diff --git a/.gitignore b/.gitignore index 6fa02d7f..e860fd86 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,7 @@ **/memory_config.yaml !**/memory_config.yaml.template tests/setup/.env.test +!tests/setup/.env.test.template # Main config (user-specific) config/config.yml @@ -14,6 +15,10 @@ config/config.yml config/plugins.yml !config/plugins.yml.template +# Individual plugin configs (may contain user-specific settings) +backends/advanced/src/advanced_omi_backend/plugins/*/config.yml +!backends/advanced/src/advanced_omi_backend/plugins/*/config.yml.template + # Config backups config/*.backup.* config/*.backup* @@ -97,3 +102,5 @@ log.html output.xml report.html .secrets + +sdk/ diff --git a/CLAUDE.md b/CLAUDE.md index faed99c2..19684385 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -373,6 +373,11 @@ curl -s -H "Authorization: Bearer YOUR_TOKEN" \ http://localhost:8000/api/conversations ``` +### Backend API Interaction Rules +- **Get token first**: Always authenticate in a separate Bash call, store the token, then use it in subsequent calls. Never chain login + API call in one command. +- **Read .env with Read tool**: Use the Read tool to get values from `.env` files. Don't use `grep | sed | cut` in Bash to extract env values. +- **Keep Bash simple**: Each Bash call should do one thing. Don't string together complex piped commands for backend queries. + ### Development Reset Commands ```bash # Reset all data (development only) diff --git a/Docs/features.md b/Docs/features.md deleted file mode 100644 index 0332c6ee..00000000 --- a/Docs/features.md +++ /dev/null @@ -1,282 +0,0 @@ -# Chronicle Features & Architecture - -## Core Features - -Chronicle supports AI-powered personal systems through multiple OMI-compatible audio devices: - -**Memory System:** -- **Advanced memory system** with pluggable providers (Chronicle native or OpenMemory MCP) -- **Memory extraction** from conversations with individual fact storage -- **Semantic memory search** with relevance threshold filtering and live results -- **Memory count display** with total count tracking from native providers -- **Speaker-based memory filtering** to control processing based on participant presence - -**Audio Processing:** -- **Action item detection** and tracking -- **Multi-device support** for comprehensive audio capture -- **Cross-client compatibility** (optional with OpenMemory MCP) - -**Device Support:** -- OMI pendants and wearables -- Smart glasses with audio capture -- Any Bluetooth-enabled audio device - -## Architecture Overview - -![Architecture Diagram](../.assets/plan.png) - -DevKit2 streams audio via Bluetooth using OPUS codec. The processing pipeline includes: - -**Audio Processing:** -- Bluetooth audio capture from OMI devices -- OPUS codec streaming to backend services -- WebSocket-based real-time audio transport - -**Transcription Services:** -- Cloud-based: Deepgram API for high-quality transcription -- Self-hosted: Local ASR services (Parakeet, Moonshine) - -**AI Processing:** -- LLM-based conversation analysis (OpenAI or local Ollama) -- **Dual memory system**: Chronicle native or OpenMemory MCP integration -- Enhanced memory extraction with individual fact storage -- **Semantic search** with relevance scoring and threshold filtering -- Smart deduplication and memory updates (ADD/UPDATE/DELETE) -- Action item detection - -**Data Storage:** -- MongoDB: User data, conversations, and transcripts -- Qdrant: Vector storage for semantic memory search -- Audio files: Optional conversation recording - -## Repository Structure - -### πŸ“± Mobile App (`app/`) -- **React Native app** for connecting to OMI devices via Bluetooth -- Streams audio in OPUS format to selected backend -- Cross-platform (iOS/Android) support -- Uses React Native Bluetooth SDK - -### πŸ–₯️ Backends (`backends/`) - -Choose one based on your needs: - -#### **Simple Backend** (`backends/simple-backend/`) -**Use case:** Getting started, basic audio processing, learning - -**Features:** -- βœ… Basic audio ingestion (OPUS β†’ PCM β†’ WAV chunks) -- βœ… File-based storage (30-second segments) -- βœ… Minimal dependencies -- βœ… Quick setup - -**Requirements:** -- Minimal resource usage -- No external services - -**Limitations:** -- No transcription -- No memory/conversation management -- No speaker recognition -- Manual file management - ---- - -#### **Advanced Backend** (`backends/advanced/`) **RECOMMENDED** -**Use case:** Production use, full feature set - -**Features:** -- Audio processing pipeline with real-time WebSocket support -- **Pluggable memory system**: Choose between Chronicle native or OpenMemory MCP -- Enhanced memory extraction with individual fact storage (no generic fallbacks) -- **Semantic memory search** with relevance threshold filtering and total count display -- **Speaker-based memory filtering**: Optional control over processing based on participant presence -- Smart memory updates with LLM-driven action proposals (ADD/UPDATE/DELETE) -- Speaker recognition and enrollment -- Action items extraction from conversations -- Audio cropping (removes silence, keeps speech) -- Conversation management with session timeouts -- Modern React web UI with live recording and advanced search -- Multiple ASR options (Deepgram API + offline ASR) -- MongoDB for structured data storage -- RESTful API for all operations -- **Cross-client compatibility** (with OpenMemory MCP provider) - -**Requirements:** -- Multiple services (MongoDB, Qdrant, Ollama) -- Higher resource usage -- Authentication configuration - ---- - -#### **OMI-Webhook-Compatible Backend** (`backends/omi-webhook-compatible/`) -**Use case:** Existing OMI users, migration from official OMI backend - -**Features:** -- βœ… Compatible with official OMI app webhook system -- βœ… Drop-in replacement for OMI backend -- βœ… Audio file storage -- βœ… ngrok integration for public endpoints - -**Requirements:** -- ngrok for public access - -**Limitations:** -- Limited features compared to advanced backend -- No built-in AI features - ---- - -#### **Example Satellite Backend** (`backends/example-satellite/`) -**Use case:** Distributed setups, external ASR integration - -**Features:** -- βœ… Audio streaming satellite -- βœ… Streams audio to remote ASR servers -- βœ… Bluetooth OMI device discovery -- βœ… Integration with external voice processing systems - -**Requirements:** -- Separate ASR server - -**Limitations:** -- Limited standalone functionality - -### πŸ”§ Additional Services (`extras/`) - -#### **ASR Services** (`extras/asr-services/`) -- **Self-hosted** ASR services -- **Moonshine** - Fast offline ASR -- **Parakeet** - Alternative offline ASR -- Self-hosted transcription options - -#### **Speaker Recognition Service** (`extras/speaker-recognition/`) -- Standalone speaker identification service -- Used by advanced backend -- REST API for speaker operations - -#### **HAVPE Relay** (`extras/havpe-relay/`) -- Audio relay service -- Protocol bridging capabilities - -## Audio Streaming Protocol - -Backends and ASR services use standardized audio streaming: -- Consistent audio streaming format -- Interoperable with external systems -- Modular ASR service architecture -- Easy to swap ASR providers - -## Deployment Scenarios - -### Single Machine (Recommended for beginners) -1. **Clone the repository** -2. **Run interactive setup**: `./wizard.sh` -3. **Start all services**: `./start.sh` -4. **Access WebUI**: `http://localhost:5173` for the React web dashboard - -### Distributed Setup (Advanced users with multiple machines) -1. **GPU Machine**: Deploy LLM services (Ollama, ASR, Speaker Recognition) - ```bash - # Ollama with GPU - docker run -d --gpus=all -p 11434:11434 ollama/ollama:latest - - # ASR services - cd extras/asr-services && docker compose up moonshine -d - - # Speaker recognition - cd extras/speaker-recognition && docker compose up --build -d - ``` - -2. **Backend Machine**: Deploy lightweight services - ```bash - cd backends/advanced - - # Configure distributed services in .env - OLLAMA_BASE_URL=http://[gpu-machine-tailscale-ip]:11434 - SPEAKER_SERVICE_URL=http://[gpu-machine-tailscale-ip]:8001 - - docker compose up --build -d - ``` - -3. **Tailscale Networking**: Connect machines securely - ```bash - # On each machine - curl -fsSL https://tailscale.com/install.sh | sh - sudo tailscale up - ``` - -## Use Case Recommendations - -### For Beginners -1. Start with **Simple Backend** to understand the basics -2. Use **mobile app** to connect your OMI device -3. Examine saved audio chunks in `./audio_chunks/` - -### For Production Use -1. Use **Advanced Backend** for full features -2. Run the orchestrated setup: `./wizard.sh` -3. Start all services: `./start.sh` -4. Access the Web UI at http://localhost:5173 for conversation management - -### For OMI Users -1. Use **OMI-Webhook-Compatible Backend** for easy migration -2. Configure ngrok for public webhook access -3. Point your OMI app to the webhook URL - -### For Home Assistant Users -1. Use **Example Satellite Backend** for audio streaming -2. Set up ASR services from `extras/asr-services/` -3. Configure external voice processing integration - -### For Distributed/Self-Hosting Users -1. Use **Advanced Backend** for full feature set -2. **Separate GPU services**: Run LLM/ASR on dedicated GPU machine -3. **Lightweight backend**: Deploy FastAPI/WebUI on VPS or Raspberry Pi -4. **Tailscale networking**: Secure VPN connection between services (automatic CORS support) -5. **Service examples**: Ollama on GPU machine, backend on lightweight server - -## Service Ports & Access - -### Advanced Backend (Primary) - -**HTTP Mode (Default):** -``` -Web Dashboard: http://localhost:5173 -Backend API: http://localhost:8000 -MongoDB: localhost:27017 -Qdrant: localhost:6333 (HTTP), 6334 (gRPC) -``` - -**HTTPS Mode:** -``` -Web Dashboard: https://localhost/ or https://your-ip/ -Backend API: https://localhost/api/ or https://your-ip/api/ -(Internal services same as HTTP mode) -``` - -### Speaker Recognition Service - -**HTTP Mode:** -``` -Web UI: http://localhost:5174 -API: http://localhost:8085 -``` - -**HTTPS Mode (nginx proxy):** -``` -Web UI: https://localhost:8444/ or https://your-ip:8444/ -API: https://localhost:8444/api/ -HTTP: http://localhost:8081/ (redirects to HTTPS) -``` - -### Additional Services - -``` -Parakeet ASR: http://localhost:8767 -OpenMemory MCP: http://localhost:8765 (API + WebUI) -``` - -**Note:** HTTPS mode requires SSL certificate setup. See individual service documentation for SSL configuration details. - -For detailed port configuration, see [ports-and-access.md](ports-and-access.md). \ No newline at end of file diff --git a/Docs/init-system.md b/Docs/init-system.md index 14d7cb3f..be7d2ae6 100644 --- a/Docs/init-system.md +++ b/Docs/init-system.md @@ -4,7 +4,7 @@ - **πŸ‘‰ [Start Here: Quick Start Guide](../quickstart.md)** - Main setup path for new users - **πŸ“š [Full Documentation](../CLAUDE.md)** - Comprehensive reference -- **πŸ—οΈ [Architecture Details](features.md)** - Technical deep dive +- **πŸ—οΈ [Architecture Details](overview.md)** - Technical deep dive --- @@ -118,20 +118,36 @@ Note (Linux): If `host.docker.internal` is unavailable, add `extra_hosts: - "hos βœ… **Unified Control** - Single command to start/stop all services βœ… **Selective Starting** - Choose which services to run based on your current needs -## Service URLs +## Ports & Access -### Default Service Endpoints -- **Backend API**: http://localhost:8000 -- **Backend WebUI**: http://localhost:5173 -- **Speaker Recognition**: http://localhost:8085 -- **Speaker Recognition WebUI**: http://localhost:5173 -- **Parakeet ASR**: http://localhost:8767 -- **OpenMemory MCP**: http://localhost:8765 +### HTTP Mode (Default - No SSL Required) + +| Service | API Port | Web UI Port | Access URL | +|---------|----------|-------------|------------| +| **Advanced Backend** | 8000 | 5173 | http://localhost:8000 (API), http://localhost:5173 (Dashboard) | +| **Speaker Recognition** | 8085 | 5175* | http://localhost:8085 (API), http://localhost:5175 (WebUI) | +| **Parakeet ASR** | 8767 | - | http://localhost:8767 (API) | +| **OpenMemory MCP** | 8765 | 8765 | http://localhost:8765 (API + WebUI) | + +*Speaker Recognition WebUI port is configurable via REACT_UI_PORT + +Note: Browsers require HTTPS for microphone access over network. + +### HTTPS Mode (For Microphone Access) + +| Service | HTTP Port | HTTPS Port | Access URL | +|---------|-----------|------------|------------| +| **Advanced Backend** | 80->443 | 443 | https://localhost/ (Main), https://localhost/api/ (API) | +| **Speaker Recognition** | 8081->8444 | 8444 | https://localhost:8444/ (Main), https://localhost:8444/api/ (API) | + +nginx services start automatically with the standard docker compose command. + +See [ssl-certificates.md](ssl-certificates.md) for HTTPS/SSL setup details. ### Container-to-Container Communication Services use `host.docker.internal` for inter-container communication: - `http://127.0.0.1:8085` - Speaker Recognition -- `http://host.docker.internal:8767` - Parakeet ASR +- `http://host.docker.internal:8767` - Parakeet ASR - `http://host.docker.internal:8765` - OpenMemory MCP ## Service Management diff --git a/Docs/overview.md b/Docs/overview.md new file mode 100644 index 00000000..927bbf16 --- /dev/null +++ b/Docs/overview.md @@ -0,0 +1,127 @@ +# Chronicle Overview + +Chronicle is an open-source, self-hosted system for building a personal timeline of your life. It captures events β€” conversations, audio, images, and more β€” processes them with AI, and extracts memories and facts that accumulate over time into a personal knowledge base. + +The goal is a personal AI that gets better the more you use it: the more context it has about you, the more useful it becomes. + +## Core Ideas + +- **Timeline of events**: Your life is a sequence of things that happen β€” someone talks, music plays, a photo is taken. Chronicle models these as timestamped events on a timeline. +- **Multimodal**: Audio is the primary input today, but the architecture supports images, visual context, and other data sources. +- **Memories from everything**: Events produce memories. A conversation yields facts about people, plans, and preferences. A photo yields location, context, and associations. +- **Self-hosted**: Runs on your hardware, your data stays with you. +- **Hackable**: Designed to be forked, modified, and extended. Pluggable providers for transcription, LLM, memory storage, and analysis. + +## How It Works + +``` +Audio/Images/Data β†’ Ingestion β†’ Processing β†’ Memories + ↓ + Vector Store + ↓ + Retrieval & Search +``` + +### Audio Pipeline (Primary) + +1. **Capture**: OMI devices, microphones, or uploaded files stream audio +2. **Transcription**: Deepgram (cloud) or Parakeet (local) converts speech to text +3. **Speaker Recognition**: Optional identification of who said what (pyannote) +4. **Memory Extraction**: LLM extracts facts, preferences, and context from transcripts +5. **Storage**: Memories stored as vectors in Qdrant for semantic search + +### Image Pipeline (In Development) + +1. **Import**: Zip upload, or sync from external services (e.g., Immich) +2. **Analysis**: Extract EXIF metadata, captions, detected objects +3. **Memory Extraction**: Same LLM pipeline, different source type +4. **Storage**: Same vector store, queryable alongside conversation memories + +## Architecture + +``` +β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” +β”‚ Chronicle System β”‚ +β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€ +β”‚ β”‚ +β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ +β”‚ β”‚ Mobile App │◄──►│ Backend β”‚β—„β–Ίβ”‚ MongoDB β”‚ β”‚ +β”‚ β”‚ (React β”‚ β”‚ (FastAPI) β”‚ β”‚ β”‚ β”‚ +β”‚ β”‚ Native) β”‚ β”‚ β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ +β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ +β”‚ β”‚ β”‚ +β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β” β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ +β”‚ β”‚ Web UI β”‚ β”‚ Workers β”‚ β”‚ Qdrant β”‚ β”‚ +β”‚ β”‚ (React) β”‚ β”‚ (RQ/Redis) β”‚ β”‚ (Vector) β”‚ β”‚ +β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ +β”‚ β”‚ +β”‚ Transcription: Deepgram (cloud) or Parakeet (local) β”‚ +β”‚ LLM: OpenAI (cloud) or Ollama (local) β”‚ +β”‚ Optional: Speaker Recognition, OpenMemory MCP β”‚ +β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ +``` + +### Key Components + +| Component | Location | Purpose | +|-----------|----------|---------| +| **Backend** | `backends/advanced/` | FastAPI server, audio processing, API | +| **Web UI** | `backends/advanced/webui/` | React dashboard for conversations and memories | +| **Mobile App** | `app/` | React Native app for OMI device pairing | +| **Speaker Recognition** | `extras/speaker-recognition/` | Voice identification service | +| **ASR Services** | `extras/asr-services/` | Local speech-to-text (Parakeet) | +| **OpenMemory MCP** | `extras/openmemory-mcp/` | Cross-client memory compatibility | +| **HAVPE Relay** | `extras/havpe-relay/` | ESP32 audio bridge | + +### Pluggable Providers + +Chronicle is designed around swappable providers: + +- **Transcription**: Deepgram API or local Parakeet ASR +- **LLM**: OpenAI or local Ollama +- **Memory Storage**: Chronicle native (Qdrant) or OpenMemory MCP +- **Speaker Recognition**: pyannote-based service (optional) + +## Repository Structure + +``` +chronicle/ +β”œβ”€β”€ app/ # React Native mobile app +β”œβ”€β”€ backends/ +β”‚ β”œβ”€β”€ advanced/ # Main backend (FastAPI + WebUI) +β”‚ β”œβ”€β”€ simple/ # Minimal backend for learning +β”‚ └── other-backends/ # Example/alternative implementations +β”œβ”€β”€ extras/ +β”‚ β”œβ”€β”€ speaker-recognition/ # Voice identification +β”‚ β”œβ”€β”€ asr-services/ # Local ASR (Parakeet) +β”‚ β”œβ”€β”€ openmemory-mcp/ # External memory server +β”‚ └── havpe-relay/ # ESP32 audio bridge +β”œβ”€β”€ config/ # Central configuration +β”œβ”€β”€ Docs/ # Documentation +β”œβ”€β”€ tests/ # Integration tests (Robot Framework) +β”œβ”€β”€ wizard.py # Setup wizard +└── services.py # Service lifecycle manager +``` + +## Getting Started + +See [quickstart.md](../quickstart.md) for setup instructions. + +```bash +# Setup +./wizard.sh + +# Start +./start.sh + +# Access +open http://localhost:5173 +``` + +## Further Reading + +- [Quick Start Guide](../quickstart.md) β€” Step-by-step setup +- [Initialization System](init-system.md) β€” Setup wizard internals and port configuration +- [Audio Pipeline Architecture](audio-pipeline-architecture.md) β€” Deep technical reference +- [SSL Certificates](ssl-certificates.md) β€” HTTPS setup +- [Backend Architecture](../backends/advanced/Docs/architecture.md) β€” Backend internals diff --git a/Docs/ports-and-access.md b/Docs/ports-and-access.md deleted file mode 100644 index 00f5ee64..00000000 --- a/Docs/ports-and-access.md +++ /dev/null @@ -1,173 +0,0 @@ -# Chronicle Port Configuration & User Journey - -## User Journey: Git Clone to Running Services - -### 1. Clone & Setup -```bash -git clone -cd chronicle - -# Configure all services (using convenience script) -./wizard.sh - -# Start all configured services -./start.sh -``` - -### 2. Service Access Points - -## HTTP Mode (Default - No SSL Required) - -| Service | API Port | Web UI Port | Access URL | -|---------|----------|-------------|------------| -| **Advanced Backend** | 8000 | 5173 | http://localhost:8000 (API)
http://localhost:5173 (Dashboard) | -| **Speaker Recognition** | 8085 | 5175* | http://localhost:8085 (API)
http://localhost:5175 (WebUI) | -| **Parakeet ASR** | 8767 | - | http://localhost:8767 (API) | -| **OpenMemory MCP** | 8765 | 8765 | http://localhost:8765 (API + WebUI) | - -*Note: Speaker Recognition WebUI port is configurable via REACT_UI_PORT (default varies by mode) - -**🌐 Main Dashboard**: http://localhost:5173 -**🎀 Speaker Recognition**: http://localhost:5174 -**❌ No microphone access** - browsers require HTTPS for microphone - ---- - -## HTTPS Mode (For Microphone Access) - -| Service | HTTP Port | HTTPS Port | Access URL | Microphone Access | -|---------|-----------|------------|------------|-------------------| -| **Advanced Backend** | 80β†’443 | 443 | https://localhost/ (Main)
https://localhost/api/ (API) | βœ… Yes | -| **Speaker Recognition** | 8081β†’8444 | 8444 | https://localhost:8444/ (Main)
https://localhost:8444/api/ (API) | βœ… Yes | - -**IMPORTANT**: nginx services start automatically with the standard docker compose command - -**🌐 Main Dashboard**: https://localhost/ (Advanced Backend with SSL) -**🎀 Speaker Recognition**: https://localhost:8444/ (Speaker Recognition with SSL) -**βœ… Full microphone access** - both services secured with SSL - -### Port Details (HTTPS Mode) -- **Advanced Backend nginx**: Ports 80 (HTTP redirect) + 443 (HTTPS) -- **Speaker Recognition nginx**: Ports 8081 (HTTP redirect) + 8444 (HTTPS) -- **No port conflicts** - different port ranges for each service - ---- - -## Why Two Modes? - -### HTTP Mode (Default) -βœ… **Simple setup** - No SSL certificates needed -βœ… **Development friendly** - Quick start for testing -❌ **No microphone access** - Browsers require HTTPS for microphone - -### HTTPS Mode (Advanced) -βœ… **Microphone access** - Browsers allow mic access over HTTPS -βœ… **Production ready** - Secure for real deployments -❌ **Complex setup** - Requires SSL certificate generation - ---- - -## Configuration Files - -### Speaker Recognition Modes - -The speaker recognition service supports both modes via configuration: - -**HTTP Mode (.env)**: -```bash -REACT_UI_PORT=5174 # Direct HTTP access -REACT_UI_HTTPS=false -``` - -**HTTPS Mode (.env)**: -```bash -REACT_UI_PORT=5175 # Internal HTTPS port (proxied through nginx) -REACT_UI_HTTPS=true -# nginx provides external access on ports 8081 (HTTP redirect) and 8444 (HTTPS) -# Start with: docker compose up -d -``` - ---- - -## Service Management Commands - -**Convenience Scripts (Recommended):** -```bash -# Check what's running -./status.sh - -# Start all configured services -./start.sh - -# Restart all services -./restart.sh - -# Stop all services -./stop.sh -``` - -**Note**: Convenience scripts wrap the longer `uv run --with-requirements setup-requirements.txt python` commands for ease of use. - -
-Full commands (click to expand) - -```bash -# Check what's running -uv run --with-requirements setup-requirements.txt python services.py status - -# Start all services -uv run --with-requirements setup-requirements.txt python services.py start --all --build - -# Start only specific services -uv run --with-requirements setup-requirements.txt python services.py start backend speaker-recognition - -# Restart all services -uv run --with-requirements setup-requirements.txt python services.py restart --all - -# Restart specific services -uv run --with-requirements setup-requirements.txt python services.py restart backend - -# Stop all services -uv run --with-requirements setup-requirements.txt python services.py stop --all -``` - -
- -**Important:** Use `restart` for configuration changes (.env updates). For code changes, use `stop` + `start --build` to rebuild images. - ---- - -## Microphone Access Requirements - -For **speaker recognition** and **live audio features** to work: - -1. **Local development**: Use HTTP mode, access via `http://localhost:5174` - - Some browsers allow localhost microphone access over HTTP - -2. **Production/Remote access**: Use HTTPS mode, access via `https://localhost:8444` - - All browsers require HTTPS for microphone access over network - -3. **Mixed setup**: Keep backend on HTTP, only enable HTTPS for speaker recognition when needed - ---- - -## Port Conflict Resolution - -If you encounter port conflicts: - -1. **Check running services**: `uv run --with-requirements setup-requirements.txt python services.py status` -2. **Stop conflicting services**: `uv run --with-requirements setup-requirements.txt python services.py stop --all` -3. **Change ports in .env files** if needed -4. **Restart services**: `uv run --with-requirements setup-requirements.txt python services.py restart --all` or `./restart.sh` - ---- - -## Summary: Default User Experience - -After `git clone` and running init + services: - -🌐 **Main Application**: http://localhost:5173 -🎀 **Speaker Recognition**: http://localhost:5174 (HTTP) or https://localhost:8444 (HTTPS) -πŸ”§ **Backend API**: http://localhost:8000 -πŸ“ **ASR Service**: http://localhost:8767 -🧠 **Memory Service**: http://localhost:8765 \ No newline at end of file diff --git a/README-K8S.md b/README-K8S.md index 0e8358c1..8bbe22fa 100644 --- a/README-K8S.md +++ b/README-K8S.md @@ -266,9 +266,9 @@ chronicle/ 1. **Clone Repository** ```bash - # Clone Friend-Lite repository with submodules + # Clone Chronicle repository with submodules git clone --recursive https://github.com/chronicle-ai/chronicle.git - cd friend-lite + cd chronicle # If you already cloned without --recursive, initialize submodules: # git submodule update --init --recursive @@ -278,7 +278,7 @@ chronicle/ ls -la backends/advanced/.env.template ``` - > **Note:** The `--recursive` flag downloads the optional Mycelia submodule (an alternative memory backend with timeline visualization). Most deployments use the default Friend-Lite memory system and don't need Mycelia. + > **Note:** The `--recursive` flag downloads the optional Mycelia submodule (an alternative memory backend with timeline visualization). Most deployments use the default Chronicle memory system and don't need Mycelia. 2. **Install Required Tools** diff --git a/README.md b/README.md index b70f4255..7e342210 100644 --- a/README.md +++ b/README.md @@ -31,7 +31,7 @@ Run setup wizard, start services, access at http://localhost:5173 - **πŸ“š [Setup Guide](quickstart.md)** - Start here - **πŸ”§ [Full Documentation](CLAUDE.md)** - Comprehensive reference -- **πŸ—οΈ [Architecture Details](Docs/features.md)** - Technical deep dive +- **πŸ—οΈ [Project Overview](Docs/overview.md)** - Architecture and vision - **🐳 [Docker/K8s](README-K8S.md)** - Container deployment ## Project Structure diff --git a/backends/advanced/.env.template b/backends/advanced/.env.template index 818b47b6..666b0b60 100644 --- a/backends/advanced/.env.template +++ b/backends/advanced/.env.template @@ -48,7 +48,9 @@ HF_TOKEN= # Optional Services # ======================================== -# Neo4j password (if using Neo4j for graph memory) +# Neo4j configuration (if using Neo4j for Obsidian or Knowledge Graph) +NEO4J_HOST=neo4j +NEO4J_USER=neo4j NEO4J_PASSWORD= # Langfuse API keys (for LLM observability) diff --git a/backends/advanced/Docs/README.md b/backends/advanced/Docs/README.md index 11e683e8..e58f94ee 100644 --- a/backends/advanced/Docs/README.md +++ b/backends/advanced/Docs/README.md @@ -29,12 +29,6 @@ Welcome to chronicle! This guide provides the optimal reading sequence to unders ### 3. **[Memory System](./memories.md)** **Memory extraction and semantic search** -### 3a. **[Memory Configuration Guide](./memory-configuration-guide.md)** 🎯 *NEW USER GUIDE* -**Easy guide for configuring memory extraction** -- 3-step setup for memory extraction -- Understanding memory types (general, facts, categories) -- Customization examples and troubleshooting -- **Perfect for**: New users wanting to customize memory behavior - How conversations become memories - Mem0 integration and vector storage - Configuration and customization options diff --git a/backends/advanced/Docs/memory-configuration-guide.md b/backends/advanced/Docs/memory-configuration-guide.md deleted file mode 100644 index 66244003..00000000 --- a/backends/advanced/Docs/memory-configuration-guide.md +++ /dev/null @@ -1,132 +0,0 @@ -# Memory Configuration Guide - -This guide helps you set up and configure the memory system for the Friend Advanced Backend. - -## Quick Start - -1. **Copy the template configuration**: -```bash -Edit the `memory` section of `config/config.yml`. -``` - -2. **Edit `config/config.yml`** with your preferred settings in the `memory` section: -```yaml -memory: - provider: "mem0" # or "basic" for simpler setup - - # Provider-specific configuration - mem0: - model_provider: "openai" # or "ollama" for local - embedding_model: "text-embedding-3-small" - llm_model: "gpt-5-mini" -``` - -3. **Set environment variables** in `.env`: -```bash -# For OpenAI -OPENAI_API_KEY=your-api-key - -# For Ollama (local) -OLLAMA_BASE_URL=http://ollama:11434 -``` - -## Configuration Options - -### Memory Providers - -#### mem0 (Recommended) -Advanced memory system with semantic search and context awareness. - -**Configuration**: -```yaml -memory: - provider: "mem0" - mem0: - model_provider: "openai" # or "ollama" - embedding_model: "text-embedding-3-small" - llm_model: "gpt-5-mini" - prompt_template: "custom_prompt_here" # Optional -``` - -#### basic -Simple memory storage without advanced features. - -**Configuration**: -```yaml -memory: - provider: "basic" - # No additional configuration needed -``` - -### Model Selection - -#### OpenAI Models -- **LLM**: `gpt-5-mini`, `gpt-5-mini`, `gpt-3.5-turbo` -- **Embeddings**: `text-embedding-3-small`, `text-embedding-3-large` - -#### Ollama Models (Local) -- **LLM**: `llama3`, `qwen2.5` -- **Embeddings**: `nomic-embed-text`, `all-minilm` - -## Hot Reload - -The configuration supports hot reloading - changes are applied automatically without restarting the service. - -## Validation - -The system validates your configuration on startup and logs any issues: -- Missing required fields -- Invalid provider names -- Incompatible model combinations - -## Troubleshooting - -### Common Issues - -1. **"Provider not found"**: Check spelling in `provider` field -2. **"API key missing"**: Ensure environment variables are set -3. **"Model not available"**: Verify model names match provider's available models -4. **"Connection refused"**: Check Ollama is running if using local models - -### Debug Mode - -Enable debug logging by setting: -```bash -DEBUG=true -``` - -This provides detailed information about memory processing and configuration loading. - -## Examples - -### OpenAI Setup -```yaml -memory: - provider: "mem0" - mem0: - model_provider: "openai" - embedding_model: "text-embedding-3-small" - llm_model: "gpt-5-mini" -``` - -### Local Ollama Setup -```yaml -memory: - provider: "mem0" - mem0: - model_provider: "ollama" - embedding_model: "nomic-embed-text" - llm_model: "llama3" -``` - -### Minimal Setup -```yaml -memory: - provider: "basic" -``` - -## Next Steps - -- Configure action items detection in `config/config.yml` (memory.extraction) -- Set up custom prompt templates for your use case -- Monitor memory processing in the debug dashboard diff --git a/backends/advanced/docs/plugin-development-guide.md b/backends/advanced/Docs/plugin-development-guide.md similarity index 100% rename from backends/advanced/docs/plugin-development-guide.md rename to backends/advanced/Docs/plugin-development-guide.md diff --git a/backends/advanced/README.md b/backends/advanced/README.md index 7f3d5a24..104137b3 100644 --- a/backends/advanced/README.md +++ b/backends/advanced/README.md @@ -1,13 +1,13 @@ -# Friend-Lite Advanced Backend +# Chronicle Advanced Backend A FastAPI backend with pluggable memory providers, real-time audio processing, and comprehensive conversation management. -[QuickStart](https://github.com/AnkushMalaker/friend-lite/blob/main/backends/advanced-backend/Docs/quickstart.md) | [Memory Providers](./MEMORY_PROVIDERS.md) | [Configuration Guide](./Docs/memory-configuration-guide.md) +[QuickStart](../../quickstart.md) | [Memory Providers](./MEMORY_PROVIDERS.md) | [Configuration Guide](./Docs/memories.md) ## Key Features ### Memory System -- **Pluggable Memory Providers**: Choose between Friend-Lite native or OpenMemory MCP +- **Pluggable Memory Providers**: Choose between Chronicle native or OpenMemory MCP - **Enhanced Memory Extraction**: Individual facts instead of generic transcripts - **Smart Memory Updates**: LLM-driven ADD/UPDATE/DELETE actions - **Cross-client Compatibility**: Use OpenMemory with Claude Desktop, Cursor, etc. @@ -33,7 +33,7 @@ Modern React-based web dashboard located in `./webui/` with: - **Authentication**: Admin email/password setup with secure keys - **Transcription Provider**: Choose between Deepgram or Offline (Parakeet) - **LLM Provider**: Choose between OpenAI (recommended) or Ollama for memory extraction -- **Memory Provider**: Choose between Friend-Lite Native or OpenMemory MCP +- **Memory Provider**: Choose between Chronicle Native or OpenMemory MCP - **HTTPS Configuration**: Optional SSL setup for microphone access (uses Caddy) - **Optional Services**: Speaker Recognition, network configuration - **API Keys**: Prompts for all required keys with helpful links @@ -58,7 +58,7 @@ docker compose --profile https up --build -d #### 3. Access URLs -**Friend-Lite Advanced Backend (Primary - ports 80/443):** +**Chronicle Advanced Backend (Primary - ports 80/443):** - **HTTPS Dashboard**: https://localhost/ or https://your-ip/ - **HTTP**: http://localhost/ (redirects to HTTPS) - **Live Recording**: Available at `/live-record` page diff --git a/backends/advanced/docker-compose-test.yml b/backends/advanced/docker-compose-test.yml index 43aa1a83..a18b0493 100644 --- a/backends/advanced/docker-compose-test.yml +++ b/backends/advanced/docker-compose-test.yml @@ -191,6 +191,23 @@ services: retries: 3 restart: unless-stopped + mock-asr: + build: + context: ../.. + dockerfile: tests/Dockerfile.mock-asr + ports: + - "8765:8765" + environment: + # Provider mode for mock ASR: mock, parakeet, vibevoice, deepgram + # vibevoice mode returns diarized segments with speaker labels + MOCK_ASR_PROVIDER: ${MOCK_ASR_PROVIDER:-mock} + healthcheck: + test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8765/health').read()"] + interval: 10s + timeout: 5s + retries: 3 + restart: unless-stopped + workers-test: build: context: . diff --git a/backends/advanced/docker-compose.yml b/backends/advanced/docker-compose.yml index 230f40c9..95cc4cab 100644 --- a/backends/advanced/docker-compose.yml +++ b/backends/advanced/docker-compose.yml @@ -103,11 +103,17 @@ services: - HA_TOKEN=${HA_TOKEN} - REDIS_URL=redis://redis:6379/0 - MONGODB_URI=mongodb://mongo:27017 + # Neo4j configuration (for knowledge graph) + - NEO4J_HOST=${NEO4J_HOST} + - NEO4J_USER=${NEO4J_USER} + - NEO4J_PASSWORD=${NEO4J_PASSWORD} # Worker orchestrator configuration (optional - defaults shown) - WORKER_CHECK_INTERVAL=${WORKER_CHECK_INTERVAL:-10} - MIN_RQ_WORKERS=${MIN_RQ_WORKERS:-6} - WORKER_STARTUP_GRACE_PERIOD=${WORKER_STARTUP_GRACE_PERIOD:-30} - WORKER_SHUTDOWN_TIMEOUT=${WORKER_SHUTDOWN_TIMEOUT:-30} + extra_hosts: + - "host.docker.internal:host-gateway" # Access host services depends_on: redis: condition: service_healthy @@ -235,9 +241,9 @@ services: timeout: 3s retries: 5 - neo4j-mem0: + neo4j: image: neo4j:5.15-community - hostname: neo4j-mem0 + hostname: neo4j ports: - "7474:7474" # HTTP - "7687:7687" # Bolt @@ -255,8 +261,15 @@ services: - ./data/neo4j_data:/data - ./data/neo4j_logs:/logs restart: unless-stopped + healthcheck: + test: ["CMD", "wget", "-q", "--spider", "http://localhost:7474"] + interval: 15s + timeout: 10s + retries: 5 + start_period: 30s profiles: - obsidian + - knowledge-graph # ollama: # image: ollama/ollama:latest diff --git a/backends/advanced/init.py b/backends/advanced/init.py index 7aa4f6aa..aad7ff0e 100644 --- a/backends/advanced/init.py +++ b/backends/advanced/init.py @@ -15,7 +15,7 @@ from pathlib import Path from typing import Any, Dict -from dotenv import get_key, set_key +from dotenv import set_key from rich.console import Console from rich.panel import Panel from rich.prompt import Confirm, Prompt @@ -24,12 +24,9 @@ # Add repo root to path for imports sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent)) from config_manager import ConfigManager -from setup_utils import ( - prompt_password as util_prompt_password, - prompt_with_existing_masked, - mask_value, - read_env_value -) +from setup_utils import detect_tailscale_info, mask_value +from setup_utils import prompt_password as util_prompt_password +from setup_utils import prompt_with_existing_masked, read_env_value class ChronicleSetup: @@ -174,47 +171,73 @@ def setup_authentication(self): self.console.print("Configure admin account for the dashboard") self.console.print() - self.config["ADMIN_EMAIL"] = self.prompt_value("Admin email", "admin@example.com") - self.config["ADMIN_PASSWORD"] = self.prompt_password("Admin password (min 8 chars)") - self.config["AUTH_SECRET_KEY"] = secrets.token_hex(32) + # Read existing values for re-run support + existing_email = self.read_existing_env_value("ADMIN_EMAIL") + default_email = existing_email if existing_email else "admin@example.com" + self.config["ADMIN_EMAIL"] = self.prompt_value("Admin email", default_email) + + # Allow reusing existing admin password + existing_password = self.read_existing_env_value("ADMIN_PASSWORD") + if existing_password: + password = prompt_with_existing_masked( + prompt_text="Admin password (min 8 chars)", + existing_value=existing_password, + is_password=True, + ) + self.config["ADMIN_PASSWORD"] = password + else: + self.config["ADMIN_PASSWORD"] = self.prompt_password("Admin password (min 8 chars)") + + # Preserve existing AUTH_SECRET_KEY to avoid invalidating JWTs + existing_secret = self.read_existing_env_value("AUTH_SECRET_KEY") + if existing_secret: + self.config["AUTH_SECRET_KEY"] = existing_secret + self.console.print("[blue][INFO][/blue] Reusing existing AUTH_SECRET_KEY (existing JWT tokens remain valid)") + else: + self.config["AUTH_SECRET_KEY"] = secrets.token_hex(32) self.console.print("[green][SUCCESS][/green] Admin account configured") def setup_transcription(self): """Configure transcription provider - updates config.yml and .env""" - self.print_section("Speech-to-Text Configuration") - - self.console.print("[blue][INFO][/blue] Provider selection is configured in config.yml (defaults.stt)") - self.console.print("[blue][INFO][/blue] API keys are stored in .env") - self.console.print() - # Check if transcription provider was provided via command line if hasattr(self.args, 'transcription_provider') and self.args.transcription_provider: provider = self.args.transcription_provider - self.console.print(f"[green][SUCCESS][/green] Transcription provider configured via wizard: {provider}") + self.console.print(f"[green]βœ…[/green] Transcription: {provider} (configured via wizard)") # Map provider to choice if provider == "deepgram": choice = "1" elif provider == "parakeet": choice = "2" - elif provider == "none": + elif provider == "vibevoice": choice = "3" + elif provider == "none": + choice = "4" else: choice = "1" # Default to Deepgram else: + self.print_section("Speech-to-Text Configuration") + + self.console.print("[blue][INFO][/blue] Provider selection is configured in config.yml (defaults.stt)") + self.console.print("[blue][INFO][/blue] API keys are stored in .env") + self.console.print() + # Interactive prompt is_macos = platform.system() == 'Darwin' if is_macos: parakeet_desc = "Offline (Parakeet ASR - CPU-based, runs locally)" + vibevoice_desc = "Offline (VibeVoice - CPU-based, built-in diarization)" else: parakeet_desc = "Offline (Parakeet ASR - GPU recommended, runs locally)" + vibevoice_desc = "Offline (VibeVoice - GPU recommended, built-in diarization)" choices = { "1": "Deepgram (recommended - high quality, cloud-based)", "2": parakeet_desc, - "3": "None (skip transcription setup)" + "3": vibevoice_desc, + "4": "None (skip transcription setup)" } choice = self.prompt_choice("Choose your transcription provider:", choices, "1") @@ -256,9 +279,24 @@ def setup_transcription(self): self.console.print("[green][SUCCESS][/green] Parakeet configured in config.yml and .env") self.console.print("[blue][INFO][/blue] Set defaults.stt: stt-parakeet-batch") - self.console.print("[yellow][WARNING][/yellow] Remember to start Parakeet service: cd ../../extras/asr-services && docker compose up parakeet") + self.console.print("[yellow][WARNING][/yellow] Remember to start Parakeet service: cd ../../extras/asr-services && docker compose up nemo-asr") elif choice == "3": + self.console.print("[blue][INFO][/blue] Offline VibeVoice ASR selected (built-in speaker diarization)") + vibevoice_url = self.prompt_value("VibeVoice ASR URL", "http://host.docker.internal:8767") + + # Write URL to .env for ${VIBEVOICE_ASR_URL} placeholder in config.yml + self.config["VIBEVOICE_ASR_URL"] = vibevoice_url + + # Update config.yml to use VibeVoice + self.config_manager.update_config_defaults({"stt": "stt-vibevoice"}) + + self.console.print("[green][SUCCESS][/green] VibeVoice configured in config.yml and .env") + self.console.print("[blue][INFO][/blue] Set defaults.stt: stt-vibevoice") + self.console.print("[blue][INFO][/blue] VibeVoice provides built-in speaker diarization - pyannote will be skipped") + self.console.print("[yellow][WARNING][/yellow] Remember to start VibeVoice service: cd ../../extras/asr-services && docker compose up vibevoice-asr") + + elif choice == "4": self.console.print("[blue][INFO][/blue] Skipping transcription setup") def setup_llm(self): @@ -375,13 +413,20 @@ def setup_memory(self): def setup_optional_services(self): """Configure optional services""" - self.print_section("Optional Services") - # Check if speaker service URL provided via args - if hasattr(self.args, 'speaker_service_url') and self.args.speaker_service_url: + has_speaker_arg = hasattr(self.args, 'speaker_service_url') and self.args.speaker_service_url + has_asr_arg = hasattr(self.args, 'parakeet_asr_url') and self.args.parakeet_asr_url + + if has_speaker_arg: self.config["SPEAKER_SERVICE_URL"] = self.args.speaker_service_url - self.console.print(f"[green][SUCCESS][/green] Speaker Recognition configured via args: {self.args.speaker_service_url}") - else: + self.console.print(f"[green]βœ…[/green] Speaker Recognition: {self.args.speaker_service_url} (configured via wizard)") + + if has_asr_arg: + self.config["PARAKEET_ASR_URL"] = self.args.parakeet_asr_url + self.console.print(f"[green]βœ…[/green] Parakeet ASR: {self.args.parakeet_asr_url} (configured via wizard)") + + # Only show interactive section if not all configured via args + if not has_speaker_arg: try: enable_speaker = Confirm.ask("Enable Speaker Recognition?", default=False) except EOFError: @@ -394,11 +439,6 @@ def setup_optional_services(self): self.console.print("[green][SUCCESS][/green] Speaker Recognition configured") self.console.print("[blue][INFO][/blue] Start with: cd ../../extras/speaker-recognition && docker compose up -d") - # Check if ASR service URL provided via args - if hasattr(self.args, 'parakeet_asr_url') and self.args.parakeet_asr_url: - self.config["PARAKEET_ASR_URL"] = self.args.parakeet_asr_url - self.console.print(f"[green][SUCCESS][/green] Parakeet ASR configured via args: {self.args.parakeet_asr_url}") - # Check if Tailscale auth key provided via args if hasattr(self.args, 'ts_authkey') and self.args.ts_authkey: self.config["TS_AUTHKEY"] = self.args.ts_authkey @@ -414,6 +454,8 @@ def setup_obsidian(self): if not neo4j_password: self.console.print("[yellow][WARNING][/yellow] --enable-obsidian provided but no password") neo4j_password = self.prompt_password("Neo4j password (min 8 chars)") + + self.console.print(f"[green]βœ…[/green] Obsidian/Neo4j: enabled (configured via wizard)") else: # Interactive prompt (fallback) self.console.print() @@ -432,7 +474,7 @@ def setup_obsidian(self): if enable_obsidian: # Update .env with credentials only (secrets, not feature flags) - self.config["NEO4J_HOST"] = "neo4j-mem0" + self.config["NEO4J_HOST"] = "neo4j" self.config["NEO4J_USER"] = "neo4j" self.config["NEO4J_PASSWORD"] = neo4j_password @@ -440,7 +482,7 @@ def setup_obsidian(self): self.config_manager.update_memory_config({ "obsidian": { "enabled": True, - "neo4j_host": "neo4j-mem0", + "neo4j_host": "neo4j", "timeout": 30 } }) @@ -452,12 +494,78 @@ def setup_obsidian(self): self.config_manager.update_memory_config({ "obsidian": { "enabled": False, - "neo4j_host": "neo4j-mem0", + "neo4j_host": "neo4j", "timeout": 30 } }) self.console.print("[blue][INFO][/blue] Obsidian/Neo4j integration disabled") + def setup_knowledge_graph(self): + """Configure Knowledge Graph (Neo4j-based entity/relationship extraction)""" + # Check if enabled via command line + if hasattr(self.args, 'enable_knowledge_graph') and self.args.enable_knowledge_graph: + enable_kg = True + neo4j_password = getattr(self.args, 'neo4j_password', None) + + if not neo4j_password: + # Check if already set from obsidian setup + neo4j_password = self.config.get("NEO4J_PASSWORD") + if not neo4j_password: + self.console.print("[yellow][WARNING][/yellow] --enable-knowledge-graph provided but no password") + neo4j_password = self.prompt_password("Neo4j password (min 8 chars)") + else: + # Interactive prompt (fallback) + self.console.print() + self.console.print("[bold cyan]Knowledge Graph (Entity Extraction)[/bold cyan]") + self.console.print("Enable graph-based entity and relationship extraction from conversations") + self.console.print("Extracts: People, Places, Organizations, Events, Promises/Tasks") + self.console.print() + + try: + enable_kg = Confirm.ask("Enable Knowledge Graph?", default=False) + except EOFError: + self.console.print("Using default: No") + enable_kg = False + + if enable_kg: + # Check if Neo4j password already set from obsidian setup + existing_password = self.config.get("NEO4J_PASSWORD") + if existing_password: + self.console.print("[blue][INFO][/blue] Using Neo4j password from Obsidian configuration") + neo4j_password = existing_password + else: + neo4j_password = self.prompt_password("Neo4j password (min 8 chars)") + + if enable_kg: + # Update .env with credentials only (secrets, not feature flags) + self.config["NEO4J_HOST"] = "neo4j" + self.config["NEO4J_USER"] = "neo4j" + if neo4j_password: + self.config["NEO4J_PASSWORD"] = neo4j_password + + # Update config.yml with feature flag (source of truth) - auto-saves via ConfigManager + self.config_manager.update_memory_config({ + "knowledge_graph": { + "enabled": True, + "neo4j_host": "neo4j", + "timeout": 30 + } + }) + + self.console.print("[green][SUCCESS][/green] Knowledge Graph configured") + self.console.print("[blue][INFO][/blue] Neo4j will start automatically with --profile knowledge-graph") + self.console.print("[blue][INFO][/blue] Entities and relationships will be extracted from conversations") + else: + # Explicitly disable Knowledge Graph in config.yml when not enabled + self.config_manager.update_memory_config({ + "knowledge_graph": { + "enabled": False, + "neo4j_host": "neo4j", + "timeout": 30 + } + }) + self.console.print("[blue][INFO][/blue] Knowledge Graph disabled") + def setup_network(self): """Configure network settings""" self.print_section("Network Configuration") @@ -471,7 +579,7 @@ def setup_https(self): if hasattr(self.args, 'enable_https') and self.args.enable_https: enable_https = True server_ip = getattr(self.args, 'server_ip', 'localhost') - self.console.print(f"[green][SUCCESS][/green] HTTPS configured via command line: {server_ip}") + self.console.print(f"[green]βœ…[/green] HTTPS: {server_ip} (configured via wizard)") else: # Interactive configuration self.print_section("HTTPS Configuration (Optional)") @@ -484,16 +592,32 @@ def setup_https(self): if enable_https: self.console.print("[blue][INFO][/blue] HTTPS enables microphone access in browsers") - self.console.print("[blue][INFO][/blue] For distributed deployments, use your Tailscale IP (e.g., 100.64.1.2)") + + # Try to auto-detect Tailscale address + ts_dns, ts_ip = detect_tailscale_info() + + if ts_dns: + self.console.print(f"[green][AUTO-DETECTED][/green] Tailscale DNS: {ts_dns}") + if ts_ip: + self.console.print(f"[green][AUTO-DETECTED][/green] Tailscale IP: {ts_ip}") + default_address = ts_dns + elif ts_ip: + self.console.print(f"[green][AUTO-DETECTED][/green] Tailscale IP: {ts_ip}") + default_address = ts_ip + else: + self.console.print("[blue][INFO][/blue] Tailscale not detected") + self.console.print("[blue][INFO][/blue] To find your Tailscale address: tailscale status --json | jq -r '.Self.DNSName'") + default_address = "localhost" + self.console.print("[blue][INFO][/blue] For local-only access, use 'localhost'") # Use the new masked prompt function (not masked for IP, but shows existing) server_ip = self.prompt_with_existing_masked( - prompt_text="Server IP/Domain for SSL certificate (Tailscale IP or localhost)", + prompt_text="Server IP/Domain for SSL certificate", env_key="SERVER_IP", placeholders=['localhost', 'your-server-ip-here'], is_password=False, - default="localhost" + default=default_address ) if enable_https: @@ -626,6 +750,12 @@ def show_summary(self): neo4j_host = obsidian_config.get("neo4j_host", "not set") self.console.print(f"βœ… Obsidian/Neo4j: Enabled ({neo4j_host})") + # Show Knowledge Graph status (read from config.yml) + kg_config = config_yml.get("memory", {}).get("knowledge_graph", {}) + if kg_config.get("enabled", False): + neo4j_host = kg_config.get("neo4j_host", "not set") + self.console.print(f"βœ… Knowledge Graph: Enabled ({neo4j_host})") + # Auto-determine URLs based on HTTPS configuration if self.config.get('HTTPS_ENABLED') == 'true': server_ip = self.config.get('SERVER_IP', 'localhost') @@ -646,11 +776,23 @@ def show_next_steps(self): config_yml = self.config_manager.get_full_config() self.console.print("1. Start the main services:") - # Include --profile obsidian if Obsidian is enabled (read from config.yml) + # Include --profile obsidian/knowledge-graph if enabled (read from config.yml) obsidian_enabled = config_yml.get("memory", {}).get("obsidian", {}).get("enabled", False) + kg_enabled = config_yml.get("memory", {}).get("knowledge_graph", {}).get("enabled", False) + + profiles = [] + profile_notes = [] if obsidian_enabled: - self.console.print(" [cyan]docker compose --profile obsidian up --build -d[/cyan]") - self.console.print(" [dim](Includes Neo4j for Obsidian integration)[/dim]") + profiles.append("obsidian") + profile_notes.append("Obsidian integration") + if kg_enabled: + profiles.append("knowledge-graph") + profile_notes.append("Knowledge Graph") + + if profiles: + profile_args = " ".join([f"--profile {p}" for p in profiles]) + self.console.print(f" [cyan]docker compose {profile_args} up --build -d[/cyan]") + self.console.print(f" [dim](Includes Neo4j for: {', '.join(profile_notes)})[/dim]") else: self.console.print(" [cyan]docker compose up --build -d[/cyan]") self.console.print() @@ -686,7 +828,8 @@ def run(self): """Run the complete setup process""" self.print_header("πŸš€ Chronicle Interactive Setup") self.console.print("This wizard will help you configure Chronicle with all necessary services.") - self.console.print("We'll ask for your API keys and preferences step by step.") + self.console.print("[dim]Safe to run again β€” it backs up your config and preserves previous values.[/dim]") + self.console.print("[dim]When unsure, just press Enter β€” the defaults will work.[/dim]") self.console.print() try: @@ -700,6 +843,7 @@ def run(self): self.setup_memory() self.setup_optional_services() self.setup_obsidian() + self.setup_knowledge_graph() self.setup_network() self.setup_https() @@ -741,7 +885,7 @@ def main(): parser.add_argument("--parakeet-asr-url", help="Parakeet ASR service URL (default: prompt user)") parser.add_argument("--transcription-provider", - choices=["deepgram", "parakeet", "none"], + choices=["deepgram", "parakeet", "vibevoice", "none"], help="Transcription provider (default: prompt user)") parser.add_argument("--enable-https", action="store_true", help="Enable HTTPS configuration (default: prompt user)") @@ -749,6 +893,8 @@ def main(): help="Server IP/domain for SSL certificate (default: prompt user)") parser.add_argument("--enable-obsidian", action="store_true", help="Enable Obsidian/Neo4j integration (default: prompt user)") + parser.add_argument("--enable-knowledge-graph", action="store_true", + help="Enable Knowledge Graph entity extraction (default: prompt user)") parser.add_argument("--neo4j-password", help="Neo4j password (default: prompt user)") parser.add_argument("--ts-authkey", diff --git a/backends/advanced/src/advanced_omi_backend/app_config.py b/backends/advanced/src/advanced_omi_backend/app_config.py index c87398f3..5ed50618 100644 --- a/backends/advanced/src/advanced_omi_backend/app_config.py +++ b/backends/advanced/src/advanced_omi_backend/app_config.py @@ -13,9 +13,13 @@ from dotenv import load_dotenv from motor.motor_asyncio import AsyncIOMotorClient -from advanced_omi_backend.constants import OMI_CHANNELS, OMI_SAMPLE_RATE, OMI_SAMPLE_WIDTH -from advanced_omi_backend.services.transcription import get_transcription_provider +from advanced_omi_backend.constants import ( + OMI_CHANNELS, + OMI_SAMPLE_RATE, + OMI_SAMPLE_WIDTH, +) from advanced_omi_backend.model_registry import get_models_registry +from advanced_omi_backend.services.transcription import get_transcription_provider # Load environment variables load_dotenv() diff --git a/backends/advanced/src/advanced_omi_backend/app_factory.py b/backends/advanced/src/advanced_omi_backend/app_factory.py index 4458ed9e..763967f1 100644 --- a/backends/advanced/src/advanced_omi_backend/app_factory.py +++ b/backends/advanced/src/advanced_omi_backend/app_factory.py @@ -23,20 +23,25 @@ fastapi_users, websocket_auth, ) +from advanced_omi_backend.client_manager import get_client_manager +from advanced_omi_backend.middleware.app_middleware import setup_middleware +from advanced_omi_backend.routers.api_router import router as api_router +from advanced_omi_backend.routers.modules.health_routes import router as health_router +from advanced_omi_backend.routers.modules.websocket_routes import ( + router as websocket_router, +) +from advanced_omi_backend.services.audio_service import get_audio_stream_service +from advanced_omi_backend.services.memory import ( + get_memory_service, + shutdown_memory_service, +) +from advanced_omi_backend.task_manager import get_task_manager, init_task_manager from advanced_omi_backend.users import ( User, UserRead, UserUpdate, register_client_to_user, ) -from advanced_omi_backend.client_manager import get_client_manager -from advanced_omi_backend.services.memory import get_memory_service, shutdown_memory_service -from advanced_omi_backend.middleware.app_middleware import setup_middleware -from advanced_omi_backend.routers.api_router import router as api_router -from advanced_omi_backend.routers.modules.health_routes import router as health_router -from advanced_omi_backend.routers.modules.websocket_routes import router as websocket_router -from advanced_omi_backend.services.audio_service import get_audio_stream_service -from advanced_omi_backend.task_manager import init_task_manager, get_task_manager logger = logging.getLogger(__name__) application_logger = logging.getLogger("audio_processing") @@ -51,7 +56,10 @@ async def initialize_openmemory_user() -> None: - Creates a test memory and deletes it to trigger user creation - Logs success or warning if OpenMemory is not reachable """ - from advanced_omi_backend.services.memory.config import build_memory_config_from_env, MemoryProvider + from advanced_omi_backend.services.memory.config import ( + MemoryProvider, + build_memory_config_from_env, + ) memory_provider_config = build_memory_config_from_env() @@ -99,11 +107,12 @@ async def lifespan(app: FastAPI): # Initialize Beanie for all document models try: from beanie import init_beanie - from advanced_omi_backend.models.conversation import Conversation + + from advanced_omi_backend.models.annotation import Annotation from advanced_omi_backend.models.audio_chunk import AudioChunkDocument + from advanced_omi_backend.models.conversation import Conversation from advanced_omi_backend.models.user import User from advanced_omi_backend.models.waveform import WaveformData - from advanced_omi_backend.models.annotation import Annotation await init_beanie( database=config.db, @@ -139,6 +148,27 @@ async def lifespan(app: FastAPI): application_logger.error(f"Failed to connect to Redis for RQ: {e}") application_logger.warning("RQ queue system will not be available - check Redis connection") + # Initialize BackgroundTaskManager (must happen before any code path uses it) + try: + task_manager = init_task_manager() + await task_manager.start() + application_logger.info("BackgroundTaskManager initialized and started") + except Exception as e: + application_logger.error(f"Failed to initialize task manager: {e}") + raise # Task manager is essential + + # Initialize ClientManager eagerly (prevents lazy race on first WebSocket connect) + get_client_manager() + application_logger.info("ClientManager initialized") + + # Initialize LLM client eagerly (catch config errors at startup, not on first request) + try: + from advanced_omi_backend.llm_client import get_llm_client + get_llm_client() + application_logger.info("LLM client initialized from config.yml") + except Exception as e: + application_logger.warning(f"LLM client initialization deferred: {e}") + # Initialize audio stream service for Redis Streams try: audio_service = get_audio_stream_service() @@ -161,7 +191,9 @@ async def lifespan(app: FastAPI): application_logger.info("βœ… Redis client for audio streaming producer initialized") # Initialize ClientManager Redis for cross-container clientβ†’user mapping - from advanced_omi_backend.client_manager import initialize_redis_for_client_manager + from advanced_omi_backend.client_manager import ( + initialize_redis_for_client_manager, + ) initialize_redis_for_client_manager(config.redis_url) except Exception as e: @@ -180,7 +212,10 @@ async def lifespan(app: FastAPI): # Initialize plugins using plugin service try: - from advanced_omi_backend.services.plugin_service import init_plugin_router, set_plugin_router + from advanced_omi_backend.services.plugin_service import ( + init_plugin_router, + set_plugin_router, + ) plugin_router = init_plugin_router() @@ -221,11 +256,23 @@ async def lifespan(app: FastAPI): client_manager = get_client_manager() for client_id in client_manager.get_all_client_ids(): try: - from advanced_omi_backend.controllers.websocket_controller import cleanup_client_state + from advanced_omi_backend.controllers.websocket_controller import ( + cleanup_client_state, + ) await cleanup_client_state(client_id) except Exception as e: application_logger.error(f"Error cleaning up client {client_id}: {e}") + # Shutdown BackgroundTaskManager + try: + task_mgr = get_task_manager() + await task_mgr.shutdown() + application_logger.info("BackgroundTaskManager shut down") + except RuntimeError: + pass # Never initialized + except Exception as e: + application_logger.error(f"Error shutting down task manager: {e}") + # RQ workers shut down automatically when process ends # No special cleanup needed for Redis connections @@ -250,7 +297,9 @@ async def lifespan(app: FastAPI): # Shutdown plugins try: - from advanced_omi_backend.services.plugin_service import cleanup_plugin_router + from advanced_omi_backend.services.plugin_service import ( + cleanup_plugin_router, + ) await cleanup_plugin_router() application_logger.info("Plugins shut down") except Exception as e: diff --git a/backends/advanced/src/advanced_omi_backend/chat_service.py b/backends/advanced/src/advanced_omi_backend/chat_service.py index 16cba331..f3184b74 100644 --- a/backends/advanced/src/advanced_omi_backend/chat_service.py +++ b/backends/advanced/src/advanced_omi_backend/chat_service.py @@ -26,8 +26,8 @@ from advanced_omi_backend.services.memory import get_memory_service from advanced_omi_backend.services.memory.base import MemoryEntry from advanced_omi_backend.services.obsidian_service import ( - get_obsidian_service, ObsidianSearchError, + get_obsidian_service, ) from advanced_omi_backend.users import User diff --git a/backends/advanced/src/advanced_omi_backend/client.py b/backends/advanced/src/advanced_omi_backend/client.py index be92716e..a92fbc10 100644 --- a/backends/advanced/src/advanced_omi_backend/client.py +++ b/backends/advanced/src/advanced_omi_backend/client.py @@ -12,7 +12,6 @@ from pathlib import Path from typing import Dict, List, Optional, Tuple -from advanced_omi_backend.task_manager import get_task_manager from wyoming.audio import AudioChunk # Get loggers diff --git a/backends/advanced/src/advanced_omi_backend/client_manager.py b/backends/advanced/src/advanced_omi_backend/client_manager.py index e55b3502..68fd6ef8 100644 --- a/backends/advanced/src/advanced_omi_backend/client_manager.py +++ b/backends/advanced/src/advanced_omi_backend/client_manager.py @@ -9,6 +9,7 @@ import logging import uuid from typing import TYPE_CHECKING, Dict, Optional + import redis.asyncio as redis if TYPE_CHECKING: @@ -39,17 +40,6 @@ def __init__(self): self._initialized = True # Self-initializing, no external dict needed logger.info("ClientManager initialized as single source of truth") - def initialize(self, active_clients_dict: Optional[Dict[str, "ClientState"]] = None): - """ - Legacy initialization method for backward compatibility. - - New design: ClientManager is self-initializing and doesn't need external dict. - This method is kept for compatibility but does nothing. - """ - if active_clients_dict is not None: - logger.warning("ClientManager no longer uses external dictionaries - ignoring active_clients_dict") - logger.info("ClientManager initialization (legacy compatibility mode)") - def is_initialized(self) -> bool: """Check if the client manager has been initialized.""" return self._initialized @@ -314,40 +304,6 @@ def get_client_manager() -> ClientManager: return _client_manager -def init_client_manager(active_clients_dict: Dict[str, "ClientState"]): - """ - Initialize the global client manager with active_clients reference. - - This should be called from main.py during startup. - - Args: - active_clients_dict: Reference to the global active_clients dictionary - """ - client_manager = get_client_manager() - client_manager.initialize(active_clients_dict) - return client_manager - - -# Client-user relationship initialization and utility functions -def init_client_user_mapping( - active_mapping_dict: Dict[str, str], all_mapping_dict: Optional[Dict[str, str]] = None -): - """ - Initialize the client-user mapping with references to the global mappings. - - This should be called from main.py during startup. - - Args: - active_mapping_dict: Reference to the active client_to_user_mapping dictionary - all_mapping_dict: Reference to the all_client_user_mappings dictionary (optional) - """ - global _client_to_user_mapping, _all_client_user_mappings - _client_to_user_mapping = active_mapping_dict - if all_mapping_dict is not None: - _all_client_user_mappings = all_mapping_dict - logger.info("Client-user mapping initialized") - - def register_client_user_mapping(client_id: str, user_id: str): """ Register a client-user mapping for active clients. diff --git a/backends/advanced/src/advanced_omi_backend/clients/audio_stream_client.py b/backends/advanced/src/advanced_omi_backend/clients/audio_stream_client.py index 1f3c695a..5686f1ef 100644 --- a/backends/advanced/src/advanced_omi_backend/clients/audio_stream_client.py +++ b/backends/advanced/src/advanced_omi_backend/clients/audio_stream_client.py @@ -48,7 +48,11 @@ async def main(): import websockets from websockets.client import WebSocketClientProtocol -from advanced_omi_backend.constants import OMI_CHANNELS, OMI_SAMPLE_RATE, OMI_SAMPLE_WIDTH +from advanced_omi_backend.constants import ( + OMI_CHANNELS, + OMI_SAMPLE_RATE, + OMI_SAMPLE_WIDTH, +) logger = logging.getLogger(__name__) diff --git a/backends/advanced/src/advanced_omi_backend/clients/gdrive_audio_client.py b/backends/advanced/src/advanced_omi_backend/clients/gdrive_audio_client.py index 5a6271e1..9d93d884 100644 --- a/backends/advanced/src/advanced_omi_backend/clients/gdrive_audio_client.py +++ b/backends/advanced/src/advanced_omi_backend/clients/gdrive_audio_client.py @@ -1,6 +1,8 @@ -import os +import os + from google.oauth2.service_account import Credentials from googleapiclient.discovery import build + from advanced_omi_backend.app_config import get_app_config _drive_client_cache = None diff --git a/backends/advanced/src/advanced_omi_backend/controllers/__init__.py b/backends/advanced/src/advanced_omi_backend/controllers/__init__.py index 25d660f9..f40145ed 100644 --- a/backends/advanced/src/advanced_omi_backend/controllers/__init__.py +++ b/backends/advanced/src/advanced_omi_backend/controllers/__init__.py @@ -3,11 +3,11 @@ """ from . import ( - memory_controller, - user_controller, - conversation_controller, client_controller, + conversation_controller, + memory_controller, system_controller, + user_controller, ) __all__ = [ diff --git a/backends/advanced/src/advanced_omi_backend/controllers/conversation_controller.py b/backends/advanced/src/advanced_omi_backend/controllers/conversation_controller.py index c142aeee..f327a545 100644 --- a/backends/advanced/src/advanced_omi_backend/controllers/conversation_controller.py +++ b/backends/advanced/src/advanced_omi_backend/controllers/conversation_controller.py @@ -25,6 +25,7 @@ from advanced_omi_backend.models.conversation import Conversation from advanced_omi_backend.models.job import JobPriority from advanced_omi_backend.users import User +from advanced_omi_backend.workers.conversation_jobs import generate_title_summary_job from advanced_omi_backend.workers.memory_jobs import ( enqueue_memory_processing, process_memory_job, @@ -34,6 +35,7 @@ logger = logging.getLogger(__name__) audio_logger = logging.getLogger("audio_processing") + async def close_current_conversation(client_id: str, user: User, client_manager: ClientManager): """Close the current conversation for a specific client. Users can only close their own conversations.""" # Validate client ownership @@ -121,7 +123,9 @@ async def get_conversation(conversation_id: str, user: User): "processing_status": conversation.processing_status, "always_persist": conversation.always_persist, "end_reason": conversation.end_reason.value if conversation.end_reason else None, - "completed_at": conversation.completed_at.isoformat() if conversation.completed_at else None, + "completed_at": ( + conversation.completed_at.isoformat() if conversation.completed_at else None + ), "title": conversation.title, "summary": conversation.summary, "detailed_summary": conversation.detailed_summary, @@ -154,54 +158,65 @@ async def get_conversations(user: User, include_deleted: bool = False): # Regular users can only see their own conversations # Filter by deleted status if not include_deleted: - user_conversations = await Conversation.find( - Conversation.user_id == str(user.user_id), - Conversation.deleted == False - ).sort(-Conversation.created_at).to_list() + user_conversations = ( + await Conversation.find( + Conversation.user_id == str(user.user_id), Conversation.deleted == False + ) + .sort(-Conversation.created_at) + .to_list() + ) else: - user_conversations = await Conversation.find( - Conversation.user_id == str(user.user_id) - ).sort(-Conversation.created_at).to_list() + user_conversations = ( + await Conversation.find(Conversation.user_id == str(user.user_id)) + .sort(-Conversation.created_at) + .to_list() + ) else: # Admins see all conversations # Filter by deleted status if not include_deleted: - user_conversations = await Conversation.find( - Conversation.deleted == False - ).sort(-Conversation.created_at).to_list() + user_conversations = ( + await Conversation.find(Conversation.deleted == False) + .sort(-Conversation.created_at) + .to_list() + ) else: - user_conversations = await Conversation.find_all().sort(-Conversation.created_at).to_list() + user_conversations = ( + await Conversation.find_all().sort(-Conversation.created_at).to_list() + ) # Build response with explicit curated fields - minimal for list view conversations = [] for conv in user_conversations: - conversations.append({ - "conversation_id": conv.conversation_id, - "user_id": conv.user_id, - "client_id": conv.client_id, - "audio_chunks_count": conv.audio_chunks_count, - "audio_total_duration": conv.audio_total_duration, - "audio_compression_ratio": conv.audio_compression_ratio, - "created_at": conv.created_at.isoformat() if conv.created_at else None, - "deleted": conv.deleted, - "deletion_reason": conv.deletion_reason, - "deleted_at": conv.deleted_at.isoformat() if conv.deleted_at else None, - "processing_status": conv.processing_status, - "always_persist": conv.always_persist, - "title": conv.title, - "summary": conv.summary, - "detailed_summary": conv.detailed_summary, - "active_transcript_version": conv.active_transcript_version, - "active_memory_version": conv.active_memory_version, - # Computed fields (counts only, no heavy data) - "segment_count": conv.segment_count, - "has_memory": conv.has_memory, - "memory_count": conv.memory_count, - "transcript_version_count": conv.transcript_version_count, - "memory_version_count": conv.memory_version_count, - "active_transcript_version_number": conv.active_transcript_version_number, - "active_memory_version_number": conv.active_memory_version_number, - }) + conversations.append( + { + "conversation_id": conv.conversation_id, + "user_id": conv.user_id, + "client_id": conv.client_id, + "audio_chunks_count": conv.audio_chunks_count, + "audio_total_duration": conv.audio_total_duration, + "audio_compression_ratio": conv.audio_compression_ratio, + "created_at": conv.created_at.isoformat() if conv.created_at else None, + "deleted": conv.deleted, + "deletion_reason": conv.deletion_reason, + "deleted_at": conv.deleted_at.isoformat() if conv.deleted_at else None, + "processing_status": conv.processing_status, + "always_persist": conv.always_persist, + "title": conv.title, + "summary": conv.summary, + "detailed_summary": conv.detailed_summary, + "active_transcript_version": conv.active_transcript_version, + "active_memory_version": conv.active_memory_version, + # Computed fields (counts only, no heavy data) + "segment_count": conv.segment_count, + "has_memory": conv.has_memory, + "memory_count": conv.memory_count, + "transcript_version_count": conv.transcript_version_count, + "memory_version_count": conv.memory_version_count, + "active_transcript_version_number": conv.active_transcript_version_number, + "active_memory_version_number": conv.active_memory_version_number, + } + ) return {"conversations": conversations} @@ -211,31 +226,43 @@ async def get_conversations(user: User, include_deleted: bool = False): async def _soft_delete_conversation(conversation: Conversation, user: User) -> JSONResponse: - """Mark conversation and chunks as deleted (soft delete).""" - conversation_id = conversation.conversation_id - - # Mark conversation as deleted - conversation.deleted = True - conversation.deletion_reason = "user_deleted" - conversation.deleted_at = datetime.utcnow() - await conversation.save() + """Mark conversation and chunks as deleted (soft delete). - logger.info(f"Soft deleted conversation {conversation_id} for user {user.user_id}") + Chunks are soft-deleted first so that a crash between the two writes + leaves chunks deleted but the conversation still active β€” a safe state + where a retry will complete the operation. + """ + conversation_id = conversation.conversation_id + deleted_at = datetime.utcnow() - # Soft delete all associated audio chunks + # 1. Soft delete audio chunks FIRST (safe failure mode: orphaned-deleted chunks) result = await AudioChunkDocument.find( AudioChunkDocument.conversation_id == conversation_id, - AudioChunkDocument.deleted == False # Only update non-deleted chunks - ).update_many({ - "$set": { - "deleted": True, - "deleted_at": datetime.utcnow() - } - }) + AudioChunkDocument.deleted == False, + ).update_many({"$set": {"deleted": True, "deleted_at": deleted_at}}) deleted_chunks = result.modified_count logger.info(f"Soft deleted {deleted_chunks} audio chunks for conversation {conversation_id}") + # 2. Mark conversation as deleted + conversation.deleted = True + conversation.deletion_reason = "user_deleted" + conversation.deleted_at = deleted_at + try: + await conversation.save() + except Exception: + # Rollback: undo chunk soft-delete using the exact timestamp we set + logger.error( + f"Failed to soft-delete conversation {conversation_id}, rolling back chunk deletes" + ) + await AudioChunkDocument.find( + AudioChunkDocument.conversation_id == conversation_id, + AudioChunkDocument.deleted_at == deleted_at, + ).update_many({"$set": {"deleted": False, "deleted_at": None}}) + raise + + logger.info(f"Soft deleted conversation {conversation_id} for user {user.user_id}") + return JSONResponse( status_code=200, content={ @@ -243,21 +270,22 @@ async def _soft_delete_conversation(conversation: Conversation, user: User) -> J "deleted_chunks": deleted_chunks, "conversation_id": conversation_id, "client_id": conversation.client_id, - "deleted_at": conversation.deleted_at.isoformat() if conversation.deleted_at else None - } + "deleted_at": conversation.deleted_at.isoformat() if conversation.deleted_at else None, + }, ) async def _hard_delete_conversation(conversation: Conversation) -> JSONResponse: - """Permanently delete conversation and chunks (admin only).""" + """Permanently delete conversation and chunks (admin only). + + Chunks are deleted first so that a crash between the two writes + leaves the conversation document intact β€” an admin can retry the + delete since the conversation still exists. + """ conversation_id = conversation.conversation_id client_id = conversation.client_id - # Delete conversation document - await conversation.delete() - logger.info(f"Hard deleted conversation {conversation_id}") - - # Delete all audio chunks + # 1. Delete audio chunks FIRST (no rollback possible for hard deletes) result = await AudioChunkDocument.find( AudioChunkDocument.conversation_id == conversation_id ).delete() @@ -265,14 +293,26 @@ async def _hard_delete_conversation(conversation: Conversation) -> JSONResponse: deleted_chunks = result.deleted_count logger.info(f"Hard deleted {deleted_chunks} audio chunks for conversation {conversation_id}") + # 2. Delete conversation document + try: + await conversation.delete() + except Exception: + logger.error( + f"Failed to hard-delete conversation {conversation_id} after " + f"deleting {deleted_chunks} chunks. Conversation document remains β€” retry delete." + ) + raise + + logger.info(f"Hard deleted conversation {conversation_id}") + return JSONResponse( status_code=200, content={ "message": f"Successfully permanently deleted conversation '{conversation_id}'", "deleted_chunks": deleted_chunks, "conversation_id": conversation_id, - "client_id": client_id - } + "client_id": client_id, + }, ) @@ -287,16 +327,21 @@ async def delete_conversation(conversation_id: str, user: User, permanent: bool """ try: # Create masked identifier for logging - masked_id = f"{conversation_id[:8]}...{conversation_id[-4:]}" if len(conversation_id) > 12 else "***" - logger.info(f"Attempting to {'permanently ' if permanent else ''}delete conversation: {masked_id}") + masked_id = ( + f"{conversation_id[:8]}...{conversation_id[-4:]}" + if len(conversation_id) > 12 + else "***" + ) + logger.info( + f"Attempting to {'permanently ' if permanent else ''}delete conversation: {masked_id}" + ) # Find the conversation using Beanie conversation = await Conversation.find_one(Conversation.conversation_id == conversation_id) if not conversation: return JSONResponse( - status_code=404, - content={"error": f"Conversation '{conversation_id}' not found"} + status_code=404, content={"error": f"Conversation '{conversation_id}' not found"} ) # Check ownership for non-admin users @@ -308,8 +353,8 @@ async def delete_conversation(conversation_id: str, user: User, permanent: bool status_code=403, content={ "error": "Access forbidden. You can only delete your own conversations.", - "details": f"Conversation '{conversation_id}' does not belong to your account." - } + "details": f"Conversation '{conversation_id}' does not belong to your account.", + }, ) # Hard delete (admin only, permanent flag) @@ -322,8 +367,7 @@ async def delete_conversation(conversation_id: str, user: User, permanent: bool except Exception as e: logger.error(f"Error deleting conversation {conversation_id}: {e}") return JSONResponse( - status_code=500, - content={"error": f"Failed to delete conversation: {str(e)}"} + status_code=500, content={"error": f"Failed to delete conversation: {str(e)}"} ) @@ -336,48 +380,45 @@ async def restore_conversation(conversation_id: str, user: User) -> JSONResponse user: Requesting user """ try: - conversation = await Conversation.find_one( - Conversation.conversation_id == conversation_id - ) + conversation = await Conversation.find_one(Conversation.conversation_id == conversation_id) if not conversation: - return JSONResponse( - status_code=404, - content={"error": "Conversation not found"} - ) + return JSONResponse(status_code=404, content={"error": "Conversation not found"}) # Permission check if not user.is_superuser and conversation.user_id != str(user.user_id): - return JSONResponse( - status_code=403, - content={"error": "Access denied"} - ) + return JSONResponse(status_code=403, content={"error": "Access denied"}) if not conversation.deleted: - return JSONResponse( - status_code=400, - content={"error": "Conversation is not deleted"} - ) - - # Restore conversation - conversation.deleted = False - conversation.deletion_reason = None - conversation.deleted_at = None - await conversation.save() + return JSONResponse(status_code=400, content={"error": "Conversation is not deleted"}) - # Restore audio chunks + # 1. Restore audio chunks FIRST (safe failure mode: restored chunks, conversation still deleted) + original_deleted_at = conversation.deleted_at result = await AudioChunkDocument.find( AudioChunkDocument.conversation_id == conversation_id, - AudioChunkDocument.deleted == True - ).update_many({ - "$set": { - "deleted": False, - "deleted_at": None - } - }) + AudioChunkDocument.deleted == True, + ).update_many({"$set": {"deleted": False, "deleted_at": None}}) restored_chunks = result.modified_count + # 2. Restore conversation + conversation.deleted = False + conversation.deletion_reason = None + conversation.deleted_at = None + try: + await conversation.save() + except Exception: + # Rollback: re-soft-delete the chunks we just restored + logger.error( + f"Failed to restore conversation {conversation_id}, " + f"rolling back {restored_chunks} chunk restores" + ) + await AudioChunkDocument.find( + AudioChunkDocument.conversation_id == conversation_id, + AudioChunkDocument.deleted == False, + ).update_many({"$set": {"deleted": True, "deleted_at": original_deleted_at}}) + raise + logger.info( f"Restored conversation {conversation_id} " f"({restored_chunks} chunks) for user {user.user_id}" @@ -389,14 +430,13 @@ async def restore_conversation(conversation_id: str, user: User) -> JSONResponse "message": f"Successfully restored conversation '{conversation_id}'", "restored_chunks": restored_chunks, "conversation_id": conversation_id, - } + }, ) except Exception as e: logger.error(f"Error restoring conversation {conversation_id}: {e}") return JSONResponse( - status_code=500, - content={"error": f"Failed to restore conversation: {str(e)}"} + status_code=500, content={"error": f"Failed to restore conversation: {str(e)}"} ) @@ -404,13 +444,20 @@ async def reprocess_transcript(conversation_id: str, user: User): """Reprocess transcript for a conversation. Users can only reprocess their own conversations.""" try: # Find the conversation using Beanie - conversation_model = await Conversation.find_one(Conversation.conversation_id == conversation_id) + conversation_model = await Conversation.find_one( + Conversation.conversation_id == conversation_id + ) if not conversation_model: return JSONResponse(status_code=404, content={"error": "Conversation not found"}) # Check ownership for non-admin users if not user.is_superuser and conversation_model.user_id != str(user.user_id): - return JSONResponse(status_code=403, content={"error": "Access forbidden. You can only reprocess your own conversations."}) + return JSONResponse( + status_code=403, + content={ + "error": "Access forbidden. You can only reprocess your own conversations." + }, + ) # Get audio_uuid from conversation # Validate audio chunks exist in MongoDB @@ -423,8 +470,8 @@ async def reprocess_transcript(conversation_id: str, user: User): status_code=404, content={ "error": "No audio data found for this conversation", - "details": f"Conversation '{conversation_id}' exists but has no audio chunks in MongoDB" - } + "details": f"Conversation '{conversation_id}' exists but has no audio chunks in MongoDB", + }, ) # Create new transcript version ID @@ -445,13 +492,15 @@ async def reprocess_transcript(conversation_id: str, user: User): result_ttl=JOB_RESULT_TTL, job_id=f"reprocess_{conversation_id[:8]}", description=f"Transcribe audio for {conversation_id[:8]}", - meta={'conversation_id': conversation_id} + meta={"conversation_id": conversation_id}, ) logger.info(f"πŸ“₯ RQ: Enqueued transcription job {transcript_job.id}") # Check if speaker recognition is enabled - speaker_config = get_service_config('speaker_recognition') - speaker_enabled = speaker_config.get('enabled', True) # Default to True for backward compatibility + speaker_config = get_service_config("speaker_recognition") + speaker_enabled = speaker_config.get( + "enabled", True + ) # Default to True for backward compatibility # Job 2: Recognize speakers (conditional - only if enabled) speaker_dependency = transcript_job # Start with transcription job @@ -467,12 +516,16 @@ async def reprocess_transcript(conversation_id: str, user: User): result_ttl=JOB_RESULT_TTL, job_id=f"speaker_{conversation_id[:8]}", description=f"Recognize speakers for {conversation_id[:8]}", - meta={'conversation_id': conversation_id} + meta={"conversation_id": conversation_id}, ) speaker_dependency = speaker_job # Chain for next job - logger.info(f"πŸ“₯ RQ: Enqueued speaker recognition job {speaker_job.id} (depends on {transcript_job.id})") + logger.info( + f"πŸ“₯ RQ: Enqueued speaker recognition job {speaker_job.id} (depends on {transcript_job.id})" + ) else: - logger.info(f"⏭️ Speaker recognition disabled, skipping speaker job for conversation {conversation_id[:8]}") + logger.info( + f"⏭️ Speaker recognition disabled, skipping speaker job for conversation {conversation_id[:8]}" + ) # Job 3: Extract memories # Depends on speaker job if it was created, otherwise depends on transcription @@ -485,39 +538,73 @@ async def reprocess_transcript(conversation_id: str, user: User): result_ttl=JOB_RESULT_TTL, job_id=f"memory_{conversation_id[:8]}", description=f"Extract memories for {conversation_id[:8]}", - meta={'conversation_id': conversation_id} + meta={"conversation_id": conversation_id}, ) if speaker_job: - logger.info(f"πŸ“₯ RQ: Enqueued memory job {memory_job.id} (depends on speaker job {speaker_job.id})") + logger.info( + f"πŸ“₯ RQ: Enqueued memory job {memory_job.id} (depends on speaker job {speaker_job.id})" + ) else: - logger.info(f"πŸ“₯ RQ: Enqueued memory job {memory_job.id} (depends on transcript job {transcript_job.id})") + logger.info( + f"πŸ“₯ RQ: Enqueued memory job {memory_job.id} (depends on transcript job {transcript_job.id})" + ) + + # Job 4: Regenerate title/summary (depends on memory job to avoid race condition + # and to ensure fresh memories are available for context-enriched summaries) + title_summary_job = default_queue.enqueue( + generate_title_summary_job, + conversation_id, + job_timeout=300, + result_ttl=JOB_RESULT_TTL, + depends_on=memory_job, + job_id=f"title_summary_{conversation_id[:8]}", + description=f"Regenerate title/summary for {conversation_id[:8]}", + meta={"conversation_id": conversation_id, "trigger": "reprocess_transcript"}, + ) + logger.info( + f"πŸ“₯ RQ: Enqueued title/summary job {title_summary_job.id} (depends on memory job {memory_job.id})" + ) job = transcript_job # For backward compatibility with return value - logger.info(f"Created transcript reprocessing job {job.id} (version: {version_id}) for conversation {conversation_id}") + logger.info( + f"Created transcript reprocessing job {job.id} (version: {version_id}) for conversation {conversation_id}" + ) - return JSONResponse(content={ - "message": f"Transcript reprocessing started for conversation {conversation_id}", - "job_id": job.id, - "version_id": version_id, - "status": "queued" - }) + return JSONResponse( + content={ + "message": f"Transcript reprocessing started for conversation {conversation_id}", + "job_id": job.id, + "title_summary_job_id": title_summary_job.id, + "version_id": version_id, + "status": "queued", + } + ) except Exception as e: logger.error(f"Error starting transcript reprocessing: {e}") - return JSONResponse(status_code=500, content={"error": "Error starting transcript reprocessing"}) + return JSONResponse( + status_code=500, content={"error": "Error starting transcript reprocessing"} + ) async def reprocess_memory(conversation_id: str, transcript_version_id: str, user: User): """Reprocess memory extraction for a specific transcript version. Users can only reprocess their own conversations.""" try: # Find the conversation using Beanie - conversation_model = await Conversation.find_one(Conversation.conversation_id == conversation_id) + conversation_model = await Conversation.find_one( + Conversation.conversation_id == conversation_id + ) if not conversation_model: return JSONResponse(status_code=404, content={"error": "Conversation not found"}) # Check ownership for non-admin users if not user.is_superuser and conversation_model.user_id != str(user.user_id): - return JSONResponse(status_code=403, content={"error": "Access forbidden. You can only reprocess your own conversations."}) + return JSONResponse( + status_code=403, + content={ + "error": "Access forbidden. You can only reprocess your own conversations." + }, + ) # Resolve transcript version ID # Handle special "active" version ID @@ -538,7 +625,8 @@ async def reprocess_memory(conversation_id: str, transcript_version_id: str, use if not transcript_version: return JSONResponse( - status_code=404, content={"error": f"Transcript version '{transcript_version_id}' not found"} + status_code=404, + content={"error": f"Transcript version '{transcript_version_id}' not found"}, ) # Create new memory version ID @@ -547,33 +635,32 @@ async def reprocess_memory(conversation_id: str, transcript_version_id: str, use # Enqueue memory processing job with RQ (RQ handles job tracking) job = enqueue_memory_processing( - client_id=conversation_model.client_id, - user_id=str(user.user_id), - user_email=user.email, conversation_id=conversation_id, - priority=JobPriority.NORMAL + priority=JobPriority.NORMAL, ) - logger.info(f"Created memory reprocessing job {job.id} (version {version_id}) for conversation {conversation_id}") + logger.info( + f"Created memory reprocessing job {job.id} (version {version_id}) for conversation {conversation_id}" + ) - return JSONResponse(content={ - "message": f"Memory reprocessing started for conversation {conversation_id}", - "job_id": job.id, - "version_id": version_id, - "transcript_version_id": transcript_version_id, - "status": "queued" - }) + return JSONResponse( + content={ + "message": f"Memory reprocessing started for conversation {conversation_id}", + "job_id": job.id, + "version_id": version_id, + "transcript_version_id": transcript_version_id, + "status": "queued", + } + ) except Exception as e: logger.error(f"Error starting memory reprocessing: {e}") - return JSONResponse(status_code=500, content={"error": "Error starting memory reprocessing"}) + return JSONResponse( + status_code=500, content={"error": "Error starting memory reprocessing"} + ) -async def reprocess_speakers( - conversation_id: str, - transcript_version_id: str, - user: User -): +async def reprocess_speakers(conversation_id: str, transcript_version_id: str, user: User): """ Reprocess speaker identification for a specific transcript version. Users can only reprocess their own conversations. @@ -587,16 +674,15 @@ async def reprocess_speakers( Conversation.conversation_id == conversation_id ) if not conversation_model: - return JSONResponse( - status_code=404, - content={"error": "Conversation not found"} - ) + return JSONResponse(status_code=404, content={"error": "Conversation not found"}) # Check ownership for non-admin users if not user.is_superuser and conversation_model.user_id != str(user.user_id): return JSONResponse( status_code=403, - content={"error": "Access forbidden. You can only reprocess your own conversations."} + content={ + "error": "Access forbidden. You can only reprocess your own conversations." + }, ) # 2. Resolve source transcript version ID (handle "active" special case) @@ -605,8 +691,7 @@ async def reprocess_speakers( active_version_id = conversation_model.active_transcript_version if not active_version_id: return JSONResponse( - status_code=404, - content={"error": "No active transcript version found"} + status_code=404, content={"error": "No active transcript version found"} ) source_version_id = active_version_id @@ -620,54 +705,76 @@ async def reprocess_speakers( if not source_version: return JSONResponse( status_code=404, - content={"error": f"Transcript version '{source_version_id}' not found"} + content={"error": f"Transcript version '{source_version_id}' not found"}, ) - # 4. Validate transcript has content and words + # 4. Validate transcript has content and words (or provider-diarized segments) if not source_version.transcript: return JSONResponse( status_code=400, - content={"error": "Cannot re-diarize empty transcript. Transcript version has no text."} + content={ + "error": "Cannot re-diarize empty transcript. Transcript version has no text." + }, ) - if not source_version.words: + provider_capabilities = source_version.metadata.get("provider_capabilities", {}) + provider_has_diarization = ( + provider_capabilities.get("diarization", False) + or source_version.diarization_source == "provider" + ) + + if not source_version.words and not (provider_has_diarization and source_version.segments): return JSONResponse( status_code=400, - content={"error": "Cannot re-diarize transcript without word timings. Words are required for diarization."