Huizhu.AI

Huizhu.AI ("慧助"，智能业务伙伴 - Intelligent Business Partner) is a comprehensive platform designed to empower enterprise customers with AI-driven solutions for their daily business operations. It integrates Generative AI (GenAI), AIOps (AI for IT Operations), and AI Security capabilities into a unified, conversational interface.

The primary goal of Huizhu.AI is to enhance operational efficiency, reduce costs, and improve decision-making by automating and augmenting tasks across various business functions.

• 中文: README-zh.md.

Customer Pain Points & Value Proposition

Huizhu.AI aims to address several critical challenges faced by modern enterprises:

Pain Points:

• Operational Inefficiencies: Businesses grapple with numerous repetitive, information-intensive tasks in marketing, customer service, internal knowledge management, IT operations, and security.
• Lack of Specialized Tools: Many companies lack dedicated professional tools and AI assistants, often resorting to expensive outsourcing or high-cost consultants.
• Marketing & Sales Challenges: Difficulty in professional market analysis, creating engaging marketing content, and gaining timely competitor insights, leading to suboptimal campaign performance.
• IT Complexity & Security Risks: Complex IT environments and frequent security incidents are hard to manage without specialized personnel, resulting in delays in fault resolution and risk mitigation.
• Knowledge & Collaboration Barriers: Cumbersome internal processes, dispersed documentation, and inefficient knowledge retrieval hinder team productivity and collaboration.

Our Value Proposition:

Huizhu.AI provides a one-stop, conversational, intelligent business operations support platform, delivering tangible value:

• Smart Marketing (慧营销):
  • Automates the generation of diverse marketing materials (copy, images, video scripts).
  • Delivers one-click market trend reports and intelligent competitor analysis.
  • Streamlines the "Data → Insight → Creativity → Campaign Delivery" lifecycle.
• Smart Operations & Security (慧运维 / 慧安全):
  • Offers natural language-based IT fault diagnosis, log correlation analysis, and actionable root cause analysis.
  • Provides intelligent interpretation of security events with clear disposition advisories.
  • Includes a built-in compliance self-assessment assistant for generating reports and remediation plans.
• Smart Collaboration (慧协同):
  • Enables intelligent Q&A based on the enterprise's own knowledge base.
  • Automates comparison and review of documents and contracts, highlighting risks.
  • Generates, archives, and distributes meeting minutes automatically.
  • Offers a unified portal to track key business progress and manage tasks.

Key Functional Features

• Conversational AI Assistant: Natural language interaction for accessing all platform capabilities.
• Unified Business Portal: A centralized dashboard for visualizations, reports, and module access.
• Multi-Scenario Marketing Support: AI-powered content generation, market analysis, and competitor insights.
• Intelligent Customer Service: Automated responses, multi-turn dialogue support, and FAQ generation from historical data.
• Proactive IT Operations: Real-time monitoring, automated alerting, and AI-driven root cause analysis for IT infrastructure.
• Responsive AI Security: Automated detection of security events (anomalous logins, suspicious traffic) with actionable remediation advice and compliance reporting.
• Enhanced Knowledge Management: Intelligent search, document comparison, and automated summarization for internal documents and meeting recordings.
• Multi-Tenancy & RBAC: Secure data isolation between enterprise clients and fine-grained role-based access control.

Architecture Overview

Huizhu.AI is built upon a microservices architecture, ensuring scalability, resilience, and maintainability. Key architectural layers include:

• User Interface Layer: Provides a conversational AI assistant and a unified business portal.
• API Gateway Layer: Acts as the single entry point for all client requests.
• Core Smart Services Layer: Houses the main AI engines (GenAI, AIOps, AISec, Data Analytics).
• Domain Services Layer: Contains business logic services like User Management, Knowledge Management, etc.
• Infrastructure Layer: Provides foundational support like databases, message queues, and observability tools.
• Data & Knowledge Layer: Manages various data sources for AI model training and inference.

All services are designed to be containerized and orchestrated using Kubernetes.

For a detailed understanding of the system architecture, components, design decisions, and technology stack, please refer to the Huizhu.AI Architecture Document (docs/architecture.md).

Technology Stack (Highlights)

• Backend: Go (Golang)
• API & Communication: gRPC (inter-service), RESTful APIs (via API Gateway), WebSockets
• Orchestration: Kubernetes, Docker
• Databases: PostgreSQL/MySQL, Elasticsearch, Prometheus/Mimir, Vector Databases (e.g., Qdrant, Milvus), MinIO/S3-compatible
• Messaging: NATS / Kafka / RabbitMQ
• Observability: Prometheus, Grafana, OpenTelemetry, ELK Stack (or similar)
• AI/ML: LangChain (Go version), Hugging Face models, OpenAI API integration.

Getting Started

This section guides you through setting up Huizhu.AI for development.

Prerequisites

• Go: Version 1.21 or higher.
• Docker & Docker Compose: For managing services and dependencies locally.
• Kubernetes Cluster: Optional, for deployment (e.g., Minikube, Kind).