} + content={ + "error": "Cannot re-diarize transcript without word timings. Words are required for diarization." + }, ) # 5. Check if speaker recognition is enabled - speaker_config = get_service_config('speaker_recognition') - if not speaker_config.get('enabled', True): + speaker_config = get_service_config("speaker_recognition") + if not speaker_config.get("enabled", True): return JSONResponse( status_code=400, content={ "error": "Speaker recognition is disabled", - "details": "Enable speaker service in config to use this feature" - } + "details": "Enable speaker service in config to use this feature", + }, ) - # 6. Create NEW transcript version (copy text/words, empty segments) + # 6. Create NEW transcript version (copy text/words, segments for provider-diarized) new_version_id = str(uuid.uuid4()) - # Add new version with copied text/words but empty segments - # Speaker job will populate segments with re-identified speakers - conversation_model.add_transcript_version( + # For provider-diarized transcripts, copy segments so the speaker job can + # identify speakers per-segment. For word-based transcripts, leave segments + # empty so pyannote can re-diarize. + new_metadata = { + "reprocessing_type": "speaker_diarization", + "source_version_id": source_version_id, + "trigger": "manual_reprocess", + } + if provider_has_diarization: + new_segments = source_version.segments # COPY provider segments + new_metadata["provider_capabilities"] = provider_capabilities + else: + new_segments = [] # Empty - will be populated by speaker job + + new_version = conversation_model.add_transcript_version( version_id=new_version_id, transcript=source_version.transcript, # COPY transcript text words=source_version.words, # COPY word timings - segments=[], # Empty - will be populated by speaker job + segments=new_segments, provider=source_version.provider, model=source_version.model, processing_time_seconds=None, # Will be updated by job - metadata={ - "reprocessing_type": "speaker_diarization", - "source_version_id": source_version_id, - "trigger": "manual_reprocess" - }, - set_as_active=True # Set new version as active + metadata=new_metadata, + set_as_active=True, # Set new version as active ) + # Carry over diarization_source so speaker job knows to use segment identification + if provider_has_diarization: + new_version.diarization_source = "provider" + # Save conversation with new version await conversation_model.save() @@ -686,11 +793,11 @@ async def reprocess_speakers( job_id=f"reprocess_speaker_{conversation_id[:12]}", description=f"Re-diarize speakers for {conversation_id[:8]}", meta={ - 'conversation_id': conversation_id, - 'version_id': new_version_id, - 'source_version_id': source_version_id, - 'trigger': 'reprocess' - } + "conversation_id": conversation_id, + "version_id": new_version_id, + "source_version_id": source_version_id, + "trigger": "reprocess", + }, ) logger.info( @@ -707,10 +814,7 @@ async def reprocess_speakers( result_ttl=JOB_RESULT_TTL, job_id=f"memory_{conversation_id[:12]}", description=f"Extract memories for {conversation_id[:8]}", - meta={ - 'conversation_id': conversation_id, - 'trigger': 'reprocess_after_speaker' - } + meta={"conversation_id": conversation_id, "trigger": "reprocess_after_speaker"}, ) logger.info( @@ -718,20 +822,41 @@ async def reprocess_speakers( f"after speaker job {speaker_job.id}" ) + # 8b. Chain title/summary regeneration after memory job + # Depends on memory_job to avoid race condition (both save conversation document) + # and to ensure fresh memories are available for context-enriched summaries + title_summary_job = default_queue.enqueue( + generate_title_summary_job, + conversation_id, + job_timeout=300, + result_ttl=JOB_RESULT_TTL, + depends_on=memory_job, + job_id=f"title_summary_{conversation_id[:12]}", + description=f"Regenerate title/summary for {conversation_id[:8]}", + meta={"conversation_id": conversation_id, "trigger": "reprocess_after_speaker"}, + ) + + logger.info( + f"Chained title/summary job {title_summary_job.id} " f"after memory job {memory_job.id}" + ) + # 9. Return job information - return JSONResponse(content={ - "message": "Speaker reprocessing started", - "job_id": speaker_job.id, - "version_id": new_version_id, # NEW version ID - "source_version_id": source_version_id, # Original version used as source - "status": "queued" - }) + return JSONResponse( + content={ + "message": "Speaker reprocessing started", + "job_id": speaker_job.id, + "memory_job_id": memory_job.id, + "title_summary_job_id": title_summary_job.id, + "version_id": new_version_id, # NEW version ID + "source_version_id": source_version_id, # Original version used as source + "status": "queued", + } + ) except Exception as e: logger.error(f"Error starting speaker reprocessing: {e}") return JSONResponse( - status_code=500, - content={"error": "Error starting speaker reprocessing"} + status_code=500, content={"error": "Error starting speaker reprocessing"} ) @@ -739,13 +864,18 @@ async def activate_transcript_version(conversation_id: str, version_id: str, use """Activate a specific transcript version. Users can only modify their own conversations.""" try: # Find the conversation using Beanie - conversation_model = await Conversation.find_one(Conversation.conversation_id == conversation_id) + conversation_model = await Conversation.find_one( + Conversation.conversation_id == conversation_id + ) if not conversation_model: return JSONResponse(status_code=404, content={"error": "Conversation not found"}) # Check ownership for non-admin users if not user.is_superuser and conversation_model.user_id != str(user.user_id): - return JSONResponse(status_code=403, content={"error": "Access forbidden. You can only modify your own conversations."}) + return JSONResponse( + status_code=403, + content={"error": "Access forbidden. You can only modify your own conversations."}, + ) # Activate the transcript version using Beanie model method success = conversation_model.set_active_transcript_version(version_id) @@ -759,29 +889,40 @@ async def activate_transcript_version(conversation_id: str, version_id: str, use # TODO: Trigger speaker recognition if configured # This would integrate with existing speaker recognition logic - logger.info(f"Activated transcript version {version_id} for conversation {conversation_id} by user {user.user_id}") + logger.info( + f"Activated transcript version {version_id} for conversation {conversation_id} by user {user.user_id}" + ) - return JSONResponse(content={ - "message": f"Transcript version {version_id} activated successfully", - "active_transcript_version": version_id - }) + return JSONResponse( + content={ + "message": f"Transcript version {version_id} activated successfully", + "active_transcript_version": version_id, + } + ) except Exception as e: logger.error(f"Error activating transcript version: {e}") - return JSONResponse(status_code=500, content={"error": "Error activating transcript version"}) + return JSONResponse( + status_code=500, content={"error": "Error activating transcript version"} + ) async def activate_memory_version(conversation_id: str, version_id: str, user: User): """Activate a specific memory version. Users can only modify their own conversations.""" try: # Find the conversation using Beanie - conversation_model = await Conversation.find_one(Conversation.conversation_id == conversation_id) + conversation_model = await Conversation.find_one( + Conversation.conversation_id == conversation_id + ) if not conversation_model: return JSONResponse(status_code=404, content={"error": "Conversation not found"}) # Check ownership for non-admin users if not user.is_superuser and conversation_model.user_id != str(user.user_id): - return JSONResponse(status_code=403, content={"error": "Access forbidden. You can only modify your own conversations."}) + return JSONResponse( + status_code=403, + content={"error": "Access forbidden. You can only modify your own conversations."}, + ) # Activate the memory version using Beanie model method success = conversation_model.set_active_memory_version(version_id) @@ -792,12 +933,16 @@ async def activate_memory_version(conversation_id: str, version_id: str, user: U await conversation_model.save() - logger.info(f"Activated memory version {version_id} for conversation {conversation_id} by user {user.user_id}") + logger.info( + f"Activated memory version {version_id} for conversation {conversation_id} by user {user.user_id}" + ) - return JSONResponse(content={ - "message": f"Memory version {version_id} activated successfully", - "active_memory_version": version_id - }) + return JSONResponse( + content={ + "message": f"Memory version {version_id} activated successfully", + "active_memory_version": version_id, + } + ) except Exception as e: logger.error(f"Error activating memory version: {e}") @@ -808,28 +953,33 @@ async def get_conversation_version_history(conversation_id: str, user: User): """Get version history for a conversation. Users can only access their own conversations.""" try: # Find the conversation using Beanie to check ownership - conversation_model = await Conversation.find_one(Conversation.conversation_id == conversation_id) + conversation_model = await Conversation.find_one( + Conversation.conversation_id == conversation_id + ) if not conversation_model: return JSONResponse(status_code=404, content={"error": "Conversation not found"}) # Check ownership for non-admin users if not user.is_superuser and conversation_model.user_id != str(user.user_id): - return JSONResponse(status_code=403, content={"error": "Access forbidden. You can only access your own conversations."}) + return JSONResponse( + status_code=403, + content={"error": "Access forbidden. You can only access your own conversations."}, + ) # Get version history from model # Convert datetime objects to ISO strings for JSON serialization transcript_versions = [] for v in conversation_model.transcript_versions: version_dict = v.model_dump() - if version_dict.get('created_at'): - version_dict['created_at'] = version_dict['created_at'].isoformat() + if version_dict.get("created_at"): + version_dict["created_at"] = version_dict["created_at"].isoformat() transcript_versions.append(version_dict) memory_versions = [] for v in conversation_model.memory_versions: version_dict = v.model_dump() - if version_dict.get('created_at'): - version_dict['created_at'] = version_dict['created_at'].isoformat() + if version_dict.get("created_at"): + version_dict["created_at"] = version_dict["created_at"].isoformat() memory_versions.append(version_dict) history = { @@ -837,7 +987,7 @@ async def get_conversation_version_history(conversation_id: str, user: User): "active_transcript_version": conversation_model.active_transcript_version, "active_memory_version": conversation_model.active_memory_version, "transcript_versions": transcript_versions, - "memory_versions": memory_versions + "memory_versions": memory_versions, } return JSONResponse(content=history) diff --git a/backends/advanced/src/advanced_omi_backend/controllers/queue_controller.py b/backends/advanced/src/advanced_omi_backend/controllers/queue_controller.py index 2d0577e7..76075d62 100644 --- a/backends/advanced/src/advanced_omi_backend/controllers/queue_controller.py +++ b/backends/advanced/src/advanced_omi_backend/controllers/queue_controller.py @@ -9,20 +9,20 @@ """ import asyncio -import os import logging +import os import uuid from datetime import datetime -from typing import Dict, Any, Optional +from typing import Any, Dict, Optional import redis from rq import Queue, Worker from rq.job import Job, JobStatus -from rq.registry import ScheduledJobRegistry, DeferredJobRegistry +from rq.registry import DeferredJobRegistry, ScheduledJobRegistry -from advanced_omi_backend.models.job import JobPriority -from advanced_omi_backend.models.conversation import Conversation from advanced_omi_backend.config_loader import get_service_config +from advanced_omi_backend.models.conversation import Conversation +from advanced_omi_backend.models.job import JobPriority logger = logging.getLogger(__name__) @@ -346,8 +346,10 @@ def start_streaming_jobs( - user_email is fetched from the database when needed. - always_persist setting is read from global config by the audio persistence job. """ - from advanced_omi_backend.workers.transcription_jobs import stream_speech_detection_job from advanced_omi_backend.workers.audio_jobs import audio_streaming_persistence_job + from advanced_omi_backend.workers.transcription_jobs import ( + stream_speech_detection_job, + ) # Enqueue speech detection job speech_job = transcription_queue.enqueue( @@ -444,9 +446,12 @@ def start_post_conversation_jobs( Returns: Dict with job IDs for speaker_recognition, memory, title_summary, event_dispatch """ - from advanced_omi_backend.workers.speaker_jobs import recognise_speakers_job + from advanced_omi_backend.workers.conversation_jobs import ( + dispatch_conversation_complete_event_job, + generate_title_summary_job, + ) from advanced_omi_backend.workers.memory_jobs import process_memory_job - from advanced_omi_backend.workers.conversation_jobs import generate_title_summary_job, dispatch_conversation_complete_event_job + from advanced_omi_backend.workers.speaker_jobs import recognise_speakers_job version_id = transcript_version_id or str(uuid.uuid4()) @@ -517,7 +522,9 @@ def start_post_conversation_jobs( logger.info(f"⏭️ Memory extraction disabled, skipping memory job for conversation {conversation_id[:8]}") # Step 3: Title/summary generation job - # Depends on speaker job if enabled, otherwise on upstream dependency + # Depends on memory job to avoid race condition (both jobs save the conversation document) + # and to ensure fresh memories are available for context-enriched summaries + title_dependency = memory_job if memory_job else speaker_dependency title_job_id = f"title_summary_{conversation_id[:12]}" logger.info(f"πŸ” DEBUG: Creating title/summary job with job_id={title_job_id}, conversation_id={conversation_id[:12]}") @@ -526,12 +533,14 @@ def start_post_conversation_jobs( conversation_id, job_timeout=300, # 5 minutes result_ttl=JOB_RESULT_TTL, - depends_on=speaker_dependency, # Depends on speaker job if enabled, NOT memory job + depends_on=title_dependency, job_id=title_job_id, description=f"Generate title and summary for conversation {conversation_id[:8]}", meta=job_meta ) - if speaker_job: + if memory_job: + logger.info(f"πŸ“₯ RQ: Enqueued title/summary job {title_summary_job.id}, meta={title_summary_job.meta} (depends on memory job {memory_job.id})") + elif speaker_job: logger.info(f"πŸ“₯ RQ: Enqueued title/summary job {title_summary_job.id}, meta={title_summary_job.meta} (depends on speaker job {speaker_job.id})") elif depends_on_job: logger.info(f"πŸ“₯ RQ: Enqueued title/summary job {title_summary_job.id}, meta={title_summary_job.meta} (depends on {depends_on_job.id})") @@ -639,6 +648,7 @@ def get_queue_health() -> Dict[str, Any]: async def cleanup_stuck_stream_workers(request): """Clean up stuck Redis Stream consumers and pending messages from all active streams.""" import time + from fastapi.responses import JSONResponse try: diff --git a/backends/advanced/src/advanced_omi_backend/controllers/session_controller.py b/backends/advanced/src/advanced_omi_backend/controllers/session_controller.py index fe9b87cd..7d7d5f2e 100644 --- a/backends/advanced/src/advanced_omi_backend/controllers/session_controller.py +++ b/backends/advanced/src/advanced_omi_backend/controllers/session_controller.py @@ -9,7 +9,7 @@ import logging import time -from typing import Dict, List, Optional, Literal +from typing import Dict, List, Literal, Optional from fastapi.responses import JSONResponse @@ -197,10 +197,10 @@ async def increment_session_conversation_count(redis_client, session_id: str) -> async def get_streaming_status(request): """Get status of active streaming sessions and Redis Streams health.""" from advanced_omi_backend.controllers.queue_controller import ( - transcription_queue, - memory_queue, + all_jobs_complete_for_client, default_queue, - all_jobs_complete_for_client + memory_queue, + transcription_queue, ) try: @@ -497,6 +497,7 @@ async def get_streaming_status(request): async def cleanup_old_sessions(request, max_age_seconds: int = 3600): """Clean up old session tracking metadata and old audio streams from Redis.""" import time + from fastapi.responses import JSONResponse try: diff --git a/backends/advanced/src/advanced_omi_backend/controllers/system_controller.py b/backends/advanced/src/advanced_omi_backend/controllers/system_controller.py index 55a4b43e..53e8ff95 100644 --- a/backends/advanced/src/advanced_omi_backend/controllers/system_controller.py +++ b/backends/advanced/src/advanced_omi_backend/controllers/system_controller.py @@ -15,11 +15,11 @@ from advanced_omi_backend.config import ( get_diarization_settings as load_diarization_settings, - get_misc_settings as load_misc_settings, - save_misc_settings, ) +from advanced_omi_backend.config import get_misc_settings as load_misc_settings from advanced_omi_backend.config import ( save_diarization_settings, + save_misc_settings, ) from advanced_omi_backend.config_loader import get_plugins_yml_path from advanced_omi_backend.model_registry import _find_config_path, load_models_config @@ -47,7 +47,7 @@ async def get_config_diagnostics(): # Test OmegaConf configuration loading try: from advanced_omi_backend.config_loader import load_config - + # Capture warnings during config load with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") @@ -1102,8 +1102,8 @@ async def update_plugin_config_structured(plugin_id: str, config: dict) -> dict: Success message with list of updated files """ try: - from advanced_omi_backend.services.plugin_service import discover_plugins import advanced_omi_backend.plugins + from advanced_omi_backend.services.plugin_service import discover_plugins # Validate plugin exists discovered_plugins = discover_plugins() @@ -1254,7 +1254,10 @@ async def test_plugin_connection(plugin_id: str, config: dict) -> dict: Test result with success status and details """ try: - from advanced_omi_backend.services.plugin_service import discover_plugins, expand_env_vars + from advanced_omi_backend.services.plugin_service import ( + discover_plugins, + expand_env_vars, + ) # Validate plugin exists discovered_plugins = discover_plugins() diff --git a/backends/advanced/src/advanced_omi_backend/controllers/user_controller.py b/backends/advanced/src/advanced_omi_backend/controllers/user_controller.py index a1b9c140..ce801327 100644 --- a/backends/advanced/src/advanced_omi_backend/controllers/user_controller.py +++ b/backends/advanced/src/advanced_omi_backend/controllers/user_controller.py @@ -11,13 +11,13 @@ from advanced_omi_backend.auth import ( ADMIN_EMAIL, - get_user_db, UserManager, + get_user_db, ) from advanced_omi_backend.client_manager import get_user_clients_all from advanced_omi_backend.database import db, users_col -from advanced_omi_backend.services.memory import get_memory_service from advanced_omi_backend.models.conversation import Conversation +from advanced_omi_backend.services.memory import get_memory_service from advanced_omi_backend.users import User, UserCreate, UserUpdate logger = logging.getLogger(__name__) diff --git a/backends/advanced/src/advanced_omi_backend/controllers/websocket_controller.py b/backends/advanced/src/advanced_omi_backend/controllers/websocket_controller.py index 89e5b46f..fcf80de4 100644 --- a/backends/advanced/src/advanced_omi_backend/controllers/websocket_controller.py +++ b/backends/advanced/src/advanced_omi_backend/controllers/websocket_controller.py @@ -15,18 +15,24 @@ from functools import partial from typing import Optional -from fastapi import WebSocket, WebSocketDisconnect, Query -from starlette.websockets import WebSocketState -from friend_lite.decoder import OmiOpusDecoder import redis.asyncio as redis +from fastapi import Query, WebSocket, WebSocketDisconnect +from friend_lite.decoder import OmiOpusDecoder +from starlette.websockets import WebSocketState from advanced_omi_backend.auth import websocket_auth from advanced_omi_backend.client_manager import generate_client_id, get_client_manager -from advanced_omi_backend.constants import OMI_CHANNELS, OMI_SAMPLE_RATE, OMI_SAMPLE_WIDTH +from advanced_omi_backend.constants import ( + OMI_CHANNELS, + OMI_SAMPLE_RATE, + OMI_SAMPLE_WIDTH, +) from advanced_omi_backend.controllers.session_controller import mark_session_complete -from advanced_omi_backend.utils.audio_utils import process_audio_chunk from advanced_omi_backend.services.audio_stream import AudioStreamProducer -from advanced_omi_backend.services.audio_stream.producer import get_audio_stream_producer +from advanced_omi_backend.services.audio_stream.producer import ( + get_audio_stream_producer, +) +from advanced_omi_backend.utils.audio_utils import process_audio_chunk # Thread pool executors for audio decoding _DEC_IO_EXECUTOR = concurrent.futures.ThreadPoolExecutor( @@ -227,7 +233,9 @@ async def cleanup_client_state(client_id: str): async_redis = redis.from_url(redis_url, decode_responses=False) # Get audio stream producer for finalization - from advanced_omi_backend.services.audio_stream.producer import get_audio_stream_producer + from advanced_omi_backend.services.audio_stream.producer import ( + get_audio_stream_producer, + ) audio_stream_producer = get_audio_stream_producer() # Find all session keys for this client and mark them complete @@ -442,8 +450,11 @@ async def _initialize_streaming_session( ) # Store audio format in Redis session (not in ClientState) - from advanced_omi_backend.services.audio_stream.producer import get_audio_stream_producer import json + + from advanced_omi_backend.services.audio_stream.producer import ( + get_audio_stream_producer, + ) session_key = f"audio:session:{client_state.stream_session_id}" redis_client = audio_stream_producer.redis_client await redis_client.hset(session_key, "audio_format", json.dumps(audio_format)) @@ -995,10 +1006,12 @@ async def _process_rolling_batch( # Enqueue transcription job from advanced_omi_backend.controllers.queue_controller import ( + JOB_RESULT_TTL, transcription_queue, - JOB_RESULT_TTL ) - from advanced_omi_backend.workers.transcription_jobs import transcribe_full_audio_job + from advanced_omi_backend.workers.transcription_jobs import ( + transcribe_full_audio_job, + ) version_id = str(uuid.uuid4()) transcribe_job_id = f"transcribe_rolling_{conversation_id[:12]}_{batch_number}" @@ -1108,11 +1121,13 @@ async def _process_batch_audio_complete( # Enqueue batch transcription job first (file uploads need transcription) from advanced_omi_backend.controllers.queue_controller import ( + JOB_RESULT_TTL, start_post_conversation_jobs, transcription_queue, - JOB_RESULT_TTL ) - from advanced_omi_backend.workers.transcription_jobs import transcribe_full_audio_job + from advanced_omi_backend.workers.transcription_jobs import ( + transcribe_full_audio_job, + ) version_id = str(uuid.uuid4()) transcribe_job_id = f"transcribe_{conversation_id[:12]}" diff --git a/backends/advanced/src/advanced_omi_backend/llm_client.py b/backends/advanced/src/advanced_omi_backend/llm_client.py index e6b5a14d..417eed5f 100644 --- a/backends/advanced/src/advanced_omi_backend/llm_client.py +++ b/backends/advanced/src/advanced_omi_backend/llm_client.py @@ -9,12 +9,13 @@ import logging import os from abc import ABC, abstractmethod -from typing import Dict, Any, Optional - -from advanced_omi_backend.services.memory.config import load_config_yml as _load_root_config -from advanced_omi_backend.services.memory.config import resolve_value as _resolve_value +from typing import Any, Dict, Optional from advanced_omi_backend.model_registry import get_models_registry +from advanced_omi_backend.services.memory.config import ( + load_config_yml as _load_root_config, +) +from advanced_omi_backend.services.memory.config import resolve_value as _resolve_value logger = logging.getLogger(__name__) diff --git a/backends/advanced/src/advanced_omi_backend/model_registry.py b/backends/advanced/src/advanced_omi_backend/model_registry.py index 382674da..5e88d91a 100644 --- a/backends/advanced/src/advanced_omi_backend/model_registry.py +++ b/backends/advanced/src/advanced_omi_backend/model_registry.py @@ -9,12 +9,19 @@ from __future__ import annotations -import yaml +import logging from pathlib import Path from typing import Any, Dict, List, Optional -import logging -from pydantic import BaseModel, Field, field_validator, model_validator, ConfigDict, ValidationError +import yaml +from pydantic import ( + BaseModel, + ConfigDict, + Field, + ValidationError, + field_validator, + model_validator, +) # Import config merging for defaults.yml + config.yml integration # OmegaConf handles environment variable resolution (${VAR:-default} syntax) @@ -36,7 +43,7 @@ class ModelDef(BaseModel): name: str = Field(..., min_length=1, description="Unique model identifier") model_type: str = Field(..., description="Model type: llm, embedding, stt, tts, etc.") - model_provider: str = Field(default="unknown", description="Provider name: openai, ollama, deepgram, parakeet, etc.") + model_provider: str = Field(default="unknown", description="Provider name: openai, ollama, deepgram, parakeet, vibevoice, etc.") api_family: str = Field(default="openai", description="API family: openai, http, websocket, etc.") model_name: str = Field(default="", description="Provider-specific model name") model_url: str = Field(default="", description="Base URL for API requests") @@ -46,6 +53,10 @@ class ModelDef(BaseModel): model_output: Optional[str] = Field(default=None, description="Output format: json, text, vector, etc.") embedding_dimensions: Optional[int] = Field(default=None, ge=1, description="Embedding vector dimensions") operations: Dict[str, Any] = Field(default_factory=dict, description="API operation definitions") + capabilities: List[str] = Field( + default_factory=list, + description="Provider capabilities: word_timestamps, segments, diarization (for STT providers)" + ) @field_validator('model_name', mode='before') @classmethod diff --git a/backends/advanced/src/advanced_omi_backend/models/annotation.py b/backends/advanced/src/advanced_omi_backend/models/annotation.py index b2a986a5..ac8ceefe 100644 --- a/backends/advanced/src/advanced_omi_backend/models/annotation.py +++ b/backends/advanced/src/advanced_omi_backend/models/annotation.py @@ -5,10 +5,10 @@ Enables both user edits and AI-powered suggestions. """ +import uuid +from datetime import datetime, timezone from enum import Enum from typing import Optional -from datetime import datetime, timezone -import uuid from beanie import Document, Indexed from pydantic import BaseModel, Field diff --git a/backends/advanced/src/advanced_omi_backend/models/audio_chunk.py b/backends/advanced/src/advanced_omi_backend/models/audio_chunk.py index cea20ef7..5f3b4c1d 100644 --- a/backends/advanced/src/advanced_omi_backend/models/audio_chunk.py +++ b/backends/advanced/src/advanced_omi_backend/models/audio_chunk.py @@ -8,9 +8,10 @@ from datetime import datetime from typing import Optional -from pydantic import ConfigDict, Field, field_serializer + from beanie import Document, Indexed from bson import Binary +from pydantic import ConfigDict, Field, field_serializer class AudioChunkDocument(Document): diff --git a/backends/advanced/src/advanced_omi_backend/models/conversation.py b/backends/advanced/src/advanced_omi_backend/models/conversation.py index e4446f0f..2ec45f33 100644 --- a/backends/advanced/src/advanced_omi_backend/models/conversation.py +++ b/backends/advanced/src/advanced_omi_backend/models/conversation.py @@ -5,13 +5,13 @@ transcript versions, and memory versions. """ +import uuid from datetime import datetime -from typing import Dict, List, Optional, Any, Union -from pydantic import BaseModel, Field, model_validator, computed_field, field_validator from enum import Enum -import uuid +from typing import Any, Dict, List, Optional, Union from beanie import Document, Indexed +from pydantic import BaseModel, Field, computed_field, field_validator, model_validator from pymongo import IndexModel @@ -56,6 +56,7 @@ class SpeakerSegment(BaseModel): end: float = Field(description="End time in seconds") text: str = Field(description="Transcript text for this segment") speaker: str = Field(description="Speaker identifier") + identified_as: Optional[str] = Field(None, description="Speaker name from speaker recognition (None if not identified)") confidence: Optional[float] = Field(None, description="Confidence score (0-1)") words: List["Conversation.Word"] = Field(default_factory=list, description="Word-level timestamps for this segment") @@ -71,10 +72,14 @@ class TranscriptVersion(BaseModel): default_factory=list, description="Speaker segments (filled by speaker recognition)" ) - provider: Optional[str] = Field(None, description="Transcription provider used (deepgram, parakeet, etc.)") + provider: Optional[str] = Field(None, description="Transcription provider used (deepgram, parakeet, vibevoice, etc.)") model: Optional[str] = Field(None, description="Model used (e.g., nova-3, parakeet)") created_at: datetime = Field(description="When this version was created") processing_time_seconds: Optional[float] = Field(None, description="Time taken to process") + diarization_source: Optional[str] = Field( + None, + description="Source of speaker diarization: 'provider' (transcription service), 'pyannote' (speaker recognition), or None" + ) metadata: Dict[str, Any] = Field(default_factory=dict, description="Additional provider-specific metadata") class MemoryVersion(BaseModel): diff --git a/backends/advanced/src/advanced_omi_backend/models/job.py b/backends/advanced/src/advanced_omi_backend/models/job.py index 5d906865..a3d93f96 100644 --- a/backends/advanced/src/advanced_omi_backend/models/job.py +++ b/backends/advanced/src/advanced_omi_backend/models/job.py @@ -13,8 +13,8 @@ from abc import ABC, abstractmethod from datetime import datetime, timezone from enum import Enum -from typing import Any, Dict, Optional, Callable from functools import wraps +from typing import Any, Callable, Dict, Optional import redis.asyncio as redis_async @@ -32,13 +32,15 @@ async def _ensure_beanie_initialized(): return try: import os - from motor.motor_asyncio import AsyncIOMotorClient + from beanie import init_beanie - from advanced_omi_backend.models.conversation import Conversation + from motor.motor_asyncio import AsyncIOMotorClient + from pymongo.errors import ConfigurationError + from advanced_omi_backend.models.audio_chunk import AudioChunkDocument + from advanced_omi_backend.models.conversation import Conversation from advanced_omi_backend.models.user import User from advanced_omi_backend.models.waveform import WaveformData - from pymongo.errors import ConfigurationError # Get MongoDB URI from environment mongodb_uri = os.getenv("MONGODB_URI", "mongodb://localhost:27017") @@ -254,7 +256,9 @@ async def process(): # Create Redis client if requested if redis: - from advanced_omi_backend.controllers.queue_controller import REDIS_URL + from advanced_omi_backend.controllers.queue_controller import ( + REDIS_URL, + ) redis_client = redis_async.from_url(REDIS_URL) kwargs['redis_client'] = redis_client logger.debug(f"Redis client created") diff --git a/backends/advanced/src/advanced_omi_backend/plugins/base.py b/backends/advanced/src/advanced_omi_backend/plugins/base.py index dbd13301..fefcc6a0 100644 --- a/backends/advanced/src/advanced_omi_backend/plugins/base.py +++ b/backends/advanced/src/advanced_omi_backend/plugins/base.py @@ -7,8 +7,8 @@ - BasePlugin: Abstract base class for all plugins """ from abc import ABC, abstractmethod -from typing import Optional, Dict, Any, List from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional @dataclass diff --git a/backends/advanced/src/advanced_omi_backend/plugins/email_summarizer/setup.py b/backends/advanced/src/advanced_omi_backend/plugins/email_summarizer/setup.py index 728ae607..df179e26 100755 --- a/backends/advanced/src/advanced_omi_backend/plugins/email_summarizer/setup.py +++ b/backends/advanced/src/advanced_omi_backend/plugins/email_summarizer/setup.py @@ -23,10 +23,7 @@ project_root = Path(__file__).resolve().parents[6] sys.path.insert(0, str(project_root)) -from setup_utils import ( - prompt_with_existing_masked, - prompt_value -) +from setup_utils import prompt_value, prompt_with_existing_masked console = Console() diff --git a/backends/advanced/src/advanced_omi_backend/plugins/homeassistant/entity_cache.py b/backends/advanced/src/advanced_omi_backend/plugins/homeassistant/entity_cache.py index e8624f1b..90c952e5 100644 --- a/backends/advanced/src/advanced_omi_backend/plugins/homeassistant/entity_cache.py +++ b/backends/advanced/src/advanced_omi_backend/plugins/homeassistant/entity_cache.py @@ -4,10 +4,10 @@ This module provides caching and lookup functionality for Home Assistant areas and entities. """ +import logging from dataclasses import dataclass, field from datetime import datetime from typing import Dict, List, Optional -import logging logger = logging.getLogger(__name__) diff --git a/backends/advanced/src/advanced_omi_backend/plugins/test_event/plugin.py b/backends/advanced/src/advanced_omi_backend/plugins/test_event/plugin.py index 59dd652e..d75a88df 100644 --- a/backends/advanced/src/advanced_omi_backend/plugins/test_event/plugin.py +++ b/backends/advanced/src/advanced_omi_backend/plugins/test_event/plugin.py @@ -8,6 +8,7 @@ from typing import Any, Dict, List, Optional from advanced_omi_backend.plugins.base import BasePlugin, PluginContext, PluginResult + from .event_storage import EventStorage logger = logging.getLogger(__name__) diff --git a/backends/advanced/src/advanced_omi_backend/routers/api_router.py b/backends/advanced/src/advanced_omi_backend/routers/api_router.py index 5a135c7e..e4c89531 100644 --- a/backends/advanced/src/advanced_omi_backend/routers/api_router.py +++ b/backends/advanced/src/advanced_omi_backend/routers/api_router.py @@ -18,6 +18,7 @@ client_router, conversation_router, finetuning_router, + knowledge_graph_router, memory_router, obsidian_router, queue_router, @@ -41,6 +42,7 @@ router.include_router(client_router) router.include_router(conversation_router) router.include_router(finetuning_router) +router.include_router(knowledge_graph_router) router.include_router(memory_router) router.include_router(obsidian_router) router.include_router(system_router) diff --git a/backends/advanced/src/advanced_omi_backend/routers/modules/__init__.py b/backends/advanced/src/advanced_omi_backend/routers/modules/__init__.py index 4025a6dc..501377fc 100644 --- a/backends/advanced/src/advanced_omi_backend/routers/modules/__init__.py +++ b/backends/advanced/src/advanced_omi_backend/routers/modules/__init__.py @@ -15,6 +15,7 @@ - health_routes: Health check endpoints - websocket_routes: WebSocket connection handling - admin_routes: Admin-only system management endpoints +- knowledge_graph_routes: Knowledge graph entities, relationships, and promises """ from .admin_routes import router as admin_router @@ -25,6 +26,7 @@ from .conversation_routes import router as conversation_router from .finetuning_routes import router as finetuning_router from .health_routes import router as health_router +from .knowledge_graph_routes import router as knowledge_graph_router from .memory_routes import router as memory_router from .obsidian_routes import router as obsidian_router from .queue_routes import router as queue_router @@ -41,6 +43,7 @@ "conversation_router", "finetuning_router", "health_router", + "knowledge_graph_router", "memory_router", "obsidian_router", "queue_router", diff --git a/backends/advanced/src/advanced_omi_backend/routers/modules/admin_routes.py b/backends/advanced/src/advanced_omi_backend/routers/modules/admin_routes.py index a2ef4398..49594dd0 100644 --- a/backends/advanced/src/advanced_omi_backend/routers/modules/admin_routes.py +++ b/backends/advanced/src/advanced_omi_backend/routers/modules/admin_routes.py @@ -7,7 +7,7 @@ import logging from typing import Optional -from fastapi import APIRouter, Depends, Query, HTTPException +from fastapi import APIRouter, Depends, HTTPException, Query from fastapi.responses import JSONResponse from advanced_omi_backend.auth import current_active_user @@ -50,8 +50,10 @@ async def trigger_cleanup( ): """Manually trigger cleanup of soft-deleted conversations (admin only).""" try: - from advanced_omi_backend.workers.cleanup_jobs import purge_old_deleted_conversations from advanced_omi_backend.controllers.queue_controller import get_queue + from advanced_omi_backend.workers.cleanup_jobs import ( + purge_old_deleted_conversations, + ) # Enqueue cleanup job queue = get_queue("default") @@ -90,9 +92,10 @@ async def preview_cleanup( ): """Preview what would be deleted by cleanup (admin only).""" try: + from datetime import datetime, timedelta + from advanced_omi_backend.config import get_cleanup_settings from advanced_omi_backend.models.conversation import Conversation - from datetime import datetime, timedelta # Use provided retention or default from config if retention_days is None: diff --git a/backends/advanced/src/advanced_omi_backend/routers/modules/annotation_routes.py b/backends/advanced/src/advanced_omi_backend/routers/modules/annotation_routes.py index ebee7634..f85a99ed 100644 --- a/backends/advanced/src/advanced_omi_backend/routers/modules/annotation_routes.py +++ b/backends/advanced/src/advanced_omi_backend/routers/modules/annotation_routes.py @@ -80,15 +80,11 @@ async def create_memory_annotation( content=annotation_data.corrected_text, user_id=current_user.user_id, ) - logger.info( - f"Updated memory {annotation_data.memory_id} with corrected text" - ) + logger.info(f"Updated memory {annotation_data.memory_id} with corrected text") except Exception as e: logger.error(f"Error updating memory: {e}") # Annotation is saved, but memory update failed - log but don't fail the request - logger.warning( - f"Memory annotation {annotation.id} saved but memory update failed" - ) + logger.warning(f"Memory annotation {annotation.id} saved but memory update failed") return AnnotationResponse.model_validate(annotation) @@ -127,9 +123,8 @@ async def create_transcript_annotation( # Validate segment index active_transcript = conversation.active_transcript - if ( - not active_transcript - or annotation_data.segment_index >= len(active_transcript.segments) + if not active_transcript or annotation_data.segment_index >= len( + active_transcript.segments ): raise HTTPException(status_code=400, detail="Invalid segment index") @@ -237,10 +232,7 @@ async def update_annotation_status( annotation.updated_at = datetime.now(timezone.utc) # If accepting a pending suggestion, apply the correction - if ( - status == AnnotationStatus.ACCEPTED - and old_status == AnnotationStatus.PENDING - ): + if status == AnnotationStatus.ACCEPTED and old_status == AnnotationStatus.PENDING: if annotation.is_memory_annotation(): # Update memory try: @@ -250,9 +242,7 @@ async def update_annotation_status( content=annotation.corrected_text, user_id=current_user.user_id, ) - logger.info( - f"Applied suggestion to memory {annotation.memory_id}" - ) + logger.info(f"Applied suggestion to memory {annotation.memory_id}") except Exception as e: logger.error(f"Error applying memory suggestion: {e}") # Don't fail the status update if memory update fails @@ -261,17 +251,14 @@ async def update_annotation_status( try: conversation = await Conversation.find_one( Conversation.conversation_id == annotation.conversation_id, - Conversation.user_id == annotation.user_id + Conversation.user_id == annotation.user_id, ) if conversation: transcript = conversation.active_transcript - if ( - transcript - and annotation.segment_index < len(transcript.segments) - ): - transcript.segments[ - annotation.segment_index - ].text = annotation.corrected_text + if transcript and annotation.segment_index < len(transcript.segments): + transcript.segments[annotation.segment_index].text = ( + annotation.corrected_text + ) await conversation.save() logger.info( f"Applied suggestion to transcript segment {annotation.segment_index}" @@ -322,9 +309,8 @@ async def create_diarization_annotation( # Validate segment index active_transcript = conversation.active_transcript - if ( - not active_transcript - or annotation_data.segment_index >= len(active_transcript.segments) + if not active_transcript or annotation_data.segment_index >= len( + active_transcript.segments ): raise HTTPException(status_code=400, detail="Invalid segment index") @@ -425,6 +411,7 @@ async def apply_diarization_annotations( # Create NEW transcript version with corrected speakers import uuid + new_version_id = str(uuid.uuid4()) # Copy segments and apply corrections @@ -479,19 +466,18 @@ async def apply_diarization_annotations( from advanced_omi_backend.workers.memory_jobs import enqueue_memory_processing enqueue_memory_processing( - client_id=conversation.client_id, - user_id=current_user.user_id, - user_email=current_user.email, conversation_id=conversation_id, priority=JobPriority.NORMAL, ) - return JSONResponse(content={ - "message": "Diarization annotations applied", - "version_id": new_version_id, - "applied_count": len(annotations), - "status": "success" - }) + return JSONResponse( + content={ + "message": "Diarization annotations applied", + "version_id": new_version_id, + "applied_count": len(annotations), + "status": "success", + } + ) except HTTPException: raise @@ -533,15 +519,21 @@ async def apply_all_annotations( ).to_list() if not annotations: - return JSONResponse(content={ - "message": "No pending annotations to apply", - "diarization_count": 0, - "transcript_count": 0, - }) + return JSONResponse( + content={ + "message": "No pending annotations to apply", + "diarization_count": 0, + "transcript_count": 0, + } + ) # Separate by type - diarization_annotations = [a for a in annotations if a.annotation_type == AnnotationType.DIARIZATION] - transcript_annotations = [a for a in annotations if a.annotation_type == AnnotationType.TRANSCRIPT] + diarization_annotations = [ + a for a in annotations if a.annotation_type == AnnotationType.DIARIZATION + ] + transcript_annotations = [ + a for a in annotations if a.annotation_type == AnnotationType.TRANSCRIPT + ] # Get active transcript active_transcript = conversation.active_transcript @@ -550,6 +542,7 @@ async def apply_all_annotations( # Create new version with ALL corrections applied import uuid + new_version_id = str(uuid.uuid4()) corrected_segments = [] @@ -558,16 +551,14 @@ async def apply_all_annotations( # Apply diarization correction (if exists) diar_annotation = next( - (a for a in diarization_annotations if a.segment_index == segment_idx), - None + (a for a in diarization_annotations if a.segment_index == segment_idx), None ) if diar_annotation: corrected_segment.speaker = diar_annotation.corrected_speaker # Apply transcript correction (if exists) transcript_annotation = next( - (a for a in transcript_annotations if a.segment_index == segment_idx), - None + (a for a in transcript_annotations if a.segment_index == segment_idx), None ) if transcript_annotation: corrected_segment.text = transcript_annotation.corrected_text @@ -610,20 +601,19 @@ async def apply_all_annotations( from advanced_omi_backend.workers.memory_jobs import enqueue_memory_processing enqueue_memory_processing( - client_id=conversation.client_id, - user_id=current_user.user_id, - user_email=current_user.email, conversation_id=conversation_id, priority=JobPriority.NORMAL, ) - return JSONResponse(content={ - "message": f"Applied {len(diarization_annotations)} diarization and {len(transcript_annotations)} transcript annotations", - "version_id": new_version_id, - "diarization_count": len(diarization_annotations), - "transcript_count": len(transcript_annotations), - "status": "success", - }) + return JSONResponse( + content={ + "message": f"Applied {len(diarization_annotations)} diarization and {len(transcript_annotations)} transcript annotations", + "version_id": new_version_id, + "diarization_count": len(diarization_annotations), + "transcript_count": len(transcript_annotations), + "status": "success", + } + ) except HTTPException: raise diff --git a/backends/advanced/src/advanced_omi_backend/routers/modules/audio_routes.py b/backends/advanced/src/advanced_omi_backend/routers/modules/audio_routes.py index 7cef955a..94a57e1e 100644 --- a/backends/advanced/src/advanced_omi_backend/routers/modules/audio_routes.py +++ b/backends/advanced/src/advanced_omi_backend/routers/modules/audio_routes.py @@ -7,20 +7,28 @@ import io from typing import Optional -from fastapi import APIRouter, Depends, File, HTTPException, Query, UploadFile, Request -from fastapi.responses import FileResponse, StreamingResponse, Response -from advanced_omi_backend.auth import current_superuser, current_active_user_optional, get_user_from_token_param +from fastapi import APIRouter, Depends, File, HTTPException, Query, Request, UploadFile +from fastapi.responses import FileResponse, Response, StreamingResponse + +from advanced_omi_backend.app_config import get_audio_chunk_dir +from advanced_omi_backend.auth import ( + current_active_user_optional, + current_superuser, + get_user_from_token_param, +) from advanced_omi_backend.controllers import audio_controller -from advanced_omi_backend.models.user import User from advanced_omi_backend.models.conversation import Conversation -from advanced_omi_backend.app_config import get_audio_chunk_dir -from advanced_omi_backend.utils.gdrive_audio_utils import download_audio_files_from_drive, AudioValidationError +from advanced_omi_backend.models.user import User from advanced_omi_backend.utils.audio_chunk_utils import ( + build_wav_from_pcm, + concatenate_chunks_to_pcm, reconstruct_wav_from_conversation, retrieve_audio_chunks, - concatenate_chunks_to_pcm, - build_wav_from_pcm, +) +from advanced_omi_backend.utils.gdrive_audio_utils import ( + AudioValidationError, + download_audio_files_from_drive, ) router = APIRouter(prefix="/audio", tags=["audio"]) diff --git a/backends/advanced/src/advanced_omi_backend/routers/modules/health_routes.py b/backends/advanced/src/advanced_omi_backend/routers/modules/health_routes.py index 96ee72fe..cd11c6ff 100644 --- a/backends/advanced/src/advanced_omi_backend/routers/modules/health_routes.py +++ b/backends/advanced/src/advanced_omi_backend/routers/modules/health_routes.py @@ -8,19 +8,19 @@ import logging import os import time -from typing import Dict, Any +from typing import Any, Dict import aiohttp -from fastapi import APIRouter, Request, HTTPException +from fastapi import APIRouter, HTTPException, Request from fastapi.responses import JSONResponse from motor.motor_asyncio import AsyncIOMotorClient -from advanced_omi_backend.controllers.queue_controller import redis_conn from advanced_omi_backend.client_manager import get_client_manager +from advanced_omi_backend.controllers.queue_controller import redis_conn from advanced_omi_backend.llm_client import async_health_check +from advanced_omi_backend.model_registry import get_models_registry from advanced_omi_backend.services.memory import get_memory_service from advanced_omi_backend.services.transcription import get_transcription_provider -from advanced_omi_backend.model_registry import get_models_registry # Create router router = APIRouter(tags=["health"]) diff --git a/backends/advanced/src/advanced_omi_backend/routers/modules/knowledge_graph_routes.py b/backends/advanced/src/advanced_omi_backend/routers/modules/knowledge_graph_routes.py new file mode 100644 index 00000000..d4680951 --- /dev/null +++ b/backends/advanced/src/advanced_omi_backend/routers/modules/knowledge_graph_routes.py @@ -0,0 +1,416 @@ +""" +Knowledge Graph API routes for Chronicle. + +Handles entity, relationship, promise, and timeline operations. +""" + +import logging +from datetime import datetime +from typing import Optional + +from fastapi import APIRouter, Depends, HTTPException, Query +from fastapi.responses import JSONResponse +from pydantic import BaseModel + +from advanced_omi_backend.auth import current_active_user +from advanced_omi_backend.services.knowledge_graph import ( + KnowledgeGraphService, + PromiseStatus, + get_knowledge_graph_service, +) +from advanced_omi_backend.users import User + +logger = logging.getLogger(__name__) + +router = APIRouter(prefix="/knowledge-graph", tags=["knowledge-graph"]) + + +# ============================================================================= +# REQUEST MODELS +# ============================================================================= + + +class UpdatePromiseRequest(BaseModel): + """Request model for updating promise status.""" + status: str # pending, in_progress, completed, cancelled + + +# ============================================================================= +# ENTITY ENDPOINTS +# ============================================================================= + + +@router.get("/entities") +async def get_entities( + current_user: User = Depends(current_active_user), + entity_type: Optional[str] = Query( + default=None, + description="Filter by entity type (person, place, organization, event, thing)", + ), + limit: int = Query(default=100, ge=1, le=500), +): + """Get all entities for the current user. + + Optionally filter by entity type. Returns entities with their + relationship counts. + """ + try: + service = get_knowledge_graph_service() + entities = await service.get_entities( + user_id=str(current_user.id), + entity_type=entity_type, + limit=limit, + ) + + return { + "entities": [e.to_dict() for e in entities], + "count": len(entities), + "user_id": str(current_user.id), + } + except Exception as e: + logger.error(f"Error getting entities: {e}") + return JSONResponse( + status_code=500, + content={"message": f"Error getting entities: {str(e)}"}, + ) + + +@router.get("/entities/{entity_id}") +async def get_entity( + entity_id: str, + current_user: User = Depends(current_active_user), +): + """Get a single entity by ID with its relationship count.""" + try: + service = get_knowledge_graph_service() + entity = await service.get_entity( + entity_id=entity_id, + user_id=str(current_user.id), + ) + + if not entity: + raise HTTPException(status_code=404, detail="Entity not found") + + return {"entity": entity.to_dict()} + except HTTPException: + raise + except Exception as e: + logger.error(f"Error getting entity {entity_id}: {e}") + return JSONResponse( + status_code=500, + content={"message": f"Error getting entity: {str(e)}"}, + ) + + +@router.get("/entities/{entity_id}/relationships") +async def get_entity_relationships( + entity_id: str, + current_user: User = Depends(current_active_user), +): + """Get all relationships for an entity. + + Returns both incoming and outgoing relationships with + connected entity information. + """ + try: + service = get_knowledge_graph_service() + + # First verify entity exists + entity = await service.get_entity( + entity_id=entity_id, + user_id=str(current_user.id), + ) + + if not entity: + raise HTTPException(status_code=404, detail="Entity not found") + + relationships = await service.get_entity_relationships( + entity_id=entity_id, + user_id=str(current_user.id), + ) + + return { + "entity": entity.to_dict(), + "relationships": [r.to_dict() for r in relationships], + "count": len(relationships), + } + except HTTPException: + raise + except Exception as e: + logger.error(f"Error getting relationships for {entity_id}: {e}") + return JSONResponse( + status_code=500, + content={"message": f"Error getting relationships: {str(e)}"}, + ) + + +@router.delete("/entities/{entity_id}") +async def delete_entity( + entity_id: str, + current_user: User = Depends(current_active_user), +): + """Delete an entity and all its relationships.""" + try: + service = get_knowledge_graph_service() + deleted = await service.delete_entity( + entity_id=entity_id, + user_id=str(current_user.id), + ) + + if not deleted: + raise HTTPException(status_code=404, detail="Entity not found") + + return {"message": "Entity deleted successfully"} + except HTTPException: + raise + except Exception as e: + logger.error(f"Error deleting entity {entity_id}: {e}") + return JSONResponse( + status_code=500, + content={"message": f"Error deleting entity: {str(e)}"}, + ) + + +# ============================================================================= +# SEARCH ENDPOINT +# ============================================================================= + + +@router.get("/search") +async def search_entities( + query: str = Query(..., description="Search query for entity names and details"), + current_user: User = Depends(current_active_user), + limit: int = Query(default=20, ge=1, le=100), +): + """Search entities by name or details. + + Performs case-insensitive substring matching on entity names + and details fields. + """ + try: + service = get_knowledge_graph_service() + entities = await service.search_entities( + query=query, + user_id=str(current_user.id), + limit=limit, + ) + + return { + "query": query, + "entities": [e.to_dict() for e in entities], + "count": len(entities), + } + except Exception as e: + logger.error(f"Error searching entities: {e}") + return JSONResponse( + status_code=500, + content={"message": f"Error searching entities: {str(e)}"}, + ) + + +# ============================================================================= +# PROMISE ENDPOINTS +# ============================================================================= + + +@router.get("/promises") +async def get_promises( + current_user: User = Depends(current_active_user), + status: Optional[str] = Query( + default=None, + description="Filter by status (pending, in_progress, completed, cancelled)", + ), + limit: int = Query(default=50, ge=1, le=200), +): + """Get promises/tasks for the current user. + + Optionally filter by status. Returns promises ordered by + due date (ascending) then created date (descending). + """ + try: + # Validate status if provided + if status: + try: + PromiseStatus(status) + except ValueError: + raise HTTPException( + status_code=400, + detail=f"Invalid status. Must be one of: {[s.value for s in PromiseStatus]}", + ) + + service = get_knowledge_graph_service() + promises = await service.get_promises( + user_id=str(current_user.id), + status=status, + limit=limit, + ) + + return { + "promises": [p.to_dict() for p in promises], + "count": len(promises), + "user_id": str(current_user.id), + } + except HTTPException: + raise + except Exception as e: + logger.error(f"Error getting promises: {e}") + return JSONResponse( + status_code=500, + content={"message": f"Error getting promises: {str(e)}"}, + ) + + +@router.patch("/promises/{promise_id}") +async def update_promise_status( + promise_id: str, + request: UpdatePromiseRequest, + current_user: User = Depends(current_active_user), +): + """Update a promise's status. + + Valid statuses: pending, in_progress, completed, cancelled + """ + try: + # Validate status + try: + PromiseStatus(request.status) + except ValueError: + raise HTTPException( + status_code=400, + detail=f"Invalid status. Must be one of: {[s.value for s in PromiseStatus]}", + ) + + service = get_knowledge_graph_service() + promise = await service.update_promise_status( + promise_id=promise_id, + user_id=str(current_user.id), + status=request.status, + ) + + if not promise: + raise HTTPException(status_code=404, detail="Promise not found") + + return {"promise": promise.to_dict()} + except HTTPException: + raise + except Exception as e: + logger.error(f"Error updating promise {promise_id}: {e}") + return JSONResponse( + status_code=500, + content={"message": f"Error updating promise: {str(e)}"}, + ) + + +@router.delete("/promises/{promise_id}") +async def delete_promise( + promise_id: str, + current_user: User = Depends(current_active_user), +): + """Delete a promise.""" + try: + service = get_knowledge_graph_service() + deleted = await service.delete_promise( + promise_id=promise_id, + user_id=str(current_user.id), + ) + + if not deleted: + raise HTTPException(status_code=404, detail="Promise not found") + + return {"message": "Promise deleted successfully"} + except HTTPException: + raise + except Exception as e: + logger.error(f"Error deleting promise {promise_id}: {e}") + return JSONResponse( + status_code=500, + content={"message": f"Error deleting promise: {str(e)}"}, + ) + + +# ============================================================================= +# TIMELINE ENDPOINT +# ============================================================================= + + +@router.get("/timeline") +async def get_timeline( + start: str = Query(..., description="Start date (ISO format)"), + end: str = Query(..., description="End date (ISO format)"), + current_user: User = Depends(current_active_user), + limit: int = Query(default=100, ge=1, le=500), +): + """Get entities within a time range. + + Returns entities ordered by their start_time or created_at date. + Useful for building timeline visualizations. + """ + try: + # Parse dates + try: + start_dt = datetime.fromisoformat(start.replace("Z", "+00:00")) + end_dt = datetime.fromisoformat(end.replace("Z", "+00:00")) + except ValueError as e: + raise HTTPException( + status_code=400, + detail=f"Invalid date format. Use ISO format (YYYY-MM-DDTHH:MM:SS): {e}", + ) + + if start_dt > end_dt: + raise HTTPException( + status_code=400, + detail="Start date must be before end date", + ) + + service = get_knowledge_graph_service() + entities = await service.get_timeline( + user_id=str(current_user.id), + start=start_dt, + end=end_dt, + limit=limit, + ) + + return { + "start": start, + "end": end, + "entities": [e.to_dict() for e in entities], + "count": len(entities), + } + except HTTPException: + raise + except Exception as e: + logger.error(f"Error getting timeline: {e}") + return JSONResponse( + status_code=500, + content={"message": f"Error getting timeline: {str(e)}"}, + ) + + +# ============================================================================= +# HEALTH CHECK +# ============================================================================= + + +@router.get("/health") +async def knowledge_graph_health(): + """Check knowledge graph service health. + + Tests Neo4j connection and returns status. + """ + try: + service = get_knowledge_graph_service() + is_healthy = await service.test_connection() + + if is_healthy: + return {"status": "healthy", "neo4j": "connected"} + else: + return JSONResponse( + status_code=503, + content={"status": "unhealthy", "neo4j": "disconnected"}, + ) + except Exception as e: + logger.error(f"Knowledge graph health check failed: {e}") + return JSONResponse( + status_code=503, + content={"status": "unhealthy", "error": str(e)}, + ) diff --git a/backends/advanced/src/advanced_omi_backend/routers/modules/memory_routes.py b/backends/advanced/src/advanced_omi_backend/routers/modules/memory_routes.py index 185f55ec..409f7b85 100644 --- a/backends/advanced/src/advanced_omi_backend/routers/modules/memory_routes.py +++ b/backends/advanced/src/advanced_omi_backend/routers/modules/memory_routes.py @@ -7,7 +7,7 @@ import logging from typing import Optional -from fastapi import APIRouter, Depends, Query, Body +from fastapi import APIRouter, Body, Depends, Query from pydantic import BaseModel from advanced_omi_backend.auth import current_active_user, current_superuser diff --git a/backends/advanced/src/advanced_omi_backend/routers/modules/obsidian_routes.py b/backends/advanced/src/advanced_omi_backend/routers/modules/obsidian_routes.py index f6a46a38..b02ed426 100644 --- a/backends/advanced/src/advanced_omi_backend/routers/modules/obsidian_routes.py +++ b/backends/advanced/src/advanced_omi_backend/routers/modules/obsidian_routes.py @@ -1,21 +1,21 @@ +import json import logging import os import uuid -import json +import zipfile from pathlib import Path -from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Body +from fastapi import APIRouter, Body, Depends, File, HTTPException, UploadFile +from pydantic import BaseModel from rq.exceptions import NoSuchJobError from rq.job import Job -from pydantic import BaseModel -import zipfile from advanced_omi_backend.auth import current_active_user, current_superuser from advanced_omi_backend.controllers.queue_controller import default_queue, redis_conn -from advanced_omi_backend.users import User from advanced_omi_backend.services.obsidian_service import obsidian_service -from advanced_omi_backend.utils.file_utils import extract_zip, ZipExtractionError +from advanced_omi_backend.users import User +from advanced_omi_backend.utils.file_utils import ZipExtractionError, extract_zip from advanced_omi_backend.workers.obsidian_jobs import ( count_markdown_files, ingest_obsidian_vault_job, diff --git a/backends/advanced/src/advanced_omi_backend/routers/modules/queue_routes.py b/backends/advanced/src/advanced_omi_backend/routers/modules/queue_routes.py index 51c07097..934cf0b1 100644 --- a/backends/advanced/src/advanced_omi_backend/routers/modules/queue_routes.py +++ b/backends/advanced/src/advanced_omi_backend/routers/modules/queue_routes.py @@ -4,15 +4,22 @@ """ import logging -from fastapi import APIRouter, Depends, Query, HTTPException, Request -from pydantic import BaseModel from typing import List, Optional +import redis.asyncio as aioredis +from fastapi import APIRouter, Depends, HTTPException, Query, Request +from pydantic import BaseModel +from rq.job import Job + from advanced_omi_backend.auth import current_active_user -from advanced_omi_backend.controllers.queue_controller import get_jobs, get_job_stats, redis_conn, QUEUE_NAMES, get_job_status_from_rq +from advanced_omi_backend.controllers.queue_controller import ( + QUEUE_NAMES, + get_job_stats, + get_job_status_from_rq, + get_jobs, + redis_conn, +) from advanced_omi_backend.users import User -from rq.job import Job -import redis.asyncio as aioredis logger = logging.getLogger(__name__) router = APIRouter(prefix="/queue", tags=["queue"]) @@ -187,7 +194,15 @@ async def get_jobs_by_client( ): """Get all jobs associated with a specific client device.""" try: - from rq.registry import FinishedJobRegistry, FailedJobRegistry, StartedJobRegistry, CanceledJobRegistry, DeferredJobRegistry, ScheduledJobRegistry + from rq.registry import ( + CanceledJobRegistry, + DeferredJobRegistry, + FailedJobRegistry, + FinishedJobRegistry, + ScheduledJobRegistry, + StartedJobRegistry, + ) + from advanced_omi_backend.controllers.queue_controller import get_queue from advanced_omi_backend.models.conversation import Conversation @@ -326,10 +341,10 @@ async def get_queue_worker_details( ): """Get detailed queue and worker status including task manager health.""" try: - from advanced_omi_backend.controllers.queue_controller import get_queue_health - from advanced_omi_backend.task_manager import get_task_manager import time + from advanced_omi_backend.controllers.queue_controller import get_queue_health + # Get queue health directly queue_health = get_queue_health() @@ -488,7 +503,13 @@ async def flush_jobs( try: from datetime import datetime, timedelta, timezone - from rq.registry import FinishedJobRegistry, FailedJobRegistry, CanceledJobRegistry + + from rq.registry import ( + CanceledJobRegistry, + FailedJobRegistry, + FinishedJobRegistry, + ) + from advanced_omi_backend.controllers.queue_controller import get_queue cutoff_time = datetime.now(timezone.utc) - timedelta(hours=request.older_than_hours) @@ -563,13 +584,14 @@ async def flush_all_jobs( try: from rq.registry import ( - FinishedJobRegistry, - FailedJobRegistry, CanceledJobRegistry, - StartedJobRegistry, DeferredJobRegistry, - ScheduledJobRegistry + FailedJobRegistry, + FinishedJobRegistry, + ScheduledJobRegistry, + StartedJobRegistry, ) + from advanced_omi_backend.controllers.queue_controller import get_queue total_removed = 0 @@ -705,6 +727,7 @@ async def get_redis_sessions( """Get Redis session tracking information.""" try: import redis.asyncio as aioredis + from advanced_omi_backend.controllers.queue_controller import REDIS_URL redis_client = aioredis.from_url(REDIS_URL) @@ -769,8 +792,10 @@ async def clear_old_sessions( raise HTTPException(status_code=403, detail="Admin access required") try: - import redis.asyncio as aioredis import time + + import redis.asyncio as aioredis + from advanced_omi_backend.controllers.queue_controller import REDIS_URL redis_client = aioredis.from_url(REDIS_URL) @@ -827,8 +852,13 @@ async def get_dashboard_data( - Client jobs for expanded clients """ try: + from rq.registry import ( + FailedJobRegistry, + FinishedJobRegistry, + StartedJobRegistry, + ) + from advanced_omi_backend.controllers import system_controller - from rq.registry import FinishedJobRegistry, FailedJobRegistry, StartedJobRegistry from advanced_omi_backend.controllers.queue_controller import get_queue # Parse expanded clients list @@ -912,6 +942,7 @@ async def fetch_streaming_status(): try: # Import session_controller for streaming status from advanced_omi_backend.controllers import session_controller + # Use the actual request object from the parent function return await session_controller.get_streaming_status(request) except Exception as e: @@ -943,8 +974,12 @@ def get_job_status(job): # Check all registries from rq.registry import ( - FinishedJobRegistry, FailedJobRegistry, StartedJobRegistry, - CanceledJobRegistry, DeferredJobRegistry, ScheduledJobRegistry + CanceledJobRegistry, + DeferredJobRegistry, + FailedJobRegistry, + FinishedJobRegistry, + ScheduledJobRegistry, + StartedJobRegistry, ) registries = [ diff --git a/backends/advanced/src/advanced_omi_backend/routers/modules/test_routes.py b/backends/advanced/src/advanced_omi_backend/routers/modules/test_routes.py index 6255b6d6..349fe33d 100644 --- a/backends/advanced/src/advanced_omi_backend/routers/modules/test_routes.py +++ b/backends/advanced/src/advanced_omi_backend/routers/modules/test_routes.py @@ -7,6 +7,7 @@ import logging from typing import Optional + from fastapi import APIRouter, HTTPException from advanced_omi_backend.services.plugin_service import get_plugin_router diff --git a/backends/advanced/src/advanced_omi_backend/routers/modules/websocket_routes.py b/backends/advanced/src/advanced_omi_backend/routers/modules/websocket_routes.py index 2671d7f6..4b244343 100644 --- a/backends/advanced/src/advanced_omi_backend/routers/modules/websocket_routes.py +++ b/backends/advanced/src/advanced_omi_backend/routers/modules/websocket_routes.py @@ -5,12 +5,13 @@ """ import logging -from fastapi import APIRouter, WebSocket, WebSocketDisconnect, Query from typing import Optional +from fastapi import APIRouter, Query, WebSocket, WebSocketDisconnect + from advanced_omi_backend.controllers.websocket_controller import ( handle_omi_websocket, - handle_pcm_websocket + handle_pcm_websocket, ) logger = logging.getLogger(__name__) diff --git a/backends/advanced/src/advanced_omi_backend/services/audio_stream/consumer.py b/backends/advanced/src/advanced_omi_backend/services/audio_stream/consumer.py index aeb12e02..455ebebe 100644 --- a/backends/advanced/src/advanced_omi_backend/services/audio_stream/consumer.py +++ b/backends/advanced/src/advanced_omi_backend/services/audio_stream/consumer.py @@ -11,6 +11,7 @@ import redis.asyncio as redis from redis import exceptions as redis_exceptions + logger = logging.getLogger(__name__) diff --git a/backends/advanced/src/advanced_omi_backend/services/audio_stream/producer.py b/backends/advanced/src/advanced_omi_backend/services/audio_stream/producer.py index 224d69f4..e7fae522 100644 --- a/backends/advanced/src/advanced_omi_backend/services/audio_stream/producer.py +++ b/backends/advanced/src/advanced_omi_backend/services/audio_stream/producer.py @@ -465,6 +465,7 @@ def get_audio_stream_producer() -> AudioStreamProducer: if _producer_instance is None: import os + import redis.asyncio as redis_async redis_url = os.getenv("REDIS_URL", "redis://localhost:6379/0") diff --git a/backends/advanced/src/advanced_omi_backend/services/capabilities.py b/backends/advanced/src/advanced_omi_backend/services/capabilities.py new file mode 100644 index 00000000..85837920 --- /dev/null +++ b/backends/advanced/src/advanced_omi_backend/services/capabilities.py @@ -0,0 +1,170 @@ +""" +Capability/Requirements system for transcription providers and features. + +This module provides explicit contracts between providers and features: +- Providers declare capabilities: what they can produce (diarization, word_timestamps, segments) +- Features declare requirements: what they need to function +- Pipeline validates: check compatibility before running + +This enables conditional pipeline execution based on provider capabilities. +For example, VibeVoice provides built-in diarization, so pyannote diarization +can be skipped. Parakeet provides word timestamps, enabling pyannote diarization. +""" + +import logging +from enum import Enum +from typing import TYPE_CHECKING, Set, Tuple + +if TYPE_CHECKING: + from advanced_omi_backend.models.conversation import Conversation + +logger = logging.getLogger(__name__) + + +class TranscriptCapability(str, Enum): + """What a transcription provider can produce.""" + + WORD_TIMESTAMPS = "word_timestamps" # Word-level timing data + SEGMENTS = "segments" # Speaker segments in output + DIARIZATION = "diarization" # Speaker labels in segments (Speaker 0, Speaker 1, etc.) + + +class FeatureRequirement(str, Enum): + """What a feature needs from the transcript.""" + + WORDS = "words" # Needs word-level data + SEGMENTS = "segments" # Needs speaker segments + DIARIZATION = "diarization" # Needs speaker labels + + +# Feature requirements registry +# Maps feature names to the set of requirements needed to run that feature +FEATURE_REQUIREMENTS: dict[str, Set[FeatureRequirement]] = { + # Pyannote diarization needs word timestamps to align speaker segments + "pyannote_diarization": {FeatureRequirement.WORDS}, + # Summary works with any transcript text + "conversation_summary": set(), + # Memory extraction benefits from segments but can work without + "memory_extraction": set(), + # Action items works with plain text + "action_items": set(), + # Title generation works with plain text + "title_generation": set(), +} + + +def check_requirements( + conversation: "Conversation", + feature: str, +) -> Tuple[bool, str]: + """ + Check if a conversation has what a feature needs. + + Args: + conversation: The conversation to check + feature: The feature name to validate requirements for + + Returns: + Tuple of (can_run: bool, reason: str) + - can_run: True if feature can run, False otherwise + - reason: "OK" if can run, or description of what's missing + """ + required = FEATURE_REQUIREMENTS.get(feature, set()) + + if not required: + return True, "OK" + + transcript = conversation.active_transcript + if not transcript: + return False, "No active transcript" + + available: Set[FeatureRequirement] = set() + + # Check what's available in the transcript + if transcript.words: + available.add(FeatureRequirement.WORDS) + if transcript.segments: + available.add(FeatureRequirement.SEGMENTS) + + # Check for diarization source in metadata + diarization_source = transcript.metadata.get("diarization_source") + if diarization_source: + available.add(FeatureRequirement.DIARIZATION) + + missing = required - available + if missing: + missing_names = [r.value for r in missing] + return False, f"Missing: {', '.join(missing_names)}" + + return True, "OK" + + +def get_provider_capabilities(transcript_version: "Conversation.TranscriptVersion") -> dict: + """ + Get provider capabilities from transcript version metadata. + + Args: + transcript_version: The transcript version to check + + Returns: + Dict of capability name -> bool (whether provider has that capability) + """ + if not transcript_version or not transcript_version.metadata: + return {} + + return transcript_version.metadata.get("provider_capabilities", {}) + + +def provider_has_capability( + transcript_version: "Conversation.TranscriptVersion", + capability: TranscriptCapability, +) -> bool: + """ + Check if the provider that created this transcript has a specific capability. + + Args: + transcript_version: The transcript version to check + capability: The capability to check for + + Returns: + True if provider has the capability, False otherwise + """ + caps = get_provider_capabilities(transcript_version) + return caps.get(capability.value, False) + + +def should_run_pyannote_diarization( + transcript_version: "Conversation.TranscriptVersion", +) -> Tuple[bool, str]: + """ + Determine if pyannote diarization should run for this transcript. + + Decision logic: + 1. If provider already has diarization -> skip (use provider's diarization) + 2. If provider has word timestamps -> run pyannote + 3. Otherwise -> cannot run (no word timestamps available) + + Args: + transcript_version: The transcript version to evaluate + + Returns: + Tuple of (should_run: bool, reason: str) + """ + if not transcript_version: + return False, "No transcript version" + + caps = get_provider_capabilities(transcript_version) + + # Check if provider already did diarization + if caps.get(TranscriptCapability.DIARIZATION.value, False): + return False, "Provider already diarized" + + # Check if we have word timestamps (required for pyannote) + if caps.get(TranscriptCapability.WORD_TIMESTAMPS.value, False): + return True, "Provider has word timestamps, can run pyannote" + + # Check if words exist in transcript (legacy check) + if transcript_version.words: + return True, "Words available in transcript" + + return False, "No word timestamps available for diarization" diff --git a/backends/advanced/src/advanced_omi_backend/services/knowledge_graph/__init__.py b/backends/advanced/src/advanced_omi_backend/services/knowledge_graph/__init__.py new file mode 100644 index 00000000..da675616 --- /dev/null +++ b/backends/advanced/src/advanced_omi_backend/services/knowledge_graph/__init__.py @@ -0,0 +1,30 @@ +"""Knowledge Graph service for entity and relationship extraction. + +This module provides: +- Entity extraction from conversations using LLM +- Storage of entities and relationships in Neo4j +- API for querying knowledge graph (entities, relationships, promises, timeline) +""" + +from .models import ( + Entity, + EntityType, + ExtractionResult, + Promise, + PromiseStatus, + Relationship, + RelationshipType, +) +from .service import KnowledgeGraphService, get_knowledge_graph_service + +__all__ = [ + "Entity", + "EntityType", + "Relationship", + "RelationshipType", + "Promise", + "PromiseStatus", + "ExtractionResult", + "KnowledgeGraphService", + "get_knowledge_graph_service", +] diff --git a/backends/advanced/src/advanced_omi_backend/services/knowledge_graph/entity_extractor.py b/backends/advanced/src/advanced_omi_backend/services/knowledge_graph/entity_extractor.py new file mode 100644 index 00000000..dc4724f2 --- /dev/null +++ b/backends/advanced/src/advanced_omi_backend/services/knowledge_graph/entity_extractor.py @@ -0,0 +1,344 @@ +"""LLM-based entity and relationship extraction from conversations. + +This module uses the configured LLM provider to extract entities, +relationships, and promises from conversation transcripts. +""" + +import json +import logging +from datetime import datetime, timedelta +from typing import Any, Dict, List, Optional + +from advanced_omi_backend.model_registry import get_models_registry + +from .models import ( + EntityType, + ExtractedEntity, + ExtractedPromise, + ExtractedRelationship, + ExtractionResult, + RelationshipType, +) + +logger = logging.getLogger("knowledge_graph") + + +ENTITY_EXTRACTION_PROMPT = """You are an entity extraction system. Extract entities, relationships, and promises from conversation transcripts. + +ENTITY TYPES: +- person: Named individuals (not generic roles) +- organization: Companies, institutions, groups +- place: Locations, addresses, venues +- event: Meetings, appointments, activities with time +- thing: Products, objects, concepts mentioned + +RELATIONSHIP TYPES: +- works_at: Employment relationship +- lives_in: Residence +- knows: Personal connection +- attended: Participated in event +- located_at: Place within place +- part_of: Membership or inclusion +- related_to: General association + +EXTRACTION RULES: +1. Only extract NAMED entities (not "my friend" but "John") +2. Use "speaker" as the subject when the user mentions themselves +3. Extract temporal info for events (dates, times) +4. Capture promises/commitments with deadlines +5. Skip filler words, small talk, and vague references +6. Normalize names (capitalize properly) +7. Assign appropriate emoji icons to entities + +Return a JSON object with this structure: +{ + "entities": [ + { + "name": "Entity Name", + "type": "person|organization|place|event|thing", + "details": "Brief description or context", + "icon": "Appropriate emoji", + "when": "Time reference for events (optional)" + } + ], + "relationships": [ + { + "subject": "Entity name or 'speaker'", + "relation": "works_at|lives_in|knows|attended|located_at|part_of|related_to", + "object": "Target entity name" + } + ], + "promises": [ + { + "action": "What was promised", + "to": "Person promised to (optional)", + "deadline": "When it should be done (optional)" + } + ] +} + +If no entities, relationships, or promises are found, return empty arrays. +Only return valid JSON, no additional text.""" + + +def _get_llm_client(): + """Get async OpenAI client from model registry.""" + from advanced_omi_backend.services.memory.providers.llm_providers import ( + _get_openai_client, + ) + + registry = get_models_registry() + if not registry: + raise RuntimeError("Model registry not configured") + + llm_def = registry.get_default("llm") + if not llm_def: + raise RuntimeError("No default LLM defined in config.yml") + + return _get_openai_client( + api_key=llm_def.api_key or "", + base_url=llm_def.model_url, + is_async=True + ), llm_def + + +async def extract_entities_from_transcript( + transcript: str, + conversation_id: Optional[str] = None, + custom_prompt: Optional[str] = None, +) -> ExtractionResult: + """Extract entities, relationships, and promises from a transcript. + + Args: + transcript: The conversation transcript text + conversation_id: Optional ID of the source conversation + custom_prompt: Optional custom prompt to override default + + Returns: + ExtractionResult containing extracted entities, relationships, and promises + """ + if not transcript or not transcript.strip(): + logger.debug("Empty transcript, returning empty extraction result") + return ExtractionResult() + + try: + client, llm_def = _get_llm_client() + prompt = custom_prompt or ENTITY_EXTRACTION_PROMPT + + response = await client.chat.completions.create( + model=llm_def.model_name, + messages=[ + {"role": "system", "content": prompt}, + {"role": "user", "content": transcript}, + ], + temperature=0.1, + max_tokens=2000, + response_format={"type": "json_object"}, + ) + + content = (response.choices[0].message.content or "").strip() + if not content: + logger.warning("LLM returned empty response for entity extraction") + return ExtractionResult() + + return _parse_extraction_response(content) + + except Exception as e: + logger.error(f"Entity extraction failed: {e}") + return ExtractionResult() + + +def _parse_extraction_response(content: str) -> ExtractionResult: + """Parse LLM response into ExtractionResult. + + Args: + content: JSON string from LLM response + + Returns: + Parsed ExtractionResult + """ + try: + # Handle potential tags from some models + if "" in content: + content = content.split("", 1)[1].strip() + + data = json.loads(content) + + # Parse entities + entities = [] + for e in data.get("entities", []): + if isinstance(e, dict) and e.get("name"): + entity_type = _normalize_entity_type(e.get("type", "thing")) + entities.append(ExtractedEntity( + name=e["name"].strip(), + type=entity_type, + details=e.get("details"), + icon=e.get("icon") or _get_default_icon(entity_type), + when=e.get("when"), + )) + + # Parse relationships + relationships = [] + for r in data.get("relationships", []): + if isinstance(r, dict) and r.get("subject") and r.get("object"): + relationships.append(ExtractedRelationship( + subject=r["subject"].strip(), + relation=_normalize_relation_type(r.get("relation", "related_to")), + object=r["object"].strip(), + )) + + # Parse promises + promises = [] + for p in data.get("promises", []): + if isinstance(p, dict) and p.get("action"): + promises.append(ExtractedPromise( + action=p["action"].strip(), + to=p.get("to"), + deadline=p.get("deadline"), + )) + + logger.info(f"Extracted {len(entities)} entities, {len(relationships)} relationships, {len(promises)} promises") + return ExtractionResult( + entities=entities, + relationships=relationships, + promises=promises, + ) + + except json.JSONDecodeError as e: + logger.error(f"Failed to parse extraction JSON: {e}") + return ExtractionResult() + except Exception as e: + logger.error(f"Error parsing extraction response: {e}") + return ExtractionResult() + + +def _normalize_entity_type(type_str: str) -> str: + """Normalize entity type string to valid EntityType value.""" + type_lower = type_str.lower().strip() + type_map = { + "person": "person", + "people": "person", + "individual": "person", + "organization": "organization", + "company": "organization", + "org": "organization", + "institution": "organization", + "place": "place", + "location": "place", + "venue": "place", + "address": "place", + "event": "event", + "meeting": "event", + "appointment": "event", + "activity": "event", + "thing": "thing", + "object": "thing", + "product": "thing", + "concept": "thing", + } + return type_map.get(type_lower, "thing") + + +def _normalize_relation_type(relation_str: str) -> str: + """Normalize relationship type string.""" + relation_lower = relation_str.lower().strip().replace(" ", "_").replace("-", "_") + relation_map = { + "works_at": "works_at", + "employed_at": "works_at", + "works_for": "works_at", + "lives_in": "lives_in", + "resides_in": "lives_in", + "knows": "knows", + "met": "knows", + "friends_with": "knows", + "attended": "attended", + "participated_in": "attended", + "went_to": "attended", + "located_at": "located_at", + "in": "located_at", + "at": "located_at", + "part_of": "part_of", + "member_of": "part_of", + "belongs_to": "part_of", + "related_to": "related_to", + "associated_with": "related_to", + "connected_to": "related_to", + } + return relation_map.get(relation_lower, "related_to") + + +def _get_default_icon(entity_type: str) -> str: + """Get default emoji icon for entity type.""" + icons = { + "person": "πŸ‘€", + "organization": "🏒", + "place": "πŸ“", + "event": "πŸ“…", + "thing": "πŸ“¦", + "conversation": "πŸ’¬", + "promise": "βœ…", + "fact": "πŸ’‘", + } + return icons.get(entity_type, "πŸ“Œ") + + +def parse_natural_datetime(text: Optional[str], reference_date: Optional[datetime] = None) -> Optional[datetime]: + """Parse natural language date/time into datetime. + + Args: + text: Natural language time reference (e.g., "next Tuesday 2pm", "Friday") + reference_date: Reference date for relative times (defaults to now) + + Returns: + Parsed datetime or None if parsing fails + """ + if not text: + return None + + reference = reference_date or datetime.utcnow() + text_lower = text.lower().strip() + + # Simple patterns - can be extended with dateparser library later + weekdays = { + "monday": 0, "tuesday": 1, "wednesday": 2, "thursday": 3, + "friday": 4, "saturday": 5, "sunday": 6, + "mon": 0, "tue": 1, "wed": 2, "thu": 3, "fri": 4, "sat": 5, "sun": 6, + } + + try: + # Handle "next [weekday]" + for day_name, day_num in weekdays.items(): + if day_name in text_lower: + days_ahead = day_num - reference.weekday() + if days_ahead <= 0: # Target day already happened this week + days_ahead += 7 + if "next" in text_lower: + days_ahead += 7 # "next" means the following week + return reference + timedelta(days=days_ahead) + + # Handle "tomorrow" + if "tomorrow" in text_lower: + return reference + timedelta(days=1) + + # Handle "today" + if "today" in text_lower: + return reference + + # Handle "in X days/weeks" + import re + match = re.search(r"in (\d+) (day|week|month)s?", text_lower) + if match: + num = int(match.group(1)) + unit = match.group(2) + if unit == "day": + return reference + timedelta(days=num) + elif unit == "week": + return reference + timedelta(weeks=num) + elif unit == "month": + return reference + timedelta(days=num * 30) # Approximate + + return None + + except Exception as e: + logger.debug(f"Could not parse datetime '{text}': {e}") + return None diff --git a/backends/advanced/src/advanced_omi_backend/services/knowledge_graph/models.py b/backends/advanced/src/advanced_omi_backend/services/knowledge_graph/models.py new file mode 100644 index 00000000..c4cf533c --- /dev/null +++ b/backends/advanced/src/advanced_omi_backend/services/knowledge_graph/models.py @@ -0,0 +1,236 @@ +"""Pydantic models for Knowledge Graph entities and relationships. + +This module defines the data structures used throughout the knowledge graph +service for storing and retrieving entities, relationships, and promises. +""" + +import uuid +from datetime import datetime +from enum import Enum +from typing import Any, Dict, List, Optional + +from pydantic import BaseModel, Field + + +class EntityType(str, Enum): + """Supported entity types in the knowledge graph.""" + PERSON = "person" + PLACE = "place" + ORGANIZATION = "organization" + EVENT = "event" + CONVERSATION = "conversation" + PROMISE = "promise" + FACT = "fact" + THING = "thing" # Generic fallback + + +class RelationshipType(str, Enum): + """Supported relationship types between entities.""" + MENTIONED_IN = "MENTIONED_IN" + WORKS_AT = "WORKS_AT" + LIVES_IN = "LIVES_IN" + KNOWS = "KNOWS" + PROMISED_TO = "PROMISED_TO" + EXTRACTED_FROM = "EXTRACTED_FROM" + ABOUT = "ABOUT" + ATTENDED = "ATTENDED" + LOCATED_AT = "LOCATED_AT" + PART_OF = "PART_OF" + RELATED_TO = "RELATED_TO" + + +class PromiseStatus(str, Enum): + """Status of a promise/task.""" + PENDING = "pending" + IN_PROGRESS = "in_progress" + COMPLETED = "completed" + CANCELLED = "cancelled" + OVERDUE = "overdue" + + +class Entity(BaseModel): + """Represents an entity in the knowledge graph. + + Entities are nodes in the graph representing people, places, events, etc. + Each entity belongs to a specific user and can have relationships with + other entities. + """ + id: str = Field(default_factory=lambda: str(uuid.uuid4())) + name: str + type: EntityType + user_id: str + details: Optional[str] = None + icon: Optional[str] = None # Emoji for display + embedding: Optional[List[float]] = None # For semantic search + metadata: Dict[str, Any] = Field(default_factory=dict) + created_at: datetime = Field(default_factory=datetime.utcnow) + updated_at: datetime = Field(default_factory=datetime.utcnow) + + # Type-specific fields + location: Optional[Dict[str, float]] = None # For Place: {lat, lon} + start_time: Optional[datetime] = None # For Event + end_time: Optional[datetime] = None # For Event + conversation_id: Optional[str] = None # For Conversation entity + + # Populated when fetching with relationships + relationships: Optional[List["Relationship"]] = None + relationship_count: Optional[int] = None + + class Config: + use_enum_values = True + + def to_dict(self) -> Dict[str, Any]: + """Convert to dictionary for JSON serialization.""" + data = { + "id": self.id, + "name": self.name, + "type": self.type, + "user_id": self.user_id, + "details": self.details, + "icon": self.icon, + "metadata": self.metadata, + "created_at": self.created_at.isoformat() if self.created_at else None, + "updated_at": self.updated_at.isoformat() if self.updated_at else None, + } + if self.location: + data["location"] = self.location + if self.start_time: + data["start_time"] = self.start_time.isoformat() + if self.end_time: + data["end_time"] = self.end_time.isoformat() + if self.conversation_id: + data["conversation_id"] = self.conversation_id + if self.relationships is not None: + data["relationships"] = [r.to_dict() for r in self.relationships] + if self.relationship_count is not None: + data["relationship_count"] = self.relationship_count + return data + + +class Relationship(BaseModel): + """Represents a relationship between two entities. + + Relationships are edges in the graph connecting entities with + typed connections (e.g., WORKS_AT, KNOWS, MENTIONED_IN). + """ + id: str = Field(default_factory=lambda: str(uuid.uuid4())) + type: RelationshipType + source_id: str # Entity ID + target_id: str # Entity ID + user_id: str + context: Optional[str] = None # Additional context about the relationship + timestamp: Optional[datetime] = None # When was this relationship established + metadata: Dict[str, Any] = Field(default_factory=dict) + created_at: datetime = Field(default_factory=datetime.utcnow) + + # For start/end dates on temporal relationships + start_date: Optional[datetime] = None + end_date: Optional[datetime] = None + + # Populated when fetching relationships + source_entity: Optional[Entity] = None + target_entity: Optional[Entity] = None + + class Config: + use_enum_values = True + + def to_dict(self) -> Dict[str, Any]: + """Convert to dictionary for JSON serialization.""" + data = { + "id": self.id, + "type": self.type, + "source_id": self.source_id, + "target_id": self.target_id, + "user_id": self.user_id, + "context": self.context, + "timestamp": self.timestamp.isoformat() if self.timestamp else None, + "metadata": self.metadata, + "created_at": self.created_at.isoformat() if self.created_at else None, + } + if self.start_date: + data["start_date"] = self.start_date.isoformat() + if self.end_date: + data["end_date"] = self.end_date.isoformat() + if self.source_entity: + data["source_entity"] = self.source_entity.to_dict() + if self.target_entity: + data["target_entity"] = self.target_entity.to_dict() + return data + + +class Promise(BaseModel): + """Represents a promise or task extracted from conversations. + + Promises are commitments made during conversations that can be + tracked and followed up on. + """ + id: str = Field(default_factory=lambda: str(uuid.uuid4())) + user_id: str + action: str # What was promised + to_entity_id: Optional[str] = None # Who the promise was made to + to_entity_name: Optional[str] = None # Name for display + status: PromiseStatus = PromiseStatus.PENDING + due_date: Optional[datetime] = None + completed_at: Optional[datetime] = None + source_conversation_id: Optional[str] = None # Where this was extracted from + context: Optional[str] = None # Additional context + metadata: Dict[str, Any] = Field(default_factory=dict) + created_at: datetime = Field(default_factory=datetime.utcnow) + updated_at: datetime = Field(default_factory=datetime.utcnow) + + class Config: + use_enum_values = True + + def to_dict(self) -> Dict[str, Any]: + """Convert to dictionary for JSON serialization.""" + return { + "id": self.id, + "user_id": self.user_id, + "action": self.action, + "to_entity_id": self.to_entity_id, + "to_entity_name": self.to_entity_name, + "status": self.status, + "due_date": self.due_date.isoformat() if self.due_date else None, + "completed_at": self.completed_at.isoformat() if self.completed_at else None, + "source_conversation_id": self.source_conversation_id, + "context": self.context, + "metadata": self.metadata, + "created_at": self.created_at.isoformat() if self.created_at else None, + "updated_at": self.updated_at.isoformat() if self.updated_at else None, + } + + +class ExtractedEntity(BaseModel): + """Entity as extracted by LLM before Neo4j storage.""" + name: str + type: str # Will be validated against EntityType + details: Optional[str] = None + icon: Optional[str] = None + # For events + when: Optional[str] = None # Natural language time reference + + +class ExtractedRelationship(BaseModel): + """Relationship as extracted by LLM.""" + subject: str # Entity name or "speaker" + relation: str # Relationship type + object: str # Entity name + + +class ExtractedPromise(BaseModel): + """Promise as extracted by LLM.""" + action: str + to: Optional[str] = None # Entity name + deadline: Optional[str] = None # Natural language deadline + + +class ExtractionResult(BaseModel): + """Result of entity extraction from a conversation.""" + entities: List[ExtractedEntity] = Field(default_factory=list) + relationships: List[ExtractedRelationship] = Field(default_factory=list) + promises: List[ExtractedPromise] = Field(default_factory=list) + + # Populated after storage + stored_entity_ids: List[str] = Field(default_factory=list) + stored_relationship_ids: List[str] = Field(default_factory=list) + stored_promise_ids: List[str] = Field(default_factory=list) diff --git a/backends/advanced/src/advanced_omi_backend/services/knowledge_graph/queries.py b/backends/advanced/src/advanced_omi_backend/services/knowledge_graph/queries.py new file mode 100644 index 00000000..bd42be9f --- /dev/null +++ b/backends/advanced/src/advanced_omi_backend/services/knowledge_graph/queries.py @@ -0,0 +1,328 @@ +"""Cypher query templates for Knowledge Graph operations. + +This module contains all Cypher queries used by the KnowledgeGraphService +for CRUD operations on entities, relationships, and promises in Neo4j. +""" + +# ============================================================================= +# ENTITY QUERIES +# ============================================================================= + +CREATE_ENTITY = """ +MERGE (e:Entity {id: $id}) +SET e.name = $name, + e.type = $type, + e.user_id = $user_id, + e.details = $details, + e.icon = $icon, + e.metadata = $metadata, + e.created_at = datetime($created_at), + e.updated_at = datetime($updated_at) +WITH e +CALL apoc.do.when( + $type = 'person', + 'SET e:Person RETURN e', + 'RETURN e', + {e: e} +) YIELD value AS v1 +CALL apoc.do.when( + $type = 'place', + 'SET e:Place, e.location = $location RETURN e', + 'RETURN e', + {e: e, location: $location} +) YIELD value AS v2 +CALL apoc.do.when( + $type = 'organization', + 'SET e:Organization RETURN e', + 'RETURN e', + {e: e} +) YIELD value AS v3 +CALL apoc.do.when( + $type = 'event', + 'SET e:Event, e.start_time = datetime($start_time), e.end_time = datetime($end_time) RETURN e', + 'RETURN e', + {e: e, start_time: $start_time, end_time: $end_time} +) YIELD value AS v4 +CALL apoc.do.when( + $type = 'conversation', + 'SET e:Conversation, e.conversation_id = $conversation_id RETURN e', + 'RETURN e', + {e: e, conversation_id: $conversation_id} +) YIELD value AS v5 +CALL apoc.do.when( + $type = 'promise', + 'SET e:Promise RETURN e', + 'RETURN e', + {e: e} +) YIELD value AS v6 +CALL apoc.do.when( + $type = 'fact', + 'SET e:Fact RETURN e', + 'RETURN e', + {e: e} +) YIELD value AS v7 +RETURN e +""" + +# Simpler version without APOC for basic installations +CREATE_ENTITY_SIMPLE = """ +MERGE (e:Entity {id: $id}) +SET e.name = $name, + e.type = $type, + e.user_id = $user_id, + e.details = $details, + e.icon = $icon, + e.metadata = $metadata, + e.created_at = datetime($created_at), + e.updated_at = datetime($updated_at), + e.location = $location, + e.start_time = CASE WHEN $start_time IS NOT NULL THEN datetime($start_time) ELSE NULL END, + e.end_time = CASE WHEN $end_time IS NOT NULL THEN datetime($end_time) ELSE NULL END, + e.conversation_id = $conversation_id +RETURN e +""" + +GET_ENTITY_BY_ID = """ +MATCH (e:Entity {id: $id, user_id: $user_id}) +OPTIONAL MATCH (e)-[r]-() +RETURN e, count(r) as relationship_count +""" + +GET_ENTITIES_BY_USER = """ +MATCH (e:Entity {user_id: $user_id}) +WHERE $type IS NULL OR e.type = $type +OPTIONAL MATCH (e)-[r]-() +WITH e, count(r) as relationship_count +RETURN e, relationship_count +ORDER BY e.updated_at DESC +LIMIT $limit +""" + +SEARCH_ENTITIES_BY_NAME = """ +MATCH (e:Entity {user_id: $user_id}) +WHERE toLower(e.name) CONTAINS toLower($query) + OR ($query IS NOT NULL AND e.details IS NOT NULL AND toLower(e.details) CONTAINS toLower($query)) +OPTIONAL MATCH (e)-[r]-() +WITH e, count(r) as relationship_count +RETURN e, relationship_count +ORDER BY e.updated_at DESC +LIMIT $limit +""" + +FIND_ENTITY_BY_NAME = """ +MATCH (e:Entity {user_id: $user_id}) +WHERE toLower(e.name) = toLower($name) +RETURN e +LIMIT 1 +""" + +DELETE_ENTITY = """ +MATCH (e:Entity {id: $id, user_id: $user_id}) +DETACH DELETE e +RETURN count(e) as deleted_count +""" + +UPDATE_ENTITY = """ +MATCH (e:Entity {id: $id, user_id: $user_id}) +SET e.name = COALESCE($name, e.name), + e.details = COALESCE($details, e.details), + e.icon = COALESCE($icon, e.icon), + e.metadata = COALESCE($metadata, e.metadata), + e.updated_at = datetime() +RETURN e +""" + +# ============================================================================= +# RELATIONSHIP QUERIES +# ============================================================================= + +CREATE_RELATIONSHIP = """ +MATCH (source:Entity {id: $source_id, user_id: $user_id}) +MATCH (target:Entity {id: $target_id, user_id: $user_id}) +MERGE (source)-[r:RELATED_TO {id: $id}]->(target) +SET r.type = $type, + r.user_id = $user_id, + r.context = $context, + r.timestamp = CASE WHEN $timestamp IS NOT NULL THEN datetime($timestamp) ELSE NULL END, + r.start_date = CASE WHEN $start_date IS NOT NULL THEN datetime($start_date) ELSE NULL END, + r.end_date = CASE WHEN $end_date IS NOT NULL THEN datetime($end_date) ELSE NULL END, + r.metadata = $metadata, + r.created_at = datetime($created_at) +RETURN r, source, target +""" + +GET_ENTITY_RELATIONSHIPS = """ +MATCH (e:Entity {id: $entity_id, user_id: $user_id}) +OPTIONAL MATCH (e)-[r]->(target:Entity) +OPTIONAL MATCH (source:Entity)-[r2]->(e) +WITH e, + collect(DISTINCT {rel: r, target: target, direction: 'outgoing'}) as outgoing, + collect(DISTINCT {rel: r2, source: source, direction: 'incoming'}) as incoming +RETURN e, outgoing, incoming +""" + +GET_RELATIONSHIPS_BETWEEN = """ +MATCH (source:Entity {id: $source_id, user_id: $user_id}) +MATCH (target:Entity {id: $target_id, user_id: $user_id}) +MATCH (source)-[r]->(target) +RETURN r, source, target +""" + +DELETE_RELATIONSHIP = """ +MATCH ()-[r {id: $id}]->() +WHERE r.user_id = $user_id +DELETE r +RETURN count(r) as deleted_count +""" + +# ============================================================================= +# PROMISE QUERIES +# ============================================================================= + +CREATE_PROMISE = """ +MERGE (p:Promise:Entity {id: $id}) +SET p.user_id = $user_id, + p.action = $action, + p.name = $action, + p.type = 'promise', + p.to_entity_id = $to_entity_id, + p.to_entity_name = $to_entity_name, + p.status = $status, + p.due_date = CASE WHEN $due_date IS NOT NULL THEN datetime($due_date) ELSE NULL END, + p.completed_at = CASE WHEN $completed_at IS NOT NULL THEN datetime($completed_at) ELSE NULL END, + p.source_conversation_id = $source_conversation_id, + p.context = $context, + p.metadata = $metadata, + p.created_at = datetime($created_at), + p.updated_at = datetime($updated_at) +WITH p +OPTIONAL MATCH (target:Entity {id: $to_entity_id, user_id: $user_id}) +FOREACH (_ IN CASE WHEN target IS NOT NULL THEN [1] ELSE [] END | + MERGE (p)-[:PROMISED_TO]->(target) +) +OPTIONAL MATCH (conv:Entity {conversation_id: $source_conversation_id, user_id: $user_id}) +FOREACH (_ IN CASE WHEN conv IS NOT NULL THEN [1] ELSE [] END | + MERGE (p)-[:EXTRACTED_FROM]->(conv) +) +RETURN p +""" + +GET_PROMISES_BY_USER = """ +MATCH (p:Promise {user_id: $user_id}) +WHERE $status IS NULL OR p.status = $status +OPTIONAL MATCH (p)-[:PROMISED_TO]->(target:Entity) +RETURN p, target +ORDER BY + CASE WHEN p.due_date IS NOT NULL THEN p.due_date ELSE datetime('9999-12-31') END ASC, + p.created_at DESC +LIMIT $limit +""" + +GET_PROMISE_BY_ID = """ +MATCH (p:Promise {id: $id, user_id: $user_id}) +OPTIONAL MATCH (p)-[:PROMISED_TO]->(target:Entity) +OPTIONAL MATCH (p)-[:EXTRACTED_FROM]->(conv:Entity) +RETURN p, target, conv +""" + +UPDATE_PROMISE_STATUS = """ +MATCH (p:Promise {id: $id, user_id: $user_id}) +SET p.status = $status, + p.updated_at = datetime(), + p.completed_at = CASE WHEN $status = 'completed' THEN datetime() ELSE p.completed_at END +RETURN p +""" + +DELETE_PROMISE = """ +MATCH (p:Promise {id: $id, user_id: $user_id}) +DETACH DELETE p +RETURN count(p) as deleted_count +""" + +# ============================================================================= +# TIMELINE QUERIES +# ============================================================================= + +GET_TIMELINE = """ +MATCH (e:Entity {user_id: $user_id}) +WHERE (e.start_time IS NOT NULL AND e.start_time >= datetime($start) AND e.start_time <= datetime($end)) + OR (e.created_at >= datetime($start) AND e.created_at <= datetime($end)) +OPTIONAL MATCH (e)-[r]-() +WITH e, count(r) as relationship_count +RETURN e, relationship_count +ORDER BY COALESCE(e.start_time, e.created_at) ASC +LIMIT $limit +""" + +# ============================================================================= +# CONVERSATION ENTITY QUERIES +# ============================================================================= + +CREATE_CONVERSATION_ENTITY = """ +MERGE (c:Conversation:Entity {conversation_id: $conversation_id, user_id: $user_id}) +SET c.id = COALESCE(c.id, $id), + c.name = $name, + c.type = 'conversation', + c.details = $details, + c.metadata = $metadata, + c.created_at = COALESCE(c.created_at, datetime($created_at)), + c.updated_at = datetime($updated_at) +RETURN c +""" + +LINK_ENTITY_TO_CONVERSATION = """ +MATCH (e:Entity {id: $entity_id, user_id: $user_id}) +MATCH (c:Conversation {conversation_id: $conversation_id, user_id: $user_id}) +MERGE (e)-[r:MENTIONED_IN {id: $rel_id}]->(c) +SET r.timestamp = datetime($timestamp), + r.context = $context, + r.user_id = $user_id, + r.created_at = datetime() +RETURN r +""" + +GET_ENTITIES_FROM_CONVERSATION = """ +MATCH (e:Entity)-[:MENTIONED_IN]->(c:Conversation {conversation_id: $conversation_id, user_id: $user_id}) +RETURN e +ORDER BY e.name +""" + +# ============================================================================= +# GRAPH QUERIES +# ============================================================================= + +GET_ENTITY_GRAPH = """ +MATCH (center:Entity {id: $entity_id, user_id: $user_id}) +OPTIONAL MATCH path = (center)-[r*1..2]-(connected:Entity) +WHERE connected.user_id = $user_id +WITH center, collect(DISTINCT connected) as connected_nodes, + collect(DISTINCT relationships(path)) as rels +RETURN center, connected_nodes, rels +""" + +GET_USER_GRAPH = """ +MATCH (e:Entity {user_id: $user_id}) +OPTIONAL MATCH (e)-[r]->(e2:Entity {user_id: $user_id}) +WITH collect(DISTINCT e) as nodes, collect(DISTINCT {source: startNode(r).id, target: endNode(r).id, type: type(r), properties: properties(r)}) as edges +RETURN nodes, edges +LIMIT $limit +""" + +# ============================================================================= +# CLEANUP QUERIES +# ============================================================================= + +DELETE_USER_ENTITIES = """ +MATCH (e:Entity {user_id: $user_id}) +DETACH DELETE e +RETURN count(e) as deleted_count +""" + +DELETE_CONVERSATION_ENTITIES = """ +MATCH (e:Entity)-[:MENTIONED_IN]->(c:Conversation {conversation_id: $conversation_id, user_id: $user_id}) +DETACH DELETE e +WITH count(e) as entity_count +MATCH (c:Conversation {conversation_id: $conversation_id, user_id: $user_id}) +DETACH DELETE c +RETURN entity_count + count(c) as deleted_count +""" diff --git a/backends/advanced/src/advanced_omi_backend/services/knowledge_graph/service.py b/backends/advanced/src/advanced_omi_backend/services/knowledge_graph/service.py new file mode 100644 index 00000000..6562dccc --- /dev/null +++ b/backends/advanced/src/advanced_omi_backend/services/knowledge_graph/service.py @@ -0,0 +1,840 @@ +"""Knowledge Graph Service for entity and relationship management. + +This module provides the main service for: +- Extracting entities and relationships from conversations +- Storing and retrieving entities from Neo4j +- Managing promises and tracking their status +- Querying the knowledge graph +""" + +import logging +import os +import threading +import uuid +from datetime import datetime +from typing import Any, Dict, List, Optional + +from ..neo4j_client import Neo4jClient, Neo4jReadInterface, Neo4jWriteInterface +from . import queries +from .entity_extractor import extract_entities_from_transcript, parse_natural_datetime +from .models import ( + Entity, + EntityType, + ExtractionResult, + Promise, + PromiseStatus, + Relationship, + RelationshipType, +) + +logger = logging.getLogger("knowledge_graph") + +# Global service instance +_knowledge_graph_service: Optional["KnowledgeGraphService"] = None +_service_lock = threading.Lock() + + +class KnowledgeGraphService: + """Service for managing knowledge graph entities and relationships. + + This service handles: + - Entity extraction from conversation transcripts + - CRUD operations on entities and relationships + - Promise tracking and management + - Graph queries (timeline, search, related entities) + """ + + def __init__( + self, + neo4j_uri: Optional[str] = None, + neo4j_user: Optional[str] = None, + neo4j_password: Optional[str] = None, + ): + """Initialize the knowledge graph service. + + Args: + neo4j_uri: Neo4j connection URI (defaults to env var) + neo4j_user: Neo4j username (defaults to env var) + neo4j_password: Neo4j password (defaults to env var) + """ + # Construct URI from host if URI not provided + if neo4j_uri: + self.neo4j_uri = neo4j_uri + elif os.getenv("NEO4J_URI"): + self.neo4j_uri = os.getenv("NEO4J_URI") + else: + neo4j_host = os.getenv("NEO4J_HOST", "neo4j") + self.neo4j_uri = f"bolt://{neo4j_host}:7687" + + self.neo4j_user = neo4j_user or os.getenv("NEO4J_USER", "neo4j") + self.neo4j_password = neo4j_password or os.getenv("NEO4J_PASSWORD", "password") + + self._client: Optional[Neo4jClient] = None + self._read: Optional[Neo4jReadInterface] = None + self._write: Optional[Neo4jWriteInterface] = None + self._initialized = False + + def _ensure_initialized(self) -> None: + """Ensure Neo4j client is initialized.""" + if not self._initialized: + self._client = Neo4jClient( + uri=self.neo4j_uri, + user=self.neo4j_user, + password=self.neo4j_password, + ) + self._read = Neo4jReadInterface(self._client) + self._write = Neo4jWriteInterface(self._client) + self._initialized = True + logger.info("Knowledge Graph Service initialized with Neo4j connection") + + # ========================================================================= + # CONVERSATION PROCESSING + # ========================================================================= + + async def process_conversation( + self, + conversation_id: str, + transcript: str, + user_id: str, + conversation_name: Optional[str] = None, + ) -> Dict[str, Any]: + """Process a conversation to extract and store entities. + + This is the main entry point called from memory jobs after + memory extraction completes. + + Args: + conversation_id: Unique ID of the conversation + transcript: Full conversation transcript + user_id: User who owns the conversation + conversation_name: Optional display name for the conversation + + Returns: + Dictionary with extraction and storage results + """ + self._ensure_initialized() + + if not transcript or not transcript.strip(): + logger.debug(f"Empty transcript for conversation {conversation_id}") + return {"entities": 0, "relationships": 0, "promises": 0} + + try: + # Extract entities using LLM + extraction = await extract_entities_from_transcript( + transcript=transcript, + conversation_id=conversation_id, + ) + + if not extraction.entities and not extraction.promises: + logger.debug(f"No entities extracted from conversation {conversation_id}") + return {"entities": 0, "relationships": 0, "promises": 0} + + # Create conversation entity node + conv_entity_id = await self._create_conversation_entity( + conversation_id=conversation_id, + user_id=user_id, + name=conversation_name or f"Conversation {conversation_id[:8]}", + ) + + # Store extracted entities + entity_id_map = await self._store_entities( + extraction=extraction, + user_id=user_id, + conversation_id=conversation_id, + ) + + # Store relationships + rel_count = await self._store_relationships( + extraction=extraction, + user_id=user_id, + entity_id_map=entity_id_map, + conversation_id=conversation_id, + ) + + # Store promises + promise_count = await self._store_promises( + extraction=extraction, + user_id=user_id, + entity_id_map=entity_id_map, + conversation_id=conversation_id, + ) + + # Link entities to conversation + await self._link_entities_to_conversation( + entity_ids=list(entity_id_map.values()), + conversation_id=conversation_id, + user_id=user_id, + ) + + logger.info( + f"Processed conversation {conversation_id}: " + f"{len(entity_id_map)} entities, {rel_count} relationships, {promise_count} promises" + ) + + return { + "entities": len(entity_id_map), + "relationships": rel_count, + "promises": promise_count, + "entity_ids": list(entity_id_map.values()), + } + + except Exception as e: + logger.error(f"Error processing conversation {conversation_id}: {e}") + return {"entities": 0, "relationships": 0, "promises": 0, "error": str(e)} + + async def _create_conversation_entity( + self, + conversation_id: str, + user_id: str, + name: str, + ) -> str: + """Create or update a conversation entity node.""" + entity_id = str(uuid.uuid4()) + now = datetime.utcnow().isoformat() + + params = { + "id": entity_id, + "conversation_id": conversation_id, + "user_id": user_id, + "name": name, + "details": None, + "metadata": "{}", + "created_at": now, + "updated_at": now, + } + + self._write.run(queries.CREATE_CONVERSATION_ENTITY, **params) + return entity_id + + async def _store_entities( + self, + extraction: ExtractionResult, + user_id: str, + conversation_id: str, + ) -> Dict[str, str]: + """Store extracted entities in Neo4j. + + Returns: + Mapping of entity name (lowercase) to entity ID + """ + entity_id_map: Dict[str, str] = {} + now = datetime.utcnow().isoformat() + + for extracted in extraction.entities: + # Check if entity already exists for this user + existing = self._find_entity_by_name(extracted.name, user_id) + if existing: + entity_id_map[extracted.name.lower()] = existing["id"] + continue + + entity_id = str(uuid.uuid4()) + + # Parse event times if present + start_time = None + end_time = None + if extracted.type == "event" and extracted.when: + start_time = parse_natural_datetime(extracted.when) + if start_time: + start_time = start_time.isoformat() + + params = { + "id": entity_id, + "name": extracted.name, + "type": extracted.type, + "user_id": user_id, + "details": extracted.details, + "icon": extracted.icon, + "metadata": "{}", + "created_at": now, + "updated_at": now, + "location": None, + "start_time": start_time, + "end_time": end_time, + "conversation_id": None, + } + + self._write.run(queries.CREATE_ENTITY_SIMPLE, **params) + entity_id_map[extracted.name.lower()] = entity_id + extraction.stored_entity_ids.append(entity_id) + + return entity_id_map + + async def _store_relationships( + self, + extraction: ExtractionResult, + user_id: str, + entity_id_map: Dict[str, str], + conversation_id: str, + ) -> int: + """Store extracted relationships in Neo4j.""" + count = 0 + now = datetime.utcnow().isoformat() + + for rel in extraction.relationships: + # Handle "speaker" as a special case - could be linked to user profile + source_name = rel.subject.lower() + target_name = rel.object.lower() + + # Skip if we don't have both entities + if source_name not in entity_id_map and source_name != "speaker": + continue + if target_name not in entity_id_map: + continue + + # For "speaker", we could create a user entity or skip + if source_name == "speaker": + # For now, skip speaker relationships - could be enhanced later + continue + + rel_id = str(uuid.uuid4()) + + params = { + "id": rel_id, + "source_id": entity_id_map[source_name], + "target_id": entity_id_map[target_name], + "type": rel.relation.upper(), + "user_id": user_id, + "context": None, + "timestamp": now, + "start_date": None, + "end_date": None, + "metadata": "{}", + "created_at": now, + } + + self._write.run(queries.CREATE_RELATIONSHIP, **params) + extraction.stored_relationship_ids.append(rel_id) + count += 1 + + return count + + async def _store_promises( + self, + extraction: ExtractionResult, + user_id: str, + entity_id_map: Dict[str, str], + conversation_id: str, + ) -> int: + """Store extracted promises in Neo4j.""" + count = 0 + now = datetime.utcnow().isoformat() + + for promise in extraction.promises: + promise_id = str(uuid.uuid4()) + + # Find target entity if specified + to_entity_id = None + to_entity_name = promise.to + if promise.to: + to_entity_id = entity_id_map.get(promise.to.lower()) + + # Parse deadline + due_date = None + if promise.deadline: + parsed = parse_natural_datetime(promise.deadline) + if parsed: + due_date = parsed.isoformat() + + params = { + "id": promise_id, + "user_id": user_id, + "action": promise.action, + "to_entity_id": to_entity_id, + "to_entity_name": to_entity_name, + "status": PromiseStatus.PENDING.value, + "due_date": due_date, + "completed_at": None, + "source_conversation_id": conversation_id, + "context": None, + "metadata": "{}", + "created_at": now, + "updated_at": now, + } + + self._write.run(queries.CREATE_PROMISE, **params) + extraction.stored_promise_ids.append(promise_id) + count += 1 + + return count + + async def _link_entities_to_conversation( + self, + entity_ids: List[str], + conversation_id: str, + user_id: str, + ) -> None: + """Link entities to their source conversation.""" + now = datetime.utcnow().isoformat() + + for entity_id in entity_ids: + params = { + "entity_id": entity_id, + "conversation_id": conversation_id, + "user_id": user_id, + "rel_id": str(uuid.uuid4()), + "timestamp": now, + "context": None, + } + self._write.run(queries.LINK_ENTITY_TO_CONVERSATION, **params) + + def _find_entity_by_name(self, name: str, user_id: str) -> Optional[Dict[str, Any]]: + """Find existing entity by name for a user.""" + results = self._read.run( + queries.FIND_ENTITY_BY_NAME, + name=name, + user_id=user_id, + ) + if results: + return dict(results[0]["e"]) + return None + + # ========================================================================= + # ENTITY CRUD + # ========================================================================= + + async def get_entities( + self, + user_id: str, + entity_type: Optional[str] = None, + limit: int = 100, + ) -> List[Entity]: + """Get entities for a user, optionally filtered by type. + + Args: + user_id: User ID to filter by + entity_type: Optional entity type filter + limit: Maximum number of entities to return + + Returns: + List of Entity objects + """ + self._ensure_initialized() + + results = self._read.run( + queries.GET_ENTITIES_BY_USER, + user_id=user_id, + type=entity_type, + limit=limit, + ) + + entities = [] + for row in results: + entity_data = dict(row["e"]) + entity_data["relationship_count"] = row.get("relationship_count", 0) + entities.append(self._row_to_entity(entity_data)) + + return entities + + async def get_entity( + self, + entity_id: str, + user_id: str, + ) -> Optional[Entity]: + """Get a single entity by ID. + + Args: + entity_id: Entity UUID + user_id: User ID for permission check + + Returns: + Entity object or None if not found + """ + self._ensure_initialized() + + results = self._read.run( + queries.GET_ENTITY_BY_ID, + id=entity_id, + user_id=user_id, + ) + + if not results: + return None + + entity_data = dict(results[0]["e"]) + entity_data["relationship_count"] = results[0].get("relationship_count", 0) + return self._row_to_entity(entity_data) + + async def get_entity_relationships( + self, + entity_id: str, + user_id: str, + ) -> List[Relationship]: + """Get all relationships for an entity. + + Args: + entity_id: Entity UUID + user_id: User ID for permission check + + Returns: + List of Relationship objects + """ + self._ensure_initialized() + + results = self._read.run( + queries.GET_ENTITY_RELATIONSHIPS, + entity_id=entity_id, + user_id=user_id, + ) + + relationships = [] + if not results: + return relationships + + row = results[0] + + # Process outgoing relationships + for item in row.get("outgoing", []): + if item.get("rel") and item.get("target"): + rel_data = dict(item["rel"]) + rel_data["source_id"] = entity_id + rel_data["target_id"] = item["target"]["id"] + rel_data["target_entity"] = self._row_to_entity(dict(item["target"])) + relationships.append(self._row_to_relationship(rel_data)) + + # Process incoming relationships + for item in row.get("incoming", []): + if item.get("rel") and item.get("source"): + rel_data = dict(item["rel"]) + rel_data["source_id"] = item["source"]["id"] + rel_data["target_id"] = entity_id + rel_data["source_entity"] = self._row_to_entity(dict(item["source"])) + relationships.append(self._row_to_relationship(rel_data)) + + return relationships + + async def search_entities( + self, + query: str, + user_id: str, + limit: int = 20, + ) -> List[Entity]: + """Search entities by name or details. + + Args: + query: Search query string + user_id: User ID to filter by + limit: Maximum results to return + + Returns: + List of matching Entity objects + """ + self._ensure_initialized() + + results = self._read.run( + queries.SEARCH_ENTITIES_BY_NAME, + query=query, + user_id=user_id, + limit=limit, + ) + + entities = [] + for row in results: + entity_data = dict(row["e"]) + entity_data["relationship_count"] = row.get("relationship_count", 0) + entities.append(self._row_to_entity(entity_data)) + + return entities + + async def delete_entity( + self, + entity_id: str, + user_id: str, + ) -> bool: + """Delete an entity and its relationships. + + Args: + entity_id: Entity UUID to delete + user_id: User ID for permission check + + Returns: + True if deleted, False if not found + """ + self._ensure_initialized() + + results = self._write.run( + queries.DELETE_ENTITY, + id=entity_id, + user_id=user_id, + ) + + deleted = results[0]["deleted_count"] if results else 0 + return deleted > 0 + + # ========================================================================= + # PROMISE OPERATIONS + # ========================================================================= + + async def get_promises( + self, + user_id: str, + status: Optional[str] = None, + limit: int = 50, + ) -> List[Promise]: + """Get promises for a user. + + Args: + user_id: User ID to filter by + status: Optional status filter (pending, completed, etc.) + limit: Maximum results to return + + Returns: + List of Promise objects + """ + self._ensure_initialized() + + results = self._read.run( + queries.GET_PROMISES_BY_USER, + user_id=user_id, + status=status, + limit=limit, + ) + + promises = [] + for row in results: + promise_data = dict(row["p"]) + if row.get("target"): + promise_data["to_entity_name"] = row["target"].get("name") + promises.append(self._row_to_promise(promise_data)) + + return promises + + async def update_promise_status( + self, + promise_id: str, + user_id: str, + status: str, + ) -> Optional[Promise]: + """Update a promise's status. + + Args: + promise_id: Promise UUID + user_id: User ID for permission check + status: New status value + + Returns: + Updated Promise object or None if not found + """ + self._ensure_initialized() + + results = self._write.run( + queries.UPDATE_PROMISE_STATUS, + id=promise_id, + user_id=user_id, + status=status, + ) + + if not results: + return None + + return self._row_to_promise(dict(results[0]["p"])) + + async def delete_promise( + self, + promise_id: str, + user_id: str, + ) -> bool: + """Delete a promise. + + Args: + promise_id: Promise UUID to delete + user_id: User ID for permission check + + Returns: + True if deleted, False if not found + """ + self._ensure_initialized() + + results = self._write.run( + queries.DELETE_PROMISE, + id=promise_id, + user_id=user_id, + ) + + deleted = results[0]["deleted_count"] if results else 0 + return deleted > 0 + + # ========================================================================= + # TIMELINE + # ========================================================================= + + async def get_timeline( + self, + user_id: str, + start: datetime, + end: datetime, + limit: int = 100, + ) -> List[Entity]: + """Get entities within a time range. + + Args: + user_id: User ID to filter by + start: Start of time range + end: End of time range + limit: Maximum results to return + + Returns: + List of Entity objects ordered by time + """ + self._ensure_initialized() + + results = self._read.run( + queries.GET_TIMELINE, + user_id=user_id, + start=start.isoformat(), + end=end.isoformat(), + limit=limit, + ) + + entities = [] + for row in results: + entity_data = dict(row["e"]) + entity_data["relationship_count"] = row.get("relationship_count", 0) + entities.append(self._row_to_entity(entity_data)) + + return entities + + # ========================================================================= + # HELPERS + # ========================================================================= + + def _row_to_entity(self, data: Dict[str, Any]) -> Entity: + """Convert Neo4j row data to Entity model.""" + return Entity( + id=data.get("id", ""), + name=data.get("name", ""), + type=EntityType(data.get("type", "thing")), + user_id=data.get("user_id", ""), + details=data.get("details"), + icon=data.get("icon"), + metadata=self._parse_metadata(data.get("metadata")), + created_at=self._parse_datetime(data.get("created_at")), + updated_at=self._parse_datetime(data.get("updated_at")), + location=data.get("location"), + start_time=self._parse_datetime(data.get("start_time")), + end_time=self._parse_datetime(data.get("end_time")), + conversation_id=data.get("conversation_id"), + relationship_count=data.get("relationship_count"), + ) + + def _row_to_relationship(self, data: Dict[str, Any]) -> Relationship: + """Convert Neo4j row data to Relationship model.""" + rel_type = data.get("type", "RELATED_TO") + try: + rel_type_enum = RelationshipType(rel_type) + except ValueError: + rel_type_enum = RelationshipType.RELATED_TO + + return Relationship( + id=data.get("id", ""), + type=rel_type_enum, + source_id=data.get("source_id", ""), + target_id=data.get("target_id", ""), + user_id=data.get("user_id", ""), + context=data.get("context"), + timestamp=self._parse_datetime(data.get("timestamp")), + metadata=self._parse_metadata(data.get("metadata")), + created_at=self._parse_datetime(data.get("created_at")), + start_date=self._parse_datetime(data.get("start_date")), + end_date=self._parse_datetime(data.get("end_date")), + source_entity=data.get("source_entity"), + target_entity=data.get("target_entity"), + ) + + def _row_to_promise(self, data: Dict[str, Any]) -> Promise: + """Convert Neo4j row data to Promise model.""" + status = data.get("status", "pending") + try: + status_enum = PromiseStatus(status) + except ValueError: + status_enum = PromiseStatus.PENDING + + return Promise( + id=data.get("id", ""), + user_id=data.get("user_id", ""), + action=data.get("action", ""), + to_entity_id=data.get("to_entity_id"), + to_entity_name=data.get("to_entity_name"), + status=status_enum, + due_date=self._parse_datetime(data.get("due_date")), + completed_at=self._parse_datetime(data.get("completed_at")), + source_conversation_id=data.get("source_conversation_id"), + context=data.get("context"), + metadata=self._parse_metadata(data.get("metadata")), + created_at=self._parse_datetime(data.get("created_at")), + updated_at=self._parse_datetime(data.get("updated_at")), + ) + + def _parse_datetime(self, value: Any) -> Optional[datetime]: + """Parse datetime from Neo4j.""" + if value is None: + return None + if isinstance(value, datetime): + return value + if isinstance(value, str): + try: + return datetime.fromisoformat(value.replace("Z", "+00:00")) + except ValueError: + return None + # Neo4j DateTime object + if hasattr(value, "to_native"): + return value.to_native() + return None + + def _parse_metadata(self, value: Any) -> Dict[str, Any]: + """Parse metadata JSON from Neo4j.""" + if value is None: + return {} + if isinstance(value, dict): + return value + if isinstance(value, str): + try: + import json + return json.loads(value) + except (json.JSONDecodeError, ValueError): + return {} + return {} + + def shutdown(self) -> None: + """Shutdown the service and close connections.""" + if self._client: + self._client.close() + self._client = None + self._initialized = False + logger.info("Knowledge Graph Service shut down") + + async def test_connection(self) -> bool: + """Test Neo4j connection.""" + try: + self._ensure_initialized() + # Simple query to test connection + self._read.run("RETURN 1 as test") + return True + except Exception as e: + logger.error(f"Neo4j connection test failed: {e}") + return False + + +def get_knowledge_graph_service() -> KnowledgeGraphService: + """Get the global knowledge graph service instance. + + Returns: + KnowledgeGraphService singleton instance + """ + global _knowledge_graph_service + + if _knowledge_graph_service is None: + with _service_lock: + if _knowledge_graph_service is None: + _knowledge_graph_service = KnowledgeGraphService() + logger.info("Knowledge Graph Service created") + + return _knowledge_graph_service + + +def shutdown_knowledge_graph_service() -> None: + """Shutdown the global knowledge graph service.""" + global _knowledge_graph_service + + if _knowledge_graph_service is not None: + _knowledge_graph_service.shutdown() + _knowledge_graph_service = None diff --git a/backends/advanced/src/advanced_omi_backend/services/memory/base.py b/backends/advanced/src/advanced_omi_backend/services/memory/base.py index 4abdb5b0..7df7748e 100644 --- a/backends/advanced/src/advanced_omi_backend/services/memory/base.py +++ b/backends/advanced/src/advanced_omi_backend/services/memory/base.py @@ -2,7 +2,7 @@ This module defines the core abstractions and interfaces for: - Memory service operations -- LLM provider integration +- LLM provider integration - Vector store backends - Memory entry data structures @@ -12,14 +12,9 @@ import time from abc import ABC, abstractmethod from dataclasses import dataclass, field -from typing import Dict, List, Any, Optional, Tuple +from typing import Any, Dict, List, Optional, Tuple -__all__ = [ - "MemoryEntry", - "MemoryServiceBase", - "LLMProviderBase", - "VectorStoreBase" -] +__all__ = ["MemoryEntry", "MemoryServiceBase", "LLMProviderBase", "VectorStoreBase"] @dataclass @@ -38,6 +33,7 @@ class MemoryEntry: created_at: Timestamp when memory was created updated_at: Timestamp when memory was last updated """ + id: str content: str metadata: Dict[str, Any] = field(default_factory=dict) @@ -65,30 +61,36 @@ def to_dict(self) -> Dict[str, Any]: "score": self.score, "created_at": self.created_at, "updated_at": self.updated_at, - "user_id": self.metadata.get("user_id") # Extract user_id from metadata + "user_id": self.metadata.get("user_id"), # Extract user_id from metadata } class MemoryServiceBase(ABC): """Abstract base class defining the core memory service interface. - + This class defines all the essential operations that any memory service implementation must provide. Concrete implementations should inherit from this class and implement all abstract methods. """ - + + @property + @abstractmethod + def provider_identifier(self) -> str: + """Return the provider identifier (e.g., 'chronicle', 'openmemory_mcp', 'mycelia').""" + ... + @abstractmethod async def initialize(self) -> None: """Initialize the memory service and all its components. - + This should set up connections to LLM providers, vector stores, and any other required dependencies. - + Raises: RuntimeError: If initialization fails """ pass - + @abstractmethod async def add_memory( self, @@ -98,62 +100,54 @@ async def add_memory( user_id: str, user_email: str, allow_update: bool = False, - db_helper: Any = None + db_helper: Any = None, ) -> Tuple[bool, List[str]]: """Add memories extracted from a transcript. - + Args: transcript: Raw transcript text to extract memories from - client_id: Client identifier + client_id: Client identifier source_id: Unique identifier for the source (audio session, chat session, etc.) user_id: User identifier user_email: User email address allow_update: Whether to allow updating existing memories db_helper: Optional database helper for tracking relationships - + Returns: Tuple of (success: bool, created_memory_ids: List[str]) """ pass - + @abstractmethod async def search_memories( - self, - query: str, - user_id: str, - limit: int = 10, - score_threshold: float = 0.0 + self, query: str, user_id: str, limit: int = 10, score_threshold: float = 0.0 ) -> List[MemoryEntry]: """Search memories using semantic similarity. - + Args: query: Search query text user_id: User identifier to filter memories limit: Maximum number of results to return score_threshold: Minimum similarity score (0.0 = no threshold) - + Returns: List of matching MemoryEntry objects ordered by relevance """ pass - + @abstractmethod - async def get_all_memories( - self, - user_id: str, - limit: int = 100 - ) -> List[MemoryEntry]: + async def get_all_memories(self, user_id: str, limit: int = 100) -> List[MemoryEntry]: """Get all memories for a specific user. - + Args: user_id: User identifier limit: Maximum number of memories to return - + Returns: List of MemoryEntry objects for the user """ pass - + async def count_memories(self, user_id: str) -> Optional[int]: """Count total number of memories for a user. @@ -168,7 +162,9 @@ async def count_memories(self, user_id: str) -> Optional[int]: """ return None - async def get_memory(self, memory_id: str, user_id: Optional[str] = None) -> Optional[MemoryEntry]: + async def get_memory( + self, memory_id: str, user_id: Optional[str] = None + ) -> Optional[MemoryEntry]: """Get a specific memory by ID. This is an optional method that providers can implement for fetching @@ -190,7 +186,7 @@ async def update_memory( content: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None, user_id: Optional[str] = None, - user_email: Optional[str] = None + user_email: Optional[str] = None, ) -> bool: """Update a specific memory's content and/or metadata. @@ -210,7 +206,9 @@ async def update_memory( return False @abstractmethod - async def delete_memory(self, memory_id: str, user_id: Optional[str] = None, user_email: Optional[str] = None) -> bool: + async def delete_memory( + self, memory_id: str, user_id: Optional[str] = None, user_email: Optional[str] = None + ) -> bool: """Delete a specific memory by ID. Args: @@ -222,31 +220,31 @@ async def delete_memory(self, memory_id: str, user_id: Optional[str] = None, use True if successfully deleted, False otherwise """ pass - + @abstractmethod async def delete_all_user_memories(self, user_id: str) -> int: """Delete all memories for a specific user. - + Args: user_id: User identifier - + Returns: Number of memories that were deleted """ pass - + @abstractmethod async def test_connection(self) -> bool: """Test if the memory service and its dependencies are working. - + Returns: True if all connections are healthy, False otherwise """ pass - + def shutdown(self) -> None: """Shutdown the memory service and clean up resources. - + Default implementation does nothing. Subclasses should override if they need to perform cleanup operations. """ @@ -254,19 +252,19 @@ def shutdown(self) -> None: def __init__(self): """Initialize base memory service state. - + Subclasses should call super().__init__() in their constructors. """ self._initialized = False - + async def _ensure_initialized(self) -> None: """Ensure the memory service is initialized before use. - + This method provides lazy initialization - it will automatically call initialize() the first time it's needed. This is critical for services used in RQ workers where the service instance is created in one process but used in another. - + This should be called at the start of any method that requires the service to be initialized (e.g., add_memory, search_memories). """ @@ -276,38 +274,38 @@ async def _ensure_initialized(self) -> None: class LLMProviderBase(ABC): """Abstract base class for LLM provider implementations. - + LLM providers handle: - Memory extraction from text using prompts - Text embedding generation - Memory action proposals (add/update/delete decisions) """ - + @abstractmethod async def extract_memories(self, text: str, prompt: str) -> List[str]: """Extract meaningful fact memories from text using an LLM. - + Args: text: Input text to extract memories from prompt: System prompt to guide the extraction process - + Returns: List of extracted fact memory strings """ pass - + @abstractmethod async def generate_embeddings(self, texts: List[str]) -> List[List[float]]: """Generate vector embeddings for the given texts. - + Args: texts: List of text strings to embed - + Returns: List of embedding vectors (one per input text) """ pass - + @abstractmethod async def propose_memory_actions( self, @@ -316,27 +314,27 @@ async def propose_memory_actions( custom_prompt: Optional[str] = None, ) -> Dict[str, Any]: """Propose memory management actions based on existing and new information. - + This method uses the LLM to decide whether new facts should: - ADD: Create new memories - - UPDATE: Modify existing memories + - UPDATE: Modify existing memories - DELETE: Remove outdated memories - NONE: No action needed - + Args: retrieved_old_memory: List of existing memories for context new_facts: List of new facts to process custom_prompt: Optional custom prompt to use instead of default - + Returns: Dictionary containing proposed actions in structured format """ pass - + @abstractmethod async def test_connection(self) -> bool: """Test connection to the LLM provider. - + Returns: True if connection is working, False otherwise """ @@ -345,82 +343,78 @@ async def test_connection(self) -> bool: class VectorStoreBase(ABC): """Abstract base class for vector store implementations. - + Vector stores handle: - Storing memory embeddings with metadata - Semantic search using vector similarity - CRUD operations on memory entries """ - + @abstractmethod async def initialize(self) -> None: """Initialize the vector store (create collections, etc.). - + Raises: RuntimeError: If initialization fails """ pass - + @abstractmethod async def add_memories(self, memories: List[MemoryEntry]) -> List[str]: """Add multiple memory entries to the vector store. - + Args: memories: List of MemoryEntry objects to store - + Returns: List of created memory IDs """ pass - + @abstractmethod async def search_memories( - self, - query_embedding: List[float], - user_id: str, - limit: int, - score_threshold: float = 0.0 + self, query_embedding: List[float], user_id: str, limit: int, score_threshold: float = 0.0 ) -> List[MemoryEntry]: """Search memories using vector similarity. - + Args: query_embedding: Query vector for similarity search user_id: User identifier to filter results limit: Maximum number of results to return score_threshold: Minimum similarity score (0.0 = no threshold) - + Returns: List of matching MemoryEntry objects with similarity scores """ pass - + @abstractmethod async def get_memories(self, user_id: str, limit: int) -> List[MemoryEntry]: """Get all memories for a user without similarity filtering. - + Args: user_id: User identifier limit: Maximum number of memories to return - + Returns: List of MemoryEntry objects for the user """ pass - + async def count_memories(self, user_id: str) -> Optional[int]: """Count total number of memories for a user. - + Default implementation returns None to indicate counting is unsupported. Vector stores should override this method to provide efficient counting if supported. - + Args: user_id: User identifier - + Returns: Total count of memories for the user, or None if counting is not supported by this store """ return None - + @abstractmethod async def update_memory( self, @@ -430,47 +424,47 @@ async def update_memory( new_metadata: Dict[str, Any], ) -> bool: """Update an existing memory with new content and metadata. - + Args: memory_id: ID of the memory to update new_content: Updated memory content new_embedding: Updated embedding vector new_metadata: Updated metadata - + Returns: True if update succeeded, False otherwise """ pass - + @abstractmethod async def delete_memory(self, memory_id: str) -> bool: """Delete a specific memory from the store. - + Args: memory_id: ID of the memory to delete - + Returns: True if deletion succeeded, False otherwise """ pass - + @abstractmethod async def delete_user_memories(self, user_id: str) -> int: """Delete all memories for a specific user. - + Args: user_id: User identifier - + Returns: Number of memories that were deleted """ pass - + @abstractmethod async def test_connection(self) -> bool: """Test connection to the vector store. - + Returns: True if connection is working, False otherwise """ - pass \ No newline at end of file + pass diff --git a/backends/advanced/src/advanced_omi_backend/services/memory/config.py b/backends/advanced/src/advanced_omi_backend/services/memory/config.py index 19b47bd7..db3b98e0 100644 --- a/backends/advanced/src/advanced_omi_backend/services/memory/config.py +++ b/backends/advanced/src/advanced_omi_backend/services/memory/config.py @@ -2,12 +2,13 @@ import logging import os -import yaml -from pathlib import Path from dataclasses import dataclass from enum import Enum +from pathlib import Path from typing import Any, Dict, Optional, Union +import yaml + from advanced_omi_backend.model_registry import get_models_registry from advanced_omi_backend.utils.config_utils import resolve_value diff --git a/backends/advanced/src/advanced_omi_backend/services/memory/prompts.py b/backends/advanced/src/advanced_omi_backend/services/memory/prompts.py index 4b41a51a..4325fd13 100644 --- a/backends/advanced/src/advanced_omi_backend/services/memory/prompts.py +++ b/backends/advanced/src/advanced_omi_backend/services/memory/prompts.py @@ -8,9 +8,10 @@ 5. Temporal and entity extraction (get_temporal_entity_extraction_prompt()) """ -from datetime import datetime, timedelta import json +from datetime import datetime, timedelta from typing import List, Optional + from pydantic import BaseModel, Field MEMORY_ANSWER_PROMPT = """ diff --git a/backends/advanced/src/advanced_omi_backend/services/memory/providers/__init__.py b/backends/advanced/src/advanced_omi_backend/services/memory/providers/__init__.py index 3a71f7cf..7a46dd33 100644 --- a/backends/advanced/src/advanced_omi_backend/services/memory/providers/__init__.py +++ b/backends/advanced/src/advanced_omi_backend/services/memory/providers/__init__.py @@ -10,11 +10,11 @@ """ from .chronicle import MemoryService as ChronicleMemoryService -from .openmemory_mcp import OpenMemoryMCPService -from .mycelia import MyceliaMemoryService from .llm_providers import OpenAIProvider -from .vector_stores import QdrantVectorStore from .mcp_client import MCPClient, MCPError +from .mycelia import MyceliaMemoryService +from .openmemory_mcp import OpenMemoryMCPService +from .vector_stores import QdrantVectorStore __all__ = [ "ChronicleMemoryService", diff --git a/backends/advanced/src/advanced_omi_backend/services/memory/providers/chronicle.py b/backends/advanced/src/advanced_omi_backend/services/memory/providers/chronicle.py index 1984b281..1eddae93 100644 --- a/backends/advanced/src/advanced_omi_backend/services/memory/providers/chronicle.py +++ b/backends/advanced/src/advanced_omi_backend/services/memory/providers/chronicle.py @@ -22,16 +22,16 @@ class MemoryService(MemoryServiceBase): """Main memory service that orchestrates LLM and vector store operations. - + This class implements the core memory management functionality including: - Memory extraction from transcripts using LLM providers - Semantic storage and retrieval using vector stores - Memory updates and deduplication - User-scoped memory management - + The service supports multiple LLM providers (OpenAI, Ollama) and vector stores (Qdrant), providing a flexible and extensible architecture. - + Attributes: config: Memory service configuration llm_provider: Active LLM provider instance @@ -39,9 +39,13 @@ class MemoryService(MemoryServiceBase): _initialized: Whether the service has been initialized """ + @property + def provider_identifier(self) -> str: + return "chronicle" + def __init__(self, config: MemoryConfig): """Initialize the memory service with configuration. - + Args: config: MemoryConfig instance with provider settings """ @@ -52,10 +56,10 @@ def __init__(self, config: MemoryConfig): async def initialize(self) -> None: """Initialize the memory service and all its components. - + Sets up LLM provider and vector store based on configuration, tests connections, and marks the service as ready for use. - + Raises: ValueError: If unsupported provider is configured RuntimeError: If initialization or connection tests fail @@ -74,7 +78,9 @@ async def initialize(self) -> None: if self.config.vector_store_provider == VectorStoreProvider.QDRANT: self.vector_store = QdrantVectorStore(self.config.vector_store_config) else: - raise ValueError(f"Unsupported vector store provider: {self.config.vector_store_provider}") + raise ValueError( + f"Unsupported vector store provider: {self.config.vector_store_provider}" + ) # Initialize vector store await self.vector_store.initialize() @@ -113,14 +119,14 @@ async def add_memory( user_id: str, user_email: str, allow_update: bool = False, - db_helper: Any = None + db_helper: Any = None, ) -> Tuple[bool, List[str]]: """Add memories extracted from a transcript. - + Processes a transcript to extract meaningful memories using the LLM, generates embeddings, and stores them in the vector database. Optionally allows updating existing memories through LLM-driven action proposals. - + Args: transcript: Raw transcript text to extract memories from client_id: Client identifier for tracking @@ -129,10 +135,10 @@ async def add_memory( user_email: User email address allow_update: Whether to allow updating existing memories db_helper: Optional database helper for relationship tracking - + Returns: Tuple of (success: bool, created_memory_ids: List[str]) - + Raises: asyncio.TimeoutError: If processing exceeds timeout """ @@ -149,14 +155,18 @@ async def add_memory( if self.config.extraction_enabled and self.config.extraction_prompt: fact_memories_text = await asyncio.wait_for( self.llm_provider.extract_memories(transcript, self.config.extraction_prompt), - timeout=self.config.timeout_seconds + timeout=self.config.timeout_seconds, + ) + memory_logger.info( + f"🧠 Extracted {len(fact_memories_text)} memories from transcript for {source_id}" ) - memory_logger.info(f"🧠 Extracted {len(fact_memories_text)} memories from transcript for {source_id}") - + # Fallback to storing raw transcript if no memories extracted if not fact_memories_text: fact_memories_text = [transcript] - memory_logger.info(f"πŸ’Ύ No memories extracted, storing raw transcript for {source_id}") + memory_logger.info( + f"πŸ’Ύ No memories extracted, storing raw transcript for {source_id}" + ) memory_logger.debug(f"🧠 fact_memories_text: {fact_memories_text}") # Simple deduplication of extracted memories within the same call @@ -165,14 +175,14 @@ async def add_memory( # Generate embeddings embeddings = await asyncio.wait_for( self.llm_provider.generate_embeddings(fact_memories_text), - timeout=self.config.timeout_seconds + timeout=self.config.timeout_seconds, ) memory_logger.info(f"embeddings generated") if not embeddings or len(embeddings) != len(fact_memories_text): error_msg = f"❌ Embedding generation failed for {source_id}: got {len(embeddings) if embeddings else 0} embeddings for {len(fact_memories_text)} memories" memory_logger.error(error_msg) raise RuntimeError(error_msg) - + # Create or update memory entries memory_entries = [] created_ids: List[str] = [] @@ -204,7 +214,9 @@ async def add_memory( return True, created_ids # No memories created - this is a valid outcome (duplicates, no extractable facts, etc.) - memory_logger.info(f"ℹ️ No new memories created for {source_id}: memory_entries={len(memory_entries) if memory_entries else 0}, allow_update={allow_update}") + memory_logger.info( + f"ℹ️ No new memories created for {source_id}: memory_entries={len(memory_entries) if memory_entries else 0}, allow_update={allow_update}" + ) return True, [] except asyncio.TimeoutError as e: @@ -214,18 +226,20 @@ async def add_memory( memory_logger.error(f"❌ Add memory failed for {source_id}: {e}") raise e - async def search_memories(self, query: str, user_id: str, limit: int = 10, score_threshold: float = 0.0) -> List[MemoryEntry]: + async def search_memories( + self, query: str, user_id: str, limit: int = 10, score_threshold: float = 0.0 + ) -> List[MemoryEntry]: """Search memories using semantic similarity. - + Generates an embedding for the query and searches the vector store for similar memories belonging to the specified user. - + Args: query: Search query text user_id: User identifier to filter memories limit: Maximum number of results to return score_threshold: Minimum similarity score (0.0 = no threshold) - + Returns: List of matching MemoryEntry objects ordered by relevance """ @@ -244,7 +258,9 @@ async def search_memories(self, query: str, user_id: str, limit: int = 10, score query_embeddings[0], user_id, limit, score_threshold ) - memory_logger.info(f"πŸ” Found {len(results)} memories for query '{query}' (user: {user_id})") + memory_logger.info( + f"πŸ” Found {len(results)} memories for query '{query}' (user: {user_id})" + ) return results except Exception as e: @@ -253,14 +269,14 @@ async def search_memories(self, query: str, user_id: str, limit: int = 10, score async def get_all_memories(self, user_id: str, limit: int = 100) -> List[MemoryEntry]: """Get all memories for a specific user. - + Retrieves all stored memories for the given user without similarity filtering. - + Args: user_id: User identifier limit: Maximum number of memories to return - + Returns: List of MemoryEntry objects for the user """ @@ -297,7 +313,9 @@ async def count_memories(self, user_id: str) -> Optional[int]: memory_logger.error(f"Count memories failed: {e}") return None - async def get_memory(self, memory_id: str, user_id: Optional[str] = None) -> Optional[MemoryEntry]: + async def get_memory( + self, memory_id: str, user_id: Optional[str] = None + ) -> Optional[MemoryEntry]: """Get a specific memory by ID. Args: @@ -327,7 +345,7 @@ async def update_memory( content: Optional[str] = None, metadata: Optional[dict[str, Any]] = None, user_id: Optional[str] = None, - user_email: Optional[str] = None + user_email: Optional[str] = None, ) -> bool: """Update a specific memory's content and/or metadata. @@ -380,7 +398,7 @@ async def update_memory( memory_id=memory_id, new_content=new_content, new_embedding=new_embedding, - new_metadata=new_metadata + new_metadata=new_metadata, ) if success: @@ -394,12 +412,14 @@ async def update_memory( memory_logger.error(f"Error updating memory {memory_id}: {e}", exc_info=True) return False - async def delete_memory(self, memory_id: str, user_id: Optional[str] = None, user_email: Optional[str] = None) -> bool: + async def delete_memory( + self, memory_id: str, user_id: Optional[str] = None, user_email: Optional[str] = None + ) -> bool: """Delete a specific memory by ID. - + Args: memory_id: Unique identifier of the memory to delete - + Returns: True if successfully deleted, False otherwise """ @@ -417,10 +437,10 @@ async def delete_memory(self, memory_id: str, user_id: Optional[str] = None, use async def delete_all_user_memories(self, user_id: str) -> int: """Delete all memories for a specific user. - + Args: user_id: User identifier - + Returns: Number of memories that were deleted """ @@ -437,7 +457,7 @@ async def delete_all_user_memories(self, user_id: str) -> int: async def test_connection(self) -> bool: """Test if the memory service and its dependencies are working. - + Returns: True if all connections are healthy, False otherwise """ @@ -460,13 +480,14 @@ def shutdown(self) -> None: def _deduplicate_memories(self, memories_text: List[str]) -> List[str]: """Remove near-duplicate memories from the same extraction session. - + Args: memories_text: List of extracted memory strings - + Returns: Deduplicated list of memory strings """ + def _collapse_text_for_dedup(text: str) -> str: """Normalize text for deduplication by removing common words and punctuation.""" t = text.lower() @@ -480,16 +501,18 @@ def _collapse_text_for_dedup(text: str) -> str: seen_collapsed = set() deduped_text: List[str] = [] - + for memory_text in memories_text: key = _collapse_text_for_dedup(memory_text) if key not in seen_collapsed: seen_collapsed.add(key) deduped_text.append(memory_text) - + if len(deduped_text) != len(memories_text): - memory_logger.info(f"🧹 Deduplicated memories: {len(memories_text)} -> {len(deduped_text)}") - + memory_logger.info( + f"🧹 Deduplicated memories: {len(memories_text)} -> {len(deduped_text)}" + ) + return deduped_text def _create_memory_entries( @@ -499,10 +522,10 @@ def _create_memory_entries( client_id: str, source_id: str, user_id: str, - user_email: str + user_email: str, ) -> List[MemoryEntry]: """Create MemoryEntry objects from extracted memories. - + Args: fact_memories_text: List of factmemory content strings embeddings: Corresponding embedding vectors @@ -510,7 +533,7 @@ def _create_memory_entries( source_id: Source session identifier user_id: User identifier user_email: User email - + Returns: List of MemoryEntry objects ready for storage """ @@ -547,14 +570,14 @@ async def _process_memory_updates( user_id: str, client_id: str, source_id: str, - user_email: str + user_email: str, ) -> List[str]: """Process memory updates using LLM-driven action proposals. - + This method implements the intelligent memory (can be fact or summarized facts) updating logic that decides whether to add, update, or delete memories based on existing context and new information. - + Args: memories_text: List of new memory content embeddings: Corresponding embeddings @@ -562,16 +585,16 @@ async def _process_memory_updates( client_id: Client identifier source_id: Source session identifier user_email: User email - + Returns: List of created/updated memory IDs """ created_ids: List[str] = [] - + # For each new fact, find top-5 existing memories as retrieval set retrieved_old_memory = [] new_message_embeddings = {} - + for new_mem, emb in zip(memories_text, embeddings): new_message_embeddings[new_mem] = emb try: @@ -604,7 +627,7 @@ async def _process_memory_updates( f"and {len(memories_text)} new facts" ) memory_logger.debug(f"🧠 Individual facts being sent to LLM: {memories_text}") - + # add update or delete etc actions using DEFAULT_UPDATE_MEMORY_PROMPT actions_obj = await self.llm_provider.propose_memory_actions( retrieved_old_memory=retrieved_old_memory, @@ -619,23 +642,28 @@ async def _process_memory_updates( # Process the proposed actions actions_list = self._normalize_actions(actions_obj) created_ids = await self._apply_memory_actions( - actions_list, new_message_embeddings, temp_uuid_mapping, - client_id, source_id, user_id, user_email + actions_list, + new_message_embeddings, + temp_uuid_mapping, + client_id, + source_id, + user_id, + user_email, ) return created_ids def _normalize_actions(self, actions_obj: Any) -> List[dict]: """Normalize LLM response into a list of action dictionaries. - + Args: actions_obj: Raw LLM response object - + Returns: List of normalized action dictionaries """ actions_list = [] - + try: memory_logger.debug(f"Normalizing actions from: {actions_obj}") if isinstance(actions_obj, dict): @@ -652,12 +680,12 @@ def _normalize_actions(self, actions_obj: Any) -> List[dict]: break elif isinstance(actions_obj, list): actions_list = actions_obj - + memory_logger.info(f"πŸ“‹ Normalized to {len(actions_list)} actions: {actions_list}") except Exception as normalize_err: memory_logger.warning(f"Failed to normalize actions: {normalize_err}") actions_list = [] - + return actions_list async def _apply_memory_actions( @@ -668,10 +696,10 @@ async def _apply_memory_actions( client_id: str, source_id: str, user_id: str, - user_email: str + user_email: str, ) -> List[str]: """Apply the proposed memory actions. - + Args: actions_list: List of action dictionaries new_message_embeddings: Pre-computed embeddings for new content @@ -680,15 +708,15 @@ async def _apply_memory_actions( source_id: Source session identifier user_id: User identifier user_email: User email - + Returns: List of created/updated memory IDs """ created_ids: List[str] = [] memory_entries = [] - + memory_logger.info(f"⚑ Processing {len(actions_list)} actions") - + for resp in actions_list: # Allow plain string entries β†’ ADD action if isinstance(resp, str): @@ -698,7 +726,7 @@ async def _apply_memory_actions( event_type = resp.get("event", "ADD") action_text = resp.get("text") or resp.get("memory") - + if not action_text or not isinstance(action_text, str): memory_logger.warning(f"Skipping action with no text: {resp}") continue @@ -730,7 +758,9 @@ async def _apply_memory_actions( if event_type == "ADD": if emb is None: - memory_logger.warning(f"Skipping ADD action due to missing embedding: {action_text}") + memory_logger.warning( + f"Skipping ADD action due to missing embedding: {action_text}" + ) continue memory_id = str(uuid.uuid4()) @@ -746,11 +776,11 @@ async def _apply_memory_actions( ) ) memory_logger.info(f"βž• Added new memory: {memory_id} - {action_text[:50]}...") - + elif event_type == "UPDATE": provided_id = resp.get("id") actual_id = temp_uuid_mapping.get(str(provided_id), provided_id) - + if actual_id and emb is not None: try: updated = await self.vector_store.update_memory( @@ -761,14 +791,16 @@ async def _apply_memory_actions( ) if updated: created_ids.append(str(actual_id)) - memory_logger.info(f"πŸ”„ Updated memory: {actual_id} - {action_text[:50]}...") + memory_logger.info( + f"πŸ”„ Updated memory: {actual_id} - {action_text[:50]}..." + ) else: memory_logger.warning(f"Failed to update memory {actual_id}") except Exception as update_err: memory_logger.error(f"Update memory failed: {update_err}") else: memory_logger.warning(f"Skipping UPDATE due to missing ID or embedding") - + elif event_type == "DELETE": provided_id = resp.get("id") actual_id = temp_uuid_mapping.get(str(provided_id), provided_id) @@ -783,24 +815,28 @@ async def _apply_memory_actions( memory_logger.error(f"Delete memory failed: {delete_err}") else: memory_logger.warning(f"Skipping DELETE due to missing ID: {provided_id}") - + elif event_type == "NONE": memory_logger.debug(f"NONE action - no changes for: {action_text[:50]}...") continue else: memory_logger.warning(f"Unknown event type: {event_type}") - + # Store new entries if memory_entries: stored_ids = await self.vector_store.add_memories(memory_entries) created_ids.extend(stored_ids) - memory_logger.info(f"βœ… Actions processed: {len(memory_entries)} new entries, {len(created_ids)} total changes") + memory_logger.info( + f"βœ… Actions processed: {len(memory_entries)} new entries, {len(created_ids)} total changes" + ) return created_ids - async def _update_database_relationships(self, db_helper: Any, source_id: str, created_ids: List[str]) -> None: + async def _update_database_relationships( + self, db_helper: Any, source_id: str, created_ids: List[str] + ) -> None: """Update database relationships for created memories. - + Args: db_helper: Database helper instance source_id: Source session identifier @@ -820,46 +856,42 @@ async def example_usage(): # Build config from environment config = build_memory_config_from_env() - + # Initialize service memory_service = MemoryService(config) await memory_service.initialize() - + # Add memory success, memory_ids = await memory_service.add_memory( transcript="User discussed their goals for the next quarter.", client_id="client123", source_id="audio456", user_id="user789", - user_email="user@example.com" + user_email="user@example.com", ) - + if success: print(f"βœ… Added memories: {memory_ids}") - + # Search memories results = await memory_service.search_memories( - query="quarterly goals", - user_id="user789", - limit=5 + query="quarterly goals", user_id="user789", limit=5 ) print(f"πŸ” Found {len(results)} search results") - + # Get all memories - all_memories = await memory_service.get_all_memories( - user_id="user789", - limit=100 - ) + all_memories = await memory_service.get_all_memories(user_id="user789", limit=100) print(f"πŸ“š Total memories: {len(all_memories)}") - + # Clean up test data for memory_id in memory_ids: await memory_service.delete_memory(memory_id) print("🧹 Cleaned up test data") - + memory_service.shutdown() if __name__ == "__main__": import asyncio - asyncio.run(example_usage()) \ No newline at end of file + + asyncio.run(example_usage()) diff --git a/backends/advanced/src/advanced_omi_backend/services/memory/providers/mycelia.py b/backends/advanced/src/advanced_omi_backend/services/memory/providers/mycelia.py index 6289f035..067dd954 100644 --- a/backends/advanced/src/advanced_omi_backend/services/memory/providers/mycelia.py +++ b/backends/advanced/src/advanced_omi_backend/services/memory/providers/mycelia.py @@ -13,6 +13,7 @@ import httpx from advanced_omi_backend.auth import generate_jwt_for_user +from advanced_omi_backend.model_registry import get_models_registry from advanced_omi_backend.users import User from ..base import MemoryEntry, MemoryServiceBase @@ -23,7 +24,6 @@ get_temporal_entity_extraction_prompt, ) from .llm_providers import _get_openai_client -from advanced_omi_backend.model_registry import get_models_registry memory_logger = logging.getLogger("memory_service") @@ -61,6 +61,10 @@ class MyceliaMemoryService(MemoryServiceBase): **kwargs: Additional configuration parameters """ + @property + def provider_identifier(self) -> str: + return "mycelia" + def __init__(self, config: MemoryConfig): """Initialize Mycelia memory service. @@ -253,7 +257,9 @@ async def _extract_memories_via_llm( if not llm_def: memory_logger.warning("No default LLM in config.yml; cannot extract facts") return [] - client = _get_openai_client(api_key=llm_def.api_key or "", base_url=llm_def.model_url, is_async=True) + client = _get_openai_client( + api_key=llm_def.api_key or "", base_url=llm_def.model_url, is_async=True + ) response = await client.chat.completions.create( model=llm_def.model_name, messages=[ @@ -302,13 +308,19 @@ async def _extract_temporal_entity_via_llm( # Use registry-driven default LLM with OpenAI SDK reg = get_models_registry() if not reg: - memory_logger.warning("No registry available for LLM; cannot extract temporal entity") + memory_logger.warning( + "No registry available for LLM; cannot extract temporal entity" + ) return None llm_def = reg.get_default("llm") if not llm_def: - memory_logger.warning("No default LLM in config.yml; cannot extract temporal entity") + memory_logger.warning( + "No default LLM in config.yml; cannot extract temporal entity" + ) return None - client = _get_openai_client(api_key=llm_def.api_key or "", base_url=llm_def.model_url, is_async=True) + client = _get_openai_client( + api_key=llm_def.api_key or "", base_url=llm_def.model_url, is_async=True + ) response = await client.chat.completions.create( model=llm_def.model_name, messages=[ diff --git a/backends/advanced/src/advanced_omi_backend/services/memory/providers/openmemory_mcp.py b/backends/advanced/src/advanced_omi_backend/services/memory/providers/openmemory_mcp.py index d5061a2c..babe9bf6 100644 --- a/backends/advanced/src/advanced_omi_backend/services/memory/providers/openmemory_mcp.py +++ b/backends/advanced/src/advanced_omi_backend/services/memory/providers/openmemory_mcp.py @@ -40,6 +40,10 @@ class OpenMemoryMCPService(MemoryServiceBase): _initialized: Whether the service has been initialized """ + @property + def provider_identifier(self) -> str: + return "openmemory_mcp" + def __init__( self, server_url: Optional[str] = None, diff --git a/backends/advanced/src/advanced_omi_backend/services/memory/service_factory.py b/backends/advanced/src/advanced_omi_backend/services/memory/service_factory.py index 5607d8ff..d30778e0 100644 --- a/backends/advanced/src/advanced_omi_backend/services/memory/service_factory.py +++ b/backends/advanced/src/advanced_omi_backend/services/memory/service_factory.py @@ -11,7 +11,7 @@ from typing import Optional from .base import MemoryServiceBase -from .config import build_memory_config_from_env, MemoryConfig, MemoryProvider +from .config import MemoryConfig, MemoryProvider, build_memory_config_from_env memory_logger = logging.getLogger("memory_service") @@ -23,22 +23,23 @@ def create_memory_service(config: MemoryConfig) -> MemoryServiceBase: """Create a memory service instance based on configuration. - + Args: config: Memory service configuration - + Returns: Configured memory service instance - + Raises: ValueError: If unsupported memory provider is specified RuntimeError: If required dependencies are missing """ memory_logger.info(f"🧠 Creating memory service with provider: {config.memory_provider.value}") - + if config.memory_provider == MemoryProvider.CHRONICLE: # Use the sophisticated Chronicle implementation from .providers.chronicle import MemoryService as ChronicleMemoryService + return ChronicleMemoryService(config) elif config.memory_provider == MemoryProvider.OPENMEMORY_MCP: @@ -99,9 +100,13 @@ def get_memory_service() -> MemoryServiceBase: # Don't initialize here - let it happen lazily on first use # This prevents orphaned tasks that cause "Task was destroyed but it is pending" errors - memory_logger.debug(f"Memory service created but not initialized: {type(_memory_service).__name__}") + memory_logger.debug( + f"Memory service created but not initialized: {type(_memory_service).__name__}" + ) - memory_logger.info(f"βœ… Global memory service created: {type(_memory_service).__name__}") + memory_logger.info( + f"βœ… Global memory service created: {type(_memory_service).__name__}" + ) except Exception as e: memory_logger.error(f"❌ Failed to create memory service: {e}") @@ -113,7 +118,7 @@ def get_memory_service() -> MemoryServiceBase: def shutdown_memory_service() -> None: """Shutdown the global memory service and clean up resources.""" global _memory_service - + if _memory_service is not None: try: _memory_service.shutdown() @@ -135,30 +140,24 @@ def reset_memory_service() -> None: def get_service_info() -> dict: """Get information about the current memory service. - + Returns: Dictionary with service information """ global _memory_service - + info = { "service_created": _memory_service is not None, "service_type": None, "service_initialized": False, - "memory_provider": None + "memory_provider": None, } - + if _memory_service is not None: info["service_type"] = type(_memory_service).__name__ # All memory services should have _initialized attribute per the base class info["service_initialized"] = _memory_service._initialized - # Try to determine provider from service type - if "OpenMemoryMCP" in info["service_type"]: - info["memory_provider"] = "openmemory_mcp" - elif info["service_type"] == "ChronicleMemoryService": - info["memory_provider"] = "chronicle" - elif info["service_type"] == "MyceliaMemoryService": - info["memory_provider"] = "mycelia" - - return info \ No newline at end of file + info["memory_provider"] = _memory_service.provider_identifier + + return info diff --git a/backends/advanced/src/advanced_omi_backend/services/memory/update_memory_utils.py b/backends/advanced/src/advanced_omi_backend/services/memory/update_memory_utils.py index 1fce1079..b0c6c9db 100644 --- a/backends/advanced/src/advanced_omi_backend/services/memory/update_memory_utils.py +++ b/backends/advanced/src/advanced_omi_backend/services/memory/update_memory_utils.py @@ -1,8 +1,8 @@ -from dataclasses import dataclass, asdict -from typing import List, Optional, Literal, Dict, Any, Union -import xml.etree.ElementTree as ET import re +import xml.etree.ElementTree as ET +from dataclasses import asdict, dataclass +from typing import Any, Dict, List, Literal, Optional, Union Event = Literal["ADD", "UPDATE", "DELETE", "NONE"] NUMERIC_ID = re.compile(r"^\d+$") @@ -25,7 +25,7 @@ def extract_xml_from_content(content: str) -> str: """ # Try to find XML block within the content import re - + # Look for ... block xml_match = re.search(r'.*?', content, re.DOTALL) if xml_match: diff --git a/backends/advanced/src/advanced_omi_backend/services/neo4j_client.py b/backends/advanced/src/advanced_omi_backend/services/neo4j_client.py index c0e3e633..263d01cd 100644 --- a/backends/advanced/src/advanced_omi_backend/services/neo4j_client.py +++ b/backends/advanced/src/advanced_omi_backend/services/neo4j_client.py @@ -1,7 +1,8 @@ """Shared Neo4j client utilities for the advanced OMI backend.""" from typing import Optional -from neo4j import GraphDatabase, Driver, READ_ACCESS, WRITE_ACCESS + +from neo4j import READ_ACCESS, WRITE_ACCESS, Driver, GraphDatabase class Neo4jClient: diff --git a/backends/advanced/src/advanced_omi_backend/services/obsidian_service.py b/backends/advanced/src/advanced_omi_backend/services/obsidian_service.py index 07f841b7..b02a6fa0 100644 --- a/backends/advanced/src/advanced_omi_backend/services/obsidian_service.py +++ b/backends/advanced/src/advanced_omi_backend/services/obsidian_service.py @@ -12,24 +12,27 @@ - Vector similarity search via Neo4j vector indexes """ +import hashlib import logging import os import re -import hashlib -from typing import TypedDict, List, Optional, Literal from pathlib import Path +from typing import List, Literal, Optional, TypedDict + +from advanced_omi_backend.services.memory.config import ( + load_config_yml as load_root_config, +) from advanced_omi_backend.services.memory.providers.llm_providers import ( - generate_openai_embeddings, chunk_text_with_spacy, + generate_openai_embeddings, ) -from advanced_omi_backend.services.memory.config import load_config_yml as load_root_config -from advanced_omi_backend.utils.model_utils import get_model_config -from advanced_omi_backend.utils.config_utils import resolve_value from advanced_omi_backend.services.neo4j_client import ( Neo4jClient, Neo4jReadInterface, Neo4jWriteInterface, ) +from advanced_omi_backend.utils.config_utils import resolve_value +from advanced_omi_backend.utils.model_utils import get_model_config logger = logging.getLogger(__name__) diff --git a/backends/advanced/src/advanced_omi_backend/services/plugin_service.py b/backends/advanced/src/advanced_omi_backend/services/plugin_service.py index 1ca086f2..fb3956db 100644 --- a/backends/advanced/src/advanced_omi_backend/services/plugin_service.py +++ b/backends/advanced/src/advanced_omi_backend/services/plugin_service.py @@ -48,8 +48,8 @@ def expand_env_vars(value: Any) -> Any: def replacer(match): var_expr = match.group(1) # Support default values: ${VAR:-default} - if ':-' in var_expr: - var_name, default = var_expr.split(':-', 1) + if ":-" in var_expr: + var_name, default = var_expr.split(":-", 1) return os.environ.get(var_name.strip(), default.strip()) else: var_name = var_expr.strip() @@ -62,7 +62,7 @@ def replacer(match): return match.group(0) # Keep original placeholder return env_value - return re.sub(r'\$\{([^}]+)\}', replacer, value) + return re.sub(r"\$\{([^}]+)\}", replacer, value) elif isinstance(value, dict): return {k: expand_env_vars(v) for k, v in value.items()} @@ -106,12 +106,13 @@ def load_plugin_config(plugin_id: str, orchestration_config: Dict[str, Any]) -> # 1. Load plugin-specific config.yml if it exists try: import advanced_omi_backend.plugins + plugins_dir = Path(advanced_omi_backend.plugins.__file__).parent plugin_config_path = plugins_dir / plugin_id / "config.yml" if plugin_config_path.exists(): logger.debug(f"Loading plugin config from: {plugin_config_path}") - with open(plugin_config_path, 'r') as f: + with open(plugin_config_path, "r") as f: plugin_config = yaml.safe_load(f) or {} config.update(plugin_config) logger.debug(f"Loaded {len(plugin_config)} config keys for '{plugin_id}'") @@ -125,12 +126,12 @@ def load_plugin_config(plugin_id: str, orchestration_config: Dict[str, Any]) -> config = expand_env_vars(config) # 3. Merge orchestration settings from config/plugins.yml - config['enabled'] = orchestration_config.get('enabled', False) - config['events'] = orchestration_config.get('events', []) - config['condition'] = orchestration_config.get('condition', {'type': 'always'}) + config["enabled"] = orchestration_config.get("enabled", False) + config["events"] = orchestration_config.get("events", []) + config["condition"] = orchestration_config.get("condition", {"type": "always"}) # Add plugin ID for reference - config['plugin_id'] = plugin_id + config["plugin_id"] = plugin_id logger.debug( f"Plugin '{plugin_id}' config merged: enabled={config['enabled']}, " @@ -183,7 +184,7 @@ def extract_env_var_name(value: str) -> Optional[str]: if not isinstance(value, str): return None - match = re.search(r'\$\{([^}:]+)', value) + match = re.search(r"\$\{([^}:]+)", value) if match: return match.group(1).strip() return None @@ -207,66 +208,70 @@ def infer_field_type(key: str, value: Any) -> Dict[str, Any]: {'type': 'number', 'label': 'Max Sentences', 'default': 3} """ # Generate human-readable label from key - label = key.replace('_', ' ').title() + label = key.replace("_", " ").title() # Check for environment variable reference - if isinstance(value, str) and '${' in value: + if isinstance(value, str) and "${" in value: env_var = extract_env_var_name(value) if not env_var: - return {'type': 'string', 'label': label, 'default': value} + return {"type": "string", "label": label, "default": value} # Determine if this is a secret based on env var name - secret_keywords = ['PASSWORD', 'TOKEN', 'KEY', 'SECRET', 'APIKEY', 'API_KEY'] + secret_keywords = ["PASSWORD", "TOKEN", "KEY", "SECRET", "APIKEY", "API_KEY"] is_secret = any(keyword in env_var.upper() for keyword in secret_keywords) # Extract default value if present (${VAR:-default}) default_value = None - if ':-' in value: - default_match = re.search(r':-([^}]+)', value) + if ":-" in value: + default_match = re.search(r":-([^}]+)", value) if default_match: default_value = default_match.group(1).strip() # Try to parse boolean/number defaults - if default_value.lower() in ('true', 'false'): - default_value = default_value.lower() == 'true' + if default_value.lower() in ("true", "false"): + default_value = default_value.lower() == "true" elif default_value.isdigit(): default_value = int(default_value) schema = { - 'type': 'password' if is_secret else 'string', - 'label': label, - 'secret': is_secret, - 'env_var': env_var, - 'required': is_secret, # Secrets are required + "type": "password" if is_secret else "string", + "label": label, + "secret": is_secret, + "env_var": env_var, + "required": is_secret, # Secrets are required } if default_value is not None: - schema['default'] = default_value - schema['required'] = False + schema["default"] = default_value + schema["required"] = False return schema # Boolean values elif isinstance(value, bool): - return {'type': 'boolean', 'label': label, 'default': value} + return {"type": "boolean", "label": label, "default": value} # Numeric values elif isinstance(value, int): - return {'type': 'number', 'label': label, 'default': value} + return {"type": "number", "label": label, "default": value} elif isinstance(value, float): - return {'type': 'number', 'label': label, 'default': value, 'step': 0.1} + return {"type": "number", "label": label, "default": value, "step": 0.1} # List values elif isinstance(value, list): - return {'type': 'array', 'label': label, 'default': value} + return {"type": "array", "label": label, "default": value} # Object/dict values elif isinstance(value, dict): - return {'type': 'object', 'label': label, 'default': value} + return {"type": "object", "label": label, "default": value} # String values (fallback) else: - return {'type': 'string', 'label': label, 'default': str(value) if value is not None else ''} + return { + "type": "string", + "label": label, + "default": str(value) if value is not None else "", + } def load_schema_yml(plugin_id: str) -> Optional[Dict[str, Any]]: @@ -280,12 +285,13 @@ def load_schema_yml(plugin_id: str) -> Optional[Dict[str, Any]]: """ try: import advanced_omi_backend.plugins + plugins_dir = Path(advanced_omi_backend.plugins.__file__).parent schema_path = plugins_dir / plugin_id / "schema.yml" if schema_path.exists(): logger.debug(f"Loading schema override from: {schema_path}") - with open(schema_path, 'r') as f: + with open(schema_path, "r") as f: return yaml.safe_load(f) or {} except Exception as e: logger.warning(f"Failed to load schema.yml for plugin '{plugin_id}': {e}") @@ -329,16 +335,13 @@ def infer_schema_from_config(plugin_id: str, config_dict: Dict[str, Any]) -> Dic field_schema = infer_field_type(key, value) # Separate env vars from regular settings - if field_schema.get('env_var'): - env_var_name = field_schema['env_var'] + if field_schema.get("env_var"): + env_var_name = field_schema["env_var"] env_vars_schema[env_var_name] = field_schema else: settings_schema[key] = field_schema - return { - 'settings': settings_schema, - 'env_vars': env_vars_schema - } + return {"settings": settings_schema, "env_vars": env_vars_schema} def mask_secrets_in_config(config: Dict[str, Any], schema: Dict[str, Any]) -> Dict[str, Any]: @@ -362,8 +365,8 @@ def mask_secrets_in_config(config: Dict[str, Any], schema: Dict[str, Any]) -> Di # Get list of secret environment variable names secret_env_vars = set() - for env_var, field_schema in schema.get('env_vars', {}).items(): - if field_schema.get('secret', False): + for env_var, field_schema in schema.get("env_vars", {}).items(): + if field_schema.get("secret", False): secret_env_vars.add(env_var) # Mask values that reference secret environment variables @@ -373,13 +376,14 @@ def mask_secrets_in_config(config: Dict[str, Any], schema: Dict[str, Any]) -> Di if env_var and env_var in secret_env_vars: # Check if env var is actually set is_set = bool(os.environ.get(env_var)) - masked_config[key] = 'β€’β€’β€’β€’β€’β€’β€’β€’β€’β€’β€’β€’' if is_set else '' + masked_config[key] = "β€’β€’β€’β€’β€’β€’β€’β€’β€’β€’β€’β€’" if is_set else "" return masked_config -def get_plugin_metadata(plugin_id: str, plugin_class: Type[BasePlugin], - orchestration_config: Dict[str, Any]) -> Dict[str, Any]: +def get_plugin_metadata( + plugin_id: str, plugin_class: Type[BasePlugin], orchestration_config: Dict[str, Any] +) -> Dict[str, Any]: """Get complete metadata for a plugin including schema and current config. Args: @@ -393,12 +397,13 @@ def get_plugin_metadata(plugin_id: str, plugin_class: Type[BasePlugin], # Load plugin config.yml try: import advanced_omi_backend.plugins + plugins_dir = Path(advanced_omi_backend.plugins.__file__).parent plugin_config_path = plugins_dir / plugin_id / "config.yml" config_dict = {} if plugin_config_path.exists(): - with open(plugin_config_path, 'r') as f: + with open(plugin_config_path, "r") as f: config_dict = yaml.safe_load(f) or {} except Exception as e: logger.error(f"Failed to load config for plugin '{plugin_id}': {e}") @@ -408,36 +413,36 @@ def get_plugin_metadata(plugin_id: str, plugin_class: Type[BasePlugin], config_schema = infer_schema_from_config(plugin_id, config_dict) # Get plugin metadata from class - plugin_name = getattr(plugin_class, 'name', plugin_id.replace('_', ' ').title()) - plugin_description = getattr(plugin_class, 'description', '') - supports_testing = hasattr(plugin_class, 'test_connection') + plugin_name = getattr(plugin_class, "name", plugin_id.replace("_", " ").title()) + plugin_description = getattr(plugin_class, "description", "") + supports_testing = hasattr(plugin_class, "test_connection") # Mask secrets in current config current_config = load_plugin_config(plugin_id, orchestration_config) masked_config = mask_secrets_in_config(current_config, config_schema) # Mark which env vars are set - for env_var_name, env_var_schema in config_schema.get('env_vars', {}).items(): - env_var_schema['is_set'] = bool(os.environ.get(env_var_name)) - if env_var_schema.get('secret') and env_var_schema['is_set']: - env_var_schema['value'] = 'β€’β€’β€’β€’β€’β€’β€’β€’β€’β€’β€’β€’' + for env_var_name, env_var_schema in config_schema.get("env_vars", {}).items(): + env_var_schema["is_set"] = bool(os.environ.get(env_var_name)) + if env_var_schema.get("secret") and env_var_schema["is_set"]: + env_var_schema["value"] = "β€’β€’β€’β€’β€’β€’β€’β€’β€’β€’β€’β€’" else: - env_var_schema['value'] = os.environ.get(env_var_name, '') + env_var_schema["value"] = os.environ.get(env_var_name, "") return { - 'plugin_id': plugin_id, - 'name': plugin_name, - 'description': plugin_description, - 'enabled': orchestration_config.get('enabled', False), - 'status': 'active' if orchestration_config.get('enabled', False) else 'disabled', - 'supports_testing': supports_testing, - 'config_schema': config_schema, - 'current_config': masked_config, - 'orchestration': { - 'enabled': orchestration_config.get('enabled', False), - 'events': orchestration_config.get('events', []), - 'condition': orchestration_config.get('condition', {'type': 'always'}) - } + "plugin_id": plugin_id, + "name": plugin_name, + "description": plugin_description, + "enabled": orchestration_config.get("enabled", False), + "status": "active" if orchestration_config.get("enabled", False) else "disabled", + "supports_testing": supports_testing, + "config_schema": config_schema, + "current_config": masked_config, + "orchestration": { + "enabled": orchestration_config.get("enabled", False), + "events": orchestration_config.get("events", []), + "condition": orchestration_config.get("condition", {"type": "always"}), + }, } @@ -467,6 +472,7 @@ def discover_plugins() -> Dict[str, Type[BasePlugin]]: # Get the plugins directory path try: import advanced_omi_backend.plugins + plugins_dir = Path(advanced_omi_backend.plugins.__file__).parent except Exception as e: logger.error(f"Failed to locate plugins directory: {e}") @@ -475,7 +481,7 @@ def discover_plugins() -> Dict[str, Type[BasePlugin]]: logger.info(f"πŸ” Scanning for plugins in: {plugins_dir}") # Skip these known system directories/files - skip_items = {'__pycache__', '__init__.py', 'base.py', 'router.py'} + skip_items = {"__pycache__", "__init__.py", "base.py", "router.py"} # Scan for plugin directories for item in plugins_dir.iterdir(): @@ -483,7 +489,7 @@ def discover_plugins() -> Dict[str, Type[BasePlugin]]: continue plugin_id = item.name - plugin_file = item / 'plugin.py' + plugin_file = item / "plugin.py" if not plugin_file.exists(): logger.debug(f"Skipping '{plugin_id}': no plugin.py found") @@ -492,10 +498,10 @@ def discover_plugins() -> Dict[str, Type[BasePlugin]]: try: # Convert snake_case directory name to PascalCase class name # e.g., email_summarizer -> EmailSummarizerPlugin - class_name = ''.join(word.capitalize() for word in plugin_id.split('_')) + 'Plugin' + class_name = "".join(word.capitalize() for word in plugin_id.split("_")) + "Plugin" # Import the plugin module - module_path = f'advanced_omi_backend.plugins.{plugin_id}' + module_path = f"advanced_omi_backend.plugins.{plugin_id}" logger.debug(f"Attempting to import plugin from: {module_path}") # Import the plugin package (which should export the class in __init__.py) @@ -558,24 +564,28 @@ def init_plugin_router() -> Optional[PluginRouter]: logger.info(f"πŸ” File exists: {plugins_yml.exists()}") if plugins_yml.exists(): - with open(plugins_yml, 'r') as f: + with open(plugins_yml, "r") as f: plugins_config = yaml.safe_load(f) # Expand environment variables in configuration plugins_config = expand_env_vars(plugins_config) - plugins_data = plugins_config.get('plugins', {}) + plugins_data = plugins_config.get("plugins", {}) - logger.info(f"πŸ” Loaded plugins config with {len(plugins_data)} plugin(s): {list(plugins_data.keys())}") + logger.info( + f"πŸ” Loaded plugins config with {len(plugins_data)} plugin(s): {list(plugins_data.keys())}" + ) # Discover all plugins via auto-discovery discovered_plugins = discover_plugins() # Core plugin names (for informational logging only) - CORE_PLUGIN_NAMES = {'homeassistant', 'test_event'} + CORE_PLUGIN_NAMES = {"homeassistant", "test_event"} # Initialize each plugin listed in config/plugins.yml for plugin_id, orchestration_config in plugins_data.items(): - logger.info(f"πŸ” Processing plugin '{plugin_id}', enabled={orchestration_config.get('enabled', False)}") - if not orchestration_config.get('enabled', False): + logger.info( + f"πŸ” Processing plugin '{plugin_id}', enabled={orchestration_config.get('enabled', False)}" + ) + if not orchestration_config.get("enabled", False): continue try: @@ -603,7 +613,9 @@ def init_plugin_router() -> Optional[PluginRouter]: except Exception as e: logger.error(f"Failed to register plugin '{plugin_id}': {e}", exc_info=True) - logger.info(f"πŸŽ‰ Plugin registration complete: {len(_plugin_router.plugins)} plugin(s) registered") + logger.info( + f"πŸŽ‰ Plugin registration complete: {len(_plugin_router.plugins)} plugin(s) registered" + ) else: logger.info("No plugins.yml found, plugins disabled") @@ -615,6 +627,31 @@ def init_plugin_router() -> Optional[PluginRouter]: return None +async def ensure_plugin_router() -> Optional[PluginRouter]: + """Get or initialize the plugin router with all plugins initialized. + + This is the standard pattern for worker processes that need the plugin router. + It handles the get-or-init-then-initialize sequence in one call. + + Returns: + Initialized plugin router, or None if no plugins configured + """ + plugin_router = get_plugin_router() + if plugin_router: + return plugin_router + + logger.info("Initializing plugin router in worker process...") + plugin_router = init_plugin_router() + if plugin_router: + for plugin_id, plugin in plugin_router.plugins.items(): + try: + await plugin.initialize() + logger.info(f"Plugin '{plugin_id}' initialized") + except Exception as e: + logger.error(f"Failed to initialize plugin '{plugin_id}': {e}") + return plugin_router + + async def cleanup_plugin_router() -> None: """Clean up the plugin router and all registered plugins.""" global _plugin_router diff --git a/backends/advanced/src/advanced_omi_backend/services/transcription/__init__.py b/backends/advanced/src/advanced_omi_backend/services/transcription/__init__.py index d87fd2e3..5c5c2296 100644 --- a/backends/advanced/src/advanced_omi_backend/services/transcription/__init__.py +++ b/backends/advanced/src/advanced_omi_backend/services/transcription/__init__.py @@ -70,11 +70,35 @@ def __init__(self): raise RuntimeError("No default STT model defined in config.yml") self.model = model self._name = model.model_provider or model.name + # Load capabilities from config.yml model definition + self._capabilities = set(model.capabilities) if model.capabilities else set() @property def name(self) -> str: return self._name + @property + def capabilities(self) -> set: + """Return provider capabilities from config.yml. + + Capabilities indicate what the provider can produce: + - word_timestamps: Word-level timing data + - segments: Speaker segments + - diarization: Speaker labels in segments + + Returns: + Set of capability strings + """ + return self._capabilities + + def get_capabilities_dict(self) -> dict: + """Return capabilities as a dict for metadata storage. + + Returns: + Dict mapping capability names to True + """ + return {cap: True for cap in self._capabilities} + async def transcribe(self, audio_data: bytes, sample_rate: int, diarize: bool = False) -> dict: # Special handling for mock provider (no HTTP server needed) if self.model.model_provider == "mock": @@ -124,7 +148,7 @@ async def transcribe(self, audio_data: bytes, sample_rate: int, diarize: bool = if "diarize" in query: query["diarize"] = "true" if diarize else "false" - timeout = op.get("timeout", 120) + timeout = op.get("timeout", 300) async with httpx.AsyncClient(timeout=timeout) as client: if method == "POST": if use_multipart: diff --git a/backends/advanced/src/advanced_omi_backend/services/transcription/mock_provider.py b/backends/advanced/src/advanced_omi_backend/services/transcription/mock_provider.py index 04b192df..f596214f 100644 --- a/backends/advanced/src/advanced_omi_backend/services/transcription/mock_provider.py +++ b/backends/advanced/src/advanced_omi_backend/services/transcription/mock_provider.py @@ -6,6 +6,7 @@ """ from typing import Optional + from .base import BatchTranscriptionProvider diff --git a/backends/advanced/src/advanced_omi_backend/services/transcription/streaming_consumer.py b/backends/advanced/src/advanced_omi_backend/services/transcription/streaming_consumer.py index b6c05ae8..052680d2 100644 --- a/backends/advanced/src/advanced_omi_backend/services/transcription/streaming_consumer.py +++ b/backends/advanced/src/advanced_omi_backend/services/transcription/streaming_consumer.py @@ -19,9 +19,9 @@ import redis.asyncio as redis from redis import exceptions as redis_exceptions +from advanced_omi_backend.client_manager import get_client_owner_async from advanced_omi_backend.plugins.router import PluginRouter from advanced_omi_backend.services.transcription import get_transcription_provider -from advanced_omi_backend.client_manager import get_client_owner_async logger = logging.getLogger(__name__) @@ -125,7 +125,6 @@ async def start_session_stream(self, session_id: str, sample_rate: int = 16000): self.active_sessions[session_id] = { "last_activity": time.time(), "sample_rate": sample_rate, - "audio_offset_seconds": 0.0 # Track cumulative audio duration for timestamp adjustment } logger.info(f"πŸŽ™οΈ Started Deepgram WebSocket stream for session: {session_id}") @@ -164,10 +163,22 @@ async def end_session_stream(self, session_id: str): await self.trigger_plugins(session_id, final_result) self.active_sessions.pop(session_id, None) - logger.info(f"πŸ›‘ Ended Deepgram WebSocket stream for session: {session_id}") + + # Signal that streaming transcription is complete for this session + # This allows conversation_jobs to wait for all results before reading transcript + completion_key = f"transcription:complete:{session_id}" + await self.redis_client.set(completion_key, "1", ex=300) # 5 min TTL + logger.info(f"βœ… Streaming transcription complete for {session_id} (signal set)") except Exception as e: logger.error(f"Error ending stream for {session_id}: {e}", exc_info=True) + # Still signal completion even on error so conversation job doesn't hang + try: + completion_key = f"transcription:complete:{session_id}" + await self.redis_client.set(completion_key, "error", ex=300) + logger.warning(f"⚠️ Set error completion signal for {session_id}") + except Exception: + pass # Best effort async def process_audio_chunk(self, session_id: str, audio_chunk: bytes, chunk_id: str): """ @@ -250,11 +261,11 @@ async def publish_to_client(self, session_id: str, result: Dict, is_final: bool) async def store_final_result(self, session_id: str, result: Dict, chunk_id: str = None): """ - Store final transcription result to Redis Stream with cumulative timestamp adjustment. + Store final transcription result to Redis Stream. - Transcription providers return word timestamps that reset to 0 for each chunk. - We maintain a running audio_offset_seconds to make timestamps cumulative across - the session, enabling accurate speech duration calculation for speech detection. + Note: Deepgram streaming WebSocket maintains state and returns cumulative + timestamps from the start of the stream. No offset adjustment is needed. + Previous code incorrectly assumed per-chunk timestamps starting at 0. Args: session_id: Session ID @@ -264,38 +275,9 @@ async def store_final_result(self, session_id: str, result: Dict, chunk_id: str try: stream_name = f"transcription:results:{session_id}" - # Get cumulative audio offset for this session - audio_offset = 0.0 - chunk_duration = 0.0 - if session_id in self.active_sessions: - audio_offset = self.active_sessions[session_id].get("audio_offset_seconds", 0.0) - - # Adjust word timestamps by cumulative offset + # Get words and segments directly - Deepgram returns cumulative timestamps words = result.get("words", []) - adjusted_words = [] - if words: - for word in words: - adjusted_word = word.copy() - adjusted_word["start"] = word.get("start", 0.0) + audio_offset - adjusted_word["end"] = word.get("end", 0.0) + audio_offset - adjusted_words.append(adjusted_word) - - # Calculate chunk duration from last word's end time - if adjusted_words: - last_word_end = words[-1].get("end", 0.0) # Use unadjusted for duration calc - chunk_duration = last_word_end - - logger.debug(f"➑️ [STREAMING] Adjusted {len(adjusted_words)} words by +{audio_offset:.1f}s (chunk_duration={chunk_duration:.1f}s)") - - # Adjust segment timestamps too segments = result.get("segments", []) - adjusted_segments = [] - if segments: - for seg in segments: - adjusted_seg = seg.copy() - adjusted_seg["start"] = seg.get("start", 0.0) + audio_offset - adjusted_seg["end"] = seg.get("end", 0.0) + audio_offset - adjusted_segments.append(adjusted_seg) # Prepare result entry - MUST match aggregator's expected schema # All keys and values must be bytes to match consumer.py format @@ -308,23 +290,17 @@ async def store_final_result(self, session_id: str, result: Dict, chunk_id: str b"timestamp": str(time.time()).encode(), } - # Add adjusted JSON fields - if adjusted_words: - entry[b"words"] = json.dumps(adjusted_words).encode() + # Add words and segments directly (already have cumulative timestamps from Deepgram) + if words: + entry[b"words"] = json.dumps(words).encode() - if adjusted_segments: - entry[b"segments"] = json.dumps(adjusted_segments).encode() + if segments: + entry[b"segments"] = json.dumps(segments).encode() # Write to Redis Stream await self.redis_client.xadd(stream_name, entry) - # Update cumulative offset for next chunk - if session_id in self.active_sessions and chunk_duration > 0: - self.active_sessions[session_id]["audio_offset_seconds"] += chunk_duration - new_offset = self.active_sessions[session_id]["audio_offset_seconds"] - logger.info(f"πŸ’Ύ Stored final result to {stream_name}: {result.get('text', '')[:50]}... (offset: {audio_offset:.1f}s β†’ {new_offset:.1f}s)") - else: - logger.info(f"πŸ’Ύ Stored final result to {stream_name}: {result.get('text', '')[:50]}...") + logger.info(f"πŸ’Ύ Stored final result to {stream_name}: {result.get('text', '')[:50]}... ({len(words)} words)") except Exception as e: logger.error(f"Error storing final result for {session_id}: {e}", exc_info=True) diff --git a/backends/advanced/src/advanced_omi_backend/speaker_recognition_client.py b/backends/advanced/src/advanced_omi_backend/speaker_recognition_client.py index 00aa0adc..7c14cccd 100644 --- a/backends/advanced/src/advanced_omi_backend/speaker_recognition_client.py +++ b/backends/advanced/src/advanced_omi_backend/speaker_recognition_client.py @@ -40,7 +40,9 @@ def __init__(self, service_url: Optional[str] = None): if os.getenv("USE_MOCK_SPEAKER_CLIENT") == "true": try: # Import mock client from testing module - from advanced_omi_backend.testing.mock_speaker_client import MockSpeakerRecognitionClient + from advanced_omi_backend.testing.mock_speaker_client import ( + MockSpeakerRecognitionClient, + ) self._mock_client = MockSpeakerRecognitionClient() self.enabled = True @@ -241,6 +243,247 @@ async def diarize_identify_match( logger.error(f"🎀 Error during speaker recognition: {e}") return {"error": "unknown_error", "message": str(e), "segments": []} + async def identify_segment( + self, + audio_wav_bytes: bytes, + user_id: Optional[str] = None, + similarity_threshold: Optional[float] = None, + ) -> Dict: + """ + Identify a single speaker from a WAV audio segment via POST /identify. + + Args: + audio_wav_bytes: WAV audio bytes for a single segment + user_id: Optional user ID to scope identification + similarity_threshold: Optional similarity threshold override + + Returns: + Dict with keys: found, speaker_id, speaker_name, confidence, status, duration + """ + if hasattr(self, "_mock_client"): + return await self._mock_client.identify_segment( + audio_wav_bytes, user_id, similarity_threshold + ) + + if not self.enabled: + return {"found": False, "speaker_name": None, "confidence": 0.0, "status": "unknown"} + + try: + async with aiohttp.ClientSession() as session: + form_data = aiohttp.FormData() + form_data.add_field( + "file", audio_wav_bytes, filename="segment.wav", content_type="audio/wav" + ) + if user_id is not None: + form_data.add_field("user_id", str(user_id)) + if similarity_threshold is not None: + form_data.add_field("similarity_threshold", str(similarity_threshold)) + + async with session.post( + f"{self.service_url}/identify", + data=form_data, + timeout=aiohttp.ClientTimeout(total=15), + ) as response: + if response.status != 200: + response_text = await response.text() + logger.warning(f"🎀 /identify returned status {response.status}: {response_text}") + return {"found": False, "speaker_name": None, "confidence": 0.0, "status": "error"} + + return await response.json() + + except ClientConnectorError as e: + logger.error(f"🎀 Failed to connect to speaker service /identify: {e}") + return {"found": False, "speaker_name": None, "confidence": 0.0, "status": "error"} + except asyncio.TimeoutError: + logger.error("🎀 Timeout calling speaker service /identify") + return {"found": False, "speaker_name": None, "confidence": 0.0, "status": "error"} + except aiohttp.ClientError as e: + logger.warning(f"🎀 Client error during /identify: {e}") + return {"found": False, "speaker_name": None, "confidence": 0.0, "status": "error"} + except Exception as e: + logger.error(f"🎀 Error during /identify: {e}") + return {"found": False, "speaker_name": None, "confidence": 0.0, "status": "error"} + + async def identify_provider_segments( + self, + conversation_id: str, + segments: List[Dict], + user_id: Optional[str] = None, + ) -> Dict: + """ + Identify speakers in provider-diarized segments using majority-vote per label. + + For each unique speaker label, picks the top 3 longest segments (min 1.5s), + extracts audio, calls /identify, and majority-votes to map labels to names. + + Args: + conversation_id: Conversation ID for audio extraction from MongoDB + segments: List of dicts with keys: start, end, text, speaker + user_id: Optional user ID for speaker identification + + Returns: + Dict with 'segments' list matching diarize_identify_match() format + """ + if hasattr(self, "_mock_client"): + return await self._mock_client.identify_provider_segments( + conversation_id, segments, user_id + ) + + if not self.enabled: + return {"segments": []} + + from advanced_omi_backend.config import get_diarization_settings + from advanced_omi_backend.utils.audio_chunk_utils import ( + reconstruct_audio_segment, + ) + + config = get_diarization_settings() + similarity_threshold = config.get("similarity_threshold", 0.15) + + MIN_SEGMENT_DURATION = 1.5 + MAX_SAMPLES_PER_LABEL = 3 + + # Detect non-speech segments (e.g. [Music], [Environmental Sounds], [Human Sounds]) + import re + NON_SPEECH_PATTERN = re.compile(r"^\[.*\]$") + + def _is_non_speech(seg: Dict) -> bool: + text = seg.get("text", "").strip() + if not text: + return True + if NON_SPEECH_PATTERN.match(text): + return True + label = str(seg.get("speaker", "")) + if label in ("None", "none", ""): + return True + return False + + # Separate speech and non-speech segments + speech_segments = [] + non_speech_indices = set() + for i, seg in enumerate(segments): + if _is_non_speech(seg): + non_speech_indices.add(i) + else: + speech_segments.append(seg) + + # Group speech segments by speaker label + label_groups: Dict[str, List[Dict]] = {} + for seg in speech_segments: + label = seg.get("speaker", "Unknown") + label_groups.setdefault(label, []).append(seg) + + logger.info( + f"🎀 Segment-level identification: {len(segments)} segments " + f"({len(non_speech_indices)} non-speech filtered), " + f"{len(label_groups)} unique labels: {list(label_groups.keys())}" + ) + + # For each label, pick top N longest segments >= MIN_SEGMENT_DURATION + label_samples: Dict[str, List[Dict]] = {} + for label, segs in label_groups.items(): + eligible = [s for s in segs if (s["end"] - s["start"]) >= MIN_SEGMENT_DURATION] + eligible.sort(key=lambda s: s["end"] - s["start"], reverse=True) + label_samples[label] = eligible[:MAX_SAMPLES_PER_LABEL] + if not label_samples[label]: + logger.info(f"🎀 Label '{label}': no segments >= {MIN_SEGMENT_DURATION}s, skipping identification") + + # Extract audio and identify concurrently with semaphore + semaphore = asyncio.Semaphore(3) + + async def _identify_one(seg: Dict) -> Optional[Dict]: + async with semaphore: + try: + wav_bytes = await reconstruct_audio_segment( + conversation_id, seg["start"], seg["end"] + ) + result = await self.identify_segment( + wav_bytes, user_id="1", similarity_threshold=similarity_threshold + ) + return result + except Exception as e: + logger.warning(f"🎀 Failed to identify segment [{seg['start']:.1f}-{seg['end']:.1f}]: {e}") + return None + + # Collect identification tasks + label_tasks: Dict[str, List[asyncio.Task]] = {} + all_tasks = [] + for label, samples in label_samples.items(): + tasks = [] + for seg in samples: + task = asyncio.create_task(_identify_one(seg)) + tasks.append(task) + all_tasks.append(task) + label_tasks[label] = tasks + + # Wait for all + if all_tasks: + await asyncio.gather(*all_tasks, return_exceptions=True) + + # Majority-vote per label + label_mapping: Dict[str, tuple] = {} # label -> (identified_name, confidence) + for label, tasks in label_tasks.items(): + name_votes: Dict[str, List[float]] = {} + for task in tasks: + try: + result = task.result() + except Exception: + continue + if result and result.get("found"): + name = result.get("speaker_name", "Unknown") + confidence = result.get("confidence", 0.0) + name_votes.setdefault(name, []).append(confidence) + + if name_votes: + # Pick name with most votes, break ties by average confidence + best_name = max( + name_votes.keys(), + key=lambda n: (len(name_votes[n]), sum(name_votes[n]) / len(name_votes[n])), + ) + avg_confidence = sum(name_votes[best_name]) / len(name_votes[best_name]) + label_mapping[label] = (best_name, avg_confidence) + logger.info( + f"🎀 Label '{label}' -> '{best_name}' " + f"({len(name_votes[best_name])}/{len(tasks)} votes, conf={avg_confidence:.3f})" + ) + else: + logger.info(f"🎀 Label '{label}' -> no identification (keeping original)") + + # Build result segments in same format as diarize_identify_match() + # Non-speech segments are kept but not speaker-identified + result_segments = [] + for i, seg in enumerate(segments): + label = seg.get("speaker", "Unknown") + if i in non_speech_indices: + result_segments.append({ + "start": seg["start"], + "end": seg["end"], + "text": seg.get("text", ""), + "speaker": label, + "identified_as": label, + "confidence": 0.0, + "status": "non_speech", + }) + else: + mapped = label_mapping.get(label) + result_segments.append({ + "start": seg["start"], + "end": seg["end"], + "text": seg.get("text", ""), + "speaker": label, + "identified_as": mapped[0] if mapped else label, + "confidence": mapped[1] if mapped else 0.0, + "status": "identified" if mapped else "unknown", + }) + + identified_count = sum(1 for m in label_mapping.values() if m) + logger.info( + f"🎀 Segment identification complete: {identified_count}/{len(label_groups)} labels identified, " + f"{len(result_segments)} total segments ({len(non_speech_indices)} non-speech kept as-is)" + ) + + return {"segments": result_segments} + async def diarize_and_identify( self, audio_data: bytes, words: None, user_id: Optional[str] = None # NOT IMPLEMENTED ) -> Dict: @@ -613,7 +856,7 @@ async def enroll_new_speaker( try: import uuid - + # Generate speaker ID: user_{user_id}_speaker_{random_hex} speaker_id = f"user_{user_id}_speaker_{uuid.uuid4().hex[:12]}" @@ -724,7 +967,9 @@ async def check_if_enrolled_speaker_present( - enrolled_present: True if enrolled speaker detected, False otherwise - speaker_result: Full speaker recognition result dict with segments """ - from advanced_omi_backend.utils.audio_extraction import extract_audio_for_results + from advanced_omi_backend.utils.audio_extraction import ( + extract_audio_for_results, + ) logger.info(f"🎀 [SPEAKER CHECK] Starting speaker check for session {session_id}") logger.info(f"🎀 [SPEAKER CHECK] Client: {client_id}, User: {user_id}") diff --git a/backends/advanced/src/advanced_omi_backend/testing/mock_speaker_client.py b/backends/advanced/src/advanced_omi_backend/testing/mock_speaker_client.py index e53a556e..8ba68adf 100644 --- a/backends/advanced/src/advanced_omi_backend/testing/mock_speaker_client.py +++ b/backends/advanced/src/advanced_omi_backend/testing/mock_speaker_client.py @@ -1,7 +1,7 @@ """Mock speaker recognition client for testing without heavy ML dependencies.""" import logging -from typing import Dict, Optional +from typing import Dict, List, Optional logger = logging.getLogger(__name__) @@ -158,3 +158,43 @@ async def diarize_identify_match( "confidence": 0.85 }] } + + async def identify_segment( + self, + audio_wav_bytes: bytes, + user_id: Optional[str] = None, + similarity_threshold: Optional[float] = None, + ) -> Dict: + """Mock identify_segment - returns no identification.""" + logger.info("🎀 Mock identify_segment called") + return { + "found": False, + "speaker_id": None, + "speaker_name": None, + "confidence": 0.0, + "status": "unknown", + "duration": 0.0, + } + + async def identify_provider_segments( + self, + conversation_id: str, + segments: List[Dict], + user_id: Optional[str] = None, + ) -> Dict: + """Mock identify_provider_segments - returns segments with original labels.""" + logger.info(f"🎀 Mock identify_provider_segments: {len(segments)} segments") + return { + "segments": [ + { + "start": seg.get("start", 0.0), + "end": seg.get("end", 0.0), + "text": seg.get("text", ""), + "speaker": seg.get("speaker", "Unknown"), + "identified_as": seg.get("speaker", "Unknown"), + "confidence": 0.0, + "status": "unknown", + } + for seg in segments + ] + } diff --git a/backends/advanced/src/advanced_omi_backend/utils/audio_utils.py b/backends/advanced/src/advanced_omi_backend/utils/audio_utils.py index f8ba07a3..5b5fa992 100644 --- a/backends/advanced/src/advanced_omi_backend/utils/audio_utils.py +++ b/backends/advanced/src/advanced_omi_backend/utils/audio_utils.py @@ -234,6 +234,7 @@ async def write_audio_file( AudioValidationError: If validation fails (when validate=True) """ from easy_audio_interfaces.filesystem.filesystem_interfaces import LocalFileSink + from advanced_omi_backend.config import CHUNK_DIR # Validate and prepare audio if needed @@ -374,8 +375,8 @@ def pcm_to_wav_bytes( Returns: WAV file data as bytes """ - import wave import io + import wave logger.debug( f"Converting PCM to WAV in memory: {len(pcm_data)} bytes " diff --git a/backends/advanced/src/advanced_omi_backend/utils/conversation_utils.py b/backends/advanced/src/advanced_omi_backend/utils/conversation_utils.py index 27af4bfa..2b69a47f 100644 --- a/backends/advanced/src/advanced_omi_backend/utils/conversation_utils.py +++ b/backends/advanced/src/advanced_omi_backend/utils/conversation_utils.py @@ -9,7 +9,7 @@ import time from datetime import datetime from pathlib import Path -from typing import Optional, Dict, Any, List +from typing import Any, Dict, List, Optional from advanced_omi_backend.config import get_speech_detection_settings from advanced_omi_backend.llm_client import async_generate @@ -178,7 +178,7 @@ async def generate_title(text: str, segments: Optional[list] = None) -> str: if segments: conversation_text = "" for segment in segments[:10]: # Use first 10 segments for title generation - segment_text = segment.get("text", "").strip() + segment_text = segment.text.strip() if segment.text else "" if segment_text: conversation_text += f"{segment_text}\n" text = conversation_text if conversation_text.strip() else text @@ -232,8 +232,8 @@ async def generate_short_summary(text: str, segments: Optional[list] = None) -> formatted_text = "" speakers_in_conv = set() for segment in segments: - speaker = segment.get("speaker", "") - segment_text = segment.get("text", "").strip() + speaker = segment.speaker or "" + segment_text = segment.text.strip() if segment.text else "" if segment_text: if speaker: formatted_text += f"{speaker}: {segment_text}\n" @@ -281,17 +281,12 @@ async def generate_short_summary(text: str, segments: Optional[list] = None) -> ) -# Backward compatibility alias -async def generate_summary(text: str) -> str: - """ - Backward compatibility wrapper for generate_short_summary. - Deprecated: Use generate_short_summary instead. - """ - return await generate_short_summary(text) - - -async def generate_detailed_summary(text: str, segments: Optional[list] = None) -> str: +async def generate_detailed_summary( + text: str, + segments: Optional[list] = None, + memory_context: Optional[str] = None, +) -> str: """ Generate a comprehensive, detailed summary of the conversation. @@ -305,6 +300,9 @@ async def generate_detailed_summary(text: str, segments: Optional[list] = None) segments: Optional list of speaker segments with structure: [{"speaker": str, "text": str, "start": float, "end": float}, ...] If provided, includes speaker attribution in detailed summary + memory_context: Optional context from prior conversations/memories. + When provided, injected into the prompt so the LLM can produce + more informed, contextual summaries. Returns: str: Comprehensive detailed summary (multiple paragraphs) or fallback @@ -317,8 +315,8 @@ async def generate_detailed_summary(text: str, segments: Optional[list] = None) formatted_text = "" speakers_in_conv = set() for segment in segments: - speaker = segment.get("speaker", "") - segment_text = segment.get("text", "").strip() + speaker = segment.speaker or "" + segment_text = segment.text.strip() if segment.text else "" if segment_text: if speaker: formatted_text += f"{speaker}: {segment_text}\n" @@ -343,9 +341,16 @@ async def generate_detailed_summary(text: str, segments: Optional[list] = None) else "" ) + memory_section = "" + if memory_context: + memory_section = f"""CONTEXT ABOUT THE USER (from prior conversations): +{memory_context} + +""" + prompt = f"""Generate a comprehensive, detailed summary of this conversation transcript. -TRANSCRIPT: +{memory_section}TRANSCRIPT: "{conversation_text}" INSTRUCTIONS: @@ -382,45 +387,18 @@ async def generate_detailed_summary(text: str, segments: Optional[list] = None) ) -# Backward compatibility aliases for deprecated speaker-specific methods -async def generate_title_with_speakers(segments: list) -> str: - """ - Deprecated: Use generate_title(text, segments=segments) instead. - - Backward compatibility wrapper. - """ - if not segments: - return "Conversation" - # Extract text from segments for compatibility - text = "\n".join(s.get("text", "") for s in segments if s.get("text")) - return await generate_title(text, segments=segments) - - -async def generate_summary_with_speakers(segments: list) -> str: - """ - Deprecated: Use generate_short_summary(text, segments=segments) instead. - - Backward compatibility wrapper. - """ - if not segments: - return "No content" - # Extract text from segments for compatibility - text = "\n".join(s.get("text", "") for s in segments if s.get("text")) - return await generate_short_summary(text, segments=segments) - - # ============================================================================ # Conversation Job Helpers # ============================================================================ -def extract_speakers_from_segments(segments: List[Dict[str, Any]]) -> List[str]: +def extract_speakers_from_segments(segments: list) -> List[str]: """ Extract unique speaker names from segments. Args: - segments: List of segments with speaker information + segments: List of segments (dict or SpeakerSegment objects) Returns: List of unique speaker names (excluding "Unknown") @@ -428,7 +406,7 @@ def extract_speakers_from_segments(segments: List[Dict[str, Any]]) -> List[str]: speakers = [] if segments: for seg in segments: - speaker = seg.get("speaker", "Unknown") + speaker = seg.get("speaker", "Unknown") if isinstance(seg, dict) else (seg.speaker or "Unknown") if speaker and speaker != "Unknown" and speaker not in speakers: speakers.append(speaker) return speakers diff --git a/backends/advanced/src/advanced_omi_backend/utils/gdrive_audio_utils.py b/backends/advanced/src/advanced_omi_backend/utils/gdrive_audio_utils.py index 785091e1..d9e39163 100644 --- a/backends/advanced/src/advanced_omi_backend/utils/gdrive_audio_utils.py +++ b/backends/advanced/src/advanced_omi_backend/utils/gdrive_audio_utils.py @@ -1,14 +1,15 @@ import io +import logging import tempfile from typing import List -import logging -from starlette.datastructures import UploadFile as StarletteUploadFile + from googleapiclient.http import MediaIoBaseDownload +from starlette.datastructures import UploadFile as StarletteUploadFile + from advanced_omi_backend.clients.gdrive_audio_client import get_google_drive_client from advanced_omi_backend.models.conversation import Conversation from advanced_omi_backend.utils.audio_utils import AudioValidationError - logger = logging.getLogger(__name__) audio_logger = logging.getLogger("audio_processing") diff --git a/backends/advanced/src/advanced_omi_backend/utils/logging_utils.py b/backends/advanced/src/advanced_omi_backend/utils/logging_utils.py index 3d15f49c..d0c69e34 100644 --- a/backends/advanced/src/advanced_omi_backend/utils/logging_utils.py +++ b/backends/advanced/src/advanced_omi_backend/utils/logging_utils.py @@ -7,7 +7,6 @@ import re from typing import Any, Dict, List, Optional, Set, Union - # Common patterns for identifying secret field names SECRET_KEYWORDS = [ 'PASSWORD', 'PASSWD', 'PWD', diff --git a/backends/advanced/src/advanced_omi_backend/utils/model_utils.py b/backends/advanced/src/advanced_omi_backend/utils/model_utils.py index 97b0cba5..96042ba0 100644 --- a/backends/advanced/src/advanced_omi_backend/utils/model_utils.py +++ b/backends/advanced/src/advanced_omi_backend/utils/model_utils.py @@ -5,7 +5,7 @@ from config.yml that can be used across different LLM services. """ -from typing import Dict, Any +from typing import Any, Dict def get_model_config(config_data: Dict[str, Any], model_role: str) -> Dict[str, Any]: diff --git a/backends/advanced/src/advanced_omi_backend/workers/__init__.py b/backends/advanced/src/advanced_omi_backend/workers/__init__.py index ea82056b..d4792805 100644 --- a/backends/advanced/src/advanced_omi_backend/workers/__init__.py +++ b/backends/advanced/src/advanced_omi_backend/workers/__init__.py @@ -11,16 +11,29 @@ Queue configuration and utilities are in controllers/queue_controller.py """ -# Import from transcription_jobs -from .transcription_jobs import ( - transcribe_full_audio_job, - stream_speech_detection_job, +# Import from queue_controller +from advanced_omi_backend.controllers.queue_controller import ( + DEFAULT_QUEUE, + JOB_RESULT_TTL, + MEMORY_QUEUE, + REDIS_URL, + TRANSCRIPTION_QUEUE, + default_queue, + get_job_stats, + get_jobs, + get_queue, + get_queue_health, + memory_queue, + redis_conn, + transcription_queue, ) -# Import from speaker_jobs -from .speaker_jobs import ( - check_enrolled_speakers_job, - recognise_speakers_job, +# Import from job models +from advanced_omi_backend.models.job import _ensure_beanie_initialized + +# Import from audio_jobs +from .audio_jobs import ( + audio_streaming_persistence_job, ) # Import from conversation_jobs @@ -30,35 +43,22 @@ # Import from memory_jobs from .memory_jobs import ( - process_memory_job, enqueue_memory_processing, + process_memory_job, ) -# Import from audio_jobs -from .audio_jobs import ( - audio_streaming_persistence_job, +# Import from speaker_jobs +from .speaker_jobs import ( + check_enrolled_speakers_job, + recognise_speakers_job, ) -# Import from queue_controller -from advanced_omi_backend.controllers.queue_controller import ( - get_queue, - get_job_stats, - get_jobs, - get_queue_health, - transcription_queue, - memory_queue, - default_queue, - redis_conn, - REDIS_URL, - JOB_RESULT_TTL, - TRANSCRIPTION_QUEUE, - MEMORY_QUEUE, - DEFAULT_QUEUE, +# Import from transcription_jobs +from .transcription_jobs import ( + stream_speech_detection_job, + transcribe_full_audio_job, ) -# Import from job models -from advanced_omi_backend.models.job import _ensure_beanie_initialized - __all__ = [ # Transcription jobs "transcribe_full_audio_job", diff --git a/backends/advanced/src/advanced_omi_backend/workers/annotation_jobs.py b/backends/advanced/src/advanced_omi_backend/workers/annotation_jobs.py index 8ad1a8ba..3681ab5f 100644 --- a/backends/advanced/src/advanced_omi_backend/workers/annotation_jobs.py +++ b/backends/advanced/src/advanced_omi_backend/workers/annotation_jobs.py @@ -9,7 +9,7 @@ """ import logging -from datetime import datetime, timezone, timedelta +from datetime import datetime, timedelta, timezone from typing import List from advanced_omi_backend.models.annotation import ( diff --git a/backends/advanced/src/advanced_omi_backend/workers/audio_jobs.py b/backends/advanced/src/advanced_omi_backend/workers/audio_jobs.py index de563069..f124feb8 100644 --- a/backends/advanced/src/advanced_omi_backend/workers/audio_jobs.py +++ b/backends/advanced/src/advanced_omi_backend/workers/audio_jobs.py @@ -5,18 +5,20 @@ """ import asyncio -import os import logging +import os import time -from typing import Dict, Any, Optional - -from advanced_omi_backend.models.job import JobPriority, async_job +from typing import Any, Dict, Optional from advanced_omi_backend.controllers.queue_controller import ( - default_queue, JOB_RESULT_TTL, + default_queue, +) +from advanced_omi_backend.models.job import ( + JobPriority, + _ensure_beanie_initialized, + async_job, ) -from advanced_omi_backend.models.job import _ensure_beanie_initialized logger = logging.getLogger(__name__) @@ -81,6 +83,25 @@ async def audio_streaming_persistence_job( conversation_key = f"conversation:current:{session_id}" existing_conversation_id = await redis_client.get(conversation_key) + # Guard against stale Redis keys: the conversation:current key has a 1-hour + # TTL and can survive container rebuilds (Redis uses appendonly persistence + # with a bind mount). If the key points to a MongoDB document that was deleted + # (e.g., data directory cleared during rebuild), we must create a fresh + # placeholder instead of silently reusing a non-existent conversation. + if existing_conversation_id: + existing_id_str = existing_conversation_id.decode() + from advanced_omi_backend.models.conversation import Conversation + existing_conv = await Conversation.find_one( + Conversation.conversation_id == existing_id_str + ) + if not existing_conv: + logger.warning( + f"⚠️ Stale Redis key: conversation {existing_id_str} not found in MongoDB. " + f"Clearing key and creating fresh placeholder." + ) + await redis_client.delete(conversation_key) + existing_conversation_id = None + if not existing_conversation_id: logger.info( f"πŸ“ always_persist=True - creating placeholder conversation for session {session_id[:12]}" @@ -129,10 +150,11 @@ async def audio_streaming_persistence_job( start_time = time.time() # Import MongoDB chunk utilities + from bson import Binary + from advanced_omi_backend.models.audio_chunk import AudioChunkDocument from advanced_omi_backend.models.conversation import Conversation from advanced_omi_backend.utils.audio_chunk_utils import encode_pcm_to_opus - from bson import Binary # Conversation rotation state current_conversation_id = None @@ -162,20 +184,23 @@ async def audio_streaming_persistence_job( # Get current job for zombie detection from rq import get_current_job + from advanced_omi_backend.utils.job_utils import check_job_alive current_job = get_current_job() - async def flush_pcm_buffer(): + async def flush_pcm_buffer() -> bool: """ Flush current PCM buffer to MongoDB as Opus-compressed chunk. Updates conversation metadata with chunk count and compression stats. + Returns True on success, False on failure. On failure the buffer is + NOT cleared so the caller can retry on the next incoming message. """ nonlocal pcm_buffer, chunk_index, chunk_start_time nonlocal total_pcm_bytes, total_compressed_bytes, total_mongo_chunks_written if len(pcm_buffer) == 0 or not current_conversation_id: - return + return True try: # Encode PCM β†’ Opus @@ -244,8 +269,11 @@ async def flush_pcm_buffer(): f"{chunk_index + 1} chunks, {total_duration:.1f}s total" ) + return True + except Exception as e: logger.error(f"❌ Failed to save audio chunk {chunk_index}: {e}", exc_info=True) + return False while True: # Check if job still exists in Redis (detect zombie state) @@ -290,11 +318,10 @@ async def flush_pcm_buffer(): # Flush if buffer reaches chunk size if len(pcm_buffer) >= CHUNK_SIZE_BYTES: - await flush_pcm_buffer() - # Reset for next chunk - pcm_buffer = bytearray() - chunk_index += 1 - chunk_start_time += CHUNK_DURATION_SECONDS + if await flush_pcm_buffer(): + pcm_buffer = bytearray() + chunk_index += 1 + chunk_start_time += CHUNK_DURATION_SECONDS await redis_client.xack(audio_stream_name, audio_group_name, message_id) @@ -320,11 +347,17 @@ async def flush_pcm_buffer(): if new_conversation_id != current_conversation_id: # Flush remaining buffer from previous conversation if len(pcm_buffer) > 0 and current_conversation_id: - await flush_pcm_buffer() - logger.info( - f"βœ… Finalized conversation {current_conversation_id[:12]}: " - f"{chunk_index + 1} chunks saved to MongoDB" - ) + if await flush_pcm_buffer(): + logger.info( + f"βœ… Finalized conversation {current_conversation_id[:12]}: " + f"{chunk_index + 1} chunks saved to MongoDB" + ) + else: + logger.warning( + f"⚠️ Failed to flush final chunk for conversation " + f"{current_conversation_id[:12]} during rotation β€” " + f"{len(pcm_buffer)} bytes lost" + ) # Start new conversation current_conversation_id = new_conversation_id @@ -388,12 +421,13 @@ async def flush_pcm_buffer(): # Flush if buffer reaches 10-second chunk size if len(pcm_buffer) >= CHUNK_SIZE_BYTES: - await flush_pcm_buffer() - - # Reset for next chunk - pcm_buffer = bytearray() - chunk_index += 1 - chunk_start_time += CHUNK_DURATION_SECONDS + if await flush_pcm_buffer(): + # Reset for next chunk only on success; + # on failure the buffer is retained and + # the next message triggers a retry. + pcm_buffer = bytearray() + chunk_index += 1 + chunk_start_time += CHUNK_DURATION_SECONDS # ACK the message await redis_client.xack(audio_stream_name, audio_group_name, message_id) diff --git a/backends/advanced/src/advanced_omi_backend/workers/audio_stream_worker.py b/backends/advanced/src/advanced_omi_backend/workers/audio_stream_worker.py index df133de4..dcad10dc 100644 --- a/backends/advanced/src/advanced_omi_backend/workers/audio_stream_worker.py +++ b/backends/advanced/src/advanced_omi_backend/workers/audio_stream_worker.py @@ -17,9 +17,11 @@ import redis.asyncio as redis -from advanced_omi_backend.services.plugin_service import init_plugin_router -from advanced_omi_backend.services.transcription.streaming_consumer import StreamingTranscriptionConsumer from advanced_omi_backend.client_manager import initialize_redis_for_client_manager +from advanced_omi_backend.services.plugin_service import init_plugin_router +from advanced_omi_backend.services.transcription.streaming_consumer import ( + StreamingTranscriptionConsumer, +) logging.basicConfig( level=logging.INFO, diff --git a/backends/advanced/src/advanced_omi_backend/workers/cleanup_jobs.py b/backends/advanced/src/advanced_omi_backend/workers/cleanup_jobs.py index 65240af4..99ea5869 100644 --- a/backends/advanced/src/advanced_omi_backend/workers/cleanup_jobs.py +++ b/backends/advanced/src/advanced_omi_backend/workers/cleanup_jobs.py @@ -8,11 +8,11 @@ from datetime import datetime, timedelta from typing import Optional -from advanced_omi_backend.models.conversation import Conversation +from advanced_omi_backend.config import CleanupSettings, get_cleanup_settings from advanced_omi_backend.models.audio_chunk import AudioChunkDocument -from advanced_omi_backend.models.waveform import WaveformData +from advanced_omi_backend.models.conversation import Conversation from advanced_omi_backend.models.job import async_job -from advanced_omi_backend.config import get_cleanup_settings, CleanupSettings +from advanced_omi_backend.models.waveform import WaveformData logger = logging.getLogger(__name__) diff --git a/backends/advanced/src/advanced_omi_backend/workers/conversation_jobs.py b/backends/advanced/src/advanced_omi_backend/workers/conversation_jobs.py index fd5875e2..6e10122f 100644 --- a/backends/advanced/src/advanced_omi_backend/workers/conversation_jobs.py +++ b/backends/advanced/src/advanced_omi_backend/workers/conversation_jobs.py @@ -6,31 +6,33 @@ import asyncio import logging -import time, os +import os +import time from datetime import datetime -from typing import Dict, Any, Optional -from rq.job import Job +from typing import Any, Dict, Optional + from rq.exceptions import NoSuchJobError +from rq.job import Job -from advanced_omi_backend.models.job import async_job -from advanced_omi_backend.controllers.queue_controller import redis_conn +from advanced_omi_backend.controllers.queue_controller import ( + redis_conn, + start_post_conversation_jobs, +) from advanced_omi_backend.controllers.session_controller import mark_session_complete -from advanced_omi_backend.services.plugin_service import get_plugin_router, init_plugin_router -from datetime import datetime - +from advanced_omi_backend.models.job import async_job +from advanced_omi_backend.services.plugin_service import ( + ensure_plugin_router, + get_plugin_router, +) from advanced_omi_backend.utils.conversation_utils import ( analyze_speech, extract_speakers_from_segments, - track_speech_activity, - update_job_progress_metadata, -) -from advanced_omi_backend.utils.conversation_utils import ( is_meaningful_speech, mark_conversation_deleted, + track_speech_activity, + update_job_progress_metadata, ) -from advanced_omi_backend.controllers.queue_controller import start_post_conversation_jobs - logger = logging.getLogger(__name__) @@ -98,9 +100,10 @@ async def handle_end_of_conversation( logger.info(f"🧹 Deleted conversation:current signal for session {session_id[:12]}") # Update conversation in database with end reason and completion time - from advanced_omi_backend.models.conversation import Conversation from datetime import datetime + from advanced_omi_backend.models.conversation import Conversation + conversation = await Conversation.find_one(Conversation.conversation_id == conversation_id) if conversation: # Convert string to enum @@ -112,7 +115,9 @@ async def handle_end_of_conversation( conversation.completed_at = datetime.utcnow() await conversation.save() - logger.info(f"πŸ’Ύ Saved conversation {conversation_id[:12]} end_reason: {conversation.end_reason}") + logger.info( + f"πŸ’Ύ Saved conversation {conversation_id[:12]} end_reason: {conversation.end_reason}" + ) else: logger.warning(f"⚠️ Conversation {conversation_id} not found for end reason tracking") @@ -137,11 +142,13 @@ async def handle_end_of_conversation( ) from advanced_omi_backend.controllers.queue_controller import ( - transcription_queue, - redis_conn, JOB_RESULT_TTL, + redis_conn, + transcription_queue, + ) + from advanced_omi_backend.workers.transcription_jobs import ( + stream_speech_detection_job, ) - from advanced_omi_backend.workers.transcription_jobs import stream_speech_detection_job # Enqueue speech detection job for next conversation (audio persistence keeps running) speech_job = transcription_queue.enqueue( @@ -158,7 +165,9 @@ async def handle_end_of_conversation( # Store job ID for cleanup (keyed by client_id for WebSocket cleanup) try: - redis_conn.set(f"speech_detection_job:{client_id}", speech_job.id, ex=86400) # 24 hours + redis_conn.set( + f"speech_detection_job:{client_id}", speech_job.id, ex=86400 + ) # 24 hours logger.info(f"πŸ“Œ Stored speech detection job ID for client {client_id}") except Exception as e: logger.warning(f"⚠️ Failed to store job ID for {client_id}: {e}") @@ -210,10 +219,16 @@ async def open_conversation_job( Note: user_email is fetched from the database when needed. """ - from advanced_omi_backend.services.audio_stream import TranscriptionResultsAggregator - from advanced_omi_backend.models.conversation import Conversation, create_conversation from rq import get_current_job + from advanced_omi_backend.models.conversation import ( + Conversation, + create_conversation, + ) + from advanced_omi_backend.services.audio_stream import ( + TranscriptionResultsAggregator, + ) + logger.info( f"πŸ“ Creating and opening conversation for session {session_id} (speech detected at {speech_detected_at})" ) @@ -227,7 +242,9 @@ async def open_conversation_job( conversation_key = f"conversation:current:{session_id}" existing_conversation_id_bytes = await redis_client.get(conversation_key) - logger.info(f"πŸ” Checking for placeholder: key={conversation_key}, found={existing_conversation_id_bytes is not None}") + logger.info( + f"πŸ” Checking for placeholder: key={conversation_key}, found={existing_conversation_id_bytes is not None}" + ) conversation = None if existing_conversation_id_bytes: @@ -240,8 +257,8 @@ async def open_conversation_job( ) if conversation: - always_persist = getattr(conversation, 'always_persist', False) - processing_status = getattr(conversation, 'processing_status', None) + always_persist = getattr(conversation, "always_persist", False) + processing_status = getattr(conversation, "processing_status", None) logger.info( f"πŸ” Found conversation in DB: always_persist={always_persist}, " f"processing_status={processing_status}" @@ -250,8 +267,11 @@ async def open_conversation_job( logger.warning(f"⚠️ Conversation {existing_conversation_id} not found in database!") # Verify it's a placeholder conversation (always_persist=True, processing_status='pending_transcription') - if conversation and getattr(conversation, 'always_persist', False) and \ - getattr(conversation, 'processing_status', None) == 'pending_transcription': + if ( + conversation + and getattr(conversation, "always_persist", False) + and getattr(conversation, "processing_status", None) == "pending_transcription" + ): logger.info( f"πŸ”„ Reusing placeholder conversation {conversation.conversation_id} for session {session_id}" ) @@ -315,7 +335,7 @@ async def open_conversation_job( ) else: raise - + # Signal audio persistence job to rotate to this conversation's file rotation_signal_key = f"conversation:current:{session_id}" await redis_client.set(rotation_signal_key, conversation_id, ex=86400) # 24 hour TTL @@ -337,9 +357,13 @@ async def open_conversation_job( # Inactivity timeout configuration inactivity_timeout_seconds = float(os.getenv("SPEECH_INACTIVITY_THRESHOLD_SECONDS", "60")) inactivity_timeout_minutes = inactivity_timeout_seconds / 60 - last_meaningful_speech_time = 0.0 # Initialize with audio time 0 (will be updated with first speech) + last_meaningful_speech_time = ( + 0.0 # Initialize with audio time 0 (will be updated with first speech) + ) timeout_triggered = False # Track if closure was due to timeout - last_inactivity_log_time = time.time() # Track when we last logged inactivity (wall-clock for logging) + last_inactivity_log_time = ( + time.time() + ) # Track when we last logged inactivity (wall-clock for logging) last_word_count = 0 # Track word count to detect actual new speech # Test mode: wait for audio queue to drain before timing out @@ -355,6 +379,7 @@ async def open_conversation_job( while True: # Check if job still exists in Redis (detect zombie state) from advanced_omi_backend.utils.job_utils import check_job_alive + if not await check_job_alive(redis_client, current_job, session_id): break @@ -368,7 +393,9 @@ async def open_conversation_job( # Get completion reason (guaranteed to exist with unified API) completion_reason = await redis_client.hget(session_key, "completion_reason") - completion_reason_str = completion_reason.decode() if completion_reason else "unknown" + completion_reason_str = ( + completion_reason.decode() if completion_reason else "unknown" + ) if completion_reason_str == "websocket_disconnect": logger.warning( @@ -394,7 +421,6 @@ async def open_conversation_job( # Analyze speech content using detailed analysis - transcript_data = {"text": combined["text"], "words": combined.get("words", [])} speech_analysis = analyze_speech(transcript_data) @@ -419,7 +445,9 @@ async def open_conversation_job( start = seg.get("start", 0.0) end = seg.get("end", 0.0) if end <= start: - logger.debug(f"Segment {i} has invalid timing (start={start}, end={end}), correcting") + logger.debug( + f"Segment {i} has invalid timing (start={start}, end={end}), correcting" + ) # Auto-correct: estimate duration from text length estimated_duration = len(text.split()) * 0.5 # ~0.5 seconds per word seg["end"] = start + estimated_duration @@ -519,15 +547,15 @@ async def open_conversation_job( plugin_router = get_plugin_router() if plugin_router: # Get the latest transcript text for plugin processing - transcript_text = combined.get('text', '') + transcript_text = combined.get("text", "") if transcript_text: plugin_data = { - 'transcript': transcript_text, - 'segment_id': f"{session_id}_{current_count}", - 'conversation_id': conversation_id, - 'segments': validated_segments, - 'word_count': speech_analysis.get('word_count', 0), + "transcript": transcript_text, + "segment_id": f"{session_id}_{current_count}", + "conversation_id": conversation_id, + "segments": validated_segments, + "word_count": speech_analysis.get("word_count", 0), } logger.info( @@ -536,10 +564,10 @@ async def open_conversation_job( ) plugin_results = await plugin_router.dispatch_event( - event='transcript.streaming', + event="transcript.streaming", user_id=user_id, data=plugin_data, - metadata={'client_id': client_id} + metadata={"client_id": client_id}, ) logger.info( @@ -547,7 +575,9 @@ async def open_conversation_job( ) if plugin_results: - logger.info(f"πŸ“Œ Triggered {len(plugin_results)} streaming transcript plugins") + logger.info( + f"πŸ“Œ Triggered {len(plugin_results)} streaming transcript plugins" + ) for result in plugin_results: if result.message: logger.info(f" Plugin: {result.message}") @@ -591,7 +621,39 @@ async def open_conversation_job( # This prevents empty/noise-only conversations from being processed and saved # NOTE: Speech was already validated during streaming, so we skip this check # to avoid false negatives from aggregated results lacking proper word-level data - logger.info("βœ… Conversation has meaningful speech (validated during streaming), proceeding with post-processing") + logger.info( + "βœ… Conversation has meaningful speech (validated during streaming), proceeding with post-processing" + ) + + # Wait for streaming transcription consumer to complete before reading transcript + # This fixes the race condition where conversation job reads transcript before + # streaming consumer stores all final results (seen as 24+ second delay in logs) + completion_key = f"transcription:complete:{session_id}" + max_wait_streaming = 30 # seconds + waited_streaming = 0.0 + while waited_streaming < max_wait_streaming: + completion_status = await redis_client.get(completion_key) + if completion_status: + status_str = ( + completion_status.decode() + if isinstance(completion_status, bytes) + else completion_status + ) + if status_str == "error": + logger.warning( + f"⚠️ Streaming transcription ended with error for {session_id}, proceeding anyway" + ) + else: + logger.info(f"βœ… Streaming transcription confirmed complete for {session_id}") + break + await asyncio.sleep(0.5) + waited_streaming += 0.5 + + if waited_streaming >= max_wait_streaming: + logger.warning( + f"⚠️ Timed out waiting for streaming completion signal for {session_id} " + f"(waited {max_wait_streaming}s), proceeding with available transcript" + ) # Wait for audio_streaming_persistence_job to complete and write MongoDB chunks from advanced_omi_backend.utils.audio_chunk_utils import wait_for_audio_chunks @@ -643,7 +705,7 @@ async def open_conversation_job( word=w.get("word", ""), start=w.get("start", 0.0), end=w.get("end", 0.0), - confidence=w.get("confidence") + confidence=w.get("confidence"), ) for w in words_data ] @@ -669,11 +731,14 @@ async def open_conversation_job( "chunk_count": final_transcript.get("chunk_count", 0), "word_count": len(words), }, - set_as_active=True + set_as_active=True, ) # Update placeholder conversation if it exists - if getattr(conversation, 'always_persist', False) and getattr(conversation, 'processing_status', None) == "pending_transcription": + if ( + getattr(conversation, "always_persist", False) + and getattr(conversation, "processing_status", None) == "pending_transcription" + ): # Keep placeholder status - will be updated by title_summary_job logger.info( f"πŸ“ Placeholder conversation {conversation_id} has transcript, " @@ -698,7 +763,7 @@ async def open_conversation_job( transcript_version_id=version_id, # Pass the streaming transcript version ID depends_on_job=None, # No dependency - streaming already succeeded client_id=client_id, # Pass client_id for UI tracking - end_reason=end_reason # Pass the determined end_reason (websocket_disconnect, inactivity_timeout, etc.) + end_reason=end_reason, # Pass the determined end_reason (websocket_disconnect, inactivity_timeout, etc.) ) logger.info( @@ -750,9 +815,9 @@ async def generate_title_summary_job(conversation_id: str, *, redis_client=None) """ from advanced_omi_backend.models.conversation import Conversation from advanced_omi_backend.utils.conversation_utils import ( - generate_title, - generate_short_summary, generate_detailed_summary, + generate_short_summary, + generate_title, ) logger.info(f"πŸ“ Starting title/summary generation for conversation {conversation_id}") @@ -783,21 +848,36 @@ async def generate_title_summary_job(conversation_id: str, *, redis_client=None) f"πŸ€– Generating title/summary/detailed_summary using LLM for conversation {conversation_id}" ) - # Convert segments to dict format expected by utils - segment_dicts = None - if segments and len(segments) > 0: - segment_dicts = [ - {"speaker": seg.speaker, "text": seg.text, "start": seg.start, "end": seg.end} - for seg in segments - ] + # Fetch memory context for richer detailed summaries + # Use the entire transcript as the search query for best semantic matching + # so all key topics/entities in the conversation can find relevant memories + memory_context = None + try: + from advanced_omi_backend.services.memory import get_memory_service + + memory_service = get_memory_service() + memories = await memory_service.search_memories( + transcript_text, conversation.user_id, limit=10 + ) + if memories: + memory_context = "\n".join(m.content for m in memories if m.content) + logger.info( + f"πŸ“š Retrieved {len(memories)} memories as context for detailed summary" + ) + else: + logger.info(f"πŸ“š No memories found for context enrichment") + except Exception as mem_error: + logger.warning(f"⚠️ Could not fetch memory context (continuing without): {mem_error}") # Generate all three summaries in parallel for efficiency import asyncio title, short_summary, detailed_summary = await asyncio.gather( - generate_title(transcript_text, segments=segment_dicts), - generate_short_summary(transcript_text, segments=segment_dicts), - generate_detailed_summary(transcript_text, segments=segment_dicts), + generate_title(transcript_text, segments=segments), + generate_short_summary(transcript_text, segments=segments), + generate_detailed_summary( + transcript_text, segments=segments, memory_context=memory_context + ), ) conversation.title = title @@ -809,7 +889,7 @@ async def generate_title_summary_job(conversation_id: str, *, redis_client=None) logger.info(f"βœ… Generated detailed summary: {len(conversation.detailed_summary)} chars") # Update processing status for placeholder conversations - if getattr(conversation, 'processing_status', None) == "pending_transcription": + if getattr(conversation, "processing_status", None) == "pending_transcription": conversation.processing_status = "completed" logger.info( f"βœ… Updated placeholder conversation {conversation_id} " @@ -820,7 +900,7 @@ async def generate_title_summary_job(conversation_id: str, *, redis_client=None) logger.error(f"❌ Title/summary generation failed: {gen_error}") # Mark placeholder conversation as failed - if getattr(conversation, 'processing_status', None) == "pending_transcription": + if getattr(conversation, "processing_status", None) == "pending_transcription": conversation.title = "Audio Recording (Transcription Failed)" conversation.summary = f"Title/summary generation failed: {str(gen_error)}" conversation.processing_status = "transcription_failed" @@ -884,7 +964,7 @@ async def dispatch_conversation_complete_event_job( user_id: str, end_reason: Optional[str] = None, *, - redis_client=None + redis_client=None, ) -> Dict[str, Any]: """ Dispatch conversation.complete plugin event for all conversation sources. @@ -929,35 +1009,19 @@ async def dispatch_conversation_complete_event_job( conversation.completed_at = datetime.utcnow() await conversation.save() - logger.info(f"πŸ’Ύ Saved end_reason={conversation.end_reason} to conversation {conversation_id[:12]} in event dispatch job") + logger.info( + f"πŸ’Ύ Saved end_reason={conversation.end_reason} to conversation {conversation_id[:12]} in event dispatch job" + ) # Get user email for event data from advanced_omi_backend.models.user import User + user = await User.get(user_id) user_email = user.email if user else "" # Prepare plugin event data (same format as open_conversation_job) try: - # Get or initialize plugin router (same pattern as transcription_jobs.py) - plugin_router = get_plugin_router() - - if not plugin_router: - logger.warning("πŸ”§ Plugin router not found in worker process - attempting initialization...") - plugin_router = init_plugin_router() - - if plugin_router: - logger.info(f"πŸ”§ Plugin router initialized with {len(plugin_router.plugins)} plugin(s)") - - # Initialize all plugins - for plugin_id, plugin in plugin_router.plugins.items(): - try: - logger.info(f" Initializing plugin '{plugin_id}'...") - await plugin.initialize() - logger.info(f" βœ“ Plugin '{plugin_id}' initialized") - except Exception as e: - logger.error(f" βœ— Failed to initialize plugin '{plugin_id}': {e}", exc_info=True) - else: - logger.error("πŸ”§ Plugin router initialization FAILED - router is None") + plugin_router = await ensure_plugin_router() # CRITICAL CHECK: Fail loudly if no router if not plugin_router: @@ -972,21 +1036,21 @@ async def dispatch_conversation_complete_event_job( "skipped": True, "reason": "No plugin router", "conversation_id": conversation_id, - "error": error_msg + "error": error_msg, } plugin_data = { - 'conversation': { - 'client_id': client_id, - 'user_id': user_id, + "conversation": { + "client_id": client_id, + "user_id": user_id, }, - 'transcript': conversation.transcript if conversation else "", - 'duration': 0, # Duration not tracked for file uploads - 'conversation_id': conversation_id, + "transcript": conversation.transcript if conversation else "", + "duration": 0, # Duration not tracked for file uploads + "conversation_id": conversation_id, } # Use provided end_reason or default to 'file_upload' for backward compatibility - actual_end_reason = end_reason or 'file_upload' + actual_end_reason = end_reason or "file_upload" logger.info( f"πŸ”Œ DISPATCH: conversation.complete event for {conversation_id[:12]} " @@ -994,10 +1058,10 @@ async def dispatch_conversation_complete_event_job( ) plugin_results = await plugin_router.dispatch_event( - event='conversation.complete', + event="conversation.complete", user_id=user_id, data=plugin_data, - metadata={'end_reason': actual_end_reason} + metadata={"end_reason": actual_end_reason}, ) logger.info( diff --git a/backends/advanced/src/advanced_omi_backend/workers/memory_jobs.py b/backends/advanced/src/advanced_omi_backend/workers/memory_jobs.py index 94581024..9c227bd9 100644 --- a/backends/advanced/src/advanced_omi_backend/workers/memory_jobs.py +++ b/backends/advanced/src/advanced_omi_backend/workers/memory_jobs.py @@ -7,19 +7,19 @@ import logging import time import uuid -from datetime import UTC, datetime from typing import Any, Dict from advanced_omi_backend.controllers.queue_controller import ( JOB_RESULT_TTL, memory_queue, ) -from advanced_omi_backend.models.job import BaseRQJob, JobPriority, async_job -from advanced_omi_backend.services.memory.base import MemoryEntry -from advanced_omi_backend.services.plugin_service import get_plugin_router, init_plugin_router +from advanced_omi_backend.models.job import JobPriority, async_job +from advanced_omi_backend.services.plugin_service import ensure_plugin_router logger = logging.getLogger(__name__) +MIN_CONVERSATION_LENGTH = 10 + @async_job(redis=True, beanie=True) async def process_memory_job(conversation_id: str, *, redis_client=None) -> Dict[str, Any]: @@ -73,48 +73,38 @@ async def process_memory_job(conversation_id: str, *, redis_client=None) -> Dict f"πŸ”„ Processing memory for conversation {conversation_id}, client={client_id}, user={user_id}" ) - # Extract conversation text from transcript segments - full_conversation = "" + # Extract conversation text and speakers from transcript segments in a single pass + dialogue_lines = [] + transcript_speakers = set() segments = conversation_model.segments if segments: - dialogue_lines = [] for segment in segments: - # Handle both dict and object segments - if isinstance(segment, dict): - text = segment.get("text", "").strip() - speaker = segment.get("speaker", "Unknown") - else: - text = getattr(segment, "text", "").strip() - speaker = getattr(segment, "speaker", "Unknown") - + text = segment.text.strip() + speaker = segment.speaker if text: dialogue_lines.append(f"{speaker}: {text}") - full_conversation = "\n".join(dialogue_lines) + if speaker and speaker != "Unknown": + transcript_speakers.add(speaker.strip().lower()) + full_conversation = "\n".join(dialogue_lines) # Fallback: if segments have no text content but transcript exists, use transcript # This handles cases where speaker recognition fails/is disabled - if len(full_conversation) < 10 and conversation_model.transcript and isinstance(conversation_model.transcript, str): - logger.info(f"Segments empty or too short, falling back to transcript text for {conversation_id}") + if ( + len(full_conversation) < MIN_CONVERSATION_LENGTH + and conversation_model.transcript + and isinstance(conversation_model.transcript, str) + ): + logger.info( + f"Segments empty or too short, falling back to transcript text for {conversation_id}" + ) full_conversation = conversation_model.transcript - if len(full_conversation) < 10: + if len(full_conversation) < MIN_CONVERSATION_LENGTH: logger.warning(f"Conversation too short for memory processing: {conversation_id}") return {"success": False, "error": "Conversation too short"} - # Check primary speakers filter - user = await get_user_by_id(user_id) + # Check primary speakers filter (reuse `user` from above β€” no duplicate DB call) if user and user.primary_speakers: - transcript_speakers = set() - for segment in conversation_model.segments: - # Handle both dict and object segments - if isinstance(segment, dict): - identified_as = segment.get("identified_as") - else: - identified_as = getattr(segment, "identified_as", None) - - if identified_as and identified_as != "Unknown": - transcript_speakers.add(identified_as.strip().lower()) - primary_speaker_names = {ps["name"].strip().lower() for ps in user.primary_speakers} if transcript_speakers and not transcript_speakers.intersection(primary_speaker_names): @@ -141,14 +131,7 @@ async def process_memory_job(conversation_id: str, *, redis_client=None) -> Dict processing_time = time.time() - start_time # Determine memory provider from memory service - memory_provider = "chronicle" # Default - try: - memory_service_obj = get_memory_service() - provider_name = memory_service_obj.__class__.__name__ - if "OpenMemory" in provider_name: - memory_provider = "openmemory_mcp" - except Exception: - pass + memory_provider = memory_service.provider_identifier # Only create memory version if new memories were created if created_memory_ids: @@ -158,7 +141,9 @@ async def process_memory_job(conversation_id: str, *, redis_client=None) -> Dict ) if conversation_model: # Get active transcript version for reference - transcript_version_id = conversation_model.active_transcript_version or "unknown" + transcript_version_id = ( + conversation_model.active_transcript_version or "unknown" + ) # Create version ID for this memory extraction version_id = str(uuid.uuid4()) @@ -168,7 +153,11 @@ async def process_memory_job(conversation_id: str, *, redis_client=None) -> Dict version_id=version_id, memory_count=len(created_memory_ids), transcript_version_id=transcript_version_id, - provider=conversation_model.MemoryProvider.OPENMEMORY_MCP if memory_provider == "openmemory_mcp" else conversation_model.MemoryProvider.CHRONICLE, + provider=( + conversation_model.MemoryProvider.OPENMEMORY_MCP + if memory_provider == "openmemory_mcp" + else conversation_model.MemoryProvider.CHRONICLE + ), processing_time_seconds=processing_time, metadata={"memory_ids": created_memory_ids}, set_as_active=True, @@ -193,37 +182,8 @@ async def process_memory_job(conversation_id: str, *, redis_client=None) -> Dict for memory_id in created_memory_ids[:5]: # Limit to first 5 for display memory_entry = await memory_service.get_memory(memory_id, user_id) if memory_entry: - # Handle different return types from memory service - memory_text: str - if isinstance(memory_entry, MemoryEntry): - # MemoryEntry object with content attribute - memory_text = memory_entry.content - elif isinstance(memory_entry, dict): - # Dictionary with "content" key - if "content" in memory_entry: - memory_text = memory_entry["content"] - else: - logger.error( - f"Dict memory entry missing 'content' key for {memory_id}: {list(memory_entry.keys())}" - ) - raise ValueError( - f"Dict memory entry missing 'content' key for memory {memory_id}" - ) - elif isinstance(memory_entry, str): - # String content directly - memory_text = memory_entry - else: - # Unexpected type - logger.error( - f"Unexpected memory entry type for {memory_id}: {type(memory_entry).__name__}" - ) - raise TypeError( - f"Unexpected memory entry type: {type(memory_entry).__name__}" - ) - - # Truncate to 200 chars memory_details.append( - {"memory_id": memory_id, "text": memory_text[:200]} + {"memory_id": memory_id, "text": memory_entry.content[:200]} ) except Exception as e: logger.warning(f"Failed to fetch memory details for UI: {e}") @@ -247,34 +207,58 @@ async def process_memory_job(conversation_id: str, *, redis_client=None) -> Dict # This allows users to resume talking immediately after conversation closes, # without waiting for memory processing to complete. + # Extract entities and relationships to knowledge graph (if enabled) + try: + from advanced_omi_backend.model_registry import get_config + + config = get_config() + kg_enabled = ( + config.get("memory", {}).get("knowledge_graph", {}).get("enabled", False) + ) + + if kg_enabled: + from advanced_omi_backend.services.knowledge_graph import ( + get_knowledge_graph_service, + ) + + kg_service = get_knowledge_graph_service() + kg_result = await kg_service.process_conversation( + conversation_id=conversation_id, + transcript=full_conversation, + user_id=user_id, + conversation_name=( + conversation_model.title + if hasattr(conversation_model, "title") + else None + ), + ) + if kg_result.get("entities", 0) > 0: + logger.info( + f"πŸ”— Knowledge graph: extracted {kg_result.get('entities', 0)} entities, " + f"{kg_result.get('relationships', 0)} relationships, " + f"{kg_result.get('promises', 0)} promises from {conversation_id}" + ) + else: + logger.debug("Knowledge graph extraction disabled in config") + except Exception as e: + # Knowledge graph extraction is optional - don't fail the job + logger.warning(f"⚠️ Knowledge graph extraction failed (non-fatal): {e}") + # Trigger memory-level plugins (ALWAYS dispatch when success, even with 0 new memories) try: - # Get or initialize plugin router (same pattern as conversation_jobs.py) - plugin_router = get_plugin_router() - if not plugin_router: - logger.info("πŸ”§ Initializing plugin router in worker process...") - plugin_router = init_plugin_router() - - # Initialize all plugins asynchronously (same as app_factory.py) - if plugin_router: - for plugin_id, plugin in plugin_router.plugins.items(): - try: - await plugin.initialize() - logger.info(f"βœ… Plugin '{plugin_id}' initialized") - except Exception as e: - logger.error(f"Failed to initialize plugin '{plugin_id}': {e}") + plugin_router = await ensure_plugin_router() if plugin_router: plugin_data = { - 'memories': created_memory_ids or [], - 'conversation': { - 'conversation_id': conversation_id, - 'client_id': client_id, - 'user_id': user_id, - 'user_email': user_email, + "memories": created_memory_ids or [], + "conversation": { + "conversation_id": conversation_id, + "client_id": client_id, + "user_id": user_id, + "user_email": user_email, }, - 'memory_count': len(created_memory_ids) if created_memory_ids else 0, - 'conversation_id': conversation_id, + "memory_count": len(created_memory_ids) if created_memory_ids else 0, + "conversation_id": conversation_id, } logger.info( @@ -283,13 +267,13 @@ async def process_memory_job(conversation_id: str, *, redis_client=None) -> Dict ) plugin_results = await plugin_router.dispatch_event( - event='memory.processed', + event="memory.processed", user_id=user_id, data=plugin_data, metadata={ - 'processing_time': processing_time, - 'memory_provider': memory_provider, - } + "processing_time": processing_time, + "memory_provider": memory_provider, + }, ) logger.info( @@ -318,15 +302,15 @@ async def process_memory_job(conversation_id: str, *, redis_client=None) -> Dict def enqueue_memory_processing( - client_id: str, - user_id: str, - user_email: str, conversation_id: str, priority: JobPriority = JobPriority.NORMAL, ): """ Enqueue a memory processing job. + The job fetches all needed data (client_id, user_id, user_email) from the + conversation document internally, so only conversation_id is needed. + Returns RQ Job object for tracking. """ timeout_mapping = { diff --git a/backends/advanced/src/advanced_omi_backend/workers/orchestrator/__init__.py b/backends/advanced/src/advanced_omi_backend/workers/orchestrator/__init__.py index 1c7b0d7a..43870b06 100644 --- a/backends/advanced/src/advanced_omi_backend/workers/orchestrator/__init__.py +++ b/backends/advanced/src/advanced_omi_backend/workers/orchestrator/__init__.py @@ -11,10 +11,10 @@ - health_monitor: Health checks and self-healing """ -from .config import WorkerDefinition, OrchestratorConfig, WorkerType -from .worker_registry import build_worker_definitions -from .process_manager import ManagedWorker, ProcessManager, WorkerState +from .config import OrchestratorConfig, WorkerDefinition, WorkerType from .health_monitor import HealthMonitor +from .process_manager import ManagedWorker, ProcessManager, WorkerState +from .worker_registry import build_worker_definitions __all__ = [ "WorkerDefinition", diff --git a/backends/advanced/src/advanced_omi_backend/workers/orchestrator/config.py b/backends/advanced/src/advanced_omi_backend/workers/orchestrator/config.py index 633d366a..b63e454c 100644 --- a/backends/advanced/src/advanced_omi_backend/workers/orchestrator/config.py +++ b/backends/advanced/src/advanced_omi_backend/workers/orchestrator/config.py @@ -7,7 +7,7 @@ import os from dataclasses import dataclass, field from enum import Enum -from typing import Optional, Callable, List +from typing import Callable, List, Optional class WorkerType(Enum): diff --git a/backends/advanced/src/advanced_omi_backend/workers/orchestrator/worker_registry.py b/backends/advanced/src/advanced_omi_backend/workers/orchestrator/worker_registry.py index a5cf4b74..c28b2727 100644 --- a/backends/advanced/src/advanced_omi_backend/workers/orchestrator/worker_registry.py +++ b/backends/advanced/src/advanced_omi_backend/workers/orchestrator/worker_registry.py @@ -5,8 +5,8 @@ Reuses model_registry.py for config.yml parsing. """ -import os import logging +import os from typing import List from .config import WorkerDefinition, WorkerType diff --git a/backends/advanced/src/advanced_omi_backend/workers/rq_worker_entry.py b/backends/advanced/src/advanced_omi_backend/workers/rq_worker_entry.py index c402005f..d9da1c6a 100755 --- a/backends/advanced/src/advanced_omi_backend/workers/rq_worker_entry.py +++ b/backends/advanced/src/advanced_omi_backend/workers/rq_worker_entry.py @@ -22,8 +22,8 @@ def main(): """Start RQ worker with proper logging configuration.""" - from rq import Worker from redis import Redis + from rq import Worker # Get Redis URL from environment redis_url = os.getenv('REDIS_URL', 'redis://localhost:6379/0') diff --git a/backends/advanced/src/advanced_omi_backend/workers/speaker_jobs.py b/backends/advanced/src/advanced_omi_backend/workers/speaker_jobs.py index d9adbada..8c90701e 100644 --- a/backends/advanced/src/advanced_omi_backend/workers/speaker_jobs.py +++ b/backends/advanced/src/advanced_omi_backend/workers/speaker_jobs.py @@ -149,11 +149,13 @@ async def recognise_speakers_job( """ RQ job function for identifying speakers in a transcribed conversation. - This job runs after transcription and: - 1. Reconstructs audio from MongoDB chunks - 2. Calls speaker recognition service to identify speakers - 3. Updates the transcript version with identified speaker labels - 4. Returns results for downstream jobs (memory) + This job adapts based on provider capabilities: + 1. If provider has diarization (e.g., VibeVoice) β†’ skip pyannote, do identification only + 2. If provider has word timestamps (e.g., Parakeet) β†’ full pyannote diarization + identification + 3. If no word timestamps β†’ cannot run diarization, keep existing segments + + Speaker identification always runs if enrolled speakers exist, mapping + generic labels ("Speaker 0") to enrolled speaker names ("Alice"). Args: conversation_id: Conversation ID @@ -202,6 +204,30 @@ async def recognise_speakers_job( "processing_time_seconds": 0 } + # Get provider capabilities from metadata + provider_capabilities = transcript_version.metadata.get("provider_capabilities", {}) + provider_has_diarization = provider_capabilities.get("diarization", False) + provider_has_word_timestamps = provider_capabilities.get("word_timestamps", False) + + # Check if provider already did diarization (set by transcription job) + diarization_source = transcript_version.diarization_source + + if provider_has_diarization or diarization_source == "provider": + # Provider already did diarization (e.g., VibeVoice, Deepgram batch) + # Skip pyannote diarization, go straight to speaker identification + logger.info( + f"🎀 Provider already diarized (diarization_source={diarization_source}), " + f"skipping pyannote diarization - will run speaker identification only" + ) + + # If we have existing segments from provider, proceed to identification + if transcript_version.segments: + logger.info(f"🎀 Using {len(transcript_version.segments)} segments from provider") + # Continue to speaker identification below (after this block) + else: + logger.warning(f"🎀 Provider claimed diarization but no segments found") + # Still continue - identification may work with audio analysis + # Read transcript text and words from the transcript version # (Parameters may be empty if called via job dependency) actual_transcript_text = transcript_text or transcript_version.transcript or "" @@ -248,66 +274,91 @@ async def recognise_speakers_job( "processing_time_seconds": 0 } - if not actual_words: - logger.warning(f"🎀 No words found in version {version_id}") - return { - "success": False, - "conversation_id": conversation_id, - "version_id": version_id, - "error": "No word-level timing data available", - "processing_time_seconds": 0 - } + # Check if we can run pyannote diarization + # Pyannote requires word timestamps to align speaker segments with text + can_run_pyannote = bool(actual_words) and not provider_has_diarization - transcript_data = { - "text": actual_transcript_text, - "words": actual_words - } - - # Generate backend token for speaker service to fetch audio - # Speaker service will check conversation duration and decide - # whether to chunk based on its own memory constraints - - # Get user details for token generation - try: - user = await get_user_by_id(user_id) - if not user: - logger.error(f"User {user_id} not found for token generation") + if not actual_words and not provider_has_diarization: + # No words AND provider didn't diarize - we have a problem + # This can happen with VibeVoice if it fails to return segments + logger.warning( + f"🎀 No word timestamps available and provider didn't diarize. " + f"Speaker recognition cannot improve segments." + ) + # Keep existing segments and return success (we can't do better) + if transcript_version.segments: + return { + "success": True, + "conversation_id": conversation_id, + "version_id": version_id, + "speaker_recognition_enabled": True, + "identified_speakers": [], + "segment_count": len(transcript_version.segments), + "skip_reason": "No word timestamps available for pyannote, keeping provider segments", + "processing_time_seconds": time.time() - start_time + } + else: return { "success": False, "conversation_id": conversation_id, "version_id": version_id, - "error": "User not found", + "error": "No word timestamps and no segments available", "processing_time_seconds": time.time() - start_time } - backend_token = generate_jwt_for_user(user_id, user.email) - logger.info(f"πŸ” Generated backend token for speaker service") - - except Exception as token_error: - logger.error(f"Failed to generate backend token: {token_error}", exc_info=True) - return { - "success": False, - "conversation_id": conversation_id, - "version_id": version_id, - "error": f"Token generation failed: {token_error}", - "processing_time_seconds": time.time() - start_time - } + try: + if provider_has_diarization and transcript_version.segments: + # Provider already diarized (e.g. VibeVoice) - use segment-level identification + logger.info(f"🎀 Using segment-level speaker identification for provider-diarized segments") + segments_data = [ + {"start": s.start, "end": s.end, "text": s.text, "speaker": s.speaker} + for s in transcript_version.segments + ] + speaker_result = await speaker_client.identify_provider_segments( + conversation_id=conversation_id, + segments=segments_data, + user_id=user_id, + ) + else: + # Standard path: full diarization + identification via speaker service + transcript_data = { + "text": actual_transcript_text, + "words": actual_words + } - # Call speaker recognition service with conversation_id - # Speaker service will: - # 1. Fetch conversation metadata to check duration - # 2. Decide whether to chunk based on its MAX_DIARIZE_DURATION setting - # 3. Request audio segments via backend API as needed - # 4. Return merged speaker segments - logger.info(f"🎀 Calling speaker recognition service with conversation_id...") + # Generate backend token for speaker service to fetch audio + try: + user = await get_user_by_id(user_id) + if not user: + logger.error(f"User {user_id} not found for token generation") + return { + "success": False, + "conversation_id": conversation_id, + "version_id": version_id, + "error": "User not found", + "processing_time_seconds": time.time() - start_time + } + + backend_token = generate_jwt_for_user(user_id, user.email) + logger.info(f"πŸ” Generated backend token for speaker service") + + except Exception as token_error: + logger.error(f"Failed to generate backend token: {token_error}", exc_info=True) + return { + "success": False, + "conversation_id": conversation_id, + "version_id": version_id, + "error": f"Token generation failed: {token_error}", + "processing_time_seconds": time.time() - start_time + } - try: - speaker_result = await speaker_client.diarize_identify_match( - conversation_id=conversation_id, - backend_token=backend_token, - transcript_data=transcript_data, - user_id=user_id - ) + logger.info(f"🎀 Calling speaker recognition service with conversation_id...") + speaker_result = await speaker_client.diarize_identify_match( + conversation_id=conversation_id, + backend_token=backend_token, + transcript_data=transcript_data, + user_id=user_id + ) # Check for errors from speaker service if speaker_result.get("error"): @@ -426,6 +477,7 @@ async def recognise_speakers_job( end=seg.get("end", 0), text=text, speaker=speaker_name, + identified_as=seg.get("identified_as"), confidence=seg.get("confidence"), words=segment_words # Use words from speaker service ) @@ -456,6 +508,10 @@ async def recognise_speakers_job( "processing_time_seconds": time.time() - start_time } + # Set diarization source if pyannote ran (provider didn't do diarization) + if not provider_has_diarization and transcript_version.diarization_source != "provider": + transcript_version.diarization_source = "pyannote" + await conversation.save() processing_time = time.time() - start_time diff --git a/backends/advanced/src/advanced_omi_backend/workers/transcription_jobs.py b/backends/advanced/src/advanced_omi_backend/workers/transcription_jobs.py index e54f3393..fa755bac 100644 --- a/backends/advanced/src/advanced_omi_backend/workers/transcription_jobs.py +++ b/backends/advanced/src/advanced_omi_backend/workers/transcription_jobs.py @@ -30,7 +30,7 @@ from advanced_omi_backend.models.conversation import Conversation from advanced_omi_backend.models.job import BaseRQJob, JobPriority, async_job from advanced_omi_backend.services.audio_stream import TranscriptionResultsAggregator -from advanced_omi_backend.services.plugin_service import get_plugin_router +from advanced_omi_backend.services.plugin_service import ensure_plugin_router from advanced_omi_backend.services.transcription import ( get_transcription_provider, is_transcription_available, @@ -208,20 +208,26 @@ async def transcribe_full_audio_job( logger.info( f"πŸ“¦ Reconstructed audio from MongoDB chunks: " f"{len(wav_data) / 1024 / 1024:.2f} MB" ) + except ValueError as e: + # No chunks found for conversation + raise FileNotFoundError(f"No audio chunks found for conversation {conversation_id}: {e}") + except Exception as e: + logger.error(f"Failed to reconstruct audio from MongoDB: {e}", exc_info=True) + raise RuntimeError(f"Audio reconstruction failed: {e}") + try: # Transcribe the audio directly from memory (no disk I/O needed) transcription_result = await provider.transcribe( audio_data=wav_data, # Pass bytes directly, already in memory sample_rate=16000, diarize=True, ) - - except ValueError as e: - # No chunks found for conversation - raise FileNotFoundError(f"No audio chunks found for conversation {conversation_id}: {e}") except Exception as e: - logger.error(f"Failed to reconstruct audio from MongoDB: {e}", exc_info=True) - raise RuntimeError(f"Audio reconstruction failed: {e}") + logger.error( + f"Transcription failed for conversation {conversation_id}: {type(e).__name__}: {e}", + exc_info=True, + ) + raise RuntimeError(f"Transcription failed ({type(e).__name__}): {e}") # Extract results transcript_text = transcription_result.get("text", "") @@ -239,33 +245,7 @@ async def transcribe_full_audio_job( ) if transcript_text: try: - from advanced_omi_backend.services.plugin_service import init_plugin_router - - # Initialize plugin router if not already initialized (worker context) - plugin_router = get_plugin_router() - logger.info(f"πŸ” DEBUG: Plugin router from service: {plugin_router is not None}") - - if not plugin_router: - logger.info("πŸ”§ Initializing plugin router in worker process...") - plugin_router = init_plugin_router() - logger.info( - f"πŸ”§ After init, plugin_router: {plugin_router is not None}, plugins count: {len(plugin_router.plugins) if plugin_router else 0}" - ) - - # Initialize async plugins - if plugin_router: - for plugin_id, plugin in plugin_router.plugins.items(): - try: - await plugin.initialize() - logger.info(f"βœ… Plugin '{plugin_id}' initialized in worker") - except Exception as e: - logger.exception( - f"Failed to initialize plugin '{plugin_id}' in worker: {e}" - ) - - logger.info( - f"πŸ” DEBUG: Plugin router final check: {plugin_router is not None}, has {len(plugin_router.plugins) if plugin_router else 0} plugins" - ) + plugin_router = await ensure_plugin_router() if plugin_router: logger.info( @@ -386,29 +366,83 @@ async def transcribe_full_audio_job( # Calculate processing time (transcription only) processing_time = time.time() - start_time - # Check if we should use provider segments as fallback - transcription_config = get_backend_config("transcription") - use_provider_segments = transcription_config.get("use_provider_segments", False) + # Get provider capabilities for downstream processing decisions + # Capabilities determine whether pyannote diarization is needed or can be skipped + provider_capabilities = {} + if hasattr(provider, "get_capabilities_dict"): + provider_capabilities = provider.get_capabilities_dict() + logger.info(f"πŸ“Š Provider capabilities: {list(provider_capabilities.keys())}") + + # Check if provider has diarization capability (e.g., VibeVoice, Deepgram batch) + provider_has_diarization = provider_capabilities.get("diarization", False) + + # Check speaker recognition configuration + from advanced_omi_backend.speaker_recognition_client import SpeakerRecognitionClient + + speaker_client = SpeakerRecognitionClient() + speaker_recognition_enabled = speaker_client.enabled + + # Determine how to handle segments based on capabilities and configuration + speaker_segments = [] + diarization_source = None + segments_created_by = "speaker_service" # Default + + if segments: + # Provider returned segments - use them + speaker_segments = [] + for seg in segments: + raw_speaker = seg.get("speaker") + if raw_speaker is None: + speaker = "Speaker 0" + elif isinstance(raw_speaker, int): + speaker = f"Speaker {raw_speaker}" + else: + speaker = str(raw_speaker) + speaker_segments.append( + Conversation.SpeakerSegment( + speaker=speaker, + start=seg.get("start", 0.0), + end=seg.get("end", 0.0), + text=seg.get("text", ""), + ) + ) + + if provider_has_diarization: + # Provider did diarization (e.g., VibeVoice, Deepgram) + diarization_source = "provider" + segments_created_by = "provider_diarization" + logger.info( + f"βœ… Using {len(speaker_segments)} diarized segments from provider " + f"(provider has diarization capability)" + ) + else: + # Provider gave segments but without speaker diarization + segments_created_by = "provider" + logger.info( + f"βœ… Using {len(speaker_segments)} segments from provider " + f"(no diarization, speaker service will add labels)" + ) + elif not speaker_recognition_enabled and words: + # No segments from provider AND speaker recognition is disabled + # Create a fallback segment with the full transcript + # This ensures memory extraction always has segments to work with + start_time_audio = words[0].get("start", 0.0) if words else 0.0 + end_time_audio = words[-1].get("end", 0.0) if words else 0.0 - # If flag enabled and provider returned segments, use them - # Otherwise, speaker service will create segments via diarization - if use_provider_segments and segments: - # Convert dict segments to SpeakerSegment objects speaker_segments = [ Conversation.SpeakerSegment( - speaker=str(seg.get("speaker", "0")), # Convert to string for Pydantic validation - start=seg.get("start", 0.0), - end=seg.get("end", 0.0), - text=seg.get("text", ""), + speaker="Speaker 0", + start=start_time_audio, + end=end_time_audio, + text=transcript_text, ) - for seg in segments ] + segments_created_by = "fallback" logger.info( - f"βœ… Using {len(speaker_segments)} segments from transcription provider " - f"(use_provider_segments=true)" + f"πŸ“Š Created fallback segment (speaker recognition disabled, no provider segments)" ) else: - speaker_segments = [] + # No segments from provider, but speaker recognition will create them logger.info( f"πŸ“Š Transcription complete: {len(words)} words " f"(segments will be created by speaker service)" @@ -428,28 +462,32 @@ async def transcribe_full_audio_job( for w in words ] - # Prepare metadata + # Prepare metadata with provider capabilities for downstream jobs metadata = { "trigger": trigger, "audio_file_size": len(wav_data), "word_count": len(words), - "segments_created_by": ( - "provider" if (use_provider_segments and segments) else "speaker_service" - ), + "segments_created_by": segments_created_by, + "provider_capabilities": provider_capabilities, # For speaker_jobs.py conditional logic } - conversation.add_transcript_version( + # Create the transcript version + new_version = conversation.add_transcript_version( version_id=version_id, transcript=transcript_text, - words=word_objects, # Store at version level (not in metadata!) - segments=speaker_segments, # Empty - will be filled by speaker recognition - provider=provider_normalized, # Now just a string, no enum constructor needed + words=word_objects, + segments=speaker_segments, + provider=provider_normalized, model=provider.name, processing_time_seconds=processing_time, metadata=metadata, set_as_active=True, ) + # Set diarization source if provider did diarization + if diarization_source: + new_version.diarization_source = diarization_source + # Generate title and summary from transcript using LLM if transcript_text and len(transcript_text.strip()) > 0: try: @@ -548,6 +586,8 @@ async def transcribe_full_audio_job( "segments": [seg.model_dump() for seg in speaker_segments], "words": words, # Needed by speaker recognition "provider": provider_name, + "provider_capabilities": provider_capabilities, # For downstream jobs + "diarization_source": diarization_source, # "provider" or None "processing_time_seconds": processing_time, "trigger": trigger, } diff --git a/backends/advanced/src/advanced_omi_backend/workers/waveform_jobs.py b/backends/advanced/src/advanced_omi_backend/workers/waveform_jobs.py index 911b651d..f58387cd 100644 --- a/backends/advanced/src/advanced_omi_backend/workers/waveform_jobs.py +++ b/backends/advanced/src/advanced_omi_backend/workers/waveform_jobs.py @@ -9,7 +9,7 @@ import logging import struct import time -from typing import Dict, Any, List +from typing import Any, Dict, List logger = logging.getLogger(__name__) @@ -43,8 +43,8 @@ async def generate_waveform_data( """ from advanced_omi_backend.models.waveform import WaveformData from advanced_omi_backend.utils.audio_chunk_utils import ( - retrieve_audio_chunks, decode_opus_to_pcm, + retrieve_audio_chunks, ) start_time = time.time() diff --git a/backends/advanced/src/scripts/cleanup_state.py b/backends/advanced/src/scripts/cleanup_state.py index b028ecde..f04f2c76 100644 --- a/backends/advanced/src/scripts/cleanup_state.py +++ b/backends/advanced/src/scripts/cleanup_state.py @@ -15,30 +15,31 @@ - Confirmation prompts with detailed warnings """ -import asyncio import argparse +import asyncio import json import logging import os import shutil +import struct import sys from datetime import datetime from pathlib import Path -from typing import Dict, List, Any, Optional, Tuple -import struct +from typing import Any, Dict, List, Optional, Tuple # Add parent directory to path for imports sys.path.insert(0, str(Path(__file__).parent.parent.parent)) try: import redis - from rq import Queue + from beanie import init_beanie from motor.motor_asyncio import AsyncIOMotorClient from qdrant_client import AsyncQdrantClient from qdrant_client.models import Distance, VectorParams - from beanie import init_beanie - from advanced_omi_backend.models.conversation import Conversation + from rq import Queue + from advanced_omi_backend.models.audio_chunk import AudioChunkDocument + from advanced_omi_backend.models.conversation import Conversation from advanced_omi_backend.models.user import User from advanced_omi_backend.models.waveform import WaveformData from advanced_omi_backend.services.memory.config import build_memory_config_from_env diff --git a/backends/advanced/webui/src/components/audio/MainRecordingControls.tsx b/backends/advanced/webui/src/components/audio/MainRecordingControls.tsx index 5a075829..bb1daff4 100644 --- a/backends/advanced/webui/src/components/audio/MainRecordingControls.tsx +++ b/backends/advanced/webui/src/components/audio/MainRecordingControls.tsx @@ -1,4 +1,4 @@ -import { Mic, MicOff } from 'lucide-react' +import { Mic, Square } from 'lucide-react' import { UseAudioRecordingReturn } from '../../hooks/useAudioRecording' interface MainRecordingControlsProps { @@ -11,45 +11,50 @@ export default function MainRecordingControls({ recording }: MainRecordingContro return (
-
- {recording.isRecording ? ( - - ) : ( - - )} +
+
+ {recording.isRecording && ( + + )} + {recording.isRecording ? ( + + ) : ( + + )} +

{recording.isRecording ? 'Recording...' : 'Ready to Record'}

- + {recording.isRecording && ( -

+

{recording.formatDuration(recording.recordingDuration)}

)} - +

- {recording.isRecording - ? `Audio streaming via ${isHttps ? 'WSS (secure)' : 'WS'} to backend for processing` - : recording.canAccessMicrophone - ? 'Click the microphone to start recording' + {recording.isRecording + ? `Click to stop \u00b7 Streaming via ${isHttps ? 'WSS (secure)' : 'WS'}` + : recording.canAccessMicrophone + ? 'Click to start recording' : 'Secure connection required for microphone access'}

) -} \ No newline at end of file +} diff --git a/backends/advanced/webui/src/components/audio/SimplifiedControls.tsx b/backends/advanced/webui/src/components/audio/SimplifiedControls.tsx index a3299deb..27ae58bc 100644 --- a/backends/advanced/webui/src/components/audio/SimplifiedControls.tsx +++ b/backends/advanced/webui/src/components/audio/SimplifiedControls.tsx @@ -1,4 +1,4 @@ -import { Mic, MicOff, Loader2 } from 'lucide-react' +import { Mic, Square, Loader2 } from 'lucide-react' import { RecordingContextType } from '../../contexts/RecordingContext' interface SimplifiedControlsProps { @@ -18,73 +18,82 @@ const getStepText = (step: string): string => { } } -const getButtonColor = (step: string, isRecording: boolean): string => { - if (step === 'error') return 'bg-red-600 hover:bg-red-700' - if (isRecording) return 'bg-red-600 hover:bg-red-700' - if (step === 'idle') return 'bg-blue-600 hover:bg-blue-700' - return 'bg-yellow-600 hover:bg-yellow-700' -} - const isProcessing = (step: string): boolean => { return ['mic', 'websocket', 'audio-start', 'streaming', 'stopping'].includes(step) } export default function SimplifiedControls({ recording }: SimplifiedControlsProps) { - const startButtonDisabled = !recording.canAccessMicrophone || isProcessing(recording.currentStep) || recording.isRecording - + const processing = isProcessing(recording.currentStep) + const canStart = recording.canAccessMicrophone && !processing && !recording.isRecording + + const handleClick = () => { + if (recording.isRecording) { + recording.stopRecording() + } else if (canStart) { + recording.startRecording() + } + } + + // Button appearance based on state + const getButtonClasses = (): string => { + if (recording.isRecording) return 'bg-red-600 hover:bg-red-700' + if (processing) return 'bg-yellow-600' + if (recording.currentStep === 'error') return 'bg-red-600 hover:bg-red-700' + return 'bg-blue-600 hover:bg-blue-700' + } + + const isDisabled = recording.isRecording ? false : (processing || !canStart) + return (
- {/* Control Buttons */} -
- {/* START Button */} - - - {/* STOP Button - only show when recording */} - {recording.isRecording && ( - )} +
- + {/* Status Text */}

{recording.isRecording ? 'Recording in Progress' : getStepText(recording.currentStep)}

- + {/* Recording Duration */} {recording.isRecording && ( -

+

{recording.formatDuration(recording.recordingDuration)}

)} - + {/* Action Text */}

- {recording.isRecording - ? 'Click the red STOP button to end recording' - : recording.currentStep === 'idle' - ? 'Click the blue START button to begin recording' + {recording.isRecording + ? 'Click to stop recording' + : recording.currentStep === 'idle' + ? 'Click to start recording' : recording.currentStep === 'error' - ? 'Click START to try again' + ? 'Click to try again' : 'Please wait while setting up...'}

- + {/* Error Message */} {recording.error && (
@@ -93,7 +102,7 @@ export default function SimplifiedControls({ recording }: SimplifiedControlsProp

)} - + {/* Security Warning */} {!recording.canAccessMicrophone && (
@@ -106,4 +115,4 @@ export default function SimplifiedControls({ recording }: SimplifiedControlsProp
) -} \ No newline at end of file +} diff --git a/backends/advanced/webui/src/components/knowledge-graph/EntityCard.tsx b/backends/advanced/webui/src/components/knowledge-graph/EntityCard.tsx new file mode 100644 index 00000000..76d28cf9 --- /dev/null +++ b/backends/advanced/webui/src/components/knowledge-graph/EntityCard.tsx @@ -0,0 +1,133 @@ +import { User, MapPin, Building, Calendar, Package, Link2 } from 'lucide-react' + +export interface Entity { + id: string + name: string + type: string + user_id: string + details?: string + icon?: string + metadata?: any + created_at?: string + updated_at?: string + location?: { lat: number; lon: number } + start_time?: string + end_time?: string + conversation_id?: string + relationship_count?: number +} + +interface EntityCardProps { + entity: Entity + onClick?: (entity: Entity) => void + compact?: boolean +} + +const typeIcons: Record = { + person: , + place: , + organization: , + event: , + thing: , +} + +const typeColors: Record = { + person: 'bg-blue-100 text-blue-800 dark:bg-blue-900/30 dark:text-blue-300', + place: 'bg-green-100 text-green-800 dark:bg-green-900/30 dark:text-green-300', + organization: 'bg-purple-100 text-purple-800 dark:bg-purple-900/30 dark:text-purple-300', + event: 'bg-yellow-100 text-yellow-800 dark:bg-yellow-900/30 dark:text-yellow-300', + thing: 'bg-gray-100 text-gray-800 dark:bg-gray-700 dark:text-gray-300', +} + +export default function EntityCard({ entity, onClick, compact = false }: EntityCardProps) { + const icon = typeIcons[entity.type] || + const colorClass = typeColors[entity.type] || typeColors.thing + + const formatDate = (dateStr?: string) => { + if (!dateStr) return null + try { + return new Date(dateStr).toLocaleDateString() + } catch { + return null + } + } + + if (compact) { + return ( +
onClick?.(entity)} + className={`flex items-center space-x-2 p-2 rounded-lg ${colorClass} cursor-pointer hover:opacity-80 transition-opacity`} + > + {entity.icon ? ( + {entity.icon} + ) : ( + icon + )} + {entity.name} + {entity.relationship_count != null && entity.relationship_count > 0 && ( + + + {entity.relationship_count} + + )} +
+ ) + } + + return ( +
onClick?.(entity)} + className="bg-white dark:bg-gray-800 rounded-lg border border-gray-200 dark:border-gray-700 p-4 hover:border-blue-400 dark:hover:border-blue-500 transition-colors cursor-pointer group" + > +
+
+
+ {entity.icon ? ( + {entity.icon} + ) : ( + icon + )} +
+
+

+ {entity.name} +

+ + {entity.type} + +
+
+ + {entity.relationship_count != null && entity.relationship_count > 0 && ( +
+ + {entity.relationship_count} +
+ )} +
+ + {entity.details && ( +

+ {entity.details} +

+ )} + + {/* Event-specific: show time info */} + {entity.type === 'event' && entity.start_time && ( +
+ + + {formatDate(entity.start_time)} + {entity.end_time && ` - ${formatDate(entity.end_time)}`} + +
+ )} + + {entity.created_at && ( +
+ Added {formatDate(entity.created_at)} +
+ )} +
+ ) +} diff --git a/backends/advanced/webui/src/components/knowledge-graph/EntityList.tsx b/backends/advanced/webui/src/components/knowledge-graph/EntityList.tsx new file mode 100644 index 00000000..878ec3e4 --- /dev/null +++ b/backends/advanced/webui/src/components/knowledge-graph/EntityList.tsx @@ -0,0 +1,240 @@ +import { useState, useEffect } from 'react' +import { User, MapPin, Building, Calendar, Package, Search, RefreshCw, Filter, X } from 'lucide-react' +import { knowledgeGraphApi } from '../../services/api' +import EntityCard, { Entity } from './EntityCard' + +interface EntityListProps { + onEntityClick?: (entity: Entity) => void +} + +const entityTypes = [ + { value: '', label: 'All Types', icon: null }, + { value: 'person', label: 'People', icon: }, + { value: 'place', label: 'Places', icon: }, + { value: 'organization', label: 'Organizations', icon: }, + { value: 'event', label: 'Events', icon: }, + { value: 'thing', label: 'Things', icon: }, +] + +export default function EntityList({ onEntityClick }: EntityListProps) { + const [entities, setEntities] = useState([]) + const [loading, setLoading] = useState(false) + const [error, setError] = useState(null) + const [searchQuery, setSearchQuery] = useState('') + const [searchResults, setSearchResults] = useState(null) + const [searching, setSearching] = useState(false) + const [selectedType, setSelectedType] = useState('') + + const loadEntities = async (type?: string) => { + try { + setLoading(true) + setError(null) + const response = await knowledgeGraphApi.getEntities(type || undefined) + setEntities(response.data.entities || []) + } catch (err: any) { + console.error('Failed to load entities:', err) + setError(err.response?.data?.message || 'Failed to load entities') + } finally { + setLoading(false) + } + } + + const handleSearch = async () => { + if (!searchQuery.trim()) { + setSearchResults(null) + return + } + + try { + setSearching(true) + setError(null) + const response = await knowledgeGraphApi.searchEntities(searchQuery.trim()) + setSearchResults(response.data.entities || []) + } catch (err: any) { + console.error('Search failed:', err) + setError(err.response?.data?.message || 'Search failed') + } finally { + setSearching(false) + } + } + + const clearSearch = () => { + setSearchQuery('') + setSearchResults(null) + } + + const handleTypeChange = (type: string) => { + setSelectedType(type) + loadEntities(type) + clearSearch() + } + + useEffect(() => { + loadEntities() + }, []) + + const displayedEntities = searchResults !== null ? searchResults : entities + + // Group entities by type for display + const groupedEntities = displayedEntities.reduce((acc, entity) => { + const type = entity.type || 'thing' + if (!acc[type]) acc[type] = [] + acc[type].push(entity) + return acc + }, {} as Record) + + const typeOrder = ['person', 'organization', 'place', 'event', 'thing'] + const sortedTypes = Object.keys(groupedEntities).sort( + (a, b) => typeOrder.indexOf(a) - typeOrder.indexOf(b) + ) + + return ( +
+ {/* Search and Filter Controls */} +
+ {/* Search Input */} +
+ + setSearchQuery(e.target.value)} + onKeyDown={(e) => e.key === 'Enter' && handleSearch()} + placeholder="Search entities..." + className="w-full pl-10 pr-20 py-2 border border-gray-300 dark:border-gray-600 rounded-md bg-white dark:bg-gray-800 text-gray-900 dark:text-gray-100 focus:outline-none focus:ring-2 focus:ring-blue-500" + /> + {searchResults !== null && ( + + )} + +
+ + {/* Type Filter */} +
+ + +
+ + {/* Refresh Button */} + +
+ + {/* Search Results Info */} + {searchResults !== null && ( +
+
+ Found {searchResults.length} entities matching "{searchQuery}" + +
+
+ )} + + {/* Error Message */} + {error && ( +
+

{error}

+
+ )} + + {/* Loading State */} + {loading && ( +
+
+ Loading entities... +
+ )} + + {/* Entities Display */} + {!loading && displayedEntities.length > 0 && ( +
+ {selectedType || searchResults !== null ? ( + // Flat list when filtered or searching +
+ {displayedEntities.map((entity) => ( + + ))} +
+ ) : ( + // Grouped by type when showing all + sortedTypes.map((type) => ( +
+

+ {entityTypes.find((t) => t.value === type)?.icon} + {type}s + + ({groupedEntities[type].length}) + +

+
+ {groupedEntities[type].map((entity) => ( + + ))} +
+
+ )) + )} +
+ )} + + {/* Empty State */} + {!loading && displayedEntities.length === 0 && !error && ( +
+ +

+ {searchResults !== null + ? `No entities found matching "${searchQuery}"` + : selectedType + ? `No ${selectedType}s found` + : 'No entities found'} +

+

+ Entities are automatically extracted from your conversations. +

+
+ )} +
+ ) +} diff --git a/backends/advanced/webui/src/components/knowledge-graph/PromisesList.tsx b/backends/advanced/webui/src/components/knowledge-graph/PromisesList.tsx new file mode 100644 index 00000000..ce64f1aa --- /dev/null +++ b/backends/advanced/webui/src/components/knowledge-graph/PromisesList.tsx @@ -0,0 +1,308 @@ +import { useState, useEffect } from 'react' +import { CheckSquare, Clock, CheckCircle, XCircle, AlertCircle, RefreshCw, Calendar, User, Trash2 } from 'lucide-react' +import { knowledgeGraphApi } from '../../services/api' + +export interface Promise { + id: string + user_id: string + action: string + to_entity_id?: string + to_entity_name?: string + status: string + due_date?: string + completed_at?: string + source_conversation_id?: string + context?: string + metadata?: any + created_at?: string + updated_at?: string +} + +interface PromisesListProps { + onPromiseClick?: (promise: Promise) => void +} + +const statusConfig: Record = { + pending: { + icon: , + color: 'bg-yellow-100 text-yellow-800 dark:bg-yellow-900/30 dark:text-yellow-300', + label: 'Pending', + }, + in_progress: { + icon: , + color: 'bg-blue-100 text-blue-800 dark:bg-blue-900/30 dark:text-blue-300', + label: 'In Progress', + }, + completed: { + icon: , + color: 'bg-green-100 text-green-800 dark:bg-green-900/30 dark:text-green-300', + label: 'Completed', + }, + cancelled: { + icon: , + color: 'bg-gray-100 text-gray-800 dark:bg-gray-700 dark:text-gray-300', + label: 'Cancelled', + }, + overdue: { + icon: , + color: 'bg-red-100 text-red-800 dark:bg-red-900/30 dark:text-red-300', + label: 'Overdue', + }, +} + +const statusFilters = [ + { value: '', label: 'All' }, + { value: 'pending', label: 'Pending' }, + { value: 'in_progress', label: 'In Progress' }, + { value: 'completed', label: 'Completed' }, + { value: 'cancelled', label: 'Cancelled' }, +] + +export default function PromisesList({ onPromiseClick }: PromisesListProps) { + const [promises, setPromises] = useState([]) + const [loading, setLoading] = useState(false) + const [error, setError] = useState(null) + const [statusFilter, setStatusFilter] = useState('') + const [updatingId, setUpdatingId] = useState(null) + + const loadPromises = async (status?: string) => { + try { + setLoading(true) + setError(null) + const response = await knowledgeGraphApi.getPromises(status || undefined) + setPromises(response.data.promises || []) + } catch (err: any) { + console.error('Failed to load promises:', err) + setError(err.response?.data?.message || 'Failed to load promises') + } finally { + setLoading(false) + } + } + + const handleStatusChange = async (promiseId: string, newStatus: string) => { + try { + setUpdatingId(promiseId) + await knowledgeGraphApi.updatePromiseStatus(promiseId, newStatus) + // Reload to get updated data + await loadPromises(statusFilter) + } catch (err: any) { + console.error('Failed to update promise status:', err) + setError(err.response?.data?.message || 'Failed to update status') + } finally { + setUpdatingId(null) + } + } + + const handleDelete = async (promiseId: string) => { + if (!confirm('Are you sure you want to delete this promise?')) return + + try { + await knowledgeGraphApi.deletePromise(promiseId) + setPromises(promises.filter((p) => p.id !== promiseId)) + } catch (err: any) { + console.error('Failed to delete promise:', err) + setError(err.response?.data?.message || 'Failed to delete promise') + } + } + + const handleFilterChange = (status: string) => { + setStatusFilter(status) + loadPromises(status) + } + + useEffect(() => { + loadPromises() + }, []) + + const formatDate = (dateStr?: string) => { + if (!dateStr) return null + try { + return new Date(dateStr).toLocaleDateString() + } catch { + return null + } + } + + const isOverdue = (promise: Promise) => { + if (promise.status !== 'pending' && promise.status !== 'in_progress') return false + if (!promise.due_date) return false + return new Date(promise.due_date) < new Date() + } + + return ( +
+ {/* Header and Controls */} +
+
+ +

+ Promises & Tasks +

+ + ({promises.length}) + +
+ +
+ {/* Status Filter */} + + + {/* Refresh Button */} + +
+
+ + {/* Error Message */} + {error && ( +
+

{error}

+
+ )} + + {/* Loading State */} + {loading && ( +
+
+ Loading promises... +
+ )} + + {/* Promises List */} + {!loading && promises.length > 0 && ( +
+ {promises.map((promise) => { + const status = isOverdue(promise) ? 'overdue' : promise.status + const config = statusConfig[status] || statusConfig.pending + + return ( +
+
+ {/* Left: Status and Content */} +
+
+ + {config.icon} + {config.label} + + {promise.due_date && ( + + + Due: {formatDate(promise.due_date)} + + )} +
+ +

onPromiseClick?.(promise)} + > + {promise.action} +

+ + {promise.to_entity_name && ( +
+ + To: {promise.to_entity_name} +
+ )} + + {promise.context && ( +

+ {promise.context} +

+ )} + +
+ Created {formatDate(promise.created_at)} +
+
+ + {/* Right: Actions */} +
+ {/* Status Change Buttons */} + {promise.status !== 'completed' && promise.status !== 'cancelled' && ( + + )} + + {promise.status === 'pending' && ( + + )} + + {promise.status !== 'cancelled' && promise.status !== 'completed' && ( + + )} + + {/* Delete Button */} + +
+
+
+ ) + })} +
+ )} + + {/* Empty State */} + {!loading && promises.length === 0 && !error && ( +
+ +

+ {statusFilter + ? `No ${statusFilters.find((f) => f.value === statusFilter)?.label.toLowerCase()} promises found` + : 'No promises found'} +

+

+ Promises are automatically extracted when you mention commitments in conversations. +

+
+ )} +
+ ) +} diff --git a/backends/advanced/webui/src/components/knowledge-graph/index.ts b/backends/advanced/webui/src/components/knowledge-graph/index.ts new file mode 100644 index 00000000..af097101 --- /dev/null +++ b/backends/advanced/webui/src/components/knowledge-graph/index.ts @@ -0,0 +1,5 @@ +export { default as EntityCard } from './EntityCard' +export { default as EntityList } from './EntityList' +export { default as PromisesList } from './PromisesList' +export type { Entity } from './EntityCard' +export type { Promise } from './PromisesList' diff --git a/backends/advanced/webui/src/pages/Memories.tsx b/backends/advanced/webui/src/pages/Memories.tsx index 77daca51..a201f3b0 100644 --- a/backends/advanced/webui/src/pages/Memories.tsx +++ b/backends/advanced/webui/src/pages/Memories.tsx @@ -1,8 +1,9 @@ import { useState, useEffect } from 'react' -import { useNavigate } from 'react-router-dom' -import { Brain, Search, RefreshCw, Trash2, Calendar, Tag, X, Target } from 'lucide-react' +import { useNavigate, useSearchParams } from 'react-router-dom' +import { Brain, Search, RefreshCw, Trash2, Calendar, Tag, X, Target, Users, CheckSquare } from 'lucide-react' import { memoriesApi, systemApi } from '../services/api' import { useAuth } from '../contexts/AuthContext' +import { EntityList, PromisesList } from '../components/knowledge-graph' import '../styles/slider.css' interface Memory { @@ -18,8 +19,12 @@ interface Memory { role?: string } +type Tab = 'memories' | 'entities' | 'promises' + export default function Memories() { const navigate = useNavigate() + const [searchParams, setSearchParams] = useSearchParams() + const [activeTab, setActiveTab] = useState((searchParams.get('tab') as Tab) || 'memories') const [memories, setMemories] = useState([]) const [loading, setLoading] = useState(false) const [error, setError] = useState(null) @@ -39,6 +44,11 @@ export default function Memories() { const { user } = useAuth() + const handleTabChange = (tab: Tab) => { + setActiveTab(tab) + setSearchParams({ tab }) + } + const loadSystemConfig = async () => { try { const response = await systemApi.getMetrics() @@ -256,9 +266,9 @@ export default function Memories() {

- Memory Management + Knowledge & Memory

- {memoryProvider && ( + {memoryProvider && activeTab === 'memories' && (

Provider: {memoryProvider === 'chronicle' ? 'Chronicle' : memoryProvider === 'openmemory_mcp' ? 'OpenMemory MCP' : memoryProvider}

@@ -267,6 +277,68 @@ export default function Memories() {
+ {/* Tab Navigation */} +
+ + + +
+ + {/* Tab Content */} + {activeTab === 'entities' && ( + { + console.log('Entity clicked:', entity) + // Could navigate to entity detail page in the future + }} + /> + )} + + {activeTab === 'promises' && ( + { + console.log('Promise clicked:', promise) + // Could navigate to conversation source + if (promise.source_conversation_id) { + navigate(`/conversations/${promise.source_conversation_id}`) + } + }} + /> + )} + + {activeTab === 'memories' && ( + <> + {/* Controls */}
@@ -580,6 +652,8 @@ export default function Memories() { )}
)} + + )}
) } diff --git a/backends/advanced/webui/src/services/api.ts b/backends/advanced/webui/src/services/api.ts index d8486c6a..9194a07f 100644 --- a/backends/advanced/webui/src/services/api.ts +++ b/backends/advanced/webui/src/services/api.ts @@ -414,14 +414,64 @@ export const chatApi = { export const speakerApi = { // Get current user's speaker configuration getSpeakerConfiguration: () => api.get('/api/speaker-configuration'), - + // Update current user's speaker configuration - updateSpeakerConfiguration: (primarySpeakers: Array<{speaker_id: string, name: string, user_id: number}>) => + updateSpeakerConfiguration: (primarySpeakers: Array<{speaker_id: string, name: string, user_id: number}>) => api.post('/api/speaker-configuration', primarySpeakers), - - // Get enrolled speakers from speaker recognition service + + // Get enrolled speakers from speaker recognition service getEnrolledSpeakers: () => api.get('/api/enrolled-speakers'), - + // Check speaker service status (admin only) getSpeakerServiceStatus: () => api.get('/api/speaker-service-status'), } + +export const knowledgeGraphApi = { + // Entity operations + getEntities: (entityType?: string, limit: number = 100) => + api.get('/api/knowledge-graph/entities', { + params: { + ...(entityType && { entity_type: entityType }), + limit + } + }), + + getEntity: (entityId: string) => + api.get(`/api/knowledge-graph/entities/${entityId}`), + + getEntityRelationships: (entityId: string) => + api.get(`/api/knowledge-graph/entities/${entityId}/relationships`), + + deleteEntity: (entityId: string) => + api.delete(`/api/knowledge-graph/entities/${entityId}`), + + // Search + searchEntities: (query: string, limit: number = 20) => + api.get('/api/knowledge-graph/search', { + params: { query, limit } + }), + + // Promise operations + getPromises: (status?: string, limit: number = 50) => + api.get('/api/knowledge-graph/promises', { + params: { + ...(status && { status }), + limit + } + }), + + updatePromiseStatus: (promiseId: string, status: string) => + api.patch(`/api/knowledge-graph/promises/${promiseId}`, { status }), + + deletePromise: (promiseId: string) => + api.delete(`/api/knowledge-graph/promises/${promiseId}`), + + // Timeline + getTimeline: (start: string, end: string, limit: number = 100) => + api.get('/api/knowledge-graph/timeline', { + params: { start, end, limit } + }), + + // Health check + getHealth: () => api.get('/api/knowledge-graph/health'), +} diff --git a/config/README.md b/config/README.md index 44ec2330..634417e6 100644 --- a/config/README.md +++ b/config/README.md @@ -148,6 +148,6 @@ Before committing `config/plugins.yml`, manually verify: ## Documentation For detailed configuration guides, see: -- `/Docs/memory-configuration-guide.md` - Memory settings +- `/backends/advanced/Docs/memories.md` - Memory settings - `/quickstart.md` - Setup guide - `/CLAUDE.md` - Project overview and technical reference diff --git a/config/config.yml.template b/config/config.yml.template index dcade594..f7e9f027 100644 --- a/config/config.yml.template +++ b/config/config.yml.template @@ -101,6 +101,12 @@ models: api_family: http model_url: http://${oc.env:PARAKEET_ASR_URL,172.17.0.1:8767} api_key: '' + # Capabilities: what this provider can produce + # - word_timestamps: Word-level timing data for alignment + # - segments: Speaker segments (generic labels like Speaker 0) + capabilities: + - word_timestamps + - segments operations: stt_transcribe: method: POST @@ -119,6 +125,14 @@ models: api_family: http model_url: https://api.deepgram.com/v1 api_key: ${oc.env:DEEPGRAM_API_KEY,''} + # Capabilities: what this provider can produce + # - word_timestamps: Word-level timing data + # - segments: Speaker segments with paragraphs + # - diarization: Native speaker diarization (Speaker 0, Speaker 1, etc.) + capabilities: + - word_timestamps + - segments + - diarization operations: stt_transcribe: method: POST @@ -141,6 +155,102 @@ models: text: results.channels[0].alternatives[0].transcript words: results.channels[0].alternatives[0].words segments: results.channels[0].alternatives[0].paragraphs.paragraphs +- name: stt-vibevoice + description: Microsoft VibeVoice ASR with speaker diarization + model_type: stt + model_provider: vibevoice + api_family: http + model_url: http://${oc.env:VIBEVOICE_ASR_URL,host.docker.internal:8767} + api_key: '' + # Capabilities: what this provider can produce + # - segments: Speaker segments with diarization labels + # - diarization: Built-in speaker diarization (no word timestamps) + # Note: VibeVoice does NOT provide word_timestamps + capabilities: + - segments + - diarization + operations: + stt_transcribe: + method: POST + path: /transcribe + content_type: multipart/form-data + response: + type: json + extract: + text: text + segments: segments +- name: stt-faster-whisper + description: Faster Whisper ASR (CTranslate2) + model_type: stt + model_provider: faster-whisper + api_family: http + model_url: http://${oc.env:FASTER_WHISPER_ASR_URL,host.docker.internal:8767} + api_key: '' + # Capabilities: what this provider can produce + # - word_timestamps: Word-level timing data + # - segments: Speaker segments + capabilities: + - word_timestamps + - segments + operations: + stt_transcribe: + method: POST + path: /transcribe + content_type: multipart/form-data + response: + type: json + extract: + text: text + words: words + segments: segments +- name: stt-transformers + description: HuggingFace Transformers ASR + model_type: stt + model_provider: transformers + api_family: http + model_url: http://${oc.env:TRANSFORMERS_ASR_URL,host.docker.internal:8767} + api_key: '' + # Capabilities: what this provider can produce + # - word_timestamps: Word-level timing data + # - segments: Speaker segments + capabilities: + - word_timestamps + - segments + operations: + stt_transcribe: + method: POST + path: /transcribe + content_type: multipart/form-data + response: + type: json + extract: + text: text + words: words + segments: segments +- name: stt-nemo + description: NVIDIA NeMo ASR (Parakeet, Canary) + model_type: stt + model_provider: nemo + api_family: http + model_url: http://${oc.env:NEMO_ASR_URL,host.docker.internal:8767} + api_key: '' + # Capabilities: what this provider can produce + # - word_timestamps: Word-level timing data + # - segments: Speaker segments + capabilities: + - word_timestamps + - segments + operations: + stt_transcribe: + method: POST + path: /transcribe + content_type: multipart/form-data + response: + type: json + extract: + text: text + words: words + segments: segments - name: tts-http description: Generic JSON TTS endpoint model_type: tts @@ -207,7 +317,11 @@ memory: timeout: 30 obsidian: enabled: false - neo4j_host: neo4j-mem0 + neo4j_host: neo4j + timeout: 30 + knowledge_graph: + enabled: false + neo4j_host: neo4j timeout: 30 speaker_recognition: diff --git a/extras/asr-services/.dockerignore b/extras/asr-services/.dockerignore index 391c81c7..84d177f8 100644 --- a/extras/asr-services/.dockerignore +++ b/extras/asr-services/.dockerignore @@ -5,3 +5,7 @@ !pyproject*.toml !README.md !uv.lock +!common/ +!common/** +!providers/ +!providers/** diff --git a/extras/asr-services/.env.template b/extras/asr-services/.env.template index 6992d1ff..1a905f1a 100644 --- a/extras/asr-services/.env.template +++ b/extras/asr-services/.env.template @@ -1,32 +1,73 @@ # ASR Services Configuration # Copy this file to .env and configure as needed -# PyTorch CUDA version for Docker build +# ============================================================================= +# Provider Selection +# ============================================================================= +# Choose one of: faster-whisper, transformers, nemo +ASR_PROVIDER=nemo + +# ============================================================================= +# Model Configuration +# ============================================================================= +# Model identifier (HuggingFace repo or local path) +# +# Faster-Whisper models: +# - Systran/faster-whisper-large-v3 (Best quality) +# - Systran/faster-whisper-small (Lightweight) +# - deepdml/faster-whisper-large-v3-turbo-ct2 (Speed optimized) +# +# Transformers models: +# - microsoft/VibeVoice-ASR (7B, speaker diarization) +# - Oriserve/Whisper-Hindi2Hinglish-Prime (Hindi/Hinglish) +# - openai/whisper-large-v3 (Original Whisper) +# +# NeMo models: +# - nvidia/parakeet-tdt-0.6b-v3 (Default) +# - nvidia/canary-1b (Multilingual) +# +ASR_MODEL=nvidia/parakeet-tdt-0.6b-v3 + +# ============================================================================= +# Service Port Configuration +# ============================================================================= +ASR_PORT=8767 + +# ============================================================================= +# PyTorch/CUDA Configuration +# ============================================================================= # Options: cu121 (CUDA 12.1), cu126 (CUDA 12.6), cu128 (CUDA 12.8) -# Should match your system's CUDA version (check with: nvidia-smi) PYTORCH_CUDA_VERSION=cu126 -# Parakeet ASR Model Selection -PARAKEET_MODEL=nvidia/parakeet-tdt-0.6b-v3 +# ============================================================================= +# Faster-Whisper Provider Settings +# ============================================================================= +# Quantization type: float16, int8, float32 +COMPUTE_TYPE=float16 -# Service Port Configuration -PARAKEET_HOST_PORT=8767 -PARAKEET_CONTAINER_PORT=8765 +# Device: cuda, cpu +DEVICE=cuda + +# GPU device index (for multi-GPU systems) +DEVICE_INDEX=0 -# Enhanced Chunking Configuration for Long Audio -# Enable/disable chunking for long audio processing -CHUNKING_ENABLED=true +# Enable Voice Activity Detection filtering +VAD_FILTER=true -# Duration of each audio chunk in seconds (recommended: 20-40s) -CHUNK_DURATION_SECONDS=120.0 +# Force language (empty for auto-detect) +# LANGUAGE=en -# Overlap duration between chunks in seconds (recommended: 3-7s) -OVERLAP_DURATION_SECONDS=10.0 +# ============================================================================= +# Transformers Provider Settings +# ============================================================================= +# PyTorch data type: float16, float32, bfloat16 +TORCH_DTYPE=float16 -# Minimum audio duration to trigger chunking (in seconds) -# Audio shorter than this will use single-pass processing -MIN_AUDIO_FOR_CHUNKING=120.0 +# Enable Flash Attention 2 (requires compatible GPU) +USE_FLASH_ATTENTION=false -# Confidence threshold for overlap reconciliation (0.0-1.0) -# Higher values prefer higher confidence words during overlap resolution -CONFIDENCE_THRESHOLD=0.8 \ No newline at end of file +# ============================================================================= +# NeMo Provider Settings +# ============================================================================= +# NeMo's transcribe() handles long audio natively with timestamps=True. +# No additional configuration needed. diff --git a/extras/asr-services/README.md b/extras/asr-services/README.md index c763d930..aaa151ff 100644 --- a/extras/asr-services/README.md +++ b/extras/asr-services/README.md @@ -1,208 +1,347 @@ # ASR Services -Offline Automatic Speech Recognition (ASR) services for Chronicle using the Wyoming protocol. +Provider-based Automatic Speech Recognition (ASR) services for Chronicle. ## Overview -The ASR services provide offline speech recognition capabilities using: -- **Moonshine ASR**: Fast, efficient speech recognition -- **Parakeet ASR**: Alternative ASR model with different characteristics -- **Wyoming Protocol**: Standardized audio processing communication -- **Real-time Processing**: Stream audio chunks and receive transcriptions +ASR Services uses a **provider-based architecture** where inference engines are generic and models are configurable at runtime. This allows: + +- Adding new Whisper variants without code changes +- Swapping models via environment variables +- Smaller, focused Docker images per inference engine + +## Providers + +| Provider | Engine | Use Case | Example Models | +|----------|--------|----------|----------------| +| `faster-whisper` | CTranslate2 | Fast Whisper inference (4-6x faster) | Whisper Large V3, distil-whisper | +| `transformers` | HuggingFace | General ASR models | Hindi Whisper, fine-tuned Whisper | +| `vibevoice` | VibeVoice | Speaker diarization | microsoft/VibeVoice-ASR | +| `nemo` | NVIDIA NeMo | NeMo models, long audio | Parakeet, Canary | ## Quick Start -### Start Parakeet ASR Service (Available) +### Option 1: Interactive Setup + ```bash -# Start the Parakeet ASR service -docker compose up parakeet-asr -d +# Run the setup wizard +uv run python init.py -# Check service health -docker compose logs parakeet-asr +# Follow prompts to select provider and model ``` -### Start Moonshine ASR Service (Commented Out) +### Option 2: Use Pre-configured Profile + ```bash -# Note: Moonshine service is currently commented out in docker-compose.yml -# To enable, uncomment the moonshine-asr service and change port to avoid conflicts +# Use Parakeet (NeMo) +cp configs/parakeet.env .env +docker compose up --build -d nemo-asr + +# Use Whisper Large V3 (faster-whisper) +cp configs/whisper-large-v3.env .env +docker compose up --build -d faster-whisper-asr -# Uncomment lines 25-47 in docker-compose.yml, then: -# docker compose up moonshine-asr -d +# Use VibeVoice (dedicated provider with speaker diarization) +cp configs/vibevoice.env .env +docker compose up --build -d vibevoice-asr ``` -## Architecture +### Option 3: Command Line Configuration + +```bash +# Faster-Whisper with custom model +ASR_MODEL=Systran/faster-whisper-large-v3 docker compose up -d faster-whisper-asr + +# NeMo with Parakeet +ASR_MODEL=nvidia/parakeet-tdt-0.6b-v3 docker compose up -d nemo-asr + +# VibeVoice with speaker diarization +docker compose up -d vibevoice-asr +``` + +## Available Models + +### Faster-Whisper Provider (Recommended for General Use) + +| Model | Description | Speed | +|-------|-------------|-------| +| `Systran/faster-whisper-large-v3` | Best quality | ~4x realtime | +| `Systran/faster-whisper-small` | Lightweight | ~10x realtime | +| `deepdml/faster-whisper-large-v3-turbo-ct2` | Speed optimized | ~6x realtime | + +### Transformers Provider (Fine-tuned Models) + +| Model | Description | Features | +|-------|-------------|----------| +| `Oriserve/Whisper-Hindi2Hinglish-Prime` | Hindi/Hinglish optimized | Fine-tuned for code-switching | +| `openai/whisper-large-v3` | Original Whisper | Baseline comparison | + +### VibeVoice Provider (Speaker Diarization) + +| Model | Description | Features | +|-------|-------------|----------| +| `microsoft/VibeVoice-ASR` | 7B model with diarization | Speaker identification, 60-min audio | + +### NeMo Provider (Long Audio Processing) + +| Model | Description | Features | +|-------|-------------|----------| +| `nvidia/parakeet-tdt-0.6b-v3` | Production-ready | Enhanced chunking, timestamps | +| `nvidia/canary-1b` | Multilingual | 1B parameters | + +## API Endpoints + +All providers expose the **same API**: + +### POST /transcribe -### Wyoming Protocol Support -The ASR services implement Wyoming AsyncTCPServer that: -- Accepts Wyoming AudioChunk events -- Returns StreamingTranscript events (Wyoming Transcript with final flag) -- Supports real-time audio processing -- Handles dynamic sample rates and audio formats - -### Audio Processing -- **Dynamic Sample Rate Support**: Adapts to input audio sample rates -- **VAD Integration**: Voice Activity Detection for better processing -- **Streaming Transcription**: Real-time transcript generation -- **Multiple Audio Formats**: PCM, Opus, and other common formats - -## Services - -### Parakeet ASR (Available) -- **Port**: 8765 (default) -- **Protocol**: Wyoming TCP -- **Features**: GPU-accelerated speech recognition -- **Resource**: GPU acceleration required (NVIDIA) -- **Status**: Active service - -### Moonshine ASR (Commented Out) -- **Port**: 8765 (conflicts with Parakeet, needs port change) -- **Protocol**: Wyoming TCP -- **Features**: Fast transcription, low latency -- **Resource**: CPU optimized -- **Status**: Commented out in docker-compose.yml +Transcribe uploaded audio file. + +```bash +curl -X POST http://localhost:8767/transcribe \ + -F "file=@audio.wav" +``` + +**Response:** +```json +{ + "text": "Hello world", + "words": [ + {"word": "Hello", "start": 0.0, "end": 0.5, "confidence": 0.98}, + {"word": "world", "start": 0.6, "end": 1.0, "confidence": 0.95} + ], + "segments": [ + {"text": "Hello world", "start": 0.0, "end": 1.0} + ], + "language": "en", + "duration": 1.0 +} +``` + +**Response with diarization** (VibeVoice): +```json +{ + "text": "Hello, how are you? I'm fine, thanks!", + "segments": [ + {"text": "Hello, how are you?", "start": 0.0, "end": 3.5, "speaker": "Speaker 0"}, + {"text": "I'm fine, thanks!", "start": 3.5, "end": 7.2, "speaker": "Speaker 1"} + ], + "speakers": [ + {"id": "Speaker 0", "start": 0.0, "end": 3.5}, + {"id": "Speaker 1", "start": 3.5, "end": 7.2} + ], + "duration": 7.2 +} +``` +Note: VibeVoice doesn't provide word-level timestamps, only segment-level with speaker IDs. + +### GET /health + +Health check endpoint. + +```bash +curl http://localhost:8767/health +``` + +**Response:** +```json +{ + "status": "healthy", + "model": "Systran/faster-whisper-large-v3", + "provider": "faster-whisper" +} +``` + +### GET /info + +Service information. + +```bash +curl http://localhost:8767/info +``` + +**Response:** +```json +{ + "model_id": "Systran/faster-whisper-large-v3", + "provider": "faster-whisper", + "capabilities": ["timestamps", "word_timestamps", "language_detection", "vad_filter"] +} +``` ## Configuration ### Environment Variables + +**Common:** ```bash -# Service configuration -MOONSHINE_PORT=8765 -PARAKEET_PORT=8766 - -# Audio settings (auto-detected from input) -DEFAULT_SAMPLE_RATE=16000 -SUPPORTED_RATES=8000,16000,44100,48000 - -# Resource allocation -GPU_ACCELERATION=true -WORKER_THREADS=4 -``` - -### Docker Configuration -```yaml -# docker-compose.yml -services: - moonshine: - build: . - ports: - - "8765:8765" - environment: - - SERVICE_TYPE=moonshine - - parakeet: - build: . - ports: - - "8766:8766" - environment: - - SERVICE_TYPE=parakeet - - GPU_ACCELERATION=true -``` - -## Integration - -### With Chronicle Backend -The ASR services integrate as fallback transcription when Deepgram is unavailable: -```bash -# Backend configuration -PARAKEET_ASR_URL=http://localhost:8765 - -# Or set the transcription provider explicitly -TRANSCRIPTION_PROVIDER=parakeet -``` - -### Client Usage -```python -# Example Wyoming client -import asyncio -from wyoming.client import AsyncTcpClient -from wyoming.audio import AudioChunk - -async def transcribe_audio(audio_data): - client = AsyncTcpClient("localhost", 8765) - await client.connect() - - # Send audio chunk - chunk = AudioChunk( - audio=audio_data, - rate=16000, - width=2, - channels=1 - ) - await client.send(chunk) - - # Receive transcript - transcript = await client.receive() - return transcript.text +ASR_MODEL=Systran/faster-whisper-large-v3 # Model identifier +ASR_PORT=8767 # Service port ``` -## Performance +**Faster-Whisper:** +```bash +COMPUTE_TYPE=float16 # Quantization: float16, int8, float32 +DEVICE=cuda # Device: cuda, cpu +VAD_FILTER=true # Voice Activity Detection +LANGUAGE= # Force language (empty for auto-detect) +``` -| Service | Latency | Throughput | Memory Usage | GPU Required | -|---------|---------|------------|--------------|---------------| -| Moonshine | ~200ms | 10x realtime | 2GB RAM | No | -| Parakeet | ~150ms | 15x realtime | 4GB RAM | Optional | +**Transformers:** +```bash +TORCH_DTYPE=float16 # PyTorch dtype +USE_FLASH_ATTENTION=false # Flash Attention 2 +DEVICE=cuda # Device: cuda, cpu +``` -## Troubleshooting +**VibeVoice:** +```bash +VIBEVOICE_LLM_MODEL=Qwen/Qwen2.5-7B # LLM backbone for processor +VIBEVOICE_ATTN_IMPL=sdpa # Attention: sdpa, flash_attention_2, eager +TORCH_DTYPE=bfloat16 # Recommended dtype for VibeVoice +MAX_NEW_TOKENS=8192 # Max tokens for generation +DEVICE=cuda # Device: cuda, cpu (GPU required, 16GB+ VRAM) +``` -### Common Issues +VibeVoice raw model output (for debugging): +```json +[{"Start": 0.0, "End": 3.5, "Speaker": 0, "Content": "Hello"}] +``` +Keys are normalized: `Start`β†’`start`, `End`β†’`end`, `Speaker`β†’`speaker`, `Content`β†’`text` + +**NeMo:** +```bash +CHUNKING_ENABLED=true # Enable chunking for long audio +MIN_AUDIO_FOR_CHUNKING=60.0 # Threshold for chunking (seconds) +CHUNK_DURATION_SECONDS=30.0 # Chunk size +PYTORCH_CUDA_VERSION=cu126 # CUDA version for build +``` + +### Config Profiles -1. **Service not starting** - ```bash - # Check container logs - docker compose logs moonshine - - # Verify port availability - netstat -an | grep 8765 - ``` +Pre-configured `.env` files in `configs/`: -2. **Audio format errors** - - Ensure audio is in supported format (PCM, 16-bit) - - Check sample rate compatibility - - Verify Wyoming protocol message format +- `parakeet.env` - NeMo + Parakeet +- `whisper-large-v3.env` - Faster-Whisper + Large V3 +- `whisper-hindi.env` - Transformers + Hindi Whisper +- `vibevoice.env` - VibeVoice provider with speaker diarization -3. **Low transcription quality** - - Check audio quality and noise levels - - Verify correct sample rate settings - - Consider switching between Moonshine and Parakeet +## Integration with Chronicle -4. **Performance issues** - - Monitor CPU/GPU usage - - Adjust worker thread count - - Consider hardware acceleration +Configure the Chronicle backend to use your ASR service: -### Debug Mode ```bash -# Enable debug logging -docker compose up moonshine -d --env DEBUG=true +# In backends/advanced/.env +PARAKEET_ASR_URL=http://host.docker.internal:8767 +``` + +The backend will use this for: +- Fallback when cloud transcription is unavailable +- Offline transcription mode +- Local processing without API keys + +## Architecture -# Monitor real-time logs -docker compose logs -f moonshine ``` +extras/asr-services/ +β”œβ”€β”€ common/ # Shared utilities +β”‚ β”œβ”€β”€ base_service.py # Abstract base class +β”‚ β”œβ”€β”€ audio_utils.py # Audio processing +β”‚ └── response_models.py # Pydantic models +β”œβ”€β”€ providers/ +β”‚ β”œβ”€β”€ faster_whisper/ # CTranslate2 backend +β”‚ β”œβ”€β”€ transformers/ # HuggingFace backend +β”‚ β”œβ”€β”€ vibevoice/ # Microsoft VibeVoice backend +β”‚ └── nemo/ # NVIDIA NeMo backend +β”œβ”€β”€ configs/ # Pre-configured profiles +β”œβ”€β”€ docker-compose.yml # All provider services +└── init.py # Setup wizard +``` + +### Key Design Decisions + +1. **Provider-based, not model-based**: Docker images are per inference engine +2. **Runtime model selection**: `ASR_MODEL` env var configures which model to load +3. **Unified API**: All providers expose identical endpoints +4. **Config profiles**: Pre-made .env files for common setups ## Development -### Local Setup +### Local Development + ```bash -# Install dependencies -pip install -r requirements.txt +# Install dependencies for specific provider +uv sync --group faster-whisper # Run service directly -python moonshine-online.py --port 8765 --debug +uv run python -m providers.faster_whisper.service --port 8765 ``` ### Testing + ```bash -# Test with audio file -python client.py --host localhost --port 8765 --audio test.wav +# Test transcription +curl -X POST http://localhost:8767/transcribe \ + -F "file=@test.wav" -# Run Wyoming protocol tests -python -m pytest tests/ +# Test health +curl http://localhost:8767/health ``` -## Notes +### Adding New Models + +For models compatible with existing providers, just change `ASR_MODEL`: + +```bash +# Use a custom model +ASR_MODEL=your-org/your-model docker compose up -d faster-whisper-asr +``` + +For new inference engines, add a new provider in `providers/`. + +## Performance + +| Provider | Typical Latency | Memory | GPU Required | +|----------|-----------------|--------|--------------| +| faster-whisper | ~150ms/s | 4GB | Recommended | +| transformers | ~200ms/s | 8GB | Required for large models | +| vibevoice | ~300ms/s | 16GB+ | Required (large 7B model) | +| nemo | ~100ms/s | 6GB | Required | + +## Troubleshooting + +### Service not starting + +```bash +# Check container logs +docker compose logs nemo-asr + +# Verify GPU availability +docker run --gpus all nvidia/cuda:12.1-base-ubuntu22.04 nvidia-smi +``` + +### Model download issues + +Models are cached in `./model_cache`. To reset: + +```bash +rm -rf model_cache +docker compose up --build -d nemo-asr +``` + +### Memory errors + +Try: +- Use a smaller model (e.g., `Systran/faster-whisper-small`) +- Use quantization (`COMPUTE_TYPE=int8`) +- Check GPU memory with `nvidia-smi` + +## Legacy Support + +The original `parakeet-asr` service is still available for backward compatibility: + +```bash +docker compose up --build -d parakeet-asr +``` -- Streaming is fully supported for real-time transcription -- Services auto-detect optimal sample rates from input audio -- GPU acceleration is optional but recommended for Parakeet -- Multiple clients can connect simultaneously -- Automatic fallback between services when one is unavailable \ No newline at end of file +This uses the original `Dockerfile_Parakeet` and `parakeet-offline.py`. diff --git a/extras/asr-services/common/__init__.py b/extras/asr-services/common/__init__.py new file mode 100644 index 00000000..7abc6af8 --- /dev/null +++ b/extras/asr-services/common/__init__.py @@ -0,0 +1,42 @@ +""" +Common utilities for ASR services. + +This module provides shared components used across all ASR providers: +- BaseASRService: Abstract base class for ASR service implementations +- Audio utilities: Resampling, format conversion, chunking +- Response models: Pydantic models for standardized API responses +""" + +from common.response_models import ( + TranscriptionResult, + Word, + Segment, + Speaker, + HealthResponse, + InfoResponse, +) +from common.audio_utils import ( + convert_audio_to_numpy, + resample_audio, + load_audio_file, + save_audio_file, +) +from common.base_service import BaseASRService, create_asr_app + +__all__ = [ + # Response models + "TranscriptionResult", + "Word", + "Segment", + "Speaker", + "HealthResponse", + "InfoResponse", + # Audio utilities + "convert_audio_to_numpy", + "resample_audio", + "load_audio_file", + "save_audio_file", + # Base service + "BaseASRService", + "create_asr_app", +] diff --git a/extras/asr-services/common/audio_utils.py b/extras/asr-services/common/audio_utils.py new file mode 100644 index 00000000..4dad9bff --- /dev/null +++ b/extras/asr-services/common/audio_utils.py @@ -0,0 +1,256 @@ +""" +Audio utility functions for ASR services. + +Provides common audio processing operations used across all providers. +""" + +import io +import logging +import tempfile +import wave +from pathlib import Path +from typing import Optional, Tuple, Union + +import numpy as np + +logger = logging.getLogger(__name__) + +# Standard sample rate for ASR processing +STANDARD_SAMPLE_RATE = 16000 + + +def convert_audio_to_numpy( + audio_bytes: bytes, + sample_rate: int, + sample_width: int = 2, + channels: int = 1, +) -> np.ndarray: + """ + Convert raw audio bytes to numpy float32 array. + + Args: + audio_bytes: Raw audio data + sample_rate: Sample rate in Hz + sample_width: Bytes per sample (2 = 16-bit, 4 = 32-bit) + channels: Number of audio channels + + Returns: + Numpy array of float32 audio samples normalized to [-1.0, 1.0] + """ + if sample_width == 2: + audio_array = np.frombuffer(audio_bytes, dtype=np.int16) + return audio_array.astype(np.float32) / np.iinfo(np.int16).max + elif sample_width == 4: + audio_array = np.frombuffer(audio_bytes, dtype=np.int32) + return audio_array.astype(np.float32) / np.iinfo(np.int32).max + else: + raise ValueError(f"Unsupported sample width: {sample_width}") + + +def numpy_to_audio_bytes( + audio_array: np.ndarray, sample_width: int = 2 +) -> bytes: + """ + Convert numpy float32 array to raw audio bytes. + + Args: + audio_array: Numpy array of float32 samples (normalized to [-1.0, 1.0]) + sample_width: Target bytes per sample (2 = 16-bit, 4 = 32-bit) + + Returns: + Raw audio bytes + """ + if sample_width == 2: + audio_int = (audio_array * np.iinfo(np.int16).max).astype(np.int16) + return audio_int.tobytes() + elif sample_width == 4: + audio_int = (audio_array * np.iinfo(np.int32).max).astype(np.int32) + return audio_int.tobytes() + else: + raise ValueError(f"Unsupported sample width: {sample_width}") + + +def resample_audio( + audio_array: np.ndarray, original_rate: int, target_rate: int = STANDARD_SAMPLE_RATE +) -> np.ndarray: + """ + Resample audio to target sample rate using simple linear interpolation. + + For production use, consider using librosa or scipy for better quality. + + Args: + audio_array: Input audio samples + original_rate: Original sample rate in Hz + target_rate: Target sample rate in Hz (default: 16000) + + Returns: + Resampled audio array + """ + if original_rate == target_rate: + return audio_array + + # Calculate ratio and new length + ratio = target_rate / original_rate + new_length = int(len(audio_array) * ratio) + + # Simple linear interpolation resampling + x_old = np.linspace(0, 1, len(audio_array)) + x_new = np.linspace(0, 1, new_length) + resampled = np.interp(x_new, x_old, audio_array) + + return resampled.astype(np.float32) + + +def convert_to_mono(audio_array: np.ndarray, channels: int) -> np.ndarray: + """ + Convert multi-channel audio to mono by averaging channels. + + Args: + audio_array: Input audio samples (interleaved if multi-channel) + channels: Number of channels in input + + Returns: + Mono audio array + """ + if channels == 1: + return audio_array + + # Reshape to (samples, channels) and average + audio_2d = audio_array.reshape(-1, channels) + return audio_2d.mean(axis=1).astype(np.float32) + + +def load_audio_file( + file_path: Union[str, Path], target_rate: int = STANDARD_SAMPLE_RATE +) -> Tuple[np.ndarray, int]: + """ + Load audio file and return as numpy array. + + Args: + file_path: Path to audio file (supports WAV) + target_rate: Target sample rate (will resample if different) + + Returns: + Tuple of (audio_array, sample_rate) + """ + file_path = Path(file_path) + + if not file_path.exists(): + raise FileNotFoundError(f"Audio file not found: {file_path}") + + # Use wave module for WAV files + with wave.open(str(file_path), "rb") as wav_file: + sample_rate = wav_file.getframerate() + channels = wav_file.getnchannels() + sample_width = wav_file.getsampwidth() + n_frames = wav_file.getnframes() + + audio_bytes = wav_file.readframes(n_frames) + audio_array = convert_audio_to_numpy( + audio_bytes, sample_rate, sample_width, channels + ) + + # Convert to mono if needed + if channels > 1: + audio_array = convert_to_mono(audio_array, channels) + + # Resample if needed + if sample_rate != target_rate: + logger.info(f"Resampling from {sample_rate}Hz to {target_rate}Hz") + audio_array = resample_audio(audio_array, sample_rate, target_rate) + sample_rate = target_rate + + return audio_array, sample_rate + + +def save_audio_file( + audio_array: np.ndarray, + file_path: Union[str, Path], + sample_rate: int = STANDARD_SAMPLE_RATE, + sample_width: int = 2, +) -> Path: + """ + Save numpy audio array to WAV file. + + Args: + audio_array: Audio samples as float32 normalized array + file_path: Output file path + sample_rate: Sample rate in Hz + sample_width: Bytes per sample + + Returns: + Path to saved file + """ + file_path = Path(file_path) + + # Convert to bytes + audio_bytes = numpy_to_audio_bytes(audio_array, sample_width) + + with wave.open(str(file_path), "wb") as wav_file: + wav_file.setnchannels(1) # Mono + wav_file.setsampwidth(sample_width) + wav_file.setframerate(sample_rate) + wav_file.writeframes(audio_bytes) + + return file_path + + +def save_to_temp_wav( + audio_array: np.ndarray, + sample_rate: int = STANDARD_SAMPLE_RATE, + sample_width: int = 2, + suffix: str = ".wav", +) -> str: + """ + Save audio to a temporary WAV file. + + Args: + audio_array: Audio samples as float32 normalized array + sample_rate: Sample rate in Hz + sample_width: Bytes per sample + suffix: File suffix + + Returns: + Path to temporary file (caller is responsible for cleanup) + """ + with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tmp_file: + save_audio_file(audio_array, tmp_file.name, sample_rate, sample_width) + return tmp_file.name + + +def get_audio_duration( + audio_array: np.ndarray, sample_rate: int = STANDARD_SAMPLE_RATE +) -> float: + """ + Calculate audio duration in seconds. + + Args: + audio_array: Audio samples + sample_rate: Sample rate in Hz + + Returns: + Duration in seconds + """ + return len(audio_array) / sample_rate + + +def validate_audio_format( + sample_rate: int, channels: int, sample_width: int +) -> None: + """ + Validate audio format parameters. + + Args: + sample_rate: Sample rate in Hz + channels: Number of channels + sample_width: Bytes per sample + + Raises: + ValueError: If parameters are invalid + """ + if sample_rate < 8000 or sample_rate > 48000: + raise ValueError(f"Sample rate {sample_rate} outside valid range (8000-48000)") + if channels < 1 or channels > 8: + raise ValueError(f"Channel count {channels} outside valid range (1-8)") + if sample_width not in (2, 4): + raise ValueError(f"Sample width {sample_width} must be 2 or 4 bytes") diff --git a/extras/asr-services/common/base_service.py b/extras/asr-services/common/base_service.py new file mode 100644 index 00000000..4c0bb1c9 --- /dev/null +++ b/extras/asr-services/common/base_service.py @@ -0,0 +1,207 @@ +""" +Abstract base class for ASR services. + +Provides a common interface and FastAPI app setup for all ASR providers. +""" + +import logging +import os +import tempfile +import time +from abc import ABC, abstractmethod +from typing import Optional + +from fastapi import FastAPI, File, HTTPException, UploadFile +from fastapi.responses import JSONResponse + +from common.response_models import ( + HealthResponse, + InfoResponse, + TranscriptionResult, +) + +logger = logging.getLogger(__name__) + + +class BaseASRService(ABC): + """ + Abstract base class for ASR service implementations. + + Subclasses must implement: + - transcribe(): Perform transcription on audio file + - warmup(): Initialize and warm up the model + - get_model_id(): Return the model identifier + - get_capabilities(): Return list of supported capabilities + """ + + def __init__(self, model_id: Optional[str] = None): + """ + Initialize the ASR service. + + Args: + model_id: Model identifier. If None, reads from ASR_MODEL env var. + """ + self.model_id = model_id or os.getenv("ASR_MODEL", "") + self._is_ready = False + + @property + @abstractmethod + def provider_name(self) -> str: + """Return the provider name (e.g., 'faster-whisper', 'nemo', 'transformers').""" + pass + + @abstractmethod + async def transcribe(self, audio_file_path: str) -> TranscriptionResult: + """ + Transcribe audio file and return result. + + Args: + audio_file_path: Path to audio file (WAV format, 16kHz mono preferred) + + Returns: + TranscriptionResult with text, words, segments, etc. + """ + pass + + @abstractmethod + async def warmup(self) -> None: + """ + Initialize and warm up the model. + + Called once during service startup. + """ + pass + + def get_model_id(self) -> str: + """Return the current model identifier.""" + return self.model_id + + @abstractmethod + def get_capabilities(self) -> list[str]: + """ + Return list of supported capabilities. + + Examples: ['timestamps', 'word_timestamps', 'diarization', 'language_detection'] + """ + pass + + def get_supported_languages(self) -> Optional[list[str]]: + """ + Return list of supported language codes, or None if multilingual. + + Override in subclasses for models with limited language support. + """ + return None + + @property + def is_ready(self) -> bool: + """Return True if service is ready to handle requests.""" + return self._is_ready + + +def create_asr_app(service: BaseASRService) -> FastAPI: + """ + Create a FastAPI application with standard ASR endpoints. + + Args: + service: Initialized ASR service instance + + Returns: + Configured FastAPI application + """ + app = FastAPI( + title=f"{service.provider_name.title()} ASR Service", + version="1.0.0", + description=f"ASR service using {service.provider_name} provider", + ) + + @app.on_event("startup") + async def startup_event(): + """Initialize the transcriber on startup.""" + logger.info(f"Starting {service.provider_name} ASR service...") + await service.warmup() + service._is_ready = True + logger.info(f"{service.provider_name} ASR service ready") + + @app.get("/health", response_model=HealthResponse) + async def health_check(): + """Health check endpoint.""" + return HealthResponse( + status="healthy" if service.is_ready else "initializing", + model=service.get_model_id(), + provider=service.provider_name, + ) + + @app.get("/info", response_model=InfoResponse) + async def service_info(): + """Service information endpoint.""" + return InfoResponse( + model_id=service.get_model_id(), + provider=service.provider_name, + capabilities=service.get_capabilities(), + supported_languages=service.get_supported_languages(), + ) + + @app.post("/transcribe") + async def transcribe(file: UploadFile = File(...)): + """ + Transcribe uploaded audio file. + + Accepts audio files (WAV, MP3, etc.) and returns transcription + with word-level timestamps. + """ + if not service.is_ready: + raise HTTPException(status_code=503, detail="Service not ready") + + request_start = time.time() + logger.info(f"Transcription request started") + + tmp_filename = None + try: + # Read uploaded file + file_read_start = time.time() + audio_content = await file.read() + file_read_time = time.time() - file_read_start + logger.info( + f"File read completed in {file_read_time:.3f}s " + f"(size: {len(audio_content)} bytes)" + ) + + # Save to temporary file + suffix = ".wav" + if file.filename: + ext = file.filename.rsplit(".", 1)[-1].lower() + if ext in ("wav", "mp3", "flac", "ogg", "m4a"): + suffix = f".{ext}" + + with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tmp_file: + tmp_file.write(audio_content) + tmp_filename = tmp_file.name + + # Transcribe + transcribe_start = time.time() + result = await service.transcribe(tmp_filename) + transcribe_time = time.time() - transcribe_start + logger.info(f"Transcription completed in {transcribe_time:.3f}s") + + total_time = time.time() - request_start + logger.info(f"Total request time: {total_time:.3f}s") + + return JSONResponse(content=result.to_dict()) + + except HTTPException: + raise + except Exception as e: + error_time = time.time() - request_start + logger.exception(f"Error after {error_time:.3f}s: {e}") + raise HTTPException(status_code=500, detail="Transcription failed") + + finally: + # Cleanup temporary file + if tmp_filename: + try: + os.unlink(tmp_filename) + except Exception as e: + logger.warning(f"Failed to delete temp file {tmp_filename}: {e}") + + return app diff --git a/extras/asr-services/common/response_models.py b/extras/asr-services/common/response_models.py new file mode 100644 index 00000000..e716fc7f --- /dev/null +++ b/extras/asr-services/common/response_models.py @@ -0,0 +1,98 @@ +""" +Pydantic response models for ASR services. + +These models provide a standardized API response format across all providers. +""" + +from typing import List, Optional + +from pydantic import BaseModel, Field + + +class Word(BaseModel): + """Word-level transcription with timing information.""" + + word: str = Field(..., description="The transcribed word text") + start: float = Field(..., description="Start time in seconds") + end: float = Field(..., description="End time in seconds") + confidence: Optional[float] = Field( + default=None, description="Confidence score (0.0-1.0)" + ) + + +class Segment(BaseModel): + """Segment-level transcription with timing information.""" + + text: str = Field(..., description="The transcribed segment text") + start: float = Field(..., description="Start time in seconds") + end: float = Field(..., description="End time in seconds") + speaker: Optional[str] = Field( + default=None, description="Speaker identifier if available" + ) + + +class Speaker(BaseModel): + """Speaker information from diarization.""" + + id: str = Field(..., description="Speaker identifier") + label: Optional[str] = Field(default=None, description="Human-readable speaker label") + start: float = Field(..., description="Speaker segment start time") + end: float = Field(..., description="Speaker segment end time") + + +class TranscriptionResult(BaseModel): + """Standardized transcription result from any ASR provider.""" + + text: str = Field(default="", description="Full transcribed text") + words: List[Word] = Field( + default_factory=list, description="Word-level transcriptions with timing" + ) + segments: List[Segment] = Field( + default_factory=list, description="Segment-level transcriptions" + ) + speakers: Optional[List[Speaker]] = Field( + default=None, description="Speaker diarization information (if available)" + ) + language: Optional[str] = Field( + default=None, description="Detected language code" + ) + duration: Optional[float] = Field( + default=None, description="Audio duration in seconds" + ) + + def to_dict(self) -> dict: + """Convert to dictionary, excluding None values.""" + result = { + "text": self.text, + "words": [w.model_dump() for w in self.words], + "segments": [s.model_dump() for s in self.segments], + } + if self.speakers is not None: + result["speakers"] = [s.model_dump() for s in self.speakers] + if self.language is not None: + result["language"] = self.language + if self.duration is not None: + result["duration"] = self.duration + return result + + +class HealthResponse(BaseModel): + """Health check response.""" + + status: str = Field(..., description="Service status (healthy/unhealthy)") + model: str = Field(..., description="Loaded model identifier") + provider: str = Field(..., description="ASR provider name") + + +class InfoResponse(BaseModel): + """Service information response.""" + + model_id: str = Field(..., description="Model identifier/name") + provider: str = Field(..., description="ASR provider name") + capabilities: List[str] = Field( + default_factory=list, + description="List of supported capabilities (e.g., timestamps, diarization)", + ) + supported_languages: Optional[List[str]] = Field( + default=None, description="List of supported language codes" + ) diff --git a/extras/asr-services/docker-compose.yml b/extras/asr-services/docker-compose.yml index c60b8cd5..7e4b0aa5 100644 --- a/extras/asr-services/docker-compose.yml +++ b/extras/asr-services/docker-compose.yml @@ -1,4 +1,152 @@ +# ASR Services Docker Compose +# Provider-based architecture: Run ONE provider at a time with configurable models +# +# Usage: +# # Start NeMo provider (Parakeet, Canary) +# ASR_MODEL=nvidia/parakeet-tdt-0.6b-v3 docker compose up nemo-asr -d +# +# # Start Faster-Whisper provider +# ASR_MODEL=Systran/faster-whisper-large-v3 docker compose up faster-whisper-asr -d +# +# # Start Transformers provider (Hindi Whisper, HuggingFace models) +# ASR_MODEL=openai/whisper-large-v3 docker compose up transformers-asr -d +# +# # Start VibeVoice provider (speaker diarization) +# docker compose up vibevoice-asr -d + services: + # ============================================================================ + # NeMo Provider (Parakeet, Canary, etc.) + # ============================================================================ + nemo-asr: + build: + context: . + dockerfile: providers/nemo/Dockerfile + args: + PYTORCH_CUDA_VERSION: ${PYTORCH_CUDA_VERSION:-cu126} + image: chronicle-asr-nemo:latest + ports: + - "${ASR_PORT:-8767}:8765" + volumes: + - ./model_cache:/models + - ./debug:/app/debug + - ./results:/app/results + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: all + capabilities: [gpu] + environment: + - HF_HOME=/models + - ASR_MODEL=${ASR_MODEL:-nvidia/parakeet-tdt-0.6b-v3} + # Legacy support + - PARAKEET_MODEL=${PARAKEET_MODEL:-${ASR_MODEL:-nvidia/parakeet-tdt-0.6b-v3}} + # Enhanced chunking configuration + - CHUNKING_ENABLED=${CHUNKING_ENABLED:-true} + - CHUNK_DURATION_SECONDS=${CHUNK_DURATION_SECONDS:-30.0} + - OVERLAP_DURATION_SECONDS=${OVERLAP_DURATION_SECONDS:-5.0} + - MIN_AUDIO_FOR_CHUNKING=${MIN_AUDIO_FOR_CHUNKING:-60.0} + - CONFIDENCE_THRESHOLD=${CONFIDENCE_THRESHOLD:-0.8} + restart: unless-stopped + + # ============================================================================ + # Faster-Whisper Provider (4-6x faster Whisper inference) + # ============================================================================ + faster-whisper-asr: + build: + context: . + dockerfile: providers/faster_whisper/Dockerfile + image: chronicle-asr-faster-whisper:latest + ports: + - "${ASR_PORT:-8767}:8765" + volumes: + - ./model_cache:/models + - ./debug:/app/debug + - ./results:/app/results + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: all + capabilities: [gpu] + environment: + - HF_HOME=/models + - ASR_MODEL=${ASR_MODEL:-Systran/faster-whisper-large-v3} + - COMPUTE_TYPE=${COMPUTE_TYPE:-float16} + - DEVICE=${DEVICE:-cuda} + - DEVICE_INDEX=${DEVICE_INDEX:-0} + - VAD_FILTER=${VAD_FILTER:-true} + - LANGUAGE=${LANGUAGE:-} + restart: unless-stopped + + # ============================================================================ + # VibeVoice Provider (Microsoft VibeVoice-ASR with speaker diarization) + # Transformers-based approach with speaker diarization support + # ============================================================================ + vibevoice-asr: + build: + context: . + dockerfile: providers/vibevoice/Dockerfile + image: chronicle-asr-vibevoice:latest + ports: + - "${ASR_PORT:-8767}:8765" + volumes: + - ./model_cache:/models + - ./debug:/app/debug + - ./results:/app/results + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: all + capabilities: [gpu] + environment: + - HF_HOME=/models + - ASR_MODEL=${ASR_MODEL:-microsoft/VibeVoice-ASR} + - VIBEVOICE_LLM_MODEL=${VIBEVOICE_LLM_MODEL:-Qwen/Qwen2.5-7B} + - VIBEVOICE_ATTN_IMPL=${VIBEVOICE_ATTN_IMPL:-sdpa} + - DEVICE=${DEVICE:-cuda} + - TORCH_DTYPE=${TORCH_DTYPE:-bfloat16} + - MAX_NEW_TOKENS=${MAX_NEW_TOKENS:-8192} + restart: unless-stopped + + # ============================================================================ + # Transformers Provider (Hindi Whisper, HuggingFace models) + # ============================================================================ + transformers-asr: + build: + context: . + dockerfile: providers/transformers/Dockerfile + image: chronicle-asr-transformers:latest + ports: + - "${ASR_PORT:-8767}:8765" + volumes: + - ./model_cache:/models + - ./debug:/app/debug + - ./results:/app/results + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: all + capabilities: [gpu] + environment: + - HF_HOME=/models + - ASR_MODEL=${ASR_MODEL:-openai/whisper-large-v3} + - USE_FLASH_ATTENTION=${USE_FLASH_ATTENTION:-false} + - DEVICE=${DEVICE:-cuda} + - TORCH_DTYPE=${TORCH_DTYPE:-float16} + - LANGUAGE=${LANGUAGE:-} + restart: unless-stopped + + # ============================================================================ + # Legacy Parakeet Service (backward compatibility) + # ============================================================================ parakeet-asr: build: context: . @@ -9,7 +157,6 @@ services: ports: - "${PARAKEET_HOST_PORT:-8767}:${PARAKEET_CONTAINER_PORT:-8765}" volumes: - # Persist Hugging Face cache (models + tokenizer) between restarts - ./model_cache:/models - ./debug:/app/debug - ./results:/app/results @@ -22,7 +169,7 @@ services: capabilities: [gpu] environment: - HF_HOME=/models - - PARAKEET_MODEL=$PARAKEET_MODEL + - PARAKEET_MODEL=${PARAKEET_MODEL:-nvidia/parakeet-tdt-0.6b-v3} # Enhanced chunking configuration - CHUNKING_ENABLED=${CHUNKING_ENABLED:-true} - CHUNK_DURATION_SECONDS=${CHUNK_DURATION_SECONDS:-30.0} @@ -30,27 +177,3 @@ services: - MIN_AUDIO_FOR_CHUNKING=${MIN_AUDIO_FOR_CHUNKING:-60.0} - CONFIDENCE_THRESHOLD=${CONFIDENCE_THRESHOLD:-0.8} restart: unless-stopped - - # moonshine-asr: - # build: - # context: . - # dockerfile: Dockerfile_Moonshine - # image: moonshine-asr:latest - # ports: - # - "8765:8765" # Keep Moonshine on 8765 - # volumes: - # # Persist Hugging Face cache (models + tokenizer) between restarts - # - ./model_cache:/models - # - ./audio_chunks:/app/audio_chunks - # - ./debug:/app/debug - # - ./results:/app/results - # deploy: - # resources: - # reservations: - # devices: - # - driver: nvidia - # count: all - # capabilities: [gpu] - # environment: - # - HF_HOME=/models - # restart: unless-stopped \ No newline at end of file diff --git a/extras/asr-services/init.py b/extras/asr-services/init.py index d65043cf..2c800609 100755 --- a/extras/asr-services/init.py +++ b/extras/asr-services/init.py @@ -1,26 +1,80 @@ #!/usr/bin/env python3 """ Chronicle ASR Services Setup Script -Interactive configuration for offline ASR (Parakeet) service +Interactive configuration for provider-based ASR services """ import argparse import os import platform import shutil -import subprocess import sys from datetime import datetime from pathlib import Path -from typing import Any, Dict +from typing import Any, Dict, Optional from dotenv import set_key -from rich import print as rprint from rich.console import Console from rich.panel import Panel from rich.prompt import Confirm, Prompt +from rich.table import Table from rich.text import Text +# Add repo root to path for imports +sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent)) +from config_manager import ConfigManager +from setup_utils import detect_cuda_version as _detect_cuda_version +from setup_utils import read_env_value + +# Provider and model definitions +PROVIDERS = { + "vibevoice": { + "name": "VibeVoice", + "description": "Microsoft VibeVoice-ASR with built-in speaker diarization", + "models": { + "microsoft/VibeVoice-ASR": "VibeVoice-ASR (7B, speaker diarization, 60-min audio)", + }, + "default_model": "microsoft/VibeVoice-ASR", + "service": "vibevoice-asr", + # Note: VibeVoice provides diarization but NOT word_timestamps + "capabilities": ["timestamps", "diarization", "speaker_identification", "long_form"], + }, + "faster-whisper": { + "name": "Faster-Whisper", + "description": "Fast Whisper inference (4-6x faster) using CTranslate2", + "models": { + "Systran/faster-whisper-large-v3": "Whisper Large V3 (Best quality)", + "Systran/faster-whisper-small": "Whisper Small (Lightweight)", + "deepdml/faster-whisper-large-v3-turbo-ct2": "Whisper Large V3 Turbo (Speed optimized)", + }, + "default_model": "Systran/faster-whisper-large-v3", + "service": "faster-whisper-asr", + "capabilities": ["timestamps", "word_timestamps", "language_detection", "vad_filter", "translation"], + }, + "transformers": { + "name": "Transformers", + "description": "HuggingFace models (Hindi Whisper, custom models)", + "models": { + "Oriserve/Whisper-Hindi2Hinglish-Prime": "Hindi/Hinglish Whisper (Fine-tuned Large V3)", + "openai/whisper-large-v3": "OpenAI Whisper Large V3", + }, + "default_model": "openai/whisper-large-v3", + "service": "transformers-asr", + "capabilities": ["timestamps", "word_timestamps", "language_detection"], + }, + "nemo": { + "name": "NeMo", + "description": "NVIDIA NeMo ASR models (Parakeet, Canary)", + "models": { + "nvidia/parakeet-tdt-0.6b-v3": "Parakeet TDT 0.6B v3 (Default)", + "nvidia/canary-1b": "Canary 1B (Multilingual)", + }, + "default_model": "nvidia/parakeet-tdt-0.6b-v3", + "service": "nemo-asr", + "capabilities": ["timestamps", "word_timestamps", "chunked_processing"], + }, +} + class ASRServicesSetup: def __init__(self, args=None): @@ -70,16 +124,9 @@ def prompt_choice(self, prompt: str, choices: Dict[str, str], default: str = "1" self.console.print(f"Using default choice: {default}") return default - def read_existing_env_value(self, key: str) -> str: - """Read a value from existing .env file""" - env_path = Path(".env") - if not env_path.exists(): - return None - - from dotenv import get_key - value = get_key(str(env_path), key) - # get_key returns None if key doesn't exist or value is empty - return value if value else None + def read_existing_env_value(self, key: str) -> Optional[str]: + """Read a value from existing .env file (delegates to shared utility)""" + return read_env_value(".env", key) def backup_existing_env(self): """Backup existing .env file""" @@ -91,162 +138,297 @@ def backup_existing_env(self): self.console.print(f"[blue][INFO][/blue] Backed up existing .env file to {backup_path}") def detect_cuda_version(self) -> str: - """Detect system CUDA version from nvidia-smi""" - try: - result = subprocess.run( - ["nvidia-smi", "--query-gpu=driver_version", "--format=csv,noheader"], - capture_output=True, - text=True, - timeout=5 - ) - if result.returncode == 0: - # Try to get CUDA version from nvidia-smi - result = subprocess.run( - ["nvidia-smi"], - capture_output=True, - text=True, - timeout=5 - ) - if result.returncode == 0: - output = result.stdout - # Parse CUDA Version from nvidia-smi output - # Format: "CUDA Version: 12.6" - import re - match = re.search(r'CUDA Version:\s*(\d+)\.(\d+)', output) - if match: - major, minor = match.groups() - cuda_ver = f"{major}.{minor}" - - # Map to available PyTorch CUDA versions - if cuda_ver >= "12.8": - return "cu128" - elif cuda_ver >= "12.6": - return "cu126" - elif cuda_ver >= "12.1": - return "cu121" - except (subprocess.SubprocessError, FileNotFoundError): - pass - return "cu126" # Default fallback to cu126 + """Detect system CUDA version (delegates to shared utility)""" + return _detect_cuda_version(default="cu126") + + def select_provider(self) -> str: + """Select ASR provider""" + # Check for command-line provider first (skip interactive UI) + if hasattr(self.args, 'provider') and self.args.provider: + provider = self.args.provider + provider_name = PROVIDERS.get(provider, {}).get('name', provider) + self.console.print(f"[green]βœ…[/green] ASR Provider: {provider_name} (configured via wizard)") + return provider + + self.print_section("Provider Selection") + + # Show provider comparison table + table = Table(title="Available ASR Providers") + table.add_column("Provider", style="cyan") + table.add_column("Description", style="white") + table.add_column("Best For", style="green") + + table.add_row( + "vibevoice", + "Microsoft VibeVoice-ASR", + "Built-in speaker diarization" + ) + table.add_row( + "faster-whisper", + "Fast Whisper (CTranslate2)", + "General use, speed + quality" + ) + table.add_row( + "transformers", + "HuggingFace models", + "Hindi, custom models" + ) + table.add_row( + "nemo", + "NVIDIA NeMo", + "Parakeet, long audio processing" + ) + self.console.print(table) + self.console.print() + + provider_choices = { + "1": "vibevoice - Microsoft VibeVoice-ASR (Built-in diarization)", + "2": "faster-whisper - Fast Whisper inference (Recommended for general use)", + "3": "transformers - HuggingFace models (Hindi, custom)", + "4": "nemo - NVIDIA NeMo (Parakeet)", + } + + choice = self.prompt_choice("Choose ASR provider:", provider_choices, "2") + choice_to_provider = {"1": "vibevoice", "2": "faster-whisper", "3": "transformers", "4": "nemo"} + return choice_to_provider[choice] + + def select_model(self, provider: str) -> str: + """Select model for the chosen provider""" + provider_info = PROVIDERS[provider] + models = provider_info["models"] + default_model = provider_info["default_model"] + + # Check for command-line model + if hasattr(self.args, 'model') and self.args.model: + model = self.args.model + self.console.print(f"[green]βœ…[/green] ASR Model: {model} (configured via wizard)") + return model + + self.print_section(f"Model Selection ({PROVIDERS[provider]['name']})") + + # Show available models + self.console.print(f"[blue]Available models for {provider_info['name']}:[/blue]") + model_choices = {} + for i, (model_id, description) in enumerate(models.items(), 1): + model_choices[str(i)] = f"{model_id} - {description}" + if model_id == default_model: + model_choices[str(i)] += " (Default)" + + # Find default choice number + default_choice = "1" + for i, model_id in enumerate(models.keys(), 1): + if model_id == default_model: + default_choice = str(i) + break + + # Add custom model option + model_choices[str(len(models) + 1)] = "Enter custom model URL" + + choice = self.prompt_choice("Choose model:", model_choices, default_choice) + + if choice == str(len(models) + 1): + # Custom model + custom_model = self.prompt_value("Enter model identifier (HuggingFace repo or path)") + return custom_model + else: + return list(models.keys())[int(choice) - 1] def setup_cuda_version(self): """Configure PyTorch CUDA version""" - self.print_section("PyTorch CUDA Version Configuration") + self.print_section("PyTorch CUDA Version") - # Detect macOS (Darwin) and auto-default to CPU is_macos = platform.system() == 'Darwin' - # Check if provided via command line if hasattr(self.args, 'pytorch_cuda_version') and self.args.pytorch_cuda_version: cuda_version = self.args.pytorch_cuda_version - self.console.print(f"[green][SUCCESS][/green] PyTorch CUDA version configured from command line: {cuda_version}") + self.console.print(f"[green][SUCCESS][/green] CUDA version from command line: {cuda_version}") elif is_macos: - # Auto-default to CPU on macOS cuda_version = "cpu" - self.console.print("[blue][INFO][/blue] Detected macOS - GPU acceleration not available (Apple Silicon/Intel)") - self.console.print("[green][SUCCESS][/green] Using CPU-only PyTorch build") + self.console.print("[blue][INFO][/blue] Detected macOS - using CPU-only PyTorch") else: - # Detect system CUDA version and suggest as default detected_cuda = self.detect_cuda_version() - - # Map to default choice number - cuda_to_choice = { - "cu121": "1", - "cu126": "2", - "cu128": "3" - } - default_choice = cuda_to_choice.get(detected_cuda, "2") - - self.console.print() self.console.print(f"[blue][INFO][/blue] Detected CUDA version: {detected_cuda}") - self.console.print("[blue][INFO][/blue] This controls which PyTorch version is installed for GPU acceleration") - self.console.print() cuda_choices = { "1": "CUDA 12.1 (cu121)", "2": "CUDA 12.6 (cu126) - Recommended", - "3": "CUDA 12.8 (cu128)" - } - cuda_choice = self.prompt_choice( - "Choose CUDA version for PyTorch:", - cuda_choices, - default_choice - ) - - choice_to_cuda = { - "1": "cu121", - "2": "cu126", - "3": "cu128" + "3": "CUDA 12.8 (cu128)", } - cuda_version = choice_to_cuda[cuda_choice] + cuda_to_choice = {"cu121": "1", "cu126": "2", "cu128": "3"} + default_choice = cuda_to_choice.get(detected_cuda, "2") + + choice = self.prompt_choice("Choose CUDA version:", cuda_choices, default_choice) + choice_to_cuda = {"1": "cu121", "2": "cu126", "3": "cu128"} + cuda_version = choice_to_cuda[choice] self.config["PYTORCH_CUDA_VERSION"] = cuda_version - self.console.print(f"[blue][INFO][/blue] Using PyTorch with CUDA version: {cuda_version}") + + def setup_provider_config(self, provider: str, model: str): + """Configure provider-specific settings""" + self.print_section("Provider Configuration") + + self.config["ASR_PROVIDER"] = provider + self.config["ASR_MODEL"] = model + self.config["ASR_PORT"] = "8767" + + if provider == "faster-whisper": + self.config["COMPUTE_TYPE"] = "float16" + self.config["DEVICE"] = "cuda" + self.config["VAD_FILTER"] = "true" + + # Ask about language + if Confirm.ask("Force specific language?", default=False): + lang = self.prompt_value("Language code (e.g., en, hi, es)", default="") + if lang: + self.config["LANGUAGE"] = lang + + elif provider == "vibevoice": + # VibeVoice uses transformers backend with specific optimizations + self.config["TORCH_DTYPE"] = "float16" + self.config["DEVICE"] = "cuda" + self.config["USE_FLASH_ATTENTION"] = "true" + self.console.print("[blue][INFO][/blue] Enabled Flash Attention for VibeVoice") + self.console.print("[blue][INFO][/blue] VibeVoice provides built-in speaker diarization (no pyannote needed)") + + elif provider == "transformers": + self.config["TORCH_DTYPE"] = "float16" + self.config["DEVICE"] = "cuda" + self.config["USE_FLASH_ATTENTION"] = "false" + + # Hindi model-specific + if "hindi" in model.lower(): + self.config["LANGUAGE"] = "hi" + self.console.print("[blue][INFO][/blue] Set language to Hindi for Hindi Whisper model") + + elif provider == "nemo": + # NeMo's transcribe() handles long audio natively - no extra config needed + pass def generate_env_file(self): - """Generate .env file from template and update with configuration""" + """Generate .env file from configuration""" env_path = Path(".env") env_template = Path(".env.template") - # Backup existing .env if it exists self.backup_existing_env() - # Copy template to .env if env_template.exists(): shutil.copy2(env_template, env_path) self.console.print("[blue][INFO][/blue] Copied .env.template to .env") else: - self.console.print("[yellow][WARNING][/yellow] .env.template not found, creating new .env") env_path.touch(mode=0o600) - # Update configured values using set_key env_path_str = str(env_path) for key, value in self.config.items(): - if value: # Only set non-empty values + if value: set_key(env_path_str, key, value) - # Ensure secure permissions os.chmod(env_path, 0o600) + self.console.print("[green][SUCCESS][/green] .env file configured successfully") + + def update_config_yml(self, provider: str): + """Update config/config.yml with STT model defaults.""" + provider_to_stt_model = { + "vibevoice": "stt-vibevoice", + "faster-whisper": "stt-faster-whisper", + "transformers": "stt-transformers", + "nemo": "stt-nemo", + } + + stt_model = provider_to_stt_model.get(provider) + if not stt_model: + self.console.print(f"[yellow][WARNING][/yellow] Unknown provider '{provider}', skipping config.yml update") + return + + try: + config_manager = ConfigManager(service_path="extras/asr-services") + + # Validate the model exists in config.yml + config = config_manager.get_full_config() + models = config.get("models", []) + model_names = [m.get("name") for m in models] + + if stt_model not in model_names: + self.console.print(f"[yellow][WARNING][/yellow] Model '{stt_model}' not found in config.yml") + self.console.print("[blue][INFO][/blue] Please ensure config/config.yml includes the model definition") + else: + self.console.print(f"[blue][INFO][/blue] Found STT model '{stt_model}' in config.yml") - self.console.print("[green][SUCCESS][/green] .env file configured successfully with secure permissions") + config_manager.update_config_defaults({"stt": stt_model}) + self.console.print(f"[green][SUCCESS][/green] Updated defaults.stt to '{stt_model}' in config/config.yml") + + except Exception as e: + self.console.print(f"[yellow][WARNING][/yellow] Could not update config.yml: {e}") + self.console.print("[blue][INFO][/blue] You may need to manually set defaults.stt in config/config.yml") - def show_summary(self): + def show_summary(self, provider: str, model: str): """Show configuration summary""" self.print_section("Configuration Summary") self.console.print() - self.console.print(f"βœ… PyTorch CUDA Version: {self.config.get('PYTORCH_CUDA_VERSION', 'Not configured')}") + provider_info = PROVIDERS[provider] - def show_next_steps(self): + table = Table(title="ASR Service Configuration") + table.add_column("Setting", style="cyan") + table.add_column("Value", style="green") + + table.add_row("Provider", f"{provider_info['name']} ({provider})") + table.add_row("Model", model) + table.add_row("Port", self.config.get("ASR_PORT", "8767")) + table.add_row("CUDA Version", self.config.get("PYTORCH_CUDA_VERSION", "N/A")) + table.add_row("Capabilities", ", ".join(provider_info["capabilities"])) + + self.console.print(table) + + def show_next_steps(self, provider: str): """Show next steps""" self.print_section("Next Steps") self.console.print() - self.console.print("1. Start the Parakeet ASR service:") - self.console.print(" [cyan]docker compose up --build -d parakeet-asr[/cyan]") + service_name = PROVIDERS[provider]["service"] + + self.console.print("1. Build and start the ASR service:") + self.console.print(f" [cyan]docker compose up --build -d {service_name}[/cyan]") + self.console.print() + self.console.print("2. Or use a pre-configured profile:") + self.console.print(" [cyan]cp configs/parakeet.env .env && docker compose up --build -d nemo-asr[/cyan]") self.console.print() - self.console.print("2. Service will be available at:") - self.console.print(" [cyan]http://host.docker.internal:8767[/cyan]") + self.console.print("3. Service will be available at:") + self.console.print(f" [cyan]http://localhost:{self.config.get('ASR_PORT', '8767')}[/cyan]") self.console.print() - self.console.print("3. Configure your backend to use offline ASR:") - self.console.print(" Set PARAKEET_ASR_URL=http://host.docker.internal:8767 in backend .env") + self.console.print("4. Test the service:") + self.console.print(f" [cyan]curl http://localhost:{self.config.get('ASR_PORT', '8767')}/health[/cyan]") + self.console.print() + self.console.print("5. Configure Chronicle backend:") + self.console.print(f" Set PARAKEET_ASR_URL=http://host.docker.internal:{self.config.get('ASR_PORT', '8767')}") def run(self): """Run the complete setup process""" - self.print_header("🎀 ASR Services (Parakeet) Setup") - self.console.print("Configure offline speech-to-text service") + self.print_header("🎀 ASR Services Setup (Provider-Based Architecture)") + self.console.print("Configure offline speech-to-text service with your choice of provider and model") self.console.print() try: - # Run setup steps - self.setup_cuda_version() + # Select provider and model + provider = self.select_provider() + model = self.select_model(provider) + + # Configure CUDA version (only for providers that need it) + if provider in ["nemo", "transformers"]: + self.setup_cuda_version() + + # Provider-specific configuration + self.setup_provider_config(provider, model) # Generate files self.print_header("Configuration Complete!") self.generate_env_file() + # Update config/config.yml with STT model and defaults + self.update_config_yml(provider) + # Show results - self.show_summary() - self.show_next_steps() + self.show_summary(provider, model) + self.show_next_steps(provider) self.console.print() self.console.print("[green][SUCCESS][/green] ASR Services setup complete! πŸŽ‰") @@ -262,10 +444,21 @@ def run(self): def main(): """Main entry point""" - parser = argparse.ArgumentParser(description="ASR Services (Parakeet) Setup") - parser.add_argument("--pytorch-cuda-version", - choices=["cu121", "cu126", "cu128"], - help="PyTorch CUDA version (default: auto-detect)") + parser = argparse.ArgumentParser(description="ASR Services Setup (Provider-Based)") + parser.add_argument( + "--provider", + choices=["vibevoice", "faster-whisper", "transformers", "nemo"], + help="ASR provider to use" + ) + parser.add_argument( + "--model", + help="Model identifier (HuggingFace repo or path)" + ) + parser.add_argument( + "--pytorch-cuda-version", + choices=["cu121", "cu126", "cu128"], + help="PyTorch CUDA version" + ) args = parser.parse_args() diff --git a/extras/asr-services/providers/__init__.py b/extras/asr-services/providers/__init__.py new file mode 100644 index 00000000..050346b9 --- /dev/null +++ b/extras/asr-services/providers/__init__.py @@ -0,0 +1,8 @@ +""" +ASR Provider implementations. + +Available providers: +- faster_whisper: Fast Whisper inference using CTranslate2 +- transformers: HuggingFace Transformers for general ASR models +- nemo: NVIDIA NeMo ASR for Parakeet, Canary, etc. +""" diff --git a/extras/asr-services/providers/faster_whisper/Dockerfile b/extras/asr-services/providers/faster_whisper/Dockerfile new file mode 100644 index 00000000..af67440e --- /dev/null +++ b/extras/asr-services/providers/faster_whisper/Dockerfile @@ -0,0 +1,43 @@ +# syntax=docker/dockerfile:1 + +######################### builder ################################# +FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim AS builder + +WORKDIR /app + +# Install system dependencies +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential git \ + && rm -rf /var/lib/apt/lists/* + +# Dependency manifest first for cache-friendly installs +COPY pyproject.toml uv.lock ./ +RUN uv sync --no-install-project --group faster-whisper && \ + uv cache clean + +######################### runtime ################################# +FROM python:3.12-slim-bookworm AS runtime +ENV PYTHONUNBUFFERED=1 +WORKDIR /app + +# Install runtime dependencies +RUN apt-get update && apt-get install -y --no-install-recommends \ + curl ffmpeg \ + && rm -rf /var/lib/apt/lists/* + +# Copy virtual environment and application +COPY --from=builder /app /app +COPY common/ ./common/ +COPY providers/faster_whisper/ ./providers/faster_whisper/ + +ENV PATH="/app/.venv/bin:$PATH" + +# Default environment variables +ENV ASR_MODEL="Systran/faster-whisper-large-v3" +ENV COMPUTE_TYPE="float16" +ENV DEVICE="cuda" +ENV VAD_FILTER="true" + +EXPOSE 8765 + +CMD ["python", "-m", "providers.faster_whisper.service", "--port", "8765"] diff --git a/extras/asr-services/providers/faster_whisper/__init__.py b/extras/asr-services/providers/faster_whisper/__init__.py new file mode 100644 index 00000000..78f2fb01 --- /dev/null +++ b/extras/asr-services/providers/faster_whisper/__init__.py @@ -0,0 +1,11 @@ +""" +Faster-Whisper ASR Provider. + +Fast Whisper inference using CTranslate2 backend. +Supports any Whisper-based model converted to CTranslate2 format. +""" + +from providers.faster_whisper.service import FasterWhisperService +from providers.faster_whisper.transcriber import FasterWhisperTranscriber + +__all__ = ["FasterWhisperService", "FasterWhisperTranscriber"] diff --git a/extras/asr-services/providers/faster_whisper/service.py b/extras/asr-services/providers/faster_whisper/service.py new file mode 100644 index 00000000..6092f7f4 --- /dev/null +++ b/extras/asr-services/providers/faster_whisper/service.py @@ -0,0 +1,132 @@ +""" +Faster-Whisper ASR Service. + +FastAPI service implementation for faster-whisper provider. +""" + +import argparse +import asyncio +import logging +import os +import tempfile +from typing import Optional + +import uvicorn + +from common.base_service import BaseASRService, create_asr_app +from common.response_models import TranscriptionResult +from providers.faster_whisper.transcriber import FasterWhisperTranscriber + +logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO) + + +class FasterWhisperService(BaseASRService): + """ + ASR service using faster-whisper (CTranslate2 backend). + + Environment variables: + ASR_MODEL: Model identifier (default: Systran/faster-whisper-large-v3) + COMPUTE_TYPE: Quantization type (default: float16) + DEVICE: Device to use (default: cuda) + VAD_FILTER: Enable VAD filtering (default: true) + LANGUAGE: Force language code (default: None for auto-detect) + """ + + def __init__(self, model_id: Optional[str] = None): + super().__init__(model_id) + self.transcriber: Optional[FasterWhisperTranscriber] = None + + # Configuration from environment + self.vad_filter = os.getenv("VAD_FILTER", "true").lower() == "true" + self.language = os.getenv("LANGUAGE", None) + + @property + def provider_name(self) -> str: + return "faster-whisper" + + async def warmup(self) -> None: + """Initialize and warm up the model.""" + logger.info(f"Initializing faster-whisper with model: {self.model_id}") + + # Load model (runs in thread pool to not block) + loop = asyncio.get_event_loop() + self.transcriber = FasterWhisperTranscriber(self.model_id) + await loop.run_in_executor(None, self.transcriber.load_model) + + # Warm up with short audio + logger.info("Warming up model...") + try: + import numpy as np + from common.audio_utils import save_to_temp_wav + + # Create 0.1s silence for warmup + silence = np.zeros(1600, dtype=np.float32) # 0.1s at 16kHz + tmp_path = save_to_temp_wav(silence) + + try: + await loop.run_in_executor( + None, + lambda: self.transcriber.transcribe( + tmp_path, word_timestamps=False, vad_filter=False + ), + ) + finally: + os.unlink(tmp_path) + + logger.info("Model warmed up successfully") + except Exception as e: + logger.warning(f"Warmup failed (non-critical): {e}") + + async def transcribe(self, audio_file_path: str) -> TranscriptionResult: + """Transcribe audio file.""" + if self.transcriber is None: + raise RuntimeError("Service not initialized") + + loop = asyncio.get_event_loop() + result = await loop.run_in_executor( + None, + lambda: self.transcriber.transcribe( + audio_file_path, + language=self.language, + word_timestamps=True, + vad_filter=self.vad_filter, + ), + ) + return result + + def get_capabilities(self) -> list[str]: + return [ + "timestamps", + "word_timestamps", + "language_detection", + "vad_filter", + "translation", + ] + + +def main(): + """Main entry point for faster-whisper service.""" + parser = argparse.ArgumentParser(description="Faster-Whisper ASR Service") + parser.add_argument("--host", default="0.0.0.0", help="Host to bind to") + parser.add_argument("--port", type=int, default=8765, help="Port to bind to") + parser.add_argument("--model", help="Model identifier", required=False) + args = parser.parse_args() + + # Set model via environment if provided + if args.model: + os.environ["ASR_MODEL"] = args.model + + # Get model ID + model_id = os.getenv("ASR_MODEL", "Systran/faster-whisper-large-v3") + + # Create service and app + service = FasterWhisperService(model_id) + app = create_asr_app(service) + + # Run server + uvicorn.run(app, host=args.host, port=args.port) + + +if __name__ == "__main__": + main() diff --git a/extras/asr-services/providers/faster_whisper/transcriber.py b/extras/asr-services/providers/faster_whisper/transcriber.py new file mode 100644 index 00000000..0e275dc0 --- /dev/null +++ b/extras/asr-services/providers/faster_whisper/transcriber.py @@ -0,0 +1,156 @@ +""" +Faster-Whisper transcriber implementation. + +Uses CTranslate2 backend for 4-6x faster inference than OpenAI Whisper. +""" + +import logging +import os +from typing import Optional + +from faster_whisper import WhisperModel + +from common.response_models import Segment, TranscriptionResult, Word + +logger = logging.getLogger(__name__) + + +class FasterWhisperTranscriber: + """ + Transcriber using faster-whisper (CTranslate2 backend). + + Environment variables: + ASR_MODEL: Model identifier (default: Systran/faster-whisper-large-v3) + COMPUTE_TYPE: Quantization type (default: float16) + DEVICE: Device to use (default: cuda) + DEVICE_INDEX: GPU device index (default: 0) + """ + + def __init__(self, model_id: Optional[str] = None): + """ + Initialize the faster-whisper transcriber. + + Args: + model_id: Model identifier. If None, reads from ASR_MODEL env var. + """ + self.model_id = model_id or os.getenv( + "ASR_MODEL", "Systran/faster-whisper-large-v3" + ) + self.compute_type = os.getenv("COMPUTE_TYPE", "float16") + self.device = os.getenv("DEVICE", "cuda") + self.device_index = int(os.getenv("DEVICE_INDEX", "0")) + + self.model: Optional[WhisperModel] = None + self._is_loaded = False + + logger.info( + f"FasterWhisperTranscriber initialized: " + f"model={self.model_id}, compute_type={self.compute_type}, " + f"device={self.device}" + ) + + def load_model(self) -> None: + """Load the Whisper model.""" + if self._is_loaded: + logger.info("Model already loaded") + return + + logger.info(f"Loading faster-whisper model: {self.model_id}") + logger.info(f"Compute type: {self.compute_type}, Device: {self.device}") + + self.model = WhisperModel( + self.model_id, + device=self.device, + device_index=self.device_index, + compute_type=self.compute_type, + ) + + self._is_loaded = True + logger.info("Model loaded successfully") + + def transcribe( + self, + audio_file_path: str, + language: Optional[str] = None, + task: str = "transcribe", + beam_size: int = 5, + word_timestamps: bool = True, + vad_filter: bool = True, + ) -> TranscriptionResult: + """ + Transcribe audio file using faster-whisper. + + Args: + audio_file_path: Path to audio file + language: Language code (None for auto-detect) + task: "transcribe" or "translate" + beam_size: Beam size for decoding + word_timestamps: Whether to compute word-level timestamps + vad_filter: Whether to use VAD to filter out non-speech + + Returns: + TranscriptionResult with text, words, and segments + """ + if not self._is_loaded or self.model is None: + raise RuntimeError("Model not loaded. Call load_model() first.") + + logger.info(f"Transcribing: {audio_file_path}") + + # Run transcription + segments_generator, info = self.model.transcribe( + audio_file_path, + language=language, + task=task, + beam_size=beam_size, + word_timestamps=word_timestamps, + vad_filter=vad_filter, + ) + + # Process segments + all_text_parts = [] + all_words = [] + all_segments = [] + + for segment in segments_generator: + all_text_parts.append(segment.text.strip()) + + # Create segment entry + seg = Segment( + text=segment.text.strip(), + start=segment.start, + end=segment.end, + ) + all_segments.append(seg) + + # Extract word-level timestamps if available + if word_timestamps and segment.words: + for word_info in segment.words: + word = Word( + word=word_info.word.strip(), + start=word_info.start, + end=word_info.end, + confidence=word_info.probability, + ) + all_words.append(word) + + # Combine text + full_text = " ".join(all_text_parts) + + logger.info( + f"Transcription complete: {len(full_text)} chars, " + f"{len(all_words)} words, {len(all_segments)} segments" + ) + logger.info(f"Detected language: {info.language} (prob: {info.language_probability:.2f})") + + return TranscriptionResult( + text=full_text, + words=all_words, + segments=all_segments, + language=info.language, + duration=info.duration, + ) + + @property + def is_loaded(self) -> bool: + """Return True if model is loaded.""" + return self._is_loaded diff --git a/extras/asr-services/providers/nemo/Dockerfile b/extras/asr-services/providers/nemo/Dockerfile new file mode 100644 index 00000000..87c0911a --- /dev/null +++ b/extras/asr-services/providers/nemo/Dockerfile @@ -0,0 +1,46 @@ +# syntax=docker/dockerfile:1 + +######################### builder ################################# +FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim AS builder + +# Accept PyTorch CUDA version as build argument +ARG PYTORCH_CUDA_VERSION=cu126 + +WORKDIR /app + +# NeMo and texterrors need libs and C++ compiler +RUN apt-get update && apt-get install -y --no-install-recommends \ + libsndfile1 \ + build-essential git portaudio19-dev \ + && rm -rf /var/lib/apt/lists/* + +# Dependency manifest first for cache-friendly installs +COPY pyproject.toml uv.lock ./ +RUN uv sync --no-install-project --group nemo --extra ${PYTORCH_CUDA_VERSION} && \ + uv cache clean + +######################### runtime ################################# +FROM python:3.12-slim-bookworm AS runtime +ENV PYTHONUNBUFFERED=1 +WORKDIR /app + +RUN apt-get update && apt-get install -y --no-install-recommends \ + libsndfile1 build-essential portaudio19-dev curl \ + && rm -rf /var/lib/apt/lists/* + +# Copy virtual environment and application +COPY --from=builder /app /app +COPY common/ ./common/ +COPY providers/nemo/ ./providers/nemo/ + +ENV PATH="/app/.venv/bin:$PATH" + +# Default environment variables +ENV ASR_MODEL="nvidia/parakeet-tdt-0.6b-v3" +ENV CHUNKING_ENABLED="true" +ENV MIN_AUDIO_FOR_CHUNKING="60.0" +ENV CHUNK_DURATION_SECONDS="30.0" + +EXPOSE 8765 + +CMD ["python", "-m", "providers.nemo.service", "--port", "8765"] diff --git a/extras/asr-services/providers/nemo/__init__.py b/extras/asr-services/providers/nemo/__init__.py new file mode 100644 index 00000000..28be5e2a --- /dev/null +++ b/extras/asr-services/providers/nemo/__init__.py @@ -0,0 +1,11 @@ +""" +NeMo ASR Provider. + +NVIDIA NeMo ASR backend for Parakeet, Canary, and other NeMo models. +Includes enhanced chunking support for long audio processing. +""" + +from providers.nemo.service import NemoService +from providers.nemo.transcriber import NemoTranscriber + +__all__ = ["NemoService", "NemoTranscriber"] diff --git a/extras/asr-services/providers/nemo/service.py b/extras/asr-services/providers/nemo/service.py new file mode 100644 index 00000000..a0c46b02 --- /dev/null +++ b/extras/asr-services/providers/nemo/service.py @@ -0,0 +1,116 @@ +""" +NeMo ASR Service. + +FastAPI service implementation for NVIDIA NeMo ASR provider. +""" + +import argparse +import asyncio +import logging +import os +import tempfile +from typing import Optional + +import uvicorn + +from common.base_service import BaseASRService, create_asr_app +from common.response_models import TranscriptionResult +from providers.nemo.transcriber import NemoTranscriber + +logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO) + + +class NemoService(BaseASRService): + """ + ASR service using NVIDIA NeMo. + + Supports: + - nvidia/parakeet-tdt-0.6b-v3 + - nvidia/canary-1b + - Other NeMo ASR models + + Environment variables: + ASR_MODEL: Model identifier (default: nvidia/parakeet-tdt-0.6b-v3) + CHUNKING_ENABLED: Enable chunking for long audio (default: true) + MIN_AUDIO_FOR_CHUNKING: Minimum duration to use chunking (default: 60.0) + """ + + def __init__(self, model_id: Optional[str] = None): + super().__init__(model_id) + self.transcriber: Optional[NemoTranscriber] = None + + @property + def provider_name(self) -> str: + return "nemo" + + async def warmup(self) -> None: + """Initialize and warm up the model.""" + logger.info(f"Initializing NeMo with model: {self.model_id}") + + # Load model (runs in thread pool to not block) + loop = asyncio.get_event_loop() + self.transcriber = NemoTranscriber(self.model_id) + await loop.run_in_executor(None, self.transcriber.load_model) + + # Warm up with short audio + logger.info("Warming up model...") + try: + import numpy as np + from common.audio_utils import save_to_temp_wav + + # Create 0.1s silence for warmup + silence = np.zeros(1600, dtype=np.float32) # 0.1s at 16kHz + tmp_path = save_to_temp_wav(silence) + + try: + await self.transcriber.transcribe(tmp_path) + finally: + os.unlink(tmp_path) + + logger.info("Model warmed up successfully") + except Exception as e: + logger.warning(f"Warmup failed (non-critical): {e}") + + async def transcribe(self, audio_file_path: str) -> TranscriptionResult: + """Transcribe audio file.""" + if self.transcriber is None: + raise RuntimeError("Service not initialized") + + return await self.transcriber.transcribe(audio_file_path) + + def get_capabilities(self) -> list[str]: + return [ + "timestamps", + "word_timestamps", + "chunked_processing", + ] + + +def main(): + """Main entry point for NeMo service.""" + parser = argparse.ArgumentParser(description="NeMo ASR Service") + parser.add_argument("--host", default="0.0.0.0", help="Host to bind to") + parser.add_argument("--port", type=int, default=8765, help="Port to bind to") + parser.add_argument("--model", help="Model identifier", required=False) + args = parser.parse_args() + + # Set model via environment if provided + if args.model: + os.environ["ASR_MODEL"] = args.model + + # Get model ID (support legacy PARAKEET_MODEL env var) + model_id = os.getenv("ASR_MODEL") or os.getenv( + "PARAKEET_MODEL", "nvidia/parakeet-tdt-0.6b-v3" + ) + + # Create service and app + service = NemoService(model_id) + app = create_asr_app(service) + + # Run server + uvicorn.run(app, host=args.host, port=args.port) + + +if __name__ == "__main__": + main() diff --git a/extras/asr-services/providers/nemo/transcriber.py b/extras/asr-services/providers/nemo/transcriber.py new file mode 100644 index 00000000..807edf58 --- /dev/null +++ b/extras/asr-services/providers/nemo/transcriber.py @@ -0,0 +1,135 @@ +""" +NeMo transcriber implementation. + +Uses NVIDIA NeMo ASR models (Parakeet, Canary, etc.) with native +timestamp support. NeMo handles long audio internally. +""" + +import asyncio +import logging +import os +from typing import Optional, cast + +import torch + +from common.response_models import TranscriptionResult, Word + +logger = logging.getLogger(__name__) + +# Constants +NEMO_SAMPLE_RATE = 16000 + + +class NemoTranscriber: + """ + Transcriber using NVIDIA NeMo ASR models. + + Supports: + - nvidia/parakeet-tdt-0.6b-v3 + - nvidia/canary-1b + - Other NeMo ASR models + + NeMo's transcribe() method handles long audio natively with word-level + timestamps - no custom chunking required. + + Environment variables: + ASR_MODEL: Model identifier (default: nvidia/parakeet-tdt-0.6b-v3) + """ + + def __init__(self, model_id: Optional[str] = None): + """ + Initialize the NeMo transcriber. + + Args: + model_id: Model identifier. If None, reads from ASR_MODEL env var. + """ + self.model_id = model_id or os.getenv( + "ASR_MODEL", "nvidia/parakeet-tdt-0.6b-v3" + ) + + self.model = None + self._is_loaded = False + self._lock = asyncio.Lock() + + logger.info(f"NemoTranscriber initialized: model={self.model_id}") + + def load_model(self) -> None: + """Load the NeMo ASR model.""" + if self._is_loaded: + logger.info("Model already loaded") + return + + logger.info(f"Loading NeMo ASR model: {self.model_id}") + + import nemo.collections.asr as nemo_asr + + self.model = cast( + nemo_asr.models.ASRModel, + nemo_asr.models.ASRModel.from_pretrained(model_name=self.model_id), + ) + + self._is_loaded = True + logger.info("Model loaded successfully") + + async def transcribe(self, audio_file_path: str) -> TranscriptionResult: + """ + Transcribe audio file using NeMo. + + NeMo's transcribe() handles long audio natively with timestamps=True. + No custom chunking is needed. + + Args: + audio_file_path: Path to audio file (WAV format, 16kHz mono preferred) + + Returns: + TranscriptionResult with text, words, and segments + """ + if not self._is_loaded or self.model is None: + raise RuntimeError("Model not loaded. Call load_model() first.") + + logger.info(f"Transcribing: {audio_file_path}") + + async with self._lock: + with torch.no_grad(): + results = self.model.transcribe( + [audio_file_path], batch_size=1, timestamps=True + ) + + if not results or len(results) == 0: + logger.warning("NeMo returned empty results") + return TranscriptionResult(text="", words=[], segments=[]) + + result = results[0] + + # Extract text + if hasattr(result, "text") and result.text: + text = result.text + elif isinstance(result, str): + text = result + else: + text = "" + + # Extract word-level timestamps - NeMo Parakeet format + words = [] + if hasattr(result, "timestamp") and "word" in result.timestamp: + for word_data in result.timestamp["word"]: + word = Word( + word=word_data["word"], + start=word_data["start"], + end=word_data["end"], + confidence=1.0, + ) + words.append(word) + + logger.info(f"Transcription complete: {len(text)} chars, {len(words)} words") + + return TranscriptionResult( + text=text, + words=words, + segments=[], + ) + + @property + def is_loaded(self) -> bool: + """Return True if model is loaded.""" + return self._is_loaded diff --git a/extras/asr-services/providers/transformers/Dockerfile b/extras/asr-services/providers/transformers/Dockerfile new file mode 100644 index 00000000..851539c9 --- /dev/null +++ b/extras/asr-services/providers/transformers/Dockerfile @@ -0,0 +1,43 @@ +# syntax=docker/dockerfile:1 + +######################### builder ################################# +FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim AS builder + +WORKDIR /app + +# Install system dependencies +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential git \ + && rm -rf /var/lib/apt/lists/* + +# Dependency manifest first for cache-friendly installs +COPY pyproject.toml uv.lock ./ +RUN uv sync --no-install-project --group transformers && \ + uv cache clean + +######################### runtime ################################# +FROM python:3.12-slim-bookworm AS runtime +ENV PYTHONUNBUFFERED=1 +WORKDIR /app + +# Install runtime dependencies +RUN apt-get update && apt-get install -y --no-install-recommends \ + curl ffmpeg git \ + && rm -rf /var/lib/apt/lists/* + +# Copy virtual environment and application +COPY --from=builder /app /app +COPY common/ ./common/ +COPY providers/transformers/ ./providers/transformers/ + +ENV PATH="/app/.venv/bin:$PATH" + +# Default environment variables +ENV ASR_MODEL="openai/whisper-large-v3" +ENV USE_FLASH_ATTENTION="false" +ENV DEVICE="cuda" +ENV TORCH_DTYPE="float16" + +EXPOSE 8765 + +CMD ["python", "-m", "providers.transformers.service", "--port", "8765"] diff --git a/extras/asr-services/providers/transformers/__init__.py b/extras/asr-services/providers/transformers/__init__.py new file mode 100644 index 00000000..131ca186 --- /dev/null +++ b/extras/asr-services/providers/transformers/__init__.py @@ -0,0 +1,11 @@ +""" +Transformers ASR Provider. + +HuggingFace Transformers backend for general ASR models. +Supports models like VibeVoice-ASR, Whisper variants, and custom fine-tuned models. +""" + +from providers.transformers.service import TransformersService +from providers.transformers.transcriber import TransformersTranscriber + +__all__ = ["TransformersService", "TransformersTranscriber"] diff --git a/extras/asr-services/providers/transformers/service.py b/extras/asr-services/providers/transformers/service.py new file mode 100644 index 00000000..2027353d --- /dev/null +++ b/extras/asr-services/providers/transformers/service.py @@ -0,0 +1,131 @@ +""" +Transformers ASR Service. + +FastAPI service implementation for HuggingFace Transformers provider. +""" + +import argparse +import asyncio +import logging +import os +import tempfile +from typing import Optional + +import uvicorn + +from common.base_service import BaseASRService, create_asr_app +from common.response_models import TranscriptionResult +from providers.transformers.transcriber import TransformersTranscriber + +logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO) + + +class TransformersService(BaseASRService): + """ + ASR service using HuggingFace Transformers. + + Supports: + - Standard Whisper models + - Fine-tuned Whisper variants + + For VibeVoice-ASR with speaker diarization, use the dedicated vibevoice provider. + + Environment variables: + ASR_MODEL: Model identifier (default: openai/whisper-large-v3) + USE_FLASH_ATTENTION: Enable Flash Attention 2 (default: false) + DEVICE: Device to use (default: cuda) + LANGUAGE: Force language code (default: None for auto-detect) + """ + + def __init__(self, model_id: Optional[str] = None): + super().__init__(model_id) + self.transcriber: Optional[TransformersTranscriber] = None + + # Configuration from environment + self.language = os.getenv("LANGUAGE", None) + + @property + def provider_name(self) -> str: + return "transformers" + + async def warmup(self) -> None: + """Initialize and warm up the model.""" + logger.info(f"Initializing transformers with model: {self.model_id}") + + # Load model (runs in thread pool to not block) + loop = asyncio.get_event_loop() + self.transcriber = TransformersTranscriber(self.model_id) + await loop.run_in_executor(None, self.transcriber.load_model) + + # Warm up with short audio + logger.info("Warming up model...") + try: + import numpy as np + from common.audio_utils import save_to_temp_wav + + # Create 0.5s silence for warmup + silence = np.zeros(8000, dtype=np.float32) # 0.5s at 16kHz + tmp_path = save_to_temp_wav(silence) + + try: + await loop.run_in_executor( + None, + lambda: self.transcriber.transcribe(tmp_path, return_timestamps=False), + ) + finally: + os.unlink(tmp_path) + + logger.info("Model warmed up successfully") + except Exception as e: + logger.warning(f"Warmup failed (non-critical): {e}") + + async def transcribe(self, audio_file_path: str) -> TranscriptionResult: + """Transcribe audio file.""" + if self.transcriber is None: + raise RuntimeError("Service not initialized") + + loop = asyncio.get_event_loop() + result = await loop.run_in_executor( + None, + lambda: self.transcriber.transcribe( + audio_file_path, + language=self.language, + return_timestamps=True, + ), + ) + return result + + def get_capabilities(self) -> list[str]: + return [ + "timestamps", + "word_timestamps", + "language_detection", + ] + + +def main(): + """Main entry point for transformers service.""" + parser = argparse.ArgumentParser(description="Transformers ASR Service") + parser.add_argument("--host", default="0.0.0.0", help="Host to bind to") + parser.add_argument("--port", type=int, default=8765, help="Port to bind to") + parser.add_argument("--model", help="Model identifier", required=False) + args = parser.parse_args() + + # Set model via environment if provided + if args.model: + os.environ["ASR_MODEL"] = args.model + + # Get model ID + model_id = os.getenv("ASR_MODEL", "openai/whisper-large-v3") + + # Create service and app + service = TransformersService(model_id) + app = create_asr_app(service) + + # Run server + uvicorn.run(app, host=args.host, port=args.port) + + +if __name__ == "__main__": + main() diff --git a/extras/asr-services/providers/transformers/transcriber.py b/extras/asr-services/providers/transformers/transcriber.py new file mode 100644 index 00000000..78656b40 --- /dev/null +++ b/extras/asr-services/providers/transformers/transcriber.py @@ -0,0 +1,186 @@ +""" +Transformers transcriber implementation. + +Uses HuggingFace Transformers pipeline for standard Whisper ASR models. +For VibeVoice-ASR, use the dedicated vibevoice provider instead. +""" + +import logging +import os +from typing import Optional + +import torch + +from common.response_models import Segment, TranscriptionResult, Word + +logger = logging.getLogger(__name__) + + +class TransformersTranscriber: + """ + Transcriber using HuggingFace Transformers pipeline. + + Supports standard Whisper models (openai/whisper-*) and fine-tuned variants. + For VibeVoice-ASR with speaker diarization, use the dedicated vibevoice provider. + + Environment variables: + ASR_MODEL: Model identifier (default: openai/whisper-large-v3) + USE_FLASH_ATTENTION: Enable Flash Attention 2 (default: false) + DEVICE: Device to use (default: cuda) + TORCH_DTYPE: Torch dtype (default: float16) + """ + + def __init__(self, model_id: Optional[str] = None): + """ + Initialize the transformers transcriber. + + Args: + model_id: Model identifier. If None, reads from ASR_MODEL env var. + """ + self.model_id = model_id or os.getenv("ASR_MODEL", "openai/whisper-large-v3") + self.use_flash_attn = os.getenv("USE_FLASH_ATTENTION", "false").lower() == "true" + self.device = os.getenv("DEVICE", "cuda" if torch.cuda.is_available() else "cpu") + self.torch_dtype_str = os.getenv("TORCH_DTYPE", "float16") + + # Determine torch dtype + dtype_map = { + "float16": torch.float16, + "float32": torch.float32, + "bfloat16": torch.bfloat16, + } + self.torch_dtype = dtype_map.get(self.torch_dtype_str, torch.float16) + + # Model components (initialized in load_model) + self.model = None + self.processor = None + self.pipeline = None + self._is_loaded = False + + logger.info( + f"TransformersTranscriber initialized: " + f"model={self.model_id}, device={self.device}, " + f"dtype={self.torch_dtype_str}, flash_attn={self.use_flash_attn}" + ) + + def load_model(self) -> None: + """Load the ASR model.""" + if self._is_loaded: + logger.info("Model already loaded") + return + + logger.info(f"Loading transformers model: {self.model_id}") + + from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline + + # Load processor + self.processor = AutoProcessor.from_pretrained(self.model_id) + + # Model kwargs + model_kwargs = { + "torch_dtype": self.torch_dtype, + "low_cpu_mem_usage": True, + } + + if self.use_flash_attn: + model_kwargs["attn_implementation"] = "flash_attention_2" + + # Load model + self.model = AutoModelForSpeechSeq2Seq.from_pretrained( + self.model_id, **model_kwargs + ) + + if self.device == "cuda": + self.model = self.model.to(self.device) + + # Create pipeline + self.pipeline = pipeline( + "automatic-speech-recognition", + model=self.model, + tokenizer=self.processor.tokenizer, + feature_extractor=self.processor.feature_extractor, + torch_dtype=self.torch_dtype, + device=self.device if self.device != "cpu" else -1, + ) + + self._is_loaded = True + logger.info("Whisper pipeline created and ready") + + def transcribe( + self, + audio_file_path: str, + language: Optional[str] = None, + return_timestamps: bool = True, + ) -> TranscriptionResult: + """ + Transcribe audio file. + + Args: + audio_file_path: Path to audio file + language: Language code (None for auto-detect) + return_timestamps: Whether to return word timestamps + + Returns: + TranscriptionResult with text, words, and segments + """ + if not self._is_loaded: + raise RuntimeError("Model not loaded. Call load_model() first.") + + logger.info(f"Transcribing: {audio_file_path}") + + # Pipeline options + generate_kwargs = {} + if language: + generate_kwargs["language"] = language + + # Run transcription + result = self.pipeline( + audio_file_path, + return_timestamps="word" if return_timestamps else False, + generate_kwargs=generate_kwargs if generate_kwargs else None, + ) + + # Parse result + text = result.get("text", "") + all_words = [] + all_segments = [] + + # Process chunks (timestamps) + chunks = result.get("chunks", []) + for chunk in chunks: + timestamp = chunk.get("timestamp", (0.0, 0.0)) + start_time = timestamp[0] if timestamp[0] is not None else 0.0 + end_time = timestamp[1] if timestamp[1] is not None else start_time + + chunk_text = chunk.get("text", "").strip() + if chunk_text: + word = Word( + word=chunk_text, + start=start_time, + end=end_time, + confidence=None, + ) + all_words.append(word) + + # Create single segment if we have text + if text: + end_time = all_words[-1].end if all_words else 0.0 + all_segments.append( + Segment( + text=text, + start=0.0, + end=end_time, + ) + ) + + logger.info(f"Transcription complete: {len(text)} chars, {len(all_words)} words") + + return TranscriptionResult( + text=text, + words=all_words, + segments=all_segments, + ) + + @property + def is_loaded(self) -> bool: + """Return True if model is loaded.""" + return self._is_loaded diff --git a/extras/asr-services/providers/vibevoice/Dockerfile b/extras/asr-services/providers/vibevoice/Dockerfile new file mode 100644 index 00000000..218abb0c --- /dev/null +++ b/extras/asr-services/providers/vibevoice/Dockerfile @@ -0,0 +1,56 @@ +# syntax=docker/dockerfile:1 +# +# VibeVoice ASR Provider Dockerfile +# +# Uses transformers-based approach for Microsoft VibeVoice-ASR. +# VibeVoice provides speech-to-text with speaker diarization. + +######################### builder ################################# +FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim AS builder + +WORKDIR /app + +# Install system dependencies for building +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential git \ + && rm -rf /var/lib/apt/lists/* + +# Dependency manifest first for cache-friendly installs +COPY pyproject.toml uv.lock ./ +RUN uv sync --no-install-project --group vibevoice && \ + uv cache clean + +######################### runtime ################################# +FROM python:3.12-slim-bookworm AS runtime +ENV PYTHONUNBUFFERED=1 +WORKDIR /app + +# Install runtime dependencies +RUN apt-get update && apt-get install -y --no-install-recommends \ + curl ffmpeg git libsndfile1 \ + && rm -rf /var/lib/apt/lists/* + +# Copy virtual environment from builder +COPY --from=builder /app/.venv /app/.venv +ENV PATH="/app/.venv/bin:$PATH" + +# Copy application code +COPY common/ ./common/ +COPY providers/vibevoice/ ./providers/vibevoice/ + +# Pre-clone VibeVoice repository for faster startup +ENV HF_HOME="/models" +RUN mkdir -p /models && \ + git clone https://github.com/microsoft/VibeVoice.git /models/vibevoice + +# Default environment variables +ENV ASR_MODEL="microsoft/VibeVoice-ASR" +ENV VIBEVOICE_LLM_MODEL="Qwen/Qwen2.5-7B" +ENV VIBEVOICE_ATTN_IMPL="sdpa" +ENV DEVICE="cuda" +ENV TORCH_DTYPE="bfloat16" +ENV MAX_NEW_TOKENS="8192" + +EXPOSE 8765 + +CMD ["python", "-m", "providers.vibevoice.service", "--port", "8765"] diff --git a/extras/asr-services/providers/vibevoice/__init__.py b/extras/asr-services/providers/vibevoice/__init__.py new file mode 100644 index 00000000..359231b6 --- /dev/null +++ b/extras/asr-services/providers/vibevoice/__init__.py @@ -0,0 +1,5 @@ +""" +VibeVoice ASR provider. + +Dedicated provider for Microsoft's VibeVoice-ASR model with speaker diarization. +""" diff --git a/extras/asr-services/providers/vibevoice/service.py b/extras/asr-services/providers/vibevoice/service.py new file mode 100644 index 00000000..4a55b93f --- /dev/null +++ b/extras/asr-services/providers/vibevoice/service.py @@ -0,0 +1,104 @@ +""" +VibeVoice ASR Service. + +FastAPI service implementation for Microsoft VibeVoice-ASR provider. +""" + +import argparse +import asyncio +import logging +import os +from typing import Optional + +import uvicorn + +from common.base_service import BaseASRService, create_asr_app +from common.response_models import TranscriptionResult +from providers.vibevoice.transcriber import VibeVoiceTranscriber + +logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO) + + +class VibeVoiceService(BaseASRService): + """ + ASR service using Microsoft VibeVoice-ASR. + + VibeVoice provides speech-to-text with built-in speaker diarization. + + Environment variables: + ASR_MODEL: Model identifier (default: microsoft/VibeVoice-ASR) + VIBEVOICE_LLM_MODEL: LLM backbone for processor (default: Qwen/Qwen2.5-7B) + VIBEVOICE_ATTN_IMPL: Attention implementation (default: sdpa) + DEVICE: Device to use (default: cuda) + TORCH_DTYPE: Torch dtype (default: bfloat16) + MAX_NEW_TOKENS: Max tokens for generation (default: 8192) + """ + + def __init__(self, model_id: Optional[str] = None): + super().__init__(model_id) + self.transcriber: Optional[VibeVoiceTranscriber] = None + + @property + def provider_name(self) -> str: + return "vibevoice" + + async def warmup(self) -> None: + """Initialize and warm up the model.""" + logger.info(f"Initializing VibeVoice with model: {self.model_id}") + + # Load model (runs in thread pool to not block) + loop = asyncio.get_event_loop() + self.transcriber = VibeVoiceTranscriber(self.model_id) + await loop.run_in_executor(None, self.transcriber.load_model) + + # Warmup is skipped for VibeVoice as it's a large model + # and initial inference can be slow + logger.info("VibeVoice model loaded and ready") + + async def transcribe(self, audio_file_path: str) -> TranscriptionResult: + """Transcribe audio file.""" + if self.transcriber is None: + raise RuntimeError("Service not initialized") + + loop = asyncio.get_event_loop() + result = await loop.run_in_executor( + None, + lambda: self.transcriber.transcribe(audio_file_path), + ) + return result + + def get_capabilities(self) -> list[str]: + return [ + "timestamps", + "diarization", + "speaker_identification", + "long_form", + ] + + +def main(): + """Main entry point for VibeVoice service.""" + parser = argparse.ArgumentParser(description="VibeVoice ASR Service") + parser.add_argument("--host", default="0.0.0.0", help="Host to bind to") + parser.add_argument("--port", type=int, default=8765, help="Port to bind to") + parser.add_argument("--model", help="Model identifier", required=False) + args = parser.parse_args() + + # Set model via environment if provided + if args.model: + os.environ["ASR_MODEL"] = args.model + + # Get model ID + model_id = os.getenv("ASR_MODEL", "microsoft/VibeVoice-ASR") + + # Create service and app + service = VibeVoiceService(model_id) + app = create_asr_app(service) + + # Run server + uvicorn.run(app, host=args.host, port=args.port) + + +if __name__ == "__main__": + main() diff --git a/extras/asr-services/providers/vibevoice/transcriber.py b/extras/asr-services/providers/vibevoice/transcriber.py new file mode 100644 index 00000000..16757f16 --- /dev/null +++ b/extras/asr-services/providers/vibevoice/transcriber.py @@ -0,0 +1,398 @@ +""" +VibeVoice ASR transcriber implementation. + +Uses Microsoft's VibeVoice-ASR model with speaker diarization capabilities. +VibeVoice is a speech-to-text model with built-in speaker diarization. + +Environment variables: + ASR_MODEL: HuggingFace model ID (default: microsoft/VibeVoice-ASR) + VIBEVOICE_LLM_MODEL: LLM backbone for processor (default: Qwen/Qwen2.5-7B) + VIBEVOICE_ATTN_IMPL: Attention implementation (default: sdpa) + - sdpa: Scaled dot product attention (default, most compatible) + - flash_attention_2: Faster but requires flash-attn package + - eager: Standard PyTorch attention + DEVICE: Device to use (default: cuda) + TORCH_DTYPE: Torch dtype (default: bfloat16, recommended for VibeVoice) + MAX_NEW_TOKENS: Maximum tokens for generation (default: 8192) +""" + +import json +import logging +import os +import re +import subprocess +import sys +from pathlib import Path +from typing import Optional + +import torch + +from common.response_models import Segment, Speaker, TranscriptionResult + +logger = logging.getLogger(__name__) + + +class VibeVoiceTranscriber: + """ + Transcriber using Microsoft VibeVoice-ASR. + + VibeVoice provides speech-to-text with speaker diarization. + It requires cloning the VibeVoice repository for the model and processor classes. + + Environment variables: + ASR_MODEL: Model identifier (default: microsoft/VibeVoice-ASR) + VIBEVOICE_LLM_MODEL: LLM backbone (default: Qwen/Qwen2.5-7B) + VIBEVOICE_ATTN_IMPL: Attention implementation (default: sdpa) + DEVICE: Device to use (default: cuda) + TORCH_DTYPE: Torch dtype (default: bfloat16) + MAX_NEW_TOKENS: Max tokens for generation (default: 8192) + """ + + def __init__(self, model_id: Optional[str] = None): + """ + Initialize the VibeVoice transcriber. + + Args: + model_id: Model identifier. If None, reads from ASR_MODEL env var. + """ + self.model_id = model_id or os.getenv("ASR_MODEL", "microsoft/VibeVoice-ASR") + self.llm_model = os.getenv("VIBEVOICE_LLM_MODEL", "Qwen/Qwen2.5-7B") + self.attn_impl = os.getenv("VIBEVOICE_ATTN_IMPL", "sdpa") + self.device = os.getenv("DEVICE", "cuda" if torch.cuda.is_available() else "cpu") + self.max_new_tokens = int(os.getenv("MAX_NEW_TOKENS", "8192")) + + # Determine torch dtype + torch_dtype_str = os.getenv("TORCH_DTYPE", "bfloat16") + dtype_map = { + "float16": torch.float16, + "float32": torch.float32, + "bfloat16": torch.bfloat16, + } + self.torch_dtype = dtype_map.get(torch_dtype_str, torch.bfloat16) + + # Model components (initialized in load_model) + self.model = None + self.processor = None + self._is_loaded = False + self._vibevoice_repo_path: Optional[Path] = None + + logger.info( + f"VibeVoiceTranscriber initialized: " + f"model={self.model_id}, llm={self.llm_model}, " + f"device={self.device}, dtype={torch_dtype_str}, attn={self.attn_impl}" + ) + + def _setup_vibevoice(self) -> None: + """Set up VibeVoice repository and add to path.""" + logger.info("Setting up VibeVoice-ASR...") + + # Check for pre-cloned repo in Docker image first + hf_home = Path(os.getenv("HF_HOME", "/models")) + vibevoice_dir = hf_home / "vibevoice" + + # Fallback to user cache if not in HF_HOME + if not vibevoice_dir.exists(): + cache_dir = Path.home() / ".cache/huggingface" + vibevoice_dir = cache_dir / "vibevoice" + + if not vibevoice_dir.exists(): + logger.info("Cloning VibeVoice repository...") + vibevoice_dir.parent.mkdir(parents=True, exist_ok=True) + subprocess.run( + [ + "git", + "clone", + "https://github.com/microsoft/VibeVoice.git", + str(vibevoice_dir), + ], + check=True, + ) + logger.info(f"VibeVoice repository cloned to {vibevoice_dir}") + else: + logger.info(f"VibeVoice repository found at {vibevoice_dir}") + + self._vibevoice_repo_path = vibevoice_dir + + # Add to path for imports + if str(vibevoice_dir) not in sys.path: + sys.path.insert(0, str(vibevoice_dir)) + logger.info(f"Added {vibevoice_dir} to sys.path") + + def load_model(self) -> None: + """Load the VibeVoice ASR model.""" + if self._is_loaded: + logger.info("Model already loaded") + return + + logger.info(f"Loading VibeVoice model: {self.model_id}") + + # Setup repository and imports + self._setup_vibevoice() + + # Import VibeVoice components + try: + from vibevoice.modular.modeling_vibevoice_asr import ( + VibeVoiceASRForConditionalGeneration, + ) + from vibevoice.processor.vibevoice_asr_processor import ( + VibeVoiceASRProcessor, + ) + + logger.info("VibeVoice modules imported successfully") + except ImportError as e: + logger.error(f"Failed to import VibeVoice modules: {e}") + raise RuntimeError( + f"Failed to import VibeVoice modules. " + f"Ensure the VibeVoice repository is properly cloned. Error: {e}" + ) + + # Load processor with LLM backbone + logger.info(f"Loading processor with LLM backbone: {self.llm_model}") + self.processor = VibeVoiceASRProcessor.from_pretrained( + self.model_id, + language_model_pretrained_name=self.llm_model, + ) + + # Load model + logger.info(f"Loading model with attn_implementation={self.attn_impl}") + self.model = VibeVoiceASRForConditionalGeneration.from_pretrained( + self.model_id, + torch_dtype=self.torch_dtype, + device_map="auto" if self.device == "cuda" else None, + attn_implementation=self.attn_impl, + trust_remote_code=True, + ) + + # Move to device (only needed if not using device_map) + if self.device != "cuda": + self.model = self.model.to(self.device) + logger.info(f"Model moved to {self.device}") + + self.model.eval() + + self._is_loaded = True + logger.info("VibeVoice model loaded successfully") + + def transcribe(self, audio_file_path: str) -> TranscriptionResult: + """ + Transcribe audio file using VibeVoice with speaker diarization. + + Args: + audio_file_path: Path to audio file + + Returns: + TranscriptionResult with text, segments (with speakers), and speaker list + """ + if not self._is_loaded or self.model is None or self.processor is None: + raise RuntimeError("Model not loaded. Call load_model() first.") + + logger.info(f"Transcribing: {audio_file_path}") + + # Process audio through processor (can take file paths directly) + inputs = self.processor( + audio=[audio_file_path], + sampling_rate=None, + return_tensors="pt", + padding=True, + add_generation_prompt=True, + ) + + # Move inputs to device + model_device = next(self.model.parameters()).device + inputs = { + k: v.to(model_device) if isinstance(v, torch.Tensor) else v + for k, v in inputs.items() + } + + logger.info(f"Input shapes - input_ids: {inputs['input_ids'].shape}") + + # Generation config + generation_config = { + "max_new_tokens": self.max_new_tokens, + "pad_token_id": self.processor.pad_id, + "eos_token_id": self.processor.tokenizer.eos_token_id, + "do_sample": False, # Greedy decoding for consistency + } + + # Generate transcription + logger.info("Generating transcription...") + with torch.no_grad(): + output_ids = self.model.generate(**inputs, **generation_config) + + # Decode output (skip input tokens) + input_length = inputs["input_ids"].shape[1] + generated_ids = output_ids[0, input_length:] + + # Remove eos tokens + eos_positions = (generated_ids == self.processor.tokenizer.eos_token_id).nonzero(as_tuple=True)[0] + if len(eos_positions) > 0: + generated_ids = generated_ids[: eos_positions[0] + 1] + + raw_output = self.processor.decode(generated_ids, skip_special_tokens=True) + logger.info(f"Raw output length: {len(raw_output)} chars") + + # Parse structured output using processor's post-processing + try: + segments = self.processor.post_process_transcription(raw_output) + processed = {"raw_text": raw_output, "segments": segments} + logger.info(f"Parsed {len(segments)} segments") + except Exception as e: + logger.warning(f"Failed to parse with post_process_transcription: {e}") + # Fallback to our JSON parsing + processed = self._parse_vibevoice_output(raw_output) + + # Map to TranscriptionResult + return self._map_to_result(processed, raw_output) + + def _parse_vibevoice_output(self, raw_output: str) -> dict: + """ + Parse VibeVoice raw output to extract segments with speaker info. + + VibeVoice outputs JSON in the assistant response: + <|im_start|>assistant + [{"Start":0.0,"End":3.0,"Speaker":0,"Content":"..."}]<|im_end|> + + Args: + raw_output: Raw decoded output from model + + Returns: + Dict with 'raw_text' and 'segments' list + """ + # DEBUG: Log actual output format for troubleshooting + logger.info(f"Raw output preview (first 500 chars): {raw_output[:500]}") + logger.info(f"Raw output preview (last 500 chars): {raw_output[-500:]}") + + # Extract JSON array from assistant response + # Strategy: Find the outermost [ ] that contains valid JSON + # Look for array starting with [{ which indicates segment objects + json_match = re.search(r'\[\s*\{.*\}\s*\]', raw_output, re.DOTALL) + + if not json_match: + logger.warning("Could not find JSON array in output, returning raw text only") + logger.warning(f"Output does not match pattern [{{...}}], checking for other formats...") + # Try alternate pattern: just find any array + json_match = re.search(r'\[.*\]', raw_output, re.DOTALL) + + if not json_match: + logger.warning("No JSON array found in output") + return {"raw_text": raw_output, "segments": []} + + try: + segments_raw = json.loads(json_match.group(0)) + logger.info(f"Parsed {len(segments_raw)} segments from JSON") + + # Convert to our expected format + segments = [] + for seg in segments_raw: + segments.append({ + "text": seg.get("Content", ""), + "start": float(seg.get("Start", 0.0)), + "end": float(seg.get("End", 0.0)), + "speaker": seg.get("Speaker", 0), + }) + + return {"raw_text": raw_output, "segments": segments} + + except (json.JSONDecodeError, KeyError, ValueError) as e: + logger.error(f"Failed to parse JSON segments: {e}") + return {"raw_text": raw_output, "segments": []} + + def _map_to_result(self, processed: dict, raw_output: str) -> TranscriptionResult: + """ + Map VibeVoice output to TranscriptionResult. + + Args: + processed: Post-processed output dict with segments + raw_output: Raw decoded output + + Returns: + TranscriptionResult with mapped data + """ + segments = [] + speakers_map: dict[str, tuple[float, float]] = {} + text_parts = [] + + for seg_data in processed.get("segments", []): + text = seg_data.get("text", "").strip() + start = seg_data.get("start_time", seg_data.get("start", 0.0)) + end = seg_data.get("end_time", seg_data.get("end", 0.0)) + speaker_raw = seg_data.get("speaker_id", seg_data.get("speaker")) + # Convert speaker to string, avoiding double-prefix from fallback parser + if speaker_raw is None: + speaker_id = None + elif isinstance(speaker_raw, str) and speaker_raw.startswith("Speaker "): + speaker_id = speaker_raw + else: + speaker_id = f"Speaker {speaker_raw}" + + if text: + text_parts.append(text) + + segment = Segment( + text=text, + start=start, + end=end, + speaker=speaker_id, + ) + segments.append(segment) + + # Track speaker time ranges + if speaker_id: + if speaker_id not in speakers_map: + speakers_map[speaker_id] = (start, end) + else: + prev_start, prev_end = speakers_map[speaker_id] + speakers_map[speaker_id] = ( + min(prev_start, start), + max(prev_end, end), + ) + + # Build speaker list + speakers = [ + Speaker(id=spk_id, start=times[0], end=times[1]) + for spk_id, times in speakers_map.items() + ] + + # Use raw text if no segments parsed + full_text = " ".join(text_parts) if text_parts else processed.get("raw_text", raw_output) + + # Calculate total duration + duration = None + if segments: + duration = max(s.end for s in segments) + + logger.info( + f"Transcription complete: {len(full_text)} chars, " + f"{len(segments)} segments, {len(speakers)} speakers" + ) + + return TranscriptionResult( + text=full_text, + words=[], # VibeVoice doesn't provide word-level timestamps + segments=segments, + speakers=speakers if speakers else None, + language=None, # VibeVoice auto-detects + duration=duration, + ) + + def _load_audio_fallback(self, audio_path: str): + """Fallback audio loading using torchaudio.""" + import torchaudio + + waveform, sample_rate = torchaudio.load(audio_path) + + # Resample to 16kHz if needed + if sample_rate != 16000: + resampler = torchaudio.transforms.Resample(sample_rate, 16000) + waveform = resampler(waveform) + + # Convert to mono + if waveform.shape[0] > 1: + waveform = waveform.mean(dim=0, keepdim=True) + + return waveform.squeeze().numpy() + + @property + def is_loaded(self) -> bool: + """Return True if model is loaded.""" + return self._is_loaded diff --git a/extras/asr-services/pyproject.toml b/extras/asr-services/pyproject.toml index 2a8b8a82..2147d454 100644 --- a/extras/asr-services/pyproject.toml +++ b/extras/asr-services/pyproject.toml @@ -1,27 +1,33 @@ [project] name = "asr-services" -version = "0.1.0" -description = "Wyoming compatible WebSocket ASR server for friend-lite" +version = "0.2.0" +description = "Provider-based ASR services for Chronicle" requires-python = ">=3.10" dependencies = [ - "websockets>=12.0", + "pydantic>=2.0.0", "numpy>=1.26", "soundfile>=0.12", "fastapi>=0.115.12", "uvicorn>=0.34.2", "python-multipart>=0.0.6", - "silero-vad>=5.1.2", - "wyoming>=1.7.0", - "easy-audio-interfaces>=0.7.1", "httpx>=0.28.1", ] [project.optional-dependencies] +# Optional dependencies for specific use cases local-audio = [ "easy-audio-interfaces[local-audio]>=0.7.1", ] +wyoming = [ + "websockets>=12.0", + "wyoming>=1.7.0", + "easy-audio-interfaces>=0.7.1", + "silero-vad>=5.1.2", +] + +# CUDA versions for PyTorch cu121 = [ "torch>=2.3", "torchaudio>=2.3", @@ -41,8 +47,10 @@ cu128 = [ compile-bytecode = true conflicts = [ [ - { group = "demo" }, - { group = "parakeet" } + { group = "faster-whisper" }, + { group = "transformers" }, + { group = "nemo" }, + { group = "vibevoice" }, ], [ { extra = "cu121" }, @@ -80,32 +88,62 @@ url = "https://download.pytorch.org/whl/cu128" explicit = true [dependency-groups] -demo = [ - "fastrtc>=0.0.23", - "gradio>=5.29.0", - "sounddevice>=0.5.1", +# Provider-specific dependency groups +faster-whisper = [ + "faster-whisper>=1.0.0", + "ctranslate2>=4.0.0", ] -dev = [ - "black>=25.1.0", - "requests>=2.31.0", - "pytest>=8.0.0", +transformers = [ + "transformers>=4.40.0", + "accelerate>=0.30.0", + "diffusers>=0.30.0", + "torch>=2.3", + "torchaudio>=2.3", ] -moonshine = [ - "useful-moonshine-onnx", +nemo = [ + "nemo-toolkit[asr]>=2.2.0", + "cuda-python>=12.3", + "numpy>=1.26,<2.0", +] + +vibevoice = [ + "transformers>=4.51.0,<5.0.0", + "accelerate>=0.30.0", + "diffusers>=0.30.0", + "torch>=2.3", + "torchaudio>=2.3", + "librosa>=0.10.0", ] +# Legacy groups (kept for backward compatibility) parakeet = [ "nemo-toolkit[asr]>=2.2.0", "cuda-python>=12.3", "numpy>=1.26,<2.0", ] +moonshine = [ + "useful-moonshine-onnx", +] + +# Development dependencies +dev = [ + "black>=25.1.0", + "requests>=2.31.0", + "pytest>=8.0.0", +] + +demo = [ + "fastrtc>=0.0.23", + "gradio>=5.29.0", + "sounddevice>=0.5.1", +] + [build-system] requires = ["setuptools>=64", "wheel"] build-backend = "setuptools.build_meta" [tool.setuptools] -# No packages - this project only contains standalone scripts -packages = [] +packages = ["common", "providers", "providers.faster_whisper", "providers.transformers", "providers.nemo", "providers.vibevoice"] diff --git a/extras/asr-services/uv.lock b/extras/asr-services/uv.lock index d1ddfa17..04e7cab9 100644 --- a/extras/asr-services/uv.lock +++ b/extras/asr-services/uv.lock @@ -1,99 +1,213 @@ version = 1 -revision = 2 +revision = 3 requires-python = ">=3.10" resolution-markers = [ - "python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra == 'extra-12-asr-services-cu128' and extra != 'group-12-asr-services-demo' and extra == 'group-12-asr-services-parakeet'", - "python_full_version >= '3.13' and sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra == 'extra-12-asr-services-cu128' and extra != 'group-12-asr-services-demo' and extra == 'group-12-asr-services-parakeet'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra == 'extra-12-asr-services-cu128' and extra != 'group-12-asr-services-demo' and extra == 'group-12-asr-services-parakeet'", - "python_full_version == '3.12.*' and sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra == 'extra-12-asr-services-cu128' and extra != 'group-12-asr-services-demo' and extra == 'group-12-asr-services-parakeet'", - "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 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and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra == 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-demo' and extra != 'group-12-asr-services-parakeet'", - "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra == 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-demo' and extra != 'group-12-asr-services-parakeet') or (python_full_version == '3.11.*' and sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra == 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-demo' and extra != 'group-12-asr-services-parakeet')", - "(python_full_version < '3.11' and platform_machine != 'aarch64' and extra != 'extra-12-asr-services-cu121' and extra != 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and extra != 'group-12-asr-services-faster-whisper' and extra != 'group-12-asr-services-nemo' and extra != 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice')", + "python_full_version >= '3.13' and sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra == 'extra-12-asr-services-cu128' and extra != 'group-12-asr-services-faster-whisper' and extra != 'group-12-asr-services-nemo' and extra != 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice'", + "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra == 'extra-12-asr-services-cu128' and extra != 'group-12-asr-services-faster-whisper' and extra != 'group-12-asr-services-nemo' and extra != 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice') or 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"python_full_version < '3.11' and sys_platform == 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra != 'extra-12-asr-services-cu128' and extra != 'group-12-asr-services-faster-whisper' and extra != 'group-12-asr-services-nemo' and extra != 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice'", + "python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra != 'extra-12-asr-services-cu128' and extra != 'group-12-asr-services-faster-whisper' and extra != 'group-12-asr-services-nemo' and extra != 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice'", + "(python_full_version >= '3.15' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and 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and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra == 'extra-12-asr-services-cu128' and extra != 'group-12-asr-services-faster-whisper' and extra != 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-transformers' and extra != 'group-12-asr-services-vibevoice')", + "python_full_version == '3.12.*' and sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra == 'extra-12-asr-services-cu128' and extra != 'group-12-asr-services-faster-whisper' and extra != 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-transformers' and extra != 'group-12-asr-services-vibevoice'", + "python_full_version >= '3.13' and python_full_version < '3.15' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra == 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extra == 'group-12-asr-services-vibevoice') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-transformers') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-vibevoice') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-nemo') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-transformers') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-faster-whisper' 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or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-vibevoice') or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-transformers') or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-vibevoice') or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice') or (extra != 'extra-12-asr-services-cu126' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-nemo') or (extra != 'extra-12-asr-services-cu126' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-transformers') or (extra != 'extra-12-asr-services-cu126' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-vibevoice') or (extra != 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(sys_platform != 'linux' and extra == 'extra-12-asr-services-cu126' and extra == 'extra-12-asr-services-cu128') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra == 'group-12-asr-services-demo' and extra == 'group-12-asr-services-parakeet') or (extra == 'extra-12-asr-services-cu121' and extra == 'extra-12-asr-services-cu126') or (extra == 'extra-12-asr-services-cu121' and extra == 'extra-12-asr-services-cu128') or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-demo' and extra == 'group-12-asr-services-parakeet') or (extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-demo' and extra == 'group-12-asr-services-parakeet')" }, - { name = "torchaudio", version = "2.9.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(platform_machine != 'aarch64' and extra == 'extra-12-asr-services-cu128') or (sys_platform != 'linux' and extra != 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or (sys_platform != 'linux' and extra == 'extra-12-asr-services-cu126' and extra == 'extra-12-asr-services-cu128') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-nemo') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-transformers') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-vibevoice') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-transformers') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-vibevoice') or (sys_platform != 'linux' and extra != 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(extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-transformers') or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-vibevoice') or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice') or (extra == 'extra-12-asr-services-cu126' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-nemo') or (extra == 'extra-12-asr-services-cu126' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-transformers') or (extra == 'extra-12-asr-services-cu126' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-vibevoice') or (extra == 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(platform_machine != 'aarch64' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-vibevoice') or (platform_machine != 'aarch64' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-transformers') or (platform_machine != 'aarch64' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-vibevoice') or (platform_machine != 'aarch64' and extra == 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice') or (platform_python_implementation != 'CPython' and extra == 'extra-12-asr-services-cu128') or (platform_python_implementation != 'CPython' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-nemo') or (platform_python_implementation != 'CPython' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-transformers') or (platform_python_implementation != 'CPython' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-vibevoice') or (platform_python_implementation != 'CPython' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-transformers') or (platform_python_implementation != 'CPython' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-vibevoice') or (platform_python_implementation != 'CPython' and extra == 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu126' and extra == 'extra-12-asr-services-cu128') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra == 'extra-12-asr-services-cu128') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu126' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-nemo') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu126' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-transformers') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu126' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-vibevoice') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu126' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-transformers') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu126' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-vibevoice') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu126' and extra == 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-nemo') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-transformers') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-vibevoice') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-transformers') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-vibevoice') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice') or (extra == 'extra-12-asr-services-cu121' and extra == 'extra-12-asr-services-cu126') or (extra == 'extra-12-asr-services-cu121' and extra == 'extra-12-asr-services-cu128') or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-nemo') or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-transformers') or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-vibevoice') or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-transformers') or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-vibevoice') or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice') or (extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-nemo') or (extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-transformers') or (extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-vibevoice') or (extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-transformers') or (extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-vibevoice') or (extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice')" }, ] local-audio = [ { name = "easy-audio-interfaces", extra = ["local-audio"] }, ] +wyoming = [ + { name = "easy-audio-interfaces" }, + { name = "silero-vad" }, + { name = "websockets" }, + { name = "wyoming" }, +] [package.dev-dependencies] demo = [ @@ -371,24 +487,65 @@ dev = [ { name = "pytest" }, { name = "requests" }, ] +faster-whisper = [ + { name = "ctranslate2" }, + { name = "faster-whisper" }, +] moonshine = [ { name = "useful-moonshine-onnx" }, ] +nemo = [ + { name = "cuda-python" }, + { name = "nemo-toolkit", extra = ["asr"], marker = "(extra == 'extra-12-asr-services-cu121' and extra == 'extra-12-asr-services-cu126') or (extra == 'extra-12-asr-services-cu121' and extra == 'extra-12-asr-services-cu128') or (extra == 'extra-12-asr-services-cu126' and extra == 'extra-12-asr-services-cu128') or (extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-nemo') or (extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-transformers') or (extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-vibevoice') or (extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice') or (extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra == 'group-12-asr-services-nemo') or (extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-transformers') or (extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-vibevoice') or (extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra == 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice')" }, + { name = "numpy" }, +] parakeet = [ { name = "cuda-python" }, - { name = "nemo-toolkit", extra = ["asr"], marker = "(extra == 'extra-12-asr-services-cu121' and extra == 'extra-12-asr-services-cu126') or (extra == 'extra-12-asr-services-cu121' and extra == 'extra-12-asr-services-cu128') or (extra == 'extra-12-asr-services-cu126' and extra == 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'extra-12-asr-services-cu121' and extra == 'extra-12-asr-services-cu126') or (extra == 'extra-12-asr-services-cu121' and extra == 'extra-12-asr-services-cu128') or (extra == 'extra-12-asr-services-cu126' and extra == 'extra-12-asr-services-cu128') or (extra == 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice') or (extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-nemo') or (extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-transformers') or (extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-vibevoice') or (extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-transformers') or (extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-vibevoice') or (extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra != 'extra-12-asr-services-cu128' and extra == 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"python_full_version == '3.12.*' and sys_platform == 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra != 'extra-12-asr-services-cu128' and extra != 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-nemo' and extra != 'group-12-asr-services-transformers' and extra != 'group-12-asr-services-vibevoice'", + "python_full_version == '3.12.*' and sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra != 'extra-12-asr-services-cu128' and extra != 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-nemo' and extra != 'group-12-asr-services-transformers' and extra != 'group-12-asr-services-vibevoice'", + "python_full_version == '3.11.*' and sys_platform == 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra != 'extra-12-asr-services-cu128' and extra != 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and extra == 'group-12-asr-services-faster-whisper' and extra != 'group-12-asr-services-nemo' and extra != 'group-12-asr-services-transformers' and extra != 'group-12-asr-services-vibevoice'", + "python_full_version < '3.11' and sys_platform == 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-faster-whisper' and extra != 'group-12-asr-services-nemo' and extra != 'group-12-asr-services-transformers' and extra != 'group-12-asr-services-vibevoice'", + "python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-faster-whisper' and extra != 'group-12-asr-services-nemo' and extra != 'group-12-asr-services-transformers' and extra != 'group-12-asr-services-vibevoice'", + "(python_full_version >= '3.15' and 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== 'group-12-asr-services-vibevoice') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice') or (extra == 'extra-12-asr-services-cu121' and extra == 'extra-12-asr-services-cu126') or (extra == 'extra-12-asr-services-cu121' and extra == 'extra-12-asr-services-cu128') or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-nemo') or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-transformers') or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-vibevoice') or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-transformers') or (extra == 'extra-12-asr-services-cu121' and extra == 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'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice')" }, ] wheels = [ { url = "https://download.pytorch.org/whl/cu126/torchaudio-2.9.1-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:a5a762939f0c9e4394214364e75938c2fec9bb3b698698104024063fd6b8ebdb" }, @@ -7369,13 +7500,13 @@ name = "torchaudio" version = "2.9.1" source = { registry = "https://download.pytorch.org/whl/cu128" } resolution-markers = [ - "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "python_full_version >= '3.13' and python_full_version < '3.15' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'", + "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'", + "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'", ] dependencies = [ - { name = "torch", version = "2.9.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-12-asr-services-cu128') or (platform_machine != 'aarch64' and extra == 'extra-12-asr-services-cu126' and extra == 'extra-12-asr-services-cu128') or (platform_machine != 'aarch64' and extra == 'group-12-asr-services-demo' and extra == 'group-12-asr-services-parakeet') or (sys_platform != 'linux' and extra == 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and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-vibevoice') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-nemo') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-transformers') or (sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra != 'extra-12-asr-services-cu126' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-vibevoice') or (sys_platform != 'linux' and extra != 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== 'group-12-asr-services-transformers') or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-vibevoice') or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-transformers') or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-vibevoice') or (extra == 'extra-12-asr-services-cu121' and extra == 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice') or (extra == 'extra-12-asr-services-cu126' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-nemo') or (extra == 'extra-12-asr-services-cu126' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-transformers') or (extra == 'extra-12-asr-services-cu126' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-faster-whisper' and extra == 'group-12-asr-services-vibevoice') or (extra == 'extra-12-asr-services-cu126' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-transformers') or (extra == 'extra-12-asr-services-cu126' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-nemo' and extra == 'group-12-asr-services-vibevoice') or (extra == 'extra-12-asr-services-cu126' and extra != 'extra-12-asr-services-cu128' and extra == 'group-12-asr-services-transformers' and extra == 'group-12-asr-services-vibevoice')" }, ] wheels = [ { url = "https://download.pytorch.org/whl/cu128/torchaudio-2.9.1-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:8c960a6f9c67b373c206c0311b6ccadf2e633694c9b301a1e5380745f2e80656" }, @@ -7392,25 +7523,17 @@ name = "torchaudio" version = "2.9.1+cu126" source = { registry = "https://download.pytorch.org/whl/cu126" } resolution-markers = [ - "python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-12-asr-services-cu121' and extra == 'extra-12-asr-services-cu126' and extra != 'extra-12-asr-services-cu128' and extra != 'group-12-asr-services-demo' and extra == 'group-12-asr-services-parakeet'", - "python_full_version >= '3.13' and sys_platform != 'linux' and extra != 'extra-12-asr-services-cu121' and extra == 'extra-12-asr-services-cu126' and extra != 'extra-12-asr-services-cu128' and extra != 'group-12-asr-services-demo' and extra == 'group-12-asr-services-parakeet'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-12-asr-services-cu121' and extra == 'extra-12-asr-services-cu126' and extra != 'extra-12-asr-services-cu128' and extra != 'group-12-asr-services-demo' and extra == 'group-12-asr-services-parakeet'", - 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prompt_password(self, prompt: str) -> str: sys.exit(1) def read_existing_env_value(self, key: str) -> str: - """Read a value from existing .env file""" - env_path = Path(".env") - if not env_path.exists(): - return None - - from dotenv import get_key - value = get_key(str(env_path), key) - # get_key returns None if key doesn't exist or value is empty - return value if value else None + """Read a value from existing .env file (delegates to shared utility)""" + return read_env_value(".env", key) def mask_api_key(self, key: str, show_chars: int = 5) -> str: - """Mask API key showing only first and last few characters""" - if not key or len(key) <= show_chars * 2: - return key - - # Remove quotes if present - key_clean = key.strip("'\"") - - return f"{key_clean[:show_chars]}{'*' * min(15, len(key_clean) - show_chars * 2)}{key_clean[-show_chars:]}" + """Mask API key (delegates to shared utility)""" + return mask_value(key, show_chars) def prompt_choice(self, prompt: str, choices: Dict[str, str], default: str = "1") -> str: """Prompt for a choice from options""" @@ -148,42 +138,8 @@ def setup_hf_token(self): self.console.print("[green][SUCCESS][/green] HF Token configured") def detect_cuda_version(self) -> str: - """Detect system CUDA version from nvidia-smi""" - try: - result = subprocess.run( - ["nvidia-smi", "--query-gpu=driver_version", "--format=csv,noheader"], - capture_output=True, - text=True, - timeout=5 - ) - if result.returncode == 0: - # Try to get CUDA version from nvidia-smi - result = subprocess.run( - ["nvidia-smi"], - capture_output=True, - text=True, - timeout=5 - ) - if result.returncode == 0: - output = result.stdout - # Parse CUDA Version from nvidia-smi output - # Format: "CUDA Version: 12.6" - import re - match = re.search(r'CUDA Version:\s*(\d+)\.(\d+)', output) - if match: - major, minor = match.groups() - cuda_ver = f"{major}.{minor}" - - # Map to available PyTorch CUDA versions - if cuda_ver >= "12.8": - return "cu128" - elif cuda_ver >= "12.6": - return "cu126" - elif cuda_ver >= "12.1": - return "cu121" - except (subprocess.SubprocessError, FileNotFoundError): - pass - return "cu121" # Default fallback + """Detect system CUDA version (delegates to shared utility)""" + return detect_cuda_version(default="cu121") def setup_compute_mode(self): """Configure compute mode (CPU/GPU)""" diff --git a/quickstart.md b/quickstart.md index 100a4fe2..377b9cbb 100644 --- a/quickstart.md +++ b/quickstart.md @@ -126,14 +126,14 @@ git submodule update --init --recursive πŸ“¦ About the Mycelia Submodule (Optional - Click to expand) **What is Mycelia?** -Mycelia is an optional self-hosted AI memory system that Friend-Lite can use as an alternative memory backend. It provides: +Mycelia is an optional self-hosted AI memory system that Chronicle can use as an alternative memory backend. It provides: - Timeline-based memory visualization - Advanced audio processing with speaker diarization - MongoDB-based full-text search - MCP (Model Context Protocol) integration **Do I need it?** -**Most users don't need Mycelia!** The default Friend-Lite memory system works great for most use cases. Only consider Mycelia if you: +**Most users don't need Mycelia!** The default Chronicle memory system works great for most use cases. Only consider Mycelia if you: - Want timeline-based memory visualization - Need advanced speaker diarization features - Want to use MongoDB for memory storage instead of Qdrant @@ -322,5 +322,5 @@ Before connecting your phone, make sure everything works: ## Need Help? - **Full Documentation**: [CLAUDE.md](CLAUDE.md) - Complete technical reference -- **Architecture Details**: [Docs/features.md](Docs/features.md) - How everything works +- **Architecture Details**: [Docs/overview.md](Docs/overview.md) - How everything works - **Advanced Setup**: [Docs/init-system.md](Docs/init-system.md) - Power user options diff --git a/services.py b/services.py index 35d11264..de98b328 100755 --- a/services.py +++ b/services.py @@ -92,12 +92,16 @@ def run_compose_command(service_name, command, build=False): build_cmd.extend(['--profile', 'https']) obsidian_enabled = False + kg_enabled = False config_data = load_config_yml() if config_data: memory_config = config_data.get('memory', {}) obsidian_config = memory_config.get('obsidian', {}) if obsidian_config.get('enabled', False): obsidian_enabled = True + kg_config = memory_config.get('knowledge_graph', {}) + if kg_config.get('enabled', False): + kg_enabled = True if not obsidian_enabled: env_file = service_path / '.env' @@ -108,6 +112,8 @@ def run_compose_command(service_name, command, build=False): if obsidian_enabled: build_cmd.extend(['--profile', 'obsidian']) + if kg_enabled: + build_cmd.extend(['--profile', 'knowledge-graph']) elif service_name == 'speaker-recognition': env_file = service_path / '.env' @@ -118,8 +124,33 @@ def run_compose_command(service_name, command, build=False): profile = 'gpu' if pytorch_version.startswith('cu') else 'cpu' build_cmd.extend(['--profile', profile]) + # For asr-services, only build the selected provider + asr_service_to_build = None + if service_name == 'asr-services': + env_file = service_path / '.env' + if env_file.exists(): + env_values = dotenv_values(env_file) + asr_provider = env_values.get('ASR_PROVIDER', '').strip("'\"") + + # Map provider to docker service name + provider_to_service = { + 'vibevoice': 'vibevoice-asr', + 'faster-whisper': 'faster-whisper-asr', + 'transformers': 'transformers-asr', + 'nemo': 'nemo-asr', + 'parakeet': 'parakeet-asr', + } + asr_service_to_build = provider_to_service.get(asr_provider) + + if asr_service_to_build: + console.print(f"[blue]ℹ️ Building ASR provider: {asr_provider} ({asr_service_to_build})[/blue]") + build_cmd.append('build') + # If building ASR, only build the specific service + if asr_service_to_build: + build_cmd.append(asr_service_to_build) + # Run build with streaming output (no timeout) console.print(f"[cyan]πŸ”¨ Building {service_name} (this may take several minutes for CUDA/GPU builds)...[/cyan]") try: @@ -173,12 +204,16 @@ def run_compose_command(service_name, command, build=False): cmd.extend(['--profile', 'https']) obsidian_enabled = False + kg_enabled = False config_data = load_config_yml() if config_data: memory_config = config_data.get('memory', {}) obsidian_config = memory_config.get('obsidian', {}) if obsidian_config.get('enabled', False): obsidian_enabled = True + kg_config = memory_config.get('knowledge_graph', {}) + if kg_config.get('enabled', False): + kg_enabled = True if not obsidian_enabled: env_file = service_path / '.env' @@ -190,6 +225,9 @@ def run_compose_command(service_name, command, build=False): if obsidian_enabled: cmd.extend(['--profile', 'obsidian']) console.print("[blue]ℹ️ Starting with Obsidian/Neo4j support[/blue]") + if kg_enabled: + cmd.extend(['--profile', 'knowledge-graph']) + console.print("[blue]ℹ️ Starting with Knowledge Graph (Neo4j)[/blue]") # Handle speaker-recognition service specially if service_name == 'speaker-recognition' and command in ['up', 'down']: @@ -215,6 +253,43 @@ def run_compose_command(service_name, command, build=False): cmd.extend(['up', '-d']) elif command == 'down': cmd.extend(['down']) + + # Handle asr-services - start only the configured provider + elif service_name == 'asr-services' and command in ['up', 'down', 'restart']: + env_file = service_path / '.env' + asr_service_name = None + + if env_file.exists(): + env_values = dotenv_values(env_file) + asr_provider = env_values.get('ASR_PROVIDER', '').strip("'\"") + + # Map provider to docker service name + provider_to_service = { + 'vibevoice': 'vibevoice-asr', + 'faster-whisper': 'faster-whisper-asr', + 'transformers': 'transformers-asr', + 'nemo': 'nemo-asr', + 'parakeet': 'parakeet-asr', + } + asr_service_name = provider_to_service.get(asr_provider) + + if asr_service_name: + console.print(f"[blue]ℹ️ Using ASR provider: {asr_provider} ({asr_service_name})[/blue]") + + if command == 'up': + if asr_service_name: + cmd.extend(['up', '-d', asr_service_name]) + else: + console.print("[yellow]⚠️ No ASR_PROVIDER configured, starting default service[/yellow]") + cmd.extend(['up', '-d', 'vibevoice-asr']) + elif command == 'down': + cmd.extend(['down']) + elif command == 'restart': + if asr_service_name: + cmd.extend(['restart', asr_service_name]) + else: + cmd.extend(['restart']) + else: # Standard compose commands for other services if command == 'up': diff --git a/setup_utils.py b/setup_utils.py index 200bd1e7..8a906c7b 100644 --- a/setup_utils.py +++ b/setup_utils.py @@ -6,9 +6,12 @@ """ import getpass +import json +import re import secrets +import subprocess from pathlib import Path -from typing import List, Optional +from typing import List, Optional, Tuple from dotenv import get_key @@ -324,3 +327,82 @@ def prompt_token( placeholders=placeholders, is_password=True ) + + +def detect_tailscale_info() -> Tuple[Optional[str], Optional[str]]: + """ + Detect Tailscale DNS name and IPv4 address. + + Returns: + (dns_name, ip) tuple. dns_name is the MagicDNS hostname (e.g. "myhost.tail1234.ts.net"), + ip is the Tailscale IPv4 address (e.g. "100.64.1.5"). + Either or both may be None if Tailscale is not available. + """ + dns_name = None + ip = None + + # Get MagicDNS name from tailscale status --json + try: + result = subprocess.run( + ["tailscale", "status", "--json"], + capture_output=True, + text=True, + timeout=5 + ) + if result.returncode == 0: + status = json.loads(result.stdout) + raw_dns = status.get("Self", {}).get("DNSName", "") + # DNSName has trailing dot, strip it + if raw_dns: + dns_name = raw_dns.rstrip(".") + except (subprocess.SubprocessError, FileNotFoundError, json.JSONDecodeError): + pass + + # Get IPv4 address as fallback + try: + result = subprocess.run( + ["tailscale", "ip", "-4"], + capture_output=True, + text=True, + timeout=5 + ) + if result.returncode == 0: + ip = result.stdout.strip() + except (subprocess.SubprocessError, FileNotFoundError): + pass + + return dns_name, ip + + +def detect_cuda_version(default: str = "cu126") -> str: + """ + Detect system CUDA version from nvidia-smi output. + + Parses "CUDA Version: X.Y" from nvidia-smi and maps to PyTorch CUDA version strings. + + Args: + default: Default CUDA version if detection fails (default: "cu126") + + Returns: + PyTorch CUDA version string: "cu121", "cu126", or "cu128" + """ + try: + result = subprocess.run( + ["nvidia-smi"], + capture_output=True, + text=True, + timeout=5 + ) + if result.returncode == 0: + match = re.search(r'CUDA Version:\s*(\d+)\.(\d+)', result.stdout) + if match: + major, minor = int(match.group(1)), int(match.group(2)) + if (major, minor) >= (12, 8): + return "cu128" + elif (major, minor) >= (12, 6): + return "cu126" + elif (major, minor) >= (12, 1): + return "cu121" + except (subprocess.SubprocessError, FileNotFoundError): + pass + return default diff --git a/tests/.gitignore b/tests/.gitignore index 2a915d33..aaaca1c6 100644 --- a/tests/.gitignore +++ b/tests/.gitignore @@ -8,3 +8,7 @@ results-no-api/ # Saved container logs (automatically generated) logs/* !logs/.gitkeep + +# Python cache directories for mocks +mocks/__pycache__/ +mocks/*.pyc diff --git a/tests/CONFIG_GUIDE.md b/tests/CONFIG_GUIDE.md new file mode 100644 index 00000000..dbda348f --- /dev/null +++ b/tests/CONFIG_GUIDE.md @@ -0,0 +1,216 @@ +# Test Configuration Guide + +## Quick Reference + +Use `TEST_CONFIG_FILE` to control which config the test backend uses: + +```bash +# Default: mock-services.yml (no API keys needed) +make start-rebuild + +# With API keys for full integration tests +make start-rebuild TEST_CONFIG_FILE=/app/test-configs/deepgram-openai.yml + +# With invalid Deepgram key (for transcription failure tests) +make start-rebuild TEST_CONFIG_FILE=/app/test-configs/mock-transcription-failure.yml +``` + +--- + +## Available Configs + +### 1. `mock-services.yml` (Default) +**Use for:** Tests 1-4 (no API keys required) + +**Features:** +- βœ… No external API calls +- βœ… Mock transcription (always succeeds) +- βœ… Mock LLM (always succeeds) +- βœ… Fast test execution +- ❌ No real transcription +- ❌ No real memory extraction + +**When to use:** +- Quick local development +- PR validation (CI) +- Tests that don't need real transcription + +**Tests that work:** +- Test 1: Placeholder Conversation Created Immediately +- Test 2: Normal Behavior Preserved +- Test 3: Redis Key Set Immediately +- Test 4: Multiple Sessions Create Separate Conversations + +--- + +### 2. `deepgram-openai.yml` +**Use for:** Tests 1-6 (full integration with real APIs) + +**Features:** +- βœ… Real Deepgram transcription +- βœ… Real OpenAI memory extraction +- βœ… Full end-to-end testing +- ⚠️ Requires valid API keys +- ⚠️ Costs money (minimal for tests) +- ⏱️ Slower (network calls) + +**Required environment variables:** +```bash +export DEEPGRAM_API_KEY=your-valid-key +export OPENAI_API_KEY=your-valid-key +``` + +**When to use:** +- Testing with real transcription +- Dev/main branch validation (CI) +- Before merging features + +**Tests that work:** +- All tests (Tests 1-6) + +--- + +### 3. `mock-transcription-failure.yml` +**Use for:** Test 5 (transcription failure scenario) + +**Features:** +- βœ… Triggers real Deepgram API failures (HTTP 401) +- βœ… Tests audio persistence despite failure +- ❌ Uses invalid API key (intentionally fails) +- βœ… Mock LLM (doesn't need to succeed) + +**When to use:** +- Testing Test 5: "Audio Chunks Persisted Despite Transcription Failure" +- Validating always_persist behavior on failure +- Ensuring audio isn't lost when transcription fails + +**Tests that work:** +- Test 5: Audio Chunks Persisted Despite Transcription Failure +- Tests 1-4 also work (don't need transcription) + +--- + +## Usage Examples + +### Running Tests 1-4 (No API Keys) + +```bash +# Start containers with mock config (default) +cd tests +make start-rebuild + +# Run tests +make test-quick +# OR run specific test +uv run robot --test "Placeholder Conversation Created Immediately With Always Persist" \ + --outputdir results integration/always_persist_audio_tests.robot +``` + +### Running All Tests (Tests 1-6) with API Keys + +```bash +# Set API keys +export DEEPGRAM_API_KEY=your-key +export OPENAI_API_KEY=your-key + +# Start containers with full API config +cd tests +make start-rebuild TEST_CONFIG_FILE=/app/test-configs/deepgram-openai.yml + +# Run all tests +uv run robot --outputdir results integration/always_persist_audio_tests.robot +``` + +### Running Test 5 (Transcription Failure) + +```bash +# Start containers with failure config +cd tests +make start-rebuild TEST_CONFIG_FILE=/app/test-configs/mock-transcription-failure.yml + +# Run Test 5 +uv run robot --test "Audio Chunks Persisted Despite Transcription Failure" \ + --outputdir results integration/always_persist_audio_tests.robot +``` + +--- + +## Switching Configs + +To switch configs, you need to **restart containers** because the config is loaded at startup: + +```bash +# Stop containers +make stop + +# Start with new config +make start-rebuild TEST_CONFIG_FILE=/app/test-configs/deepgram-openai.yml + +# Or use one command +make start-rebuild TEST_CONFIG_FILE=/app/test-configs/mock-services.yml +``` + +**Note:** Just changing `TEST_CONFIG_FILE` and running tests won't work - you must restart containers! + +--- + +## Verifying Active Config + +Check which config is loaded: + +```bash +docker compose -f docker-compose-test.yml exec -T chronicle-backend-test \ + env | grep CONFIG_FILE +``` + +Expected output: +``` +CONFIG_FILE=/app/test-configs/mock-services.yml +``` + +--- + +## Troubleshooting + +### Issue: Tests fail with "streaming transcription not available" +**Solution:** Using `deepgram-openai.yml` but `DEEPGRAM_API_KEY` is empty +```bash +export DEEPGRAM_API_KEY=your-key +make start-rebuild TEST_CONFIG_FILE=/app/test-configs/deepgram-openai.yml +``` + +### Issue: Test 5 doesn't trigger transcription failure +**Solution:** Not using `mock-transcription-failure.yml` config +```bash +make start-rebuild TEST_CONFIG_FILE=/app/test-configs/mock-transcription-failure.yml +``` + +### Issue: Changed config but tests still use old config +**Solution:** Must restart containers, not just rerun tests +```bash +make start-rebuild TEST_CONFIG_FILE=/app/test-configs/mock-services.yml +``` + +--- + +## CI/CD Integration + +**GitHub Actions automatically sets the right config:** + +- **PR runs:** Uses `mock-services.yml` (no API keys) +- **Dev/main runs:** Uses `deepgram-openai.yml` (with secrets) +- **Label-triggered:** Uses `deepgram-openai.yml` (with secrets) + +You don't need to set `TEST_CONFIG_FILE` in CI - it's handled automatically. + +--- + +## Summary + +| Config | API Keys Needed | Tests Supported | Use Case | +|--------|----------------|-----------------|----------| +| `mock-services.yml` | ❌ None | 1-4 | Quick local dev, PRs | +| `deepgram-openai.yml` | βœ… Both | 1-6 | Full integration, releases | +| `mock-transcription-failure.yml` | ⚠️ Invalid | 1-5 | Testing failure scenarios | + +**Default:** `mock-services.yml` (fastest, no costs, works for most tests) diff --git a/tests/DEBUG_GUIDE.md b/tests/DEBUG_GUIDE.md new file mode 100644 index 00000000..e13050d4 --- /dev/null +++ b/tests/DEBUG_GUIDE.md @@ -0,0 +1,242 @@ +# Robot Framework Test Debugging Guide + +Quick reference for debugging test failures efficiently. + +## Quick Failure Analysis + +### 1. Get Test Summary (Fastest) +```bash +# After test run, get failure summary +grep -E "tests?, [0-9]+ passed, [0-9]+ failed" results/output.xml.txt | tail -5 + +# Or from test output +grep -E "\| FAIL \|" results/log.html | wc -l +``` + +### 2. List All Failing Tests +```bash +# Get test names that failed +grep -B2 "| FAIL |" | grep -E "^[A-Z].*Test ::" + +# Example output: +# Get conversation permission Test :: Test that users can only acces... +# Save Diarization Settings Test :: Test saving diarization settings... +``` + +### 3. Get Failure Details +```bash +# Get failure reasons for each failed test +grep -A3 "| FAIL |" | grep -v "^--$" + +# Example output: +# | FAIL | +# POST /api/diarization-settings returned 500 (expected 200). +# Response: Internal Server Error +``` + +## Backend Error Correlation + +### Check Backend Logs During Test Run +```bash +# Real-time backend errors +docker logs -f advanced-backend-test-chronicle-backend-test-1 2>&1 | grep -E "(ERROR|Exception|Traceback)" + +# Recent errors from completed test run +docker logs advanced-backend-test-chronicle-backend-test-1 --since=1h 2>&1 | grep -B5 -A10 "Traceback" +``` + +### Find Errors for Specific Endpoint +```bash +# Example: Find errors related to /api/conversations +docker logs advanced-backend-test-chronicle-backend-test-1 2>&1 | grep -B10 -A10 "api/conversations" +``` + +## Common Failure Patterns + +### 1. Function Name Mismatch (500 Error) +**Symptom**: `POST /api/endpoint returned 500 (expected 200)` + +**Diagnosis**: +```bash +# Check if controller function exists +grep "async def function_name" backends/advanced/src/advanced_omi_backend/controllers/*.py + +# Check route calls the right function +grep "controller\\.function_name" backends/advanced/src/advanced_omi_backend/routers/**/*.py +``` + +**Fix**: Update route to call correct controller function name + +### 2. Connection Reset (Backend Crash) +**Symptom**: `ConnectionResetError(104, 'Connection reset by peer')` + +**Diagnosis**: +```bash +# Check if backend container is running +docker ps | grep chronicle-backend-test + +# Check backend crash logs +docker logs advanced-backend-test-chronicle-backend-test-1 --tail=100 +``` + +**Fix**: Backend likely crashed - check for unhandled exceptions, restart container + +### 3. Test Expects 400/422 but Gets 500 +**Symptom**: `'500 in [400, 422]' should be true` + +**Diagnosis**: Backend is crashing instead of returning validation error + +**Common Causes**: +- Missing function (AttributeError) +- Unhandled exception in validation logic +- Missing import + +### 4. Job Timeout (Deferred Status) +**Symptom**: `Memory job did not complete within 120s (last status: deferred)` + +**Diagnosis**: +```bash +# Check worker logs +docker logs advanced-backend-test-workers-test-1 --tail=100 + +# Check Redis queue status +redis-cli -p 6380 LLEN rq:queue:default +``` + +**Common Causes**: +- Workers not running +- Job dependency not completed +- API key missing for external services + +## Debugging Workflow + +### Step 1: Quick Scan (30 seconds) +```bash +cd tests + +# Count failures +grep "| FAIL |" results/output.xml | wc -l + +# List failed test names +grep -B2 "| FAIL |" | grep "Test ::" +``` + +### Step 2: Get Error Messages (1 minute) +```bash +# Get all failure details +grep -A3 "| FAIL |" | less + +# Or save to file for analysis +grep -B5 -A10 "| FAIL |" > failures.txt +``` + +### Step 3: Check Backend Logs (2 minutes) +```bash +# Check for backend exceptions +docker logs advanced-backend-test-chronicle-backend-test-1 --since=10m 2>&1 | grep -E "(ERROR|Exception|Traceback)" | tail -50 + +# For specific endpoint, grep for endpoint path +docker logs advanced-backend-test-chronicle-backend-test-1 2>&1 | grep -B10 "/api/endpoint-path" +``` + +### Step 4: Fix and Verify (iterative) +```bash +# After code fix, rebuild backend +cd backends/advanced +docker compose -f docker-compose-test.yml build chronicle-backend-test +docker restart advanced-backend-test-chronicle-backend-test-1 + +# Wait for healthy status +docker ps | grep chronicle-backend-test + +# Re-run failed test suite only +cd tests +uv run --with-requirements test-requirements.txt robot --outputdir results-rerun --loglevel INFO:INFO endpoints/system_admin_tests.robot +``` + +## Useful Test Commands + +### Run Specific Test Suite +```bash +# Single suite +robot endpoints/conversation_tests.robot + +# Specific test by name +robot -t "Get conversation permission Test" endpoints/conversation_tests.robot + +# By tag +robot --include permissions endpoints/ +``` + +### Run with More Debug Info +```bash +# Increase log level for debugging +robot --loglevel DEBUG:DEBUG endpoints/ + +# Keep only failed tests in log +robot --loglevel TRACE:INFO endpoints/ +``` + +### Faster Iteration +```bash +# Skip test environment setup (if containers already running) +TEST_MODE=dev robot endpoints/test_file.robot + +# Run without cleanup (keep containers running) +CLEANUP_CONTAINERS=false ./run-robot-tests.sh +``` + +## Prevention: Catch Issues Before Running Tests + +### 1. Type Checking (Recommended) +```bash +cd backends/advanced +uv run mypy src/ +``` + +### 2. Import Validation +```bash +# Quick check if all imports resolve +uv run python -c "from advanced_omi_backend.routers.modules import system_routes" +``` + +### 3. Lint Check +```bash +uv run ruff check src/ +``` + +## Quick Reference: Test Container Names + +| Service | Container Name | Logs Command | +|---------|---------------|--------------| +| Backend | `advanced-backend-test-chronicle-backend-test-1` | `docker logs ` | +| Workers | `advanced-backend-test-workers-test-1` | `docker logs ` | +| MongoDB | `advanced-backend-test-mongo-test-1` | `docker logs ` | +| Redis | `advanced-backend-test-redis-test-1` | `docker logs ` | +| Qdrant | `advanced-backend-test-qdrant-test-1` | `docker logs ` | + +## Tips for Faster Debugging + +1. **Keep test containers running** between iterations (`CLEANUP_CONTAINERS=false`) +2. **Run specific suites** instead of full test run +3. **Check backend health first** before running tests +4. **Use grep liberally** - don't read full logs +5. **Pattern match errors** - most failures follow common patterns +6. **Fix one failure type at a time** - similar failures often have same root cause + +## Common Error Messages and Solutions + +| Error Message | Likely Cause | Quick Fix | +|---------------|--------------|-----------| +| `500 Internal Server Error` | Backend exception | Check backend logs for traceback | +| `ConnectionResetError` | Backend crashed | Restart backend, check logs | +| `expected 200, got 500` | Unhandled exception | Check controller function exists | +| `Job did not complete` | Worker issue or timeout | Check worker logs, increase timeout | +| `404 Not Found` | Endpoint doesn't exist | Check route registration | +| `401 Unauthorized` | Auth token issue | Check session creation | +| `403 Forbidden` | Permission issue | Check user role in test | + +--- + +**Last Updated**: 2026-01-15 +**Maintainer**: Update this guide when you discover new debugging patterns diff --git a/tests/Dockerfile.mock-asr b/tests/Dockerfile.mock-asr new file mode 100644 index 00000000..f17709f9 --- /dev/null +++ b/tests/Dockerfile.mock-asr @@ -0,0 +1,19 @@ +FROM python:3.12-slim + +WORKDIR /app + +# Install dependencies (FastAPI + uvicorn + multipart for file uploads) +RUN pip install --no-cache-dir fastapi uvicorn python-multipart + +# Copy mock server script +COPY tests/libs/mock_asr_server.py . + +# Expose HTTP port +EXPOSE 8765 + +# Provider mode can be set via environment variable +# Options: mock, parakeet, vibevoice, deepgram +ENV MOCK_ASR_PROVIDER=mock + +# Run server (provider mode read from env var) +CMD ["sh", "-c", "python mock_asr_server.py --host 0.0.0.0 --port 8765 --provider $MOCK_ASR_PROVIDER"] diff --git a/tests/Makefile b/tests/Makefile index 1927338b..83d9126d 100644 --- a/tests/Makefile +++ b/tests/Makefile @@ -1,16 +1,17 @@ # Chronicle Test Makefile # Shortcuts for running tests and managing test containers -.PHONY: help all clean \ +.PHONY: help all clean configure \ containers-start containers-stop containers-restart containers-rebuild \ containers-start-rebuild containers-clean containers-status containers-logs \ start stop restart rebuild start-rebuild status logs \ test test-quick test-slow test-sdk test-no-api test-with-api-keys test-all-with-slow-and-sdk clean-all \ + test-asr test-asr-gpu \ results results-path results-detailed # Default output directory OUTPUTDIR ?= results -TEST_DIR = endpoints integration infrastructure +TEST_DIR = endpoints integration infrastructure asr SERVICE ?= chronicle-backend-test # Test configuration file (set this to use different configs) @@ -34,6 +35,7 @@ help: @echo "Chronicle Test Targets:" @echo "" @echo "Quick Commands:" + @echo " make configure - Set up test API keys (Deepgram, OpenAI)" @echo " make test - Start containers + run tests (uses real APIs)" @echo " make test-no-api - Run tests without API keys (CI mode)" @echo " make test-quick - Run tests on existing containers" @@ -45,11 +47,15 @@ help: @echo " make status - Show container status" @echo "" @echo "Running Tests:" - @echo " make all - Run all tests (excludes slow/sdk)" + @echo " make all - Run all tests (excludes slow/sdk/requires-gpu)" @echo " make endpoints - Run only endpoint tests" @echo " make integration - Run only integration tests" @echo " make infra - Run only infrastructure tests" @echo "" + @echo "ASR Tests:" + @echo " make test-asr - Run ASR protocol tests (no GPU required)" + @echo " make test-asr-gpu - Run ASR GPU tests (requires NVIDIA GPU)" + @echo "" @echo "Special Test Tags:" @echo " make test-slow - Run ONLY slow tests (backend restarts)" @echo " make test-sdk - Run ONLY SDK tests (unreleased)" @@ -94,16 +100,17 @@ help: @echo " make containers-logs SERVICE=workers-test # View worker logs" @echo " make show-config # Show current config" -# Run all tests (excludes slow and sdk tests for faster feedback) +# Run all tests (excludes slow, sdk, and requires-gpu tests for faster feedback) # Creates a persistent fixture conversation that won't be deleted between suites all: - @echo "Running all tests (excluding slow and sdk tests)..." + @echo "Running all tests (excluding slow, sdk, and requires-gpu tests)..." CREATE_FIXTURE=true uv run --with-requirements test-requirements.txt robot --outputdir $(OUTPUTDIR) \ --name "All Tests" \ --console verbose \ --loglevel INFO:INFO \ --exclude slow \ --exclude sdk \ + --exclude requires-gpu \ $(TEST_DIR) # Run only endpoint tests @@ -149,6 +156,10 @@ clean: rm -rf $(OUTPUTDIR) @echo "Clean complete!" +# Configure test API keys (interactive setup) +configure: + @uv run --with-requirements ../setup-requirements.txt python setup/init.py + # ============================================================================ # Container Management Targets # ============================================================================ @@ -284,6 +295,31 @@ test-all-with-slow-and-sdk: --loglevel INFO:INFO \ $(TEST_DIR) +# ============================================================================ +# ASR Tests +# ============================================================================ + +# Run ASR protocol tests (no GPU required, uses mock-asr service) +test-asr: + @echo "Running ASR protocol tests (no GPU)..." + uv run --with-requirements test-requirements.txt robot --outputdir $(OUTPUTDIR) \ + --name "ASR Protocol Tests" \ + --console verbose \ + --loglevel INFO:INFO \ + --exclude requires-gpu \ + asr + +# Run ASR GPU tests (requires NVIDIA GPU with actual ASR service) +test-asr-gpu: + @echo "Running ASR GPU tests (requires GPU)..." + @echo "NOTE: Ensure GPU ASR service is running on port 8767" + uv run --with-requirements test-requirements.txt robot --outputdir $(OUTPUTDIR) \ + --name "ASR GPU Tests" \ + --console verbose \ + --loglevel INFO:INFO \ + --include requires-gpu \ + asr + # ============================================================================ # View Test Results # ============================================================================ diff --git a/tests/asr/error_handling_tests.robot b/tests/asr/error_handling_tests.robot new file mode 100644 index 00000000..07b35e3c --- /dev/null +++ b/tests/asr/error_handling_tests.robot @@ -0,0 +1,67 @@ +*** Settings *** +Documentation ASR Service Error Handling Tests - validates error responses +... +... These tests verify proper error handling in the ASR service: +... - Missing file in transcribe request +... - Invalid endpoints return 404 +... - Proper HTTP status codes for error conditions +... +... Run with: make test-asr +Library RequestsLibrary +Library Collections +Resource ../setup/setup_keywords.robot + +Suite Setup Suite Setup + +*** Variables *** +${ASR_URL} http://localhost:8765 + +*** Test Cases *** + +ASR Transcribe Without File Returns 422 + [Documentation] POST /transcribe without file should return 422 Unprocessable Entity + [Tags] infra + + Create Session asr ${ASR_URL} verify=True + ${response}= POST On Session asr /transcribe expected_status=422 + + # 422 indicates validation error (missing required field) + Should Be Equal As Integers ${response.status_code} 422 + +ASR Invalid Endpoint Returns 404 + [Documentation] Non-existent endpoint should return 404 Not Found + [Tags] infra + + Create Session asr ${ASR_URL} verify=True + ${response}= GET On Session asr /invalid-endpoint expected_status=404 + + Should Be Equal As Integers ${response.status_code} 404 + +ASR Health Endpoint Returns JSON Content Type + [Documentation] Health endpoint should return application/json content type + [Tags] health + + Create Session asr ${ASR_URL} verify=True + ${response}= GET On Session asr /health expected_status=200 + + ${content_type}= Get From Dictionary ${response.headers} Content-Type + Should Contain ${content_type} application/json + +ASR Info Endpoint Returns JSON Content Type + [Documentation] Info endpoint should return application/json content type + [Tags] infra + + Create Session asr ${ASR_URL} verify=True + ${response}= GET On Session asr /info expected_status=200 + + ${content_type}= Get From Dictionary ${response.headers} Content-Type + Should Contain ${content_type} application/json + +ASR Transcribe POST Only + [Documentation] GET request to /transcribe should return 405 Method Not Allowed + [Tags] infra + + Create Session asr ${ASR_URL} verify=True + ${response}= GET On Session asr /transcribe expected_status=405 + + Should Be Equal As Integers ${response.status_code} 405 diff --git a/tests/asr/gpu_integration_tests.robot b/tests/asr/gpu_integration_tests.robot new file mode 100644 index 00000000..be665744 --- /dev/null +++ b/tests/asr/gpu_integration_tests.robot @@ -0,0 +1,222 @@ +*** Settings *** +Documentation GPU-based ASR Integration Tests - requires NVIDIA GPU +... +... These tests validate actual transcription with real ASR models: +... - Transformers/VibeVoice model loading and inference +... - Transcription quality (actual text output) +... - Word timestamp accuracy and ordering +... +... IMPORTANT: These tests require: +... - NVIDIA GPU with CUDA support +... - Model download (~2-5GB first time) +... +... The tests automatically start/stop the ASR service. +... Run with: make test-asr-gpu +... Excluded from default runs (requires-gpu tag) +Library RequestsLibrary +Library Collections +Resource ../resources/asr_keywords.robot +Resource ../setup/setup_keywords.robot + +Suite Setup GPU Test Suite Setup +Suite Teardown GPU Test Suite Teardown + +*** Variables *** +${GPU_ASR_URL} http://localhost:8767 +${TEST_AUDIO_FILE} ${CURDIR}/../test_assets/DIY_Experts_Glass_Blowing_16khz_mono_1min.wav +# ASR service configuration +${ASR_SERVICE} transformers-asr +${ASR_MODEL} microsoft/VibeVoice-ASR +${ASR_PORT} 8767 + +*** Keywords *** + +GPU Test Suite Setup + [Documentation] Setup for GPU tests - starts ASR service and waits for model loading + Suite Setup + Log To Console \n======================================== + Log To Console GPU ASR Integration Test Suite Setup + Log To Console ======================================== + + # Start the ASR service + Start GPU ASR Service ${ASR_SERVICE} ${ASR_MODEL} ${ASR_PORT} + + # Wait for service to be ready (model loading can take a while) + Log To Console \n⏳ Waiting for model to load (may take 2-5 minutes first time)... + Wait For ASR Service Ready ${GPU_ASR_URL} timeout=600s interval=15s + Log To Console βœ… GPU ASR service is ready! + +GPU Test Suite Teardown + [Documentation] Teardown for GPU tests - stops ASR service + Log To Console \n======================================== + Log To Console GPU ASR Integration Test Suite Teardown + Log To Console ======================================== + + # Stop and remove the ASR service + Remove GPU ASR Service ${ASR_SERVICE} + +*** Test Cases *** + +GPU ASR Service Health Check + [Documentation] Verify ASR service starts and reports healthy + [Tags] requires-gpu health + [Timeout] 300s + + Check ASR Service Health ${GPU_ASR_URL} + +GPU ASR Service Reports Model Info + [Documentation] Verify service reports model information + [Tags] requires-gpu infra + [Timeout] 60s + + ${info}= Get ASR Service Info ${GPU_ASR_URL} + + # Verify model info is present + Should Not Be Empty ${info}[model_id] + Should Not Be Empty ${info}[provider] + Log Model: ${info}[model_id], Provider: ${info}[provider] + +GPU ASR Transcription Returns Text + [Documentation] Verify actual transcription produces non-empty text + [Tags] requires-gpu e2e + [Timeout] 180s + + ${response}= Upload Audio For ASR Transcription ${TEST_AUDIO_FILE} ${GPU_ASR_URL} + Should Be Equal As Integers ${response.status_code} 200 + + ${json}= Set Variable ${response.json()} + Should Not Be Empty ${json}[text] + Log Transcription: ${json}[text] + + # Verify reasonable transcription length (should have some words) + ${text_length}= Get Length ${json}[text] + Should Be True ${text_length} > 10 Transcription should have meaningful content + +GPU ASR Transcription Has Word Timestamps + [Documentation] Verify word timestamps from actual model inference + [Tags] requires-gpu e2e + [Timeout] 180s + + ${response}= Upload Audio For ASR Transcription ${TEST_AUDIO_FILE} ${GPU_ASR_URL} + ${json}= Set Variable ${response.json()} + + # Verify words are present + Should Not Be Empty ${json}[words] + ${word_count}= Get Length ${json}[words] + Log Word count: ${word_count} + + # Verify timestamps are properly ordered and valid + ${prev_end}= Set Variable ${0} + FOR ${word} IN @{json}[words] + # Each word should have required fields + Dictionary Should Contain Key ${word} word + Dictionary Should Contain Key ${word} start + Dictionary Should Contain Key ${word} end + + # Timestamps should be in order + Should Be True ${word}[start] >= ${prev_end} + ... Word "${word}[word]" start (${word}[start]) should be >= previous end (${prev_end}) + + # End should be after start + Should Be True ${word}[end] > ${word}[start] + ... Word "${word}[word]" end (${word}[end]) should be > start (${word}[start]) + + ${prev_end}= Set Variable ${word}[end] + END + +GPU ASR Transcription Duration Reasonable + [Documentation] Verify transcription duration matches audio (approximately) + [Tags] requires-gpu e2e + [Timeout] 180s + + ${response}= Upload Audio For ASR Transcription ${TEST_AUDIO_FILE} ${GPU_ASR_URL} + ${json}= Set Variable ${response.json()} + + # Get last word timestamp (approximate audio duration) + ${word_count}= Get Length ${json}[words] + IF ${word_count} > 0 + ${last_word}= Get From List ${json}[words] -1 + ${transcription_duration}= Set Variable ${last_word}[end] + + # For a 1-minute audio file, transcription should show reasonable duration + # Allow some flexibility (at least 30 seconds, no more than 90 seconds) + Should Be True ${transcription_duration} > 30 + ... Transcription duration (${transcription_duration}s) should be > 30s for 1-min audio + Should Be True ${transcription_duration} < 90 + ... Transcription duration (${transcription_duration}s) should be < 90s for 1-min audio + + Log Transcription duration: ${transcription_duration} seconds + END + +GPU ASR Returns Diarized Segments With Speaker Labels + [Documentation] Verify VibeVoice returns segments with speaker diarization + ... This tests the core VibeVoice capability: combined ASR + diarization + [Tags] requires-gpu e2e + [Timeout] 180s + + # First check if this model reports diarization capability + ${info}= Get ASR Service Info ${GPU_ASR_URL} + ${has_diarization}= Evaluate 'diarization' in $info.get('capabilities', []) + Log Provider: ${info}[provider], Capabilities: ${info}[capabilities] + + # Skip if provider doesn't have diarization + Skip If not ${has_diarization} Provider does not have diarization capability + + # Upload audio and get transcription + ${response}= Upload Audio For ASR Transcription ${TEST_AUDIO_FILE} ${GPU_ASR_URL} + Should Be Equal As Integers ${response.status_code} 200 + + ${json}= Set Variable ${response.json()} + + # Verify segments are present and non-empty + Dictionary Should Contain Key ${json} segments + ${segments}= Set Variable ${json}[segments] + Should Not Be Empty ${segments} Diarization provider should return segments + + ${segment_count}= Get Length ${segments} + Log Found ${segment_count} diarized segments + + # Verify each segment has valid structure + # Note: Non-speech segments (like [Music]) may have speaker=null + ${speech_segments}= Create List + FOR ${index} ${segment} IN ENUMERATE @{segments} + # Each segment must have speaker, start, end, text fields + Dictionary Should Contain Key ${segment} speaker + ... Segment ${index} missing 'speaker' field + Dictionary Should Contain Key ${segment} start + ... Segment ${index} missing 'start' field + Dictionary Should Contain Key ${segment} end + ... Segment ${index} missing 'end' field + Dictionary Should Contain Key ${segment} text + ... Segment ${index} missing 'text' field + + # Timestamps should be valid + Should Be True ${segment}[start] >= 0 + ... Segment ${index} has negative start time + Should Be True ${segment}[end] > ${segment}[start] + ... Segment ${index} end (${segment}[end]) not greater than start (${segment}[start]) + + # Track speech segments (speaker is not null) + ${has_speaker}= Evaluate $segment['speaker'] is not None + IF ${has_speaker} + Append To List ${speech_segments} ${segment} + Log Segment ${index}: [${segment}[speaker]] ${segment}[start]s-${segment}[end]s + ELSE + Log Segment ${index}: [non-speech] ${segment}[start]s-${segment}[end]s: ${segment}[text] + END + END + + # Verify we have at least some speech segments with speaker labels + ${speech_count}= Get Length ${speech_segments} + Should Be True ${speech_count} > 0 + ... Expected at least one segment with speaker label, got ${speech_count} + + # Verify segments cover reasonable audio duration (should be close to 1 minute) + ${last_segment}= Get From List ${segments} -1 + ${total_duration}= Set Variable ${last_segment}[end] + Should Be True ${total_duration} > 30 + ... Segments only cover ${total_duration}s, expected > 30s for 1-min audio + Should Be True ${total_duration} < 90 + ... Segments cover ${total_duration}s, expected < 90s for 1-min audio + + Log βœ… Diarization test passed: ${segment_count} segments, ${total_duration}s total duration diff --git a/tests/asr/protocol_tests.robot b/tests/asr/protocol_tests.robot new file mode 100644 index 00000000..8e6462da --- /dev/null +++ b/tests/asr/protocol_tests.robot @@ -0,0 +1,228 @@ +*** Settings *** +Documentation ASR Service Protocol Tests - validates API endpoints and response formats +... +... These tests validate the ASR service API contract without requiring GPU. +... They use the mock ASR server to verify: +... - Health endpoint structure and response +... - Info endpoint structure and response +... - Transcribe endpoint accepts audio files +... - Response format matches expected schema +... +... Run with: make test-asr +Library RequestsLibrary +Library Collections +Resource ../resources/asr_keywords.robot +Resource ../setup/setup_keywords.robot + +Suite Setup Suite Setup + +*** Variables *** +${ASR_URL} http://localhost:8765 +${TEST_AUDIO_FILE} ${CURDIR}/../test_assets/DIY_Experts_Glass_Blowing_16khz_mono_1min.wav + +*** Test Cases *** + +ASR Health Endpoint Returns Valid Response + [Documentation] Verify /health endpoint returns correct structure with status, model, and provider + [Tags] health + + Create Session asr ${ASR_URL} verify=True + ${response}= GET On Session asr /health expected_status=200 + + # Verify response structure + ${json}= Set Variable ${response.json()} + Dictionary Should Contain Key ${json} status + Dictionary Should Contain Key ${json} model + Dictionary Should Contain Key ${json} provider + + # Verify health status + Should Be Equal ${json}[status] healthy + Should Not Be Empty ${json}[model] + Should Not Be Empty ${json}[provider] + +ASR Info Endpoint Returns Valid Response + [Documentation] Verify /info endpoint returns correct structure with capabilities + [Tags] infra + + Create Session asr ${ASR_URL} verify=True + ${response}= GET On Session asr /info expected_status=200 + + # Verify response structure + ${json}= Set Variable ${response.json()} + Dictionary Should Contain Key ${json} model_id + Dictionary Should Contain Key ${json} provider + Dictionary Should Contain Key ${json} capabilities + + # Verify capabilities is a list + Should Be True isinstance($json['capabilities'], list) + Should Not Be Empty ${json}[model_id] + Should Not Be Empty ${json}[provider] + +ASR Transcribe Endpoint Accepts Audio File + [Documentation] Verify /transcribe endpoint accepts file upload and returns 200 + [Tags] audio-upload + + ${response}= Upload Audio For ASR Transcription ${TEST_AUDIO_FILE} + + # Verify successful upload + Should Be Equal As Integers ${response.status_code} 200 + +ASR Transcribe Response Contains Required Fields + [Documentation] Verify transcription response contains text, words, and segments + [Tags] audio-upload + + ${response}= Upload Audio For ASR Transcription ${TEST_AUDIO_FILE} + ${json}= Set Variable ${response.json()} + + # Verify required fields exist + Dictionary Should Contain Key ${json} text + Dictionary Should Contain Key ${json} words + Dictionary Should Contain Key ${json} segments + + # Verify text is non-empty string + Should Be True isinstance($json['text'], str) + Should Not Be Empty ${json}[text] + + # Verify words and segments are lists + Should Be True isinstance($json['words'], list) + Should Be True isinstance($json['segments'], list) + +ASR Transcribe Response Words Have Timestamps + [Documentation] Verify word-level timestamps in transcription response + [Tags] audio-upload + + ${response}= Upload Audio For ASR Transcription ${TEST_AUDIO_FILE} + ${json}= Set Variable ${response.json()} + + # Verify words list is not empty + Should Not Be Empty ${json}[words] + + # Verify first word has required timestamp fields + ${first_word}= Get From List ${json}[words] 0 + Dictionary Should Contain Key ${first_word} word + Dictionary Should Contain Key ${first_word} start + Dictionary Should Contain Key ${first_word} end + + # Verify timestamps are numeric and ordered correctly + Should Be True isinstance($first_word['start'], (int, float)) + Should Be True isinstance($first_word['end'], (int, float)) + Should Be True ${first_word}[start] < ${first_word}[end] + Should Be True ${first_word}[start] >= 0 + +ASR Transcribe Response Words Have Confidence Scores + [Documentation] Verify confidence scores in word-level transcription + [Tags] audio-upload + + ${response}= Upload Audio For ASR Transcription ${TEST_AUDIO_FILE} + ${json}= Set Variable ${response.json()} + + # Verify first word has confidence + ${first_word}= Get From List ${json}[words] 0 + Dictionary Should Contain Key ${first_word} confidence + + # Verify confidence is between 0 and 1 + Should Be True isinstance($first_word['confidence'], (int, float)) + Should Be True ${first_word}[confidence] >= 0 + Should Be True ${first_word}[confidence] <= 1 + +ASR Info Capabilities Include Timestamps + [Documentation] Verify ASR service reports timestamp capability + [Tags] infra + + ${info}= Get ASR Service Info ${ASR_URL} + + # Verify timestamps capability is reported + ${has_timestamps}= Evaluate 'timestamps' in $info['capabilities'] or 'word_timestamps' in $info['capabilities'] + Should Be True ${has_timestamps} ASR service should report timestamp capability + + +# ============================================================================= +# Provider Capability Tests +# ============================================================================= +# These tests verify the capability-based architecture that enables +# conditional processing (e.g., skipping pyannote when provider has diarization) + +ASR Capabilities Format Is Valid List + [Documentation] Verify capabilities is a list of strings + [Tags] infra + + ${info}= Get ASR Service Info ${ASR_URL} + + # Capabilities must be a list + Should Be True isinstance($info['capabilities'], list) Capabilities should be a list + + # Each capability should be a string + FOR ${cap} IN @{info}[capabilities] + Should Be True isinstance($cap, str) Each capability should be a string: ${cap} + END + +ASR Capabilities Are From Known Set + [Documentation] Verify reported capabilities are valid known capabilities + ... Known capabilities: timestamps, word_timestamps, diarization, + ... speaker_identification, long_form, language_detection, vad_filter, + ... translation, chunked_processing + [Tags] infra + + ${info}= Get ASR Service Info ${ASR_URL} + + # Define known capabilities (union of all provider capabilities + mock server) + @{known_caps}= Create List timestamps word_timestamps diarization + ... segments speaker_identification long_form language_detection + ... vad_filter translation chunked_processing + + # All reported capabilities should be known + FOR ${cap} IN @{info}[capabilities] + ${is_known}= Evaluate $cap in $known_caps + Should Be True ${is_known} + ... Unknown capability reported: ${cap}. Known: ${known_caps} + END + +ASR Response Segments Structure When Provider Has Segments + [Documentation] Verify segment structure when provider reports segments capability + ... Segments should have speaker, start, end, and text fields + [Tags] audio-upload + + ${info}= Get ASR Service Info ${ASR_URL} + ${has_segments}= Evaluate 'segments' in $info['capabilities'] + + # Only test segment structure if provider claims segments capability + IF ${has_segments} + ${response}= Upload Audio For ASR Transcription ${TEST_AUDIO_FILE} + ${json}= Set Variable ${response.json()} + + IF len($json['segments']) > 0 + ${first_segment}= Get From List ${json}[segments] 0 + Dictionary Should Contain Key ${first_segment} start + Dictionary Should Contain Key ${first_segment} end + Dictionary Should Contain Key ${first_segment} text + + # Timestamps should be numeric + Should Be True isinstance($first_segment['start'], (int, float)) + Should Be True isinstance($first_segment['end'], (int, float)) + END + END + +ASR Response Segments Have Speaker Labels When Provider Has Diarization + [Documentation] Verify segments include speaker labels when provider has diarization + ... This is critical for VibeVoice integration - providers with built-in + ... diarization should return segments with speaker field populated + [Tags] audio-upload + + ${info}= Get ASR Service Info ${ASR_URL} + ${has_diarization}= Evaluate 'diarization' in $info['capabilities'] + + # Skip test if provider doesn't have diarization + Skip If not ${has_diarization} Provider does not have diarization capability + + ${response}= Upload Audio For ASR Transcription ${TEST_AUDIO_FILE} + ${json}= Set Variable ${response.json()} + + # With diarization capability, segments should have speaker labels + Should Not Be Empty ${json}[segments] + ... Provider with diarization should return segments + + ${first_segment}= Get From List ${json}[segments] 0 + Dictionary Should Contain Key ${first_segment} speaker + ... Segments from diarization provider must have 'speaker' field + Should Not Be Empty ${first_segment}[speaker] + ... Speaker label should not be empty diff --git a/tests/asr/vibevoice_diarization_test.robot b/tests/asr/vibevoice_diarization_test.robot new file mode 100644 index 00000000..ba61ea09 --- /dev/null +++ b/tests/asr/vibevoice_diarization_test.robot @@ -0,0 +1,107 @@ +*** Settings *** +Documentation VibeVoice Diarization Test - tests against existing VibeVoice service +... +... Run this test when VibeVoice ASR service is already running on port 8767. +... This test validates diarization capability without managing the service lifecycle. +... +... Prerequisites: +... - VibeVoice ASR service running on port 8767 +... - Start with: cd extras/asr-services && docker compose up vibevoice-asr -d +Library RequestsLibrary +Library Collections +Resource ../resources/asr_keywords.robot + +*** Variables *** +${VIBEVOICE_URL} http://localhost:8767 +${TEST_AUDIO_FILE} ${CURDIR}/../test_assets/DIY_Experts_Glass_Blowing_16khz_mono_1min.wav + +*** Test Cases *** + +VibeVoice Service Is Healthy + [Documentation] Verify VibeVoice ASR service is running and healthy + [Tags] requires-gpu health + [Timeout] 30s + + Check ASR Service Health ${VIBEVOICE_URL} + Log VibeVoice service is healthy + +VibeVoice Reports Diarization Capability + [Documentation] Verify VibeVoice reports diarization in capabilities + [Tags] requires-gpu infra + [Timeout] 30s + + ${info}= Get ASR Service Info ${VIBEVOICE_URL} + + Log Provider: ${info}[provider] + Log Model: ${info}[model_id] + Log Capabilities: ${info}[capabilities] + + # VibeVoice must report diarization capability + ${has_diarization}= Evaluate 'diarization' in $info['capabilities'] + Should Be True ${has_diarization} + ... VibeVoice should report diarization capability, got: ${info}[capabilities] + +VibeVoice Transcription Returns Diarized Segments + [Documentation] Upload 1-minute audio and verify diarized segments are returned + [Tags] requires-gpu e2e + [Timeout] 180s + + # Upload audio file + ${response}= Upload Audio For ASR Transcription ${TEST_AUDIO_FILE} ${VIBEVOICE_URL} + Should Be Equal As Integers ${response.status_code} 200 + ... Transcription request failed with status ${response.status_code} + + ${json}= Set Variable ${response.json()} + + # Log the full transcript + Log Full transcript: ${json}[text] + + # Verify segments are present + Dictionary Should Contain Key ${json} segments + ${segments}= Set Variable ${json}[segments] + Should Not Be Empty ${segments} + ... VibeVoice should return segments with speaker diarization + + ${segment_count}= Get Length ${segments} + Log Found ${segment_count} diarized segments + + # Verify each segment has valid structure + # Note: Non-speech segments (like [Music]) may have speaker=null + ${speech_segments}= Create List + FOR ${index} ${segment} IN ENUMERATE @{segments} + # Required fields + Dictionary Should Contain Key ${segment} speaker + Dictionary Should Contain Key ${segment} start + Dictionary Should Contain Key ${segment} end + Dictionary Should Contain Key ${segment} text + + # Timestamps must be valid + Should Be True ${segment}[start] >= 0 + Should Be True ${segment}[end] > ${segment}[start] + + # Track segments with speaker labels (speech segments) + ${has_speaker}= Evaluate $segment['speaker'] is not None + IF ${has_speaker} + Append To List ${speech_segments} ${segment} + Log [${segment}[speaker]] ${segment}[start]s-${segment}[end]s: ${segment}[text] + ELSE + Log [non-speech] ${segment}[start]s-${segment}[end]s: ${segment}[text] + END + END + + # Verify we have at least some speech segments with speaker labels + ${speech_count}= Get Length ${speech_segments} + Should Be True ${speech_count} > 0 + ... Expected at least one segment with speaker label, got ${speech_count} + + # Verify segments cover reasonable duration for 1-min audio + ${last_segment}= Get From List ${segments} -1 + ${total_duration}= Set Variable ${last_segment}[end] + + Should Be True ${total_duration} > 30 + ... Segments only cover ${total_duration}s, expected > 30s for 1-min audio + Should Be True ${total_duration} < 90 + ... Segments cover ${total_duration}s, expected < 90s for 1-min audio + + Log Total diarization coverage: ${total_duration} seconds + Log Number of speaker segments: ${segment_count} diff --git a/tests/bin/start-containers.sh b/tests/bin/start-containers.sh index 0918b141..3b2e69c2 100755 --- a/tests/bin/start-containers.sh +++ b/tests/bin/start-containers.sh @@ -29,6 +29,15 @@ if [ -f "$TESTS_DIR/setup/.env.test" ]; then set -a source "$TESTS_DIR/setup/.env.test" set +a + + # Warn if API keys are still placeholders + if echo "$DEEPGRAM_API_KEY" | grep -qi "your-.*-here" || echo "$OPENAI_API_KEY" | grep -qi "your-.*-here"; then + echo "" + echo "⚠️ WARNING: API keys in .env.test are still placeholder values." + echo " Tests tagged 'requires-api-keys' will fail." + echo " Run 'make configure' from tests/ to set your API keys." + echo "" + fi fi # Start containers diff --git a/tests/bin/start-rebuild-containers.sh b/tests/bin/start-rebuild-containers.sh index 28cb0667..39dbfb1e 100755 --- a/tests/bin/start-rebuild-containers.sh +++ b/tests/bin/start-rebuild-containers.sh @@ -33,6 +33,14 @@ docker compose -f docker-compose-test.yml stop echo "πŸ—οΈ Rebuilding images..." docker compose -f docker-compose-test.yml up -d --build +# Flush Redis to clear stale keys from previous test runs. +# Redis uses appendonly persistence with a bind mount, so data survives +# stop/rebuild cycles. Stale conversation:current:* keys can cause test +# failures when the audio persistence job finds a Redis key pointing to +# a MongoDB document that no longer exists. +echo "πŸ—‘οΈ Flushing Redis for clean test state..." +docker compose -f docker-compose-test.yml exec -T redis-test redis-cli FLUSHALL > /dev/null 2>&1 || true + # Wait for services echo "⏳ Waiting for services to be ready..." sleep 5 diff --git a/tests/configs/mock-vibevoice.yml b/tests/configs/mock-vibevoice.yml new file mode 100644 index 00000000..2e8b6289 --- /dev/null +++ b/tests/configs/mock-vibevoice.yml @@ -0,0 +1,120 @@ +# Test Configuration: Mock VibeVoice (simulates provider with built-in diarization) +# +# This config tests the capability-based pipeline behavior when a provider +# already has diarization (like VibeVoice), meaning: +# - Segments already have speaker labels +# - Pyannote diarization should be SKIPPED +# - Speaker identification can still run if enrolled speakers exist +# +# Use with mock ASR server in vibevoice mode: +# python mock_asr_server.py --provider vibevoice + +defaults: + llm: mock-llm + embedding: mock-embed + stt: stt-vibevoice + stt_stream: mock-stt-stream + vector_store: vs-qdrant + +models: + - name: mock-llm + description: Mock LLM server for testing (local) + model_type: llm + model_provider: openai + api_family: openai + model_name: gpt-4o-mini + model_url: http://host.docker.internal:11435 + api_key: dummy-key-not-used + model_params: + temperature: 0.2 + max_tokens: 2000 + model_output: json + + - name: mock-embed + description: Mock embedding server for testing (local) + model_type: embedding + model_provider: openai + api_family: openai + model_name: text-embedding-3-small + model_url: http://host.docker.internal:11435 + api_key: dummy-key-not-used + embedding_dimensions: 1536 + model_output: vector + + - name: vs-qdrant + description: Qdrant vector database (local) + model_type: vector_store + model_provider: qdrant + api_family: qdrant + model_url: http://${oc.env:QDRANT_BASE_URL,qdrant}:${oc.env:QDRANT_PORT,6333} + model_params: + host: ${oc.env:QDRANT_BASE_URL,qdrant} + port: ${oc.env:QDRANT_PORT,6333} + collection_name: omi_memories + + - name: stt-vibevoice + description: Mock VibeVoice ASR with built-in speaker diarization + model_type: stt + model_provider: vibevoice + api_family: http + model_url: http://host.docker.internal:8765 + api_key: '' + # VibeVoice capabilities: segments with speakers, but no word-level timestamps + capabilities: + - segments + - diarization + - timestamps + operations: + stt_transcribe: + method: POST + path: /transcribe + content_type: multipart/form-data + response: + type: json + extract: + text: text + words: words + segments: segments + + - name: mock-stt-stream + description: Mock STT for testing (streaming) + model_type: stt_stream + model_provider: mock + api_family: mock + model_url: ws://host.docker.internal:9999 + api_key: mock-key-not-used + operations: + start: + message: {} + end: + message: + type: CloseStream + chunk_header: + message: {} + expect: + interim_type: Results + final_type: Results + extract: + text: channel.alternatives[0].transcript + words: channel.alternatives[0].words + segments: [] + +memory: + provider: chronicle + timeout_seconds: 1200 + extraction: + enabled: true + prompt: '' + +backend: + audio: + always_persist_enabled: true + transcription: + # Use segments from provider (VibeVoice provides pre-diarized segments) + use_provider_segments: true + +# Speaker recognition should skip diarization step for VibeVoice +# but can still run speaker identification if enrolled speakers exist +speaker_recognition: + enabled: true + timeout: 60 diff --git a/tests/integration/websocket_transcription_e2e_test.robot b/tests/integration/websocket_transcription_e2e_test.robot index 3429b52f..3711ac54 100644 --- a/tests/integration/websocket_transcription_e2e_test.robot +++ b/tests/integration/websocket_transcription_e2e_test.robot @@ -16,6 +16,7 @@ Resource ../resources/websocket_keywords.robot Resource ../resources/conversation_keywords.robot Resource ../resources/redis_keywords.robot Resource ../resources/queue_keywords.robot +Resource ../resources/transcript_verification.robot Resource ../setup/setup_keywords.robot Resource ../setup/teardown_keywords.robot @@ -240,3 +241,149 @@ Streaming Consumer Closes Deepgram Connection On End Marker Log βœ… No Deepgram timeout errors - streaming consumer processed end_marker correctly +Word Timestamps Are Monotonically Increasing + [Documentation] Verify timestamps increase across chunks (catches offset accumulation bug) + ... + ... This test validates that word timestamps are cumulative from stream start, + ... not reset for each chunk. Real Deepgram maintains state and returns + ... timestamps relative to stream start. If the mock server or backend + ... incorrectly resets timestamps per chunk, this test will fail. + ... + ... Bug this catches: Offset accumulation bug where timestamps restart at 0 + ... for each interim result instead of being cumulative across the stream. + [Tags] audio-streaming conversation + + ${device_name}= Set Variable timestamp-test + ${client_id}= Get Client ID From Device Name ${device_name} + + # Stream audio + ${stream_id}= Open Audio Stream device_name=${device_name} + Send Audio Chunks To Stream ${stream_id} ${TEST_AUDIO_FILE} num_chunks=200 realtime_pacing=True + Close Audio Stream ${stream_id} + + # Wait for speech detection and conversation creation + Log Waiting for speech detection job... + ${speech_jobs}= Wait Until Keyword Succeeds 60s 3s + ... Get Jobs By Type And Client speech_detection ${client_id} + Should Not Be Empty ${speech_jobs} No speech detection job found + ${speech_job}= Set Variable ${speech_jobs}[0] + Wait For Job Status ${speech_job}[job_id] finished timeout=60s interval=2s + + # Wait for conversation to be created + ${conv_jobs}= Wait Until Keyword Succeeds 60s 3s + ... Job Type Exists For Client open_conversation ${client_id} + ${conv_job}= Set Variable ${conv_jobs}[0] + ${conv_meta}= Set Variable ${conv_job}[meta] + ${conversation_id}= Evaluate $conv_meta.get('conversation_id', '') + Should Not Be Empty ${conversation_id} Conversation ID not found + + # Wait for conversation to close + Wait For Job Status ${conv_job}[job_id] finished timeout=60s interval=2s + + # Get conversation with segments + ${conversation}= Get Conversation By ID ${conversation_id} + ${segments}= Set Variable ${conversation}[segments] + ${segment_count}= Get Length ${segments} + Should Be True ${segment_count} > 0 No segments found in conversation + + # Verify monotonically increasing timestamps across all segments + ${prev_end}= Set Variable ${0.0} + FOR ${segment} IN @{segments} + ${start}= Convert To Number ${segment}[start] + ${end}= Convert To Number ${segment}[end] + + # Start time must be >= previous end time (allowing small gaps between segments) + Should Be True ${start} >= ${prev_end} - 0.1 + ... Segment at ${start}s starts before previous end ${prev_end}s - timestamps not monotonically increasing! + + # End time must be > start time within segment + Should Be True ${end} > ${start} + ... Segment has invalid timing: start=${start}s, end=${end}s + + ${prev_end}= Set Variable ${end} + END + + # Verify timestamps span a reasonable duration (not all near 0) + ${last_segment}= Set Variable ${segments}[-1] + ${final_end_time}= Convert To Number ${last_segment}[end] + Should Be True ${final_end_time} > 1.0 + ... Final timestamp ${final_end_time}s is too low - timestamps may not be accumulating correctly across chunks + + Log βœ… All ${segment_count} segments have monotonically increasing timestamps (final: ${final_end_time}s) + + +Segment Timestamps Match Expected Values + [Documentation] Use existing verification keyword to check segment timing accuracy + ... + ... This test uses the Verify Segments Match Expected Timestamps keyword + ... that was created but never called. It validates that actual segment + ... timestamps match expected values from test_data.py within tolerance. + [Tags] audio-streaming conversation + + ${device_name}= Set Variable segment-timing-test + ${client_id}= Get Client ID From Device Name ${device_name} + + # Stream the test audio file + ${stream_id}= Open Audio Stream device_name=${device_name} + Send Audio Chunks To Stream ${stream_id} ${TEST_AUDIO_FILE} num_chunks=200 realtime_pacing=True + Close Audio Stream ${stream_id} + + # Wait for speech detection and conversation + ${speech_jobs}= Wait Until Keyword Succeeds 60s 3s + ... Get Jobs By Type And Client speech_detection ${client_id} + Should Not Be Empty ${speech_jobs} No speech detection job found + ${speech_job}= Set Variable ${speech_jobs}[0] + Wait For Job Status ${speech_job}[job_id] finished timeout=60s interval=2s + + # Get conversation + ${conv_jobs}= Wait Until Keyword Succeeds 60s 3s + ... Job Type Exists For Client open_conversation ${client_id} + ${conv_job}= Set Variable ${conv_jobs}[0] + ${conv_meta}= Set Variable ${conv_job}[meta] + ${conversation_id}= Evaluate $conv_meta.get('conversation_id', '') + Should Not Be Empty ${conversation_id} + + Wait For Job Status ${conv_job}[job_id] finished timeout=60s interval=2s + + # Get conversation with segments + ${conversation}= Get Conversation By ID ${conversation_id} + ${segments}= Set Variable ${conversation}[segments] + + # Use the existing (previously unused) verification keyword + # This checks against EXPECTED_SEGMENT_TIMES from test_data.py + Verify Segments Match Expected Timestamps ${segments} + + Log βœ… Segment timestamps match expected values within tolerance + + +Streaming Completion Signal Is Set Before Transcript Read + [Documentation] Verify Redis completion signal prevents race condition + ... + ... This test validates the fix for the race condition between + ... StreamingTranscriptionConsumer and conversation job: + ... - Consumer sets transcription:complete:{session_id} = "1" when done + ... - Conversation job waits for this signal before reading transcript + ... - Without this, job could read incomplete transcript data + [Tags] audio-streaming infra + + ${device_name}= Set Variable signal-test + ${client_id}= Get Client ID From Device Name ${device_name} + + # Stream audio and close + ${stream_id}= Open Audio Stream device_name=${device_name} + Send Audio Chunks To Stream ${stream_id} ${TEST_AUDIO_FILE} num_chunks=100 + Close Audio Stream ${stream_id} + + # Wait for streaming consumer to complete and set the completion signal + ${completion_key}= Set Variable transcription:complete:${client_id} + Wait Until Keyword Succeeds 30s 1s + ... Verify Redis Key Exists ${completion_key} + + # Verify the signal value is "1" (completed) + ${signal_value}= Redis Command GET ${completion_key} + Should Be Equal As Strings ${signal_value} 1 + ... Completion signal value should be "1", got: ${signal_value} + + Log βœ… Completion signal ${completion_key} = ${signal_value} (consumer completed before job reads) + + diff --git a/tests/libs/mock_asr_server.py b/tests/libs/mock_asr_server.py new file mode 100644 index 00000000..d03c0c1b --- /dev/null +++ b/tests/libs/mock_asr_server.py @@ -0,0 +1,231 @@ +#!/usr/bin/env python3 +""" +Mock ASR Server - Mimics ASR provider API for protocol testing. + +This server simulates the ASR service endpoints (health, info, transcribe) +used by Chronicle's offline transcription services. It follows the same +patterns as mock_streaming_stt_server.py for consistency. + +Architecture: +- FastAPI HTTP server on 0.0.0.0:8765 +- Implements /health, /info, /transcribe endpoints +- Returns mock transcription responses for file uploads +- No GPU required - suitable for CI/CD testing + +Endpoints: +- GET /health -> {"status": "healthy", "model": "...", "provider": "..."} +- GET /info -> {"model_id": "...", "provider": "...", "capabilities": [...]} +- POST /transcribe -> {"text": "...", "words": [...], "segments": [...]} +""" + +import argparse +import logging +import os +from typing import Optional + +import uvicorn +from fastapi import FastAPI, File, UploadFile, HTTPException +from pydantic import BaseModel + +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +logger = logging.getLogger(__name__) + +app = FastAPI(title="Mock ASR Server", version="1.0.0") + +# Provider mode determines what capabilities are reported +# Set via MOCK_ASR_PROVIDER env var or --provider CLI arg +PROVIDER_MODE = os.environ.get("MOCK_ASR_PROVIDER", "parakeet") + + +# Response models +class HealthResponse(BaseModel): + status: str + model: str + provider: str + + +class InfoResponse(BaseModel): + model_id: str + provider: str + capabilities: list[str] + supported_languages: Optional[list[str]] = None + + +class WordTimestamp(BaseModel): + word: str + start: float + end: float + confidence: float + + +class TranscriptionResult(BaseModel): + text: str + words: list[WordTimestamp] + segments: list[dict] + + +# Provider configurations - different capabilities per provider type +PROVIDER_CONFIGS = { + "parakeet": { + "model_id": "nvidia/parakeet-tdt-0.6b-v3", + "provider": "parakeet", + "capabilities": ["timestamps", "word_timestamps", "segments"], + "has_diarization": False, + "has_word_timestamps": True, + }, + "vibevoice": { + "model_id": "microsoft/VibeVoice-ASR", + "provider": "vibevoice", + "capabilities": ["segments", "diarization", "timestamps"], + "has_diarization": True, + "has_word_timestamps": False, # VibeVoice provides segments but no word-level timestamps + }, + "deepgram": { + "model_id": "nova-2", + "provider": "deepgram", + "capabilities": ["timestamps", "word_timestamps", "segments", "diarization"], + "has_diarization": True, + "has_word_timestamps": True, + }, + "mock": { + "model_id": "mock-model-v1", + "provider": "mock", + "capabilities": ["timestamps", "word_timestamps"], + "has_diarization": False, + "has_word_timestamps": True, + }, +} + + +def get_provider_config(): + """Get configuration for the current provider mode.""" + return PROVIDER_CONFIGS.get(PROVIDER_MODE, PROVIDER_CONFIGS["mock"]) + + +# Mock transcription data +MOCK_TRANSCRIPT = "This is a mock transcription for testing purposes" +MOCK_WORDS = [ + {"word": "This", "start": 0.0, "end": 0.2, "confidence": 0.99}, + {"word": "is", "start": 0.25, "end": 0.35, "confidence": 0.99}, + {"word": "a", "start": 0.4, "end": 0.45, "confidence": 0.98}, + {"word": "mock", "start": 0.5, "end": 0.7, "confidence": 0.99}, + {"word": "transcription", "start": 0.75, "end": 1.2, "confidence": 0.97}, + {"word": "for", "start": 1.25, "end": 1.4, "confidence": 0.99}, + {"word": "testing", "start": 1.45, "end": 1.75, "confidence": 0.98}, + {"word": "purposes", "start": 1.8, "end": 2.2, "confidence": 0.96}, +] + +# Mock diarized segments (for providers with built-in diarization) +MOCK_DIARIZED_SEGMENTS = [ + {"speaker": "Speaker 0", "start": 0.0, "end": 1.2, "text": "This is a mock transcription"}, + {"speaker": "Speaker 1", "start": 1.25, "end": 2.2, "text": "for testing purposes"}, +] + + +@app.get("/health", response_model=HealthResponse) +async def health(): + """Health check endpoint.""" + config = get_provider_config() + logger.debug(f"Health check requested (provider: {config['provider']})") + return HealthResponse( + status="healthy", + model=config["model_id"], + provider=config["provider"] + ) + + +@app.get("/info", response_model=InfoResponse) +async def info(): + """Service info endpoint.""" + config = get_provider_config() + logger.debug(f"Info requested (provider: {config['provider']})") + return InfoResponse( + model_id=config["model_id"], + provider=config["provider"], + capabilities=config["capabilities"], + supported_languages=None + ) + + +@app.post("/transcribe", response_model=TranscriptionResult) +async def transcribe(file: UploadFile = File(...)): + """ + Transcribe an uploaded audio file. + + In mock mode, returns a fixed transcription regardless of input. + Response varies based on provider mode: + - parakeet/mock: words with timestamps, empty segments + - vibevoice: segments with speakers (diarization), no word timestamps + - deepgram: both words and diarized segments + """ + content = await file.read() + file_size = len(content) + config = get_provider_config() + + logger.info(f"Received file: {file.filename}, size: {file_size} bytes (provider: {config['provider']})") + + # Basic validation - reject empty files + if file_size == 0: + raise HTTPException(status_code=400, detail="Empty file received") + + # Build response based on provider capabilities + words = [] + segments = [] + + if config["has_word_timestamps"]: + words = [WordTimestamp(**w) for w in MOCK_WORDS] + + if config["has_diarization"]: + segments = MOCK_DIARIZED_SEGMENTS + + return TranscriptionResult( + text=MOCK_TRANSCRIPT, + words=words, + segments=segments + ) + + +def main(host: str, port: int, provider: str = "mock", debug: bool = False): + """Start the mock ASR server.""" + global PROVIDER_MODE + PROVIDER_MODE = provider + + log_level = "debug" if debug else "info" + config = get_provider_config() + + logger.info(f"Starting Mock ASR Server on {host}:{port}") + logger.info(f"Provider mode: {provider}") + logger.info(f"Capabilities: {config['capabilities']}") + logger.info(f"Has diarization: {config['has_diarization']}") + logger.info(f"Has word timestamps: {config['has_word_timestamps']}") + logger.info("Endpoints: /health, /info, /transcribe") + + uvicorn.run( + app, + host=host, + port=port, + log_level=log_level + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Mock ASR Server") + parser.add_argument("--host", default="0.0.0.0", help="Server host (default: 0.0.0.0)") + parser.add_argument("--port", type=int, default=8765, help="Server port (default: 8765)") + parser.add_argument( + "--provider", + default="mock", + choices=["mock", "parakeet", "vibevoice", "deepgram"], + help="Provider mode - determines reported capabilities (default: mock)" + ) + parser.add_argument("--debug", action="store_true", help="Enable debug logging") + + args = parser.parse_args() + + if args.debug: + logger.setLevel(logging.DEBUG) + + main(args.host, args.port, args.provider, args.debug) diff --git a/tests/libs/mock_streaming_stt_server.py b/tests/libs/mock_streaming_stt_server.py index 8faf492a..4b2618e6 100755 --- a/tests/libs/mock_streaming_stt_server.py +++ b/tests/libs/mock_streaming_stt_server.py @@ -9,6 +9,11 @@ - Async WebSocket server on 0.0.0.0:9999 - Sends interim results every 10 audio chunks - Sends final results on CloseStream with >2s duration and >5 words (speech detection thresholds) + +IMPORTANT: Timestamps are cumulative across the entire stream +- Real Deepgram maintains state and returns timestamps relative to stream start +- Each chunk's word timestamps continue from where the previous chunk ended +- This matches production behavior and tests will catch offset accumulation bugs """ import asyncio @@ -25,34 +30,57 @@ ) logger = logging.getLogger(__name__) +# Per-connection state for tracking cumulative timestamps +# Key: client_id (ip:port), Value: {"cumulative_offset": float} +connection_state: dict[str, dict] = {} + def create_deepgram_response( transcript: str, is_final: bool, words: Optional[list] = None, - confidence: float = 0.99 -) -> dict: + confidence: float = 0.99, + cumulative_offset: float = 0.0 +) -> tuple[dict, float]: """ Create Deepgram-compatible nested response format. Format matches extraction paths in config: - channel.alternatives[0].transcript - channel.alternatives[0].words + + IMPORTANT: Returns (response, new_cumulative_offset) to track timestamps across chunks. + Real Deepgram maintains state - word timestamps are cumulative from stream start. + + Args: + transcript: The transcript text + is_final: Whether this is a final result + words: Pre-generated word list (optional) + confidence: Confidence score for transcript + cumulative_offset: Current cumulative timestamp offset from stream start + + Returns: + Tuple of (response dict, new cumulative offset after this response) """ + new_offset = cumulative_offset + if words is None: - # Generate word timestamps from transcript + # Generate word timestamps from transcript with cumulative offset words = [] - current_time = 0.0 + local_time = 0.0 for word in transcript.split(): words.append({ "word": word, - "start": current_time, - "end": current_time + 0.3, + "start": cumulative_offset + local_time, # Cumulative from stream start + "end": cumulative_offset + local_time + 0.3, "confidence": confidence }) - current_time += 0.35 # 0.3s word + 0.05s gap + local_time += 0.35 # 0.3s word + 0.05s gap + + # Update cumulative offset for next chunk + new_offset = cumulative_offset + local_time - return { + response = { "type": "Results", "is_final": is_final, "channel": { @@ -63,41 +91,55 @@ def create_deepgram_response( }] } } + return response, new_offset -def create_final_response() -> dict: +def create_final_response(cumulative_offset: float = 0.0) -> tuple[dict, float]: """ Create final response with >2s duration and >5 words. Speech detection thresholds (docker-compose-test.yml): - SPEECH_DETECTION_MIN_DURATION: 2.0s - SPEECH_DETECTION_MIN_WORDS: 5 + + Args: + cumulative_offset: Current cumulative timestamp offset from stream start + + Returns: + Tuple of (response dict, new cumulative offset after this response) """ # Create 7 words spanning >2.0 seconds words = [] transcript_words = ["This", "is", "a", "test", "conversation", "about", "hiking"] - current_time = 0.0 + local_time = 0.0 for word in transcript_words: words.append({ "word": word, - "start": current_time, - "end": current_time + 0.35, + "start": cumulative_offset + local_time, # Cumulative from stream start + "end": cumulative_offset + local_time + 0.35, "confidence": 0.99 }) - current_time += 0.4 # 0.35s word + 0.05s gap + local_time += 0.4 # 0.35s word + 0.05s gap - # Final timestamp should be >2.0s - assert words[-1]["end"] > 2.0, f"Duration {words[-1]['end']}s must be >2.0s" + # Final timestamp should be >2.0s from start of this segment + assert local_time > 2.0, f"Duration {local_time}s must be >2.0s" transcript = " ".join(transcript_words) + new_offset = cumulative_offset + local_time - return create_deepgram_response( - transcript=transcript, - is_final=True, - words=words, - confidence=0.99 - ) + response = { + "type": "Results", + "is_final": True, + "channel": { + "alternatives": [{ + "transcript": transcript, + "confidence": 0.99, + "words": words + }] + } + } + return response, new_offset async def handle_client(websocket: WebSocketServerProtocol): @@ -105,11 +147,15 @@ async def handle_client(websocket: WebSocketServerProtocol): client_id = f"{websocket.remote_address[0]}:{websocket.remote_address[1]}" logger.info(f"Client connected: {client_id}") + # Initialize per-connection state for cumulative timestamp tracking + # This matches Deepgram behavior where timestamps are relative to stream start + connection_state[client_id] = {"cumulative_offset": 0.0} + chunk_count = 0 try: - # Send initial empty result - initial = create_deepgram_response(transcript="", is_final=False) + # Send initial empty result (no timestamp advancement for empty result) + initial, _ = create_deepgram_response(transcript="", is_final=False, cumulative_offset=0.0) await websocket.send(json.dumps(initial)) logger.debug(f"Sent initial result to {client_id}") @@ -121,12 +167,16 @@ async def handle_client(websocket: WebSocketServerProtocol): # Send interim results every 10 chunks if chunk_count % 10 == 0: - interim = create_deepgram_response( + current_offset = connection_state[client_id]["cumulative_offset"] + interim, new_offset = create_deepgram_response( transcript=f"Interim transcription chunk {chunk_count // 10}", - is_final=False + is_final=False, + cumulative_offset=current_offset ) + # Update cumulative offset for next chunk + connection_state[client_id]["cumulative_offset"] = new_offset await websocket.send(json.dumps(interim)) - logger.debug(f"Sent interim result to {client_id}") + logger.debug(f"Sent interim result to {client_id} (offset: {current_offset:.2f}s β†’ {new_offset:.2f}s)") # Handle control messages elif isinstance(message, str): @@ -137,10 +187,11 @@ async def handle_client(websocket: WebSocketServerProtocol): if msg_type == "CloseStream": logger.info(f"Received CloseStream from {client_id}") - # Send final result with >2s duration and >5 words - final = create_final_response() + # Send final result with cumulative timestamps + current_offset = connection_state[client_id]["cumulative_offset"] + final, new_offset = create_final_response(cumulative_offset=current_offset) await websocket.send(json.dumps(final)) - logger.info(f"Sent final result to {client_id}: {final['channel']['alternatives'][0]['transcript']}") + logger.info(f"Sent final result to {client_id} (offset: {current_offset:.2f}s β†’ {new_offset:.2f}s): {final['channel']['alternatives'][0]['transcript']}") # Close connection gracefully await websocket.close() @@ -159,7 +210,13 @@ async def handle_client(websocket: WebSocketServerProtocol): logger.error(f"Error handling client {client_id}: {e}", exc_info=True) finally: - logger.info(f"Connection closed: {client_id}, processed {chunk_count} chunks") + # Clean up connection state + if client_id in connection_state: + final_offset = connection_state[client_id]["cumulative_offset"] + del connection_state[client_id] + logger.info(f"Connection closed: {client_id}, processed {chunk_count} chunks, final cumulative offset: {final_offset:.2f}s") + else: + logger.info(f"Connection closed: {client_id}, processed {chunk_count} chunks") async def main(host: str, port: int): diff --git a/tests/resources/asr_keywords.robot b/tests/resources/asr_keywords.robot new file mode 100644 index 00000000..dfa4d7c3 --- /dev/null +++ b/tests/resources/asr_keywords.robot @@ -0,0 +1,145 @@ +*** Settings *** +Documentation ASR Service Keywords for transcription endpoint testing +... +... This file contains keywords for testing ASR service endpoints. +... Keywords in this file should handle ASR service interactions, +... file uploads for transcription, and response validation helpers. +... +... Examples of keywords that belong here: +... - ASR health check and info retrieval +... - Audio file upload for transcription +... - ASR service URL management +... - ASR service lifecycle (start/stop via docker compose) +... +... Keywords that should NOT be in this file: +... - Verification/assertion keywords (belong in tests) +... - Backend API session management (use session_keywords.robot) +... - Audio file operations (use audio_keywords.robot) +Library RequestsLibrary +Library Collections +Library OperatingSystem +Library Process +Variables ../setup/test_env.py + +*** Variables *** +${ASR_URL} http://localhost:8765 +${GPU_ASR_URL} http://localhost:8767 + +*** Keywords *** + +Upload Audio For ASR Transcription + [Documentation] Upload audio file to ASR /transcribe endpoint + ... Returns the full response object for verification in tests + [Arguments] ${audio_file} ${base_url}=${ASR_URL} + + # Verify file exists + File Should Exist ${audio_file} + + # Create session if not exists + ${session_exists}= Run Keyword And Return Status + ... GET On Session asr-session /health expected_status=any + IF not ${session_exists} + Create Session asr-session ${base_url} verify=True + END + + # Upload file using multipart form + ${file_data}= Get Binary File ${audio_file} + &{files}= Create Dictionary file=${file_data} + + ${response}= POST On Session asr-session /transcribe + ... files=${files} expected_status=any + + RETURN ${response} + +Check ASR Service Health + [Documentation] Verify ASR service is healthy and responding + [Arguments] ${base_url}=${ASR_URL} + + Create Session asr-health ${base_url} verify=True + ${response}= GET On Session asr-health /health expected_status=200 + + ${json}= Set Variable ${response.json()} + Should Be Equal ${json}[status] healthy + +Get ASR Service Info + [Documentation] Get ASR service info and return response JSON + [Arguments] ${base_url}=${ASR_URL} + + Create Session asr-info ${base_url} verify=True + ${response}= GET On Session asr-info /info expected_status=200 + + RETURN ${response.json()} + +Wait For ASR Service Ready + [Documentation] Wait until ASR service is healthy (for GPU service startup) + [Arguments] ${base_url}=${GPU_ASR_URL} ${timeout}=180s ${interval}=10s + + Wait Until Keyword Succeeds ${timeout} ${interval} + ... Check ASR Service Health ${base_url} + +Start GPU ASR Service + [Documentation] Start a GPU ASR service via docker compose + ... Supports: transformers-asr, nemo-asr, faster-whisper-asr, parakeet-asr + [Arguments] ${service}=transformers-asr ${model}=${EMPTY} ${port}=8767 + + ${asr_dir}= Set Variable ${CURDIR}/../../extras/asr-services + + # Build environment variables + @{env_vars}= Create List + IF "${model}" != "${EMPTY}" + Append To List ${env_vars} ASR_MODEL=${model} + END + Append To List ${env_vars} ASR_PORT=${port} + + Log To Console \nπŸš€ Starting ${service} on port ${port}... + IF "${model}" != "${EMPTY}" + Log To Console Model: ${model} + END + + # Start the service + ${result}= Run Process docker compose up -d --build ${service} + ... cwd=${asr_dir} + ... env:ASR_MODEL=${model} + ... env:ASR_PORT=${port} + + IF ${result.rc} != 0 + Log STDOUT: ${result.stdout} + Log STDERR: ${result.stderr} + Fail Failed to start ${service}: ${result.stderr} + END + + Log To Console βœ… ${service} container started + +Stop GPU ASR Service + [Documentation] Stop a GPU ASR service via docker compose + [Arguments] ${service}=transformers-asr + + ${asr_dir}= Set Variable ${CURDIR}/../../extras/asr-services + + Log To Console \nπŸ›‘ Stopping ${service}... + + ${result}= Run Process docker compose stop ${service} + ... cwd=${asr_dir} + + IF ${result.rc} != 0 + Log Warning: Failed to stop ${service}: ${result.stderr} WARN + ELSE + Log To Console βœ… ${service} stopped + END + +Remove GPU ASR Service + [Documentation] Stop and remove a GPU ASR service container + [Arguments] ${service}=transformers-asr + + ${asr_dir}= Set Variable ${CURDIR}/../../extras/asr-services + + Log To Console \nπŸ—‘οΈ Removing ${service}... + + ${result}= Run Process docker compose rm -f -s ${service} + ... cwd=${asr_dir} + + IF ${result.rc} != 0 + Log Warning: Failed to remove ${service}: ${result.stderr} WARN + ELSE + Log To Console βœ… ${service} removed + END diff --git a/tests/run_failed_tests.sh b/tests/run_failed_tests.sh new file mode 100755 index 00000000..ca3de008 --- /dev/null +++ b/tests/run_failed_tests.sh @@ -0,0 +1,72 @@ +#!/bin/bash +# Run failed tests one by one + +OUTPUTDIR="results/individual" +mkdir -p "$OUTPUTDIR" + +# Define test names and their files +declare -A tests=( + ["Audio Upload Job Tracking Test"]="endpoints/audio_upload_tests.robot" + ["Audio Playback And Segment Timing Test"]="integration/integration_test.robot" + ["WebSocket Disconnect Should Trigger Conversation Complete Event"]="integration/plugin_event_tests.robot" + ["Conversation Job Created After Speech Detection"]="integration/websocket_streaming_tests.robot" + ["Speech Detection Receives Transcription From Stream"]="integration/websocket_transcription_e2e_test.robot" + ["Conversation Created With Valid Transcript"]="integration/websocket_transcription_e2e_test.robot" +) + +echo "========================================" +echo "Running Failed Tests Individually" +echo "========================================" +echo "" + +test_count=0 +passed_count=0 +failed_count=0 + +for test_name in "${!tests[@]}"; do + test_count=$((test_count + 1)) + test_file="${tests[$test_name]}" + safe_name=$(echo "$test_name" | sed 's/ /_/g') + + echo "" + echo "========================================" + echo "Test $test_count/6: $test_name" + echo "File: $test_file" + echo "========================================" + echo "" + + # Run the test + CREATE_FIXTURE=true uv run --with-requirements test-requirements.txt robot \ + --outputdir "$OUTPUTDIR/$safe_name" \ + --name "$test_name" \ + --console verbose \ + --loglevel INFO:INFO \ + --test "$test_name" \ + "$test_file" + + result=$? + + if [ $result -eq 0 ]; then + echo "" + echo "βœ“ PASSED: $test_name" + passed_count=$((passed_count + 1)) + else + echo "" + echo "βœ— FAILED: $test_name" + failed_count=$((failed_count + 1)) + fi + + echo "" + echo "----------------------------------------" + sleep 2 +done + +echo "" +echo "========================================" +echo "Summary" +echo "========================================" +echo "Total tests: $test_count" +echo "Passed: $passed_count" +echo "Failed: $failed_count" +echo "" +echo "Individual test results saved in: $OUTPUTDIR/" diff --git a/tests/scripts/mock_transcription_server.py b/tests/scripts/mock_transcription_server.py new file mode 100755 index 00000000..d4d25e6a --- /dev/null +++ b/tests/scripts/mock_transcription_server.py @@ -0,0 +1,178 @@ +#!/usr/bin/env python3 +""" +Mock WebSocket Transcription Server for Testing + +This server mimics a streaming transcription service (like Deepgram) by accepting +WebSocket connections and returning mock transcription results. + +Usage: + python mock_transcription_server.py [--port PORT] [--host HOST] +""" +import asyncio +import json +import logging +from typing import Optional + +try: + import websockets + from websockets.server import serve, WebSocketServerProtocol +except ImportError: + print("ERROR: websockets package not found. Install with: uv pip install websockets") + exit(1) + +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s [%(levelname)s] %(message)s' +) +logger = logging.getLogger(__name__) + + +class MockTranscriptionServer: + """Mock transcription WebSocket server that returns predefined transcripts.""" + + def __init__(self, host: str = "localhost", port: int = 9999): + self.host = host + self.port = port + self.server: Optional[asyncio.Server] = None + + async def handle_connection(self, websocket: WebSocketServerProtocol): + """Handle a single WebSocket connection.""" + client_id = f"{websocket.remote_address[0]}:{websocket.remote_address[1]}" + logger.info(f"πŸ“‘ New connection from {client_id}") + + try: + # Send initial connection acknowledgment (simplified format for mock config) + await websocket.send(json.dumps({ + "type": "Results", + "is_final": False, + "text": "", + "words": [], + "segments": [] + })) + + chunk_count = 0 + duration = 0.0 + + async for message in websocket: + if isinstance(message, bytes): + # Audio chunk received + chunk_count += 1 + duration += 0.1 # Assume 100ms chunks + + # Every 10 chunks, send an interim result (simplified format) + if chunk_count % 10 == 0: + await websocket.send(json.dumps({ + "type": "Results", + "is_final": False, + "text": f"This is mock transcription chunk {chunk_count // 10}", + "confidence": 0.95, + "words": [ + {"word": "This", "start": 0.0, "end": 0.1, "confidence": 0.95}, + {"word": "is", "start": 0.1, "end": 0.2, "confidence": 0.95}, + {"word": "mock", "start": 0.2, "end": 0.3, "confidence": 0.95}, + {"word": "transcription", "start": 0.3, "end": 0.5, "confidence": 0.95}, + ], + "segments": [] + })) + + # Every 50 chunks, send a final result (simplified format) + # Make sure words span at least 2 seconds for speech detection + if chunk_count % 50 == 0: + await websocket.send(json.dumps({ + "type": "Results", + "is_final": True, + "text": "This is a final mock transcription segment.", + "confidence": 0.98, + "words": [ + {"word": "This", "start": 0.0, "end": 0.3, "confidence": 0.98}, + {"word": "is", "start": 0.3, "end": 0.6, "confidence": 0.98}, + {"word": "a", "start": 0.6, "end": 0.8, "confidence": 0.98}, + {"word": "final", "start": 0.8, "end": 1.2, "confidence": 0.98}, + {"word": "mock", "start": 1.2, "end": 1.5, "confidence": 0.98}, + {"word": "transcription", "start": 1.5, "end": 2.0, "confidence": 0.98}, + {"word": "segment", "start": 2.0, "end": 2.5, "confidence": 0.98}, + ], + "segments": [] + })) + + elif isinstance(message, str): + # Control message received + try: + msg = json.loads(message) + if msg.get("type") == "CloseStream": + logger.info(f"πŸ“΄ Received CloseStream from {client_id}") + # Send final results (simplified format) with words spanning >2s + await websocket.send(json.dumps({ + "type": "Results", + "is_final": True, + "text": "This is the complete mock transcription result.", + "confidence": 0.98, + "words": [ + {"word": "This", "start": 0.0, "end": 0.4, "confidence": 0.98}, + {"word": "is", "start": 0.4, "end": 0.7, "confidence": 0.98}, + {"word": "the", "start": 0.7, "end": 1.0, "confidence": 0.98}, + {"word": "complete", "start": 1.0, "end": 1.5, "confidence": 0.98}, + {"word": "mock", "start": 1.5, "end": 1.9, "confidence": 0.98}, + {"word": "transcription", "start": 1.9, "end": 2.5, "confidence": 0.98}, + {"word": "result", "start": 2.5, "end": 3.0, "confidence": 0.98}, + ], + "segments": [] + })) + break + except json.JSONDecodeError: + logger.warning(f"⚠️ Invalid JSON from {client_id}: {message}") + + except websockets.exceptions.ConnectionClosed: + logger.info(f"πŸ”Œ Connection closed by {client_id}") + except Exception as e: + logger.error(f"❌ Error handling connection from {client_id}: {e}") + finally: + logger.info(f"βœ… Cleaned up connection from {client_id} (received {chunk_count} chunks)") + + async def start(self): + """Start the WebSocket server.""" + logger.info(f"πŸš€ Starting Mock Transcription Server on {self.host}:{self.port}") + + self.server = await serve( + self.handle_connection, + self.host, + self.port + ) + + logger.info(f"βœ… Mock Transcription Server ready at ws://{self.host}:{self.port}") + + async def run_forever(self): + """Start the server and run until interrupted.""" + await self.start() + + # Keep server running + await asyncio.Future() # Run forever + + async def stop(self): + """Stop the WebSocket server.""" + if self.server: + self.server.close() + await self.server.wait_closed() + logger.info("πŸ›‘ Mock Transcription Server stopped") + + +async def main(): + """Main entry point.""" + import argparse + + parser = argparse.ArgumentParser(description="Mock WebSocket Transcription Server") + parser.add_argument("--host", default="0.0.0.0", help="Host to bind to (default: 0.0.0.0)") + parser.add_argument("--port", type=int, default=9999, help="Port to bind to (default: 9999)") + args = parser.parse_args() + + server = MockTranscriptionServer(host=args.host, port=args.port) + + try: + await server.run_forever() + except KeyboardInterrupt: + logger.info("⏹️ Keyboard interrupt received") + await server.stop() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/tests/scripts/verify_mock_servers.py b/tests/scripts/verify_mock_servers.py new file mode 100755 index 00000000..e40aae4e --- /dev/null +++ b/tests/scripts/verify_mock_servers.py @@ -0,0 +1,212 @@ +#!/usr/bin/env python3 +""" +Verification script for mock STT and LLM servers. + +Tests: +1. Mock LLM - models endpoint +2. Mock LLM - fact extraction +3. Mock LLM - memory updates +4. Mock LLM - embeddings +5. Mock Streaming STT - WebSocket connection and final results +""" + +import asyncio +import json +import sys +import urllib.request +import websockets + + +def test_llm_models(): + """Test mock LLM models endpoint.""" + print("Testing Mock LLM - Models endpoint...") + try: + with urllib.request.urlopen('http://localhost:11435/v1/models') as response: + data = json.loads(response.read()) + assert "data" in data + assert len(data["data"]) == 2 + models = [m["id"] for m in data["data"]] + assert "gpt-4o-mini" in models + assert "text-embedding-3-small" in models + print(" βœ… Models endpoint works") + return True + except Exception as e: + print(f" ❌ Models endpoint failed: {e}") + return False + + +def test_llm_fact_extraction(): + """Test mock LLM fact extraction.""" + print("Testing Mock LLM - Fact extraction...") + try: + request_data = { + "model": "gpt-4o-mini", + "messages": [ + {"role": "system", "content": "FACT_RETRIEVAL_PROMPT extract facts"}, + {"role": "user", "content": "I like hiking"} + ] + } + + req = urllib.request.Request( + 'http://localhost:11435/v1/chat/completions', + data=json.dumps(request_data).encode(), + headers={'Content-Type': 'application/json'} + ) + + with urllib.request.urlopen(req) as response: + data = json.loads(response.read()) + content = data["choices"][0]["message"]["content"] + facts = json.loads(content) + assert "facts" in facts + assert len(facts["facts"]) > 0 + print(f" βœ… Fact extraction works ({len(facts['facts'])} facts)") + return True + except Exception as e: + print(f" ❌ Fact extraction failed: {e}") + return False + + +def test_llm_memory_update(): + """Test mock LLM memory updates.""" + print("Testing Mock LLM - Memory updates...") + try: + request_data = { + "model": "gpt-4o-mini", + "messages": [ + {"role": "system", "content": "UPDATE_MEMORY_PROMPT memory manager"}, + {"role": "user", "content": "User now likes apple pie"} + ] + } + + req = urllib.request.Request( + 'http://localhost:11435/v1/chat/completions', + data=json.dumps(request_data).encode(), + headers={'Content-Type': 'application/json'} + ) + + with urllib.request.urlopen(req) as response: + data = json.loads(response.read()) + content = data["choices"][0]["message"]["content"] + assert "" in content + assert "" in content + assert " 0 + + alternative = final_data["channel"]["alternatives"][0] + assert "transcript" in alternative + assert "words" in alternative + assert final_data["is_final"] == True + + # Verify speech detection thresholds + words = alternative["words"] + assert len(words) > 5, f"Need >5 words, got {len(words)}" + + duration = words[-1]["end"] + assert duration > 2.0, f"Need >2.0s duration, got {duration}s" + + print(f" βœ… Streaming STT works ({len(words)} words, {duration:.2f}s)") + return True + except Exception as e: + print(f" ❌ Streaming STT failed: {e}") + return False + + +def main(): + """Run all verification tests.""" + print("\n" + "="*60) + print("Mock Servers Verification") + print("="*60 + "\n") + + results = [] + + # Test LLM server + results.append(test_llm_models()) + results.append(test_llm_fact_extraction()) + results.append(test_llm_memory_update()) + results.append(test_llm_embeddings()) + + # Test streaming STT server + results.append(asyncio.run(test_streaming_stt())) + + # Summary + print("\n" + "="*60) + passed = sum(results) + total = len(results) + print(f"Results: {passed}/{total} tests passed") + print("="*60 + "\n") + + if passed == total: + print("βœ… All mock servers are working correctly!") + return 0 + else: + print(f"❌ {total - passed} test(s) failed") + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tests/setup/.env.test.template b/tests/setup/.env.test.template new file mode 100644 index 00000000..470c09eb --- /dev/null +++ b/tests/setup/.env.test.template @@ -0,0 +1,5 @@ +# Test API Keys - Configure with: make configure (from tests/) +# Or run: uv run --with-requirements ../setup-requirements.txt python setup/init.py + +DEEPGRAM_API_KEY=your-deepgram-api-key-here +OPENAI_API_KEY=your-openai-api-key-here diff --git a/tests/setup/init.py b/tests/setup/init.py new file mode 100644 index 00000000..8223e619 --- /dev/null +++ b/tests/setup/init.py @@ -0,0 +1,121 @@ +""" +Chronicle Test Environment Setup Script. + +Interactive configuration for test API keys (Deepgram, OpenAI). +Follows the same pattern as backends/advanced/init.py. +""" + +import argparse +import shutil +import sys +from pathlib import Path + +from dotenv import set_key +from rich.console import Console +from rich.panel import Panel +from rich.text import Text + +# Add repo root to path for imports +sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent)) +from setup_utils import is_placeholder, prompt_with_existing_masked + +SETUP_DIR = Path(__file__).resolve().parent +ENV_TEST_PATH = SETUP_DIR / ".env.test" +ENV_TEST_TEMPLATE = SETUP_DIR / ".env.test.template" + +DEEPGRAM_PLACEHOLDERS = ["your-deepgram-api-key-here", "your_deepgram_api_key_here"] +OPENAI_PLACEHOLDERS = ["your-openai-api-key-here", "your_openai_api_key_here"] + + +def main(): + parser = argparse.ArgumentParser(description="Chronicle Test Environment Setup") + parser.add_argument( + "--deepgram-api-key", help="Deepgram API key (skips interactive prompt)" + ) + parser.add_argument( + "--openai-api-key", help="OpenAI API key (skips interactive prompt)" + ) + args = parser.parse_args() + + console = Console() + + console.print() + panel = Panel( + Text("Chronicle Test Environment Setup", style="cyan bold"), + style="cyan", + expand=False, + ) + console.print(panel) + console.print() + + # Ensure template exists + if not ENV_TEST_TEMPLATE.exists(): + console.print( + f"[red][ERROR][/red] Template not found: {ENV_TEST_TEMPLATE}" + ) + sys.exit(1) + + # Copy template to .env.test if it doesn't exist + if not ENV_TEST_PATH.exists(): + shutil.copy2(ENV_TEST_TEMPLATE, ENV_TEST_PATH) + console.print("[blue][INFO][/blue] Created .env.test from template") + else: + console.print("[blue][INFO][/blue] Found existing .env.test") + + env_path_str = str(ENV_TEST_PATH) + + # --- Deepgram API Key --- + if args.deepgram_api_key: + deepgram_key = args.deepgram_api_key + console.print("[green][OK][/green] Deepgram API key provided via argument") + else: + deepgram_key = prompt_with_existing_masked( + prompt_text="Deepgram API key", + env_file_path=env_path_str, + env_key="DEEPGRAM_API_KEY", + placeholders=DEEPGRAM_PLACEHOLDERS, + is_password=True, + ) + + if deepgram_key and not is_placeholder(deepgram_key, *DEEPGRAM_PLACEHOLDERS): + set_key(env_path_str, "DEEPGRAM_API_KEY", deepgram_key) + console.print("[green][OK][/green] Deepgram API key saved") + else: + console.print( + "[yellow][WARNING][/yellow] No Deepgram key configured - " + "tests tagged requires-api-keys will fail" + ) + + # --- OpenAI API Key --- + if args.openai_api_key: + openai_key = args.openai_api_key + console.print("[green][OK][/green] OpenAI API key provided via argument") + else: + openai_key = prompt_with_existing_masked( + prompt_text="OpenAI API key", + env_file_path=env_path_str, + env_key="OPENAI_API_KEY", + placeholders=OPENAI_PLACEHOLDERS, + is_password=True, + ) + + if openai_key and not is_placeholder(openai_key, *OPENAI_PLACEHOLDERS): + set_key(env_path_str, "OPENAI_API_KEY", openai_key) + console.print("[green][OK][/green] OpenAI API key saved") + else: + console.print( + "[yellow][WARNING][/yellow] No OpenAI key configured - " + "tests tagged requires-api-keys will fail" + ) + + console.print() + console.print("[green][DONE][/green] Test environment configured") + console.print(f" Config file: {ENV_TEST_PATH}") + console.print() + console.print("Next steps:") + console.print(" [cyan]make start[/cyan] - Start test containers") + console.print(" [cyan]make test[/cyan] - Start containers + run tests") + + +if __name__ == "__main__": + main() diff --git a/tests/show_results.py b/tests/show_results.py new file mode 100755 index 00000000..0d2e1b2c --- /dev/null +++ b/tests/show_results.py @@ -0,0 +1,338 @@ +#!/usr/bin/env python3 +""" +Chronicle Test Results Viewer + +Quick command-line tool to view Robot Framework test results. +SSH-friendly with multiple display modes. +""" + +import argparse +import sys +from pathlib import Path +from xml.etree import ElementTree as ET +from datetime import datetime + + +# ANSI color codes +class Colors: + GREEN = "\033[92m" + RED = "\033[91m" + YELLOW = "\033[93m" + BLUE = "\033[94m" + BOLD = "\033[1m" + RESET = "\033[0m" + + +def colorize(text, color): + """Add color to text if stdout is a TTY""" + if sys.stdout.isatty(): + return f"{color}{text}{Colors.RESET}" + return text + + +def find_latest_results(result_dir=None): + """ + Find the most recent output.xml file. + + Args: + result_dir: Specific directory to search, or None to search all + + Returns: + Path to output.xml, or None if not found + """ + tests_dir = Path(__file__).parent + + if result_dir: + # Search specific directory + search_path = tests_dir / result_dir / "output.xml" + if search_path.exists(): + return search_path + return None + + # Search all result directories + result_dirs = [ + tests_dir / "results", + tests_dir / "results-no-api", + tests_dir / "results-slow", + tests_dir / "results-sdk", + ] + + # Find all output.xml files + xml_files = [] + for result_dir in result_dirs: + xml_path = result_dir / "output.xml" + if xml_path.exists(): + xml_files.append(xml_path) + + if not xml_files: + return None + + # Return most recently modified + return max(xml_files, key=lambda p: p.stat().st_mtime) + + +def parse_results(xml_file): + """ + Parse Robot Framework output.xml file. + + Returns: + dict with keys: passed, failed, skipped, total, duration_seconds, + failed_tests (list of dicts with name, suite, error, duration) + """ + tree = ET.parse(xml_file) + root = tree.getroot() + + # Get statistics + stats = root.find(".//statistics/total/stat") + if stats is None: + return None + + passed = int(stats.get("pass", 0)) + failed = int(stats.get("fail", 0)) + skipped = int(stats.get("skip", 0)) + total = passed + failed + skipped + + # Get suite duration from robot element + robot_elem = root.find("./suite") + status_elem = robot_elem.find("./status") if robot_elem is not None else None + + duration_seconds = 0 + if status_elem is not None and status_elem.get("elapsed"): + # Duration is in milliseconds + elapsed_ms = status_elem.get("elapsed") + try: + duration_seconds = int(elapsed_ms) / 1000.0 + except (ValueError, TypeError): + pass + + # Find failed tests + failed_tests = [] + for test in root.findall(".//test"): + status = test.find("./status") + if status is not None and status.get("status") == "FAIL": + # Get suite path + suite_path = [] + parent = test + while True: + parent = find_parent_suite(root, parent) + if parent is None: + break + suite_name = parent.get("name") + if suite_name: + suite_path.insert(0, suite_name) + + # Get error message + error_msg = status.text or "No error message" + + # Get test duration + test_duration = 0 + if status.get("elapsed"): + try: + test_duration = int(status.get("elapsed")) / 1000.0 + except (ValueError, TypeError): + pass + + failed_tests.append({ + "name": test.get("name"), + "suite": " > ".join(suite_path) if suite_path else "Unknown Suite", + "error": error_msg.strip(), + "duration": test_duration + }) + + return { + "passed": passed, + "failed": failed, + "skipped": skipped, + "total": total, + "duration_seconds": duration_seconds, + "failed_tests": failed_tests + } + + +def find_parent_suite(root, element): + """Find parent suite of an element""" + for suite in root.findall(".//suite"): + for child in suite: + if child == element: + return suite + # Check nested tests + for test in suite.findall(".//test"): + if test == element: + return suite + return None + + +def format_duration(seconds): + """Format duration in human-readable format""" + if seconds < 60: + return f"{seconds:.1f}s" + elif seconds < 3600: + minutes = int(seconds / 60) + secs = int(seconds % 60) + return f"{minutes}m {secs}s" + else: + hours = int(seconds / 3600) + minutes = int((seconds % 3600) / 60) + return f"{hours}h {minutes}m" + + +def print_summary(results, xml_file, show_detailed=False): + """Print terminal summary of test results""" + + # Header + print() + print(colorize("Chronicle Test Results", Colors.BOLD)) + print("=" * 50) + print() + + # File info + file_mtime = datetime.fromtimestamp(xml_file.stat().st_mtime) + print(f"Results from: {colorize(str(xml_file.relative_to(Path.cwd())), Colors.BLUE)}") + print(f"Last modified: {file_mtime.strftime('%Y-%m-%d %H:%M:%S')}") + print(f"Total duration: {format_duration(results['duration_seconds'])}") + print() + + # Statistics + print(colorize("Test Statistics:", Colors.BOLD)) + print(f" {colorize('βœ“', Colors.GREEN)} Passed: {colorize(str(results['passed']), Colors.GREEN)}") + print(f" {colorize('βœ—', Colors.RED)} Failed: {colorize(str(results['failed']), Colors.RED)}") + if results['skipped'] > 0: + print(f" {colorize('β—‹', Colors.YELLOW)} Skipped: {colorize(str(results['skipped']), Colors.YELLOW)}") + print(f" Total: {results['total']}") + + if results['total'] > 0: + pass_rate = (results['passed'] / results['total']) * 100 + color = Colors.GREEN if pass_rate == 100 else Colors.YELLOW if pass_rate >= 90 else Colors.RED + print(f" Pass rate: {colorize(f'{pass_rate:.1f}%', color)}") + print() + + # Failed tests + if results['failed'] > 0: + print(colorize(f"Failed Tests ({results['failed']}):", Colors.BOLD)) + print() + + for i, test in enumerate(results['failed_tests'], 1): + print(f"{colorize(f'{i}.', Colors.RED)} {colorize(test['name'], Colors.BOLD)}") + print(f" Suite: {test['suite']}") + + if show_detailed: + # Show full error in detailed mode + print(f" Error: {test['error']}") + print(f" Duration: {format_duration(test['duration'])}") + else: + # Truncate error in summary mode + error_lines = test['error'].split('\n') + first_line = error_lines[0] + if len(first_line) > 100: + first_line = first_line[:97] + "..." + print(f" Error: {first_line}") + if len(error_lines) > 1: + print(f" {colorize('(Use --detailed for full error)', Colors.YELLOW)}") + print() + else: + print(colorize("πŸŽ‰ All tests passed!", Colors.GREEN)) + print() + + +def print_report_path(xml_file): + """Print paths to HTML reports""" + result_dir = xml_file.parent + report_file = result_dir / "report.html" + log_file = result_dir / "log.html" + + print() + print(colorize("Test Report Files:", Colors.BOLD)) + print() + + if report_file.exists(): + print(f"HTML report: {colorize(str(report_file.absolute()), Colors.BLUE)}") + else: + print(f"HTML report: {colorize('Not found', Colors.RED)}") + + if log_file.exists(): + print(f"Detailed log: {colorize(str(log_file.absolute()), Colors.BLUE)}") + else: + print(f"Detailed log: {colorize('Not found', Colors.RED)}") + + print() + + if report_file.exists(): + print(colorize("To view reports:", Colors.BOLD)) + print(f" 1. Copy to local machine: scp user@server:{report_file.absolute()} .") + print(f" 2. Open in browser: file://{report_file.absolute()}") + print() + + +def main(): + parser = argparse.ArgumentParser( + description="View Chronicle Robot Framework test results", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + %(prog)s # Quick summary (default, SSH-friendly) + %(prog)s --path # Print path to HTML report + %(prog)s --detailed # Detailed terminal output with full errors + %(prog)s --dir results-no-api # Specific result directory + """ + ) + + parser.add_argument( + "--dir", + help="Specific result directory to use (default: auto-find most recent)" + ) + + parser.add_argument( + "--path", + action="store_true", + help="Print path to HTML report instead of showing summary" + ) + + parser.add_argument( + "--detailed", + action="store_true", + help="Show detailed output with full error messages" + ) + + args = parser.parse_args() + + # Find results file + xml_file = find_latest_results(args.dir) + + if xml_file is None: + if args.dir: + print(colorize(f"Error: No test results found in '{args.dir}' directory", Colors.RED)) + else: + print(colorize("Error: No test results found", Colors.RED)) + print() + print("Run tests first:") + print(" make test # Full test run") + print(" make test-quick # Quick test on existing containers") + print() + return 1 + + # Show path mode + if args.path: + print_report_path(xml_file) + return 0 + + # Parse results + try: + results = parse_results(xml_file) + if results is None: + print(colorize("Error: Could not parse test results", Colors.RED)) + print(f"File: {xml_file}") + return 1 + except Exception as e: + print(colorize(f"Error parsing results: {e}", Colors.RED)) + print(f"File: {xml_file}") + return 1 + + # Show summary + print_summary(results, xml_file, show_detailed=args.detailed) + + # Return exit code based on test results + return 1 if results['failed'] > 0 else 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tests/tags.md b/tests/tags.md index 0ee5cb56..514dcc08 100644 --- a/tests/tags.md +++ b/tests/tags.md @@ -4,7 +4,7 @@ This document defines the standard tags used across the Chronicle test suite. ## Simplified Tag Set -Chronicle uses a **minimal, focused tag set** for test organization. Only 14 tags are permitted. +Chronicle uses a **minimal, focused tag set** for test organization. Only 15 tags are permitted. ## Tag Format @@ -111,6 +111,13 @@ Chronicle uses a **minimal, focused tag set** for test organization. Only 14 tag - Excluded from default `make test` runs until SDK is released - Run explicitly with `make test-sdk` when developing SDK features +**`requires-gpu`** - Tests requiring GPU hardware and CUDA +- Actual ASR model loading and inference +- GPU-accelerated transcription quality validation +- Tests using real NeMo/Parakeet models +- Excluded from standard CI runs (no GPU available) +- Run explicitly with `make test-asr-gpu` on GPU-enabled systems + ## Tag Usage Guidelines ### Single Tag per Test (Preferred) @@ -158,6 +165,7 @@ Use 2-3 tags only when testing interactions between components: 11. **Does it require external API keys?** β†’ Add `requires-api-keys` tag 12. **Does it take >30s or restart services?** β†’ Add `slow` tag 13. **Is it for unreleased SDK features?** β†’ Add `sdk` tag +14. **Does it require GPU hardware?** β†’ Add `requires-gpu` tag ### Examples @@ -255,10 +263,11 @@ Current distribution (approximate): - `slow`: 2 tests (backend restart tests) - `sdk`: 2 tests (SDK integration tests) - `requires-api-keys`: 1 test (integration_test.robot) +- `requires-gpu`: 5 tests (ASR GPU integration tests) - `audio-batch`: 0 tests (reserved for future use) --- -**Last Updated:** 2026-01-18 -**Total Approved Tags:** 14 +**Last Updated:** 2026-01-29 +**Total Approved Tags:** 15 **Enforcement:** Mandatory - no exceptions diff --git a/wizard.py b/wizard.py index fc69557e..2ede3a6a 100755 --- a/wizard.py +++ b/wizard.py @@ -6,23 +6,20 @@ import shutil import subprocess -import sys from datetime import datetime from pathlib import Path import yaml -from rich import print as rprint from rich.console import Console from rich.prompt import Confirm, Prompt # Import shared setup utilities from setup_utils import ( - prompt_password, - prompt_value, + detect_tailscale_info, + is_placeholder, + mask_value, prompt_with_existing_masked, read_env_value, - mask_value, - is_placeholder ) console = Console() @@ -112,36 +109,46 @@ def check_service_exists(service_name, service_config): return True, "OK" -def select_services(): +def select_services(transcription_provider=None): """Let user select which services to setup""" console.print("πŸš€ [bold cyan]Chronicle Service Setup[/bold cyan]") console.print("Select which services to configure:\n") - + selected = [] - + # Backend is required console.print("πŸ“± [bold]Backend (Required):[/bold]") console.print(" βœ… Advanced Backend - Full AI features") selected.append('advanced') - + + # Services that will be auto-added based on transcription provider choice + auto_added = set() + if transcription_provider in ("parakeet", "vibevoice"): + auto_added.add('asr-services') + # Optional extras console.print("\nπŸ”§ [bold]Optional Services:[/bold]") for service_name, service_config in SERVICES['extras'].items(): + # Skip services that will be auto-added based on earlier choices + if service_name in auto_added: + console.print(f" βœ… {service_config['description']} [dim](auto-selected for {transcription_provider})[/dim]") + continue + # Check if service exists exists, msg = check_service_exists(service_name, service_config) if not exists: console.print(f" ⏸️ {service_config['description']} - [dim]{msg}[/dim]") continue - + try: enable_service = Confirm.ask(f" Setup {service_config['description']}?", default=False) except EOFError: console.print("Using default: No") enable_service = False - + if enable_service: selected.append(service_name) - + return selected def cleanup_unselected_services(selected_services): @@ -222,8 +229,18 @@ def run_service_setup(service_name, selected_services, https_enabled=False, serv cmd.extend(['--compute-mode', compute_mode]) console.print(f"[blue][INFO][/blue] Found existing COMPUTE_MODE ({compute_mode}), reusing") - # For asr-services, try to reuse PYTORCH_CUDA_VERSION from speaker-recognition + # For asr-services, pass provider from wizard's transcription choice and reuse CUDA version if service_name == 'asr-services': + # Map wizard transcription provider to asr-services provider name + wizard_to_asr_provider = { + 'vibevoice': 'vibevoice', + 'parakeet': 'nemo', + } + asr_provider = wizard_to_asr_provider.get(transcription_provider) + if asr_provider: + cmd.extend(['--provider', asr_provider]) + console.print(f"[blue][INFO][/blue] Pre-selecting ASR provider: {asr_provider} (from wizard choice: {transcription_provider})") + speaker_env_path = 'extras/speaker-recognition/.env' cuda_version = read_env_value(speaker_env_path, 'PYTORCH_CUDA_VERSION') if cuda_version and cuda_version in ['cu121', 'cu126', 'cu128']: @@ -259,12 +276,20 @@ def run_service_setup(service_name, selected_services, https_enabled=False, serv except FileNotFoundError as e: console.print(f"❌ {service_name} setup failed: {e}") + console.print(f"[yellow] Check that the service directory exists: {service['path']}[/yellow]") + console.print(f"[yellow] And that 'uv' is installed and on your PATH[/yellow]") return False except subprocess.TimeoutExpired as e: - console.print(f"❌ {service_name} setup timed out after {e.timeout} seconds") + console.print(f"❌ {service_name} setup timed out after {e.timeout}s") + console.print(f"[yellow] Configuration may be partially written.[/yellow]") + console.print(f"[yellow] To retry just this service:[/yellow]") + console.print(f"[yellow] cd {service['path']} && {' '.join(service['cmd'])}[/yellow]") return False except subprocess.CalledProcessError as e: console.print(f"❌ {service_name} setup failed with exit code {e.returncode}") + console.print(f"[yellow] Check the error output above for details.[/yellow]") + console.print(f"[yellow] To retry just this service:[/yellow]") + console.print(f"[yellow] cd {service['path']} && {' '.join(service['cmd'])}[/yellow]") return False except Exception as e: console.print(f"❌ {service_name} setup failed: {e}") @@ -399,7 +424,7 @@ def setup_hf_token_if_needed(selected_services): HF_TOKEN string if provided, None otherwise """ # Check if any selected services need HF_TOKEN - needs_hf_token = 'speaker-recognition' in selected_services or 'advanced' in selected_services + needs_hf_token = 'speaker-recognition' in selected_services if not needs_hf_token: return None @@ -454,7 +479,8 @@ def select_transcription_provider(): choices = { "1": "Deepgram (cloud-based, high quality, requires API key)", "2": "Parakeet ASR (offline, runs locally, requires GPU)", - "3": "None (skip transcription setup)" + "3": "VibeVoice ASR (offline, built-in speaker diarization, requires GPU)", + "4": "None (skip transcription setup)" } for key, desc in choices.items(): @@ -470,6 +496,8 @@ def select_transcription_provider(): elif choice == "2": return "parakeet" elif choice == "3": + return "vibevoice" + elif choice == "4": return "none" console.print(f"[red]Invalid choice. Please select from {list(choices.keys())}[/red]") except EOFError: @@ -479,6 +507,9 @@ def select_transcription_provider(): def main(): """Main orchestration logic""" console.print("πŸŽ‰ [bold green]Welcome to Chronicle![/bold green]\n") + console.print("[dim]This wizard is safe to run as many times as you like.[/dim]") + console.print("[dim]It backs up your existing config and preserves previously entered values.[/dim]") + console.print("[dim]When unsure, just press Enter β€” the defaults will work.[/dim]\n") # Setup config file from template setup_config_file() @@ -492,12 +523,12 @@ def main(): # Ask about transcription provider FIRST (determines which services are needed) transcription_provider = select_transcription_provider() - # Service Selection - selected_services = select_services() + # Service Selection (pass transcription_provider so we skip asking about ASR when already chosen) + selected_services = select_services(transcription_provider) - # Auto-add asr-services if Parakeet was chosen - if transcription_provider == "parakeet" and 'asr-services' not in selected_services: - console.print("[blue][INFO][/blue] Auto-adding ASR services for Parakeet transcription") + # Auto-add asr-services if local ASR was chosen (Parakeet or VibeVoice) + if transcription_provider in ("parakeet", "vibevoice") and 'asr-services' not in selected_services: + console.print(f"[blue][INFO][/blue] Auto-adding ASR services for {transcription_provider.capitalize()} transcription") selected_services.append('asr-services') if not selected_services: @@ -526,21 +557,38 @@ def main(): https_enabled = False if https_enabled: - console.print("\n[blue][INFO][/blue] For distributed deployments, use your Tailscale IP") + # Try to auto-detect Tailscale address + ts_dns, ts_ip = detect_tailscale_info() + + if ts_dns: + console.print(f"\n[green][AUTO-DETECTED][/green] Tailscale DNS: {ts_dns}") + if ts_ip: + console.print(f"[green][AUTO-DETECTED][/green] Tailscale IP: {ts_ip}") + default_address = ts_dns + elif ts_ip: + console.print(f"\n[green][AUTO-DETECTED][/green] Tailscale IP: {ts_ip}") + default_address = ts_ip + else: + console.print("\n[blue][INFO][/blue] Tailscale not detected") + console.print("[blue][INFO][/blue] To find your Tailscale address: tailscale status --json | jq -r '.Self.DNSName'") + default_address = None + console.print("[blue][INFO][/blue] For local-only access, use 'localhost'") - console.print("Examples: localhost, 100.64.1.2, your-domain.com") + console.print("Examples: localhost, myhost.tail1234.ts.net, 100.64.1.2") # Check for existing SERVER_IP from backend .env backend_env_path = 'backends/advanced/.env' existing_ip = read_env_value(backend_env_path, 'SERVER_IP') - # Use the new masked prompt function + # Use existing value, or auto-detected address, or localhost as default + effective_default = default_address or "localhost" + server_ip = prompt_with_existing_masked( prompt_text="Server IP/Domain for SSL certificates", existing_value=existing_ip, placeholders=['localhost', 'your-server-ip-here'], is_password=False, - default="localhost" + default=effective_default ) console.print(f"[green]βœ…[/green] HTTPS configured for: {server_ip}")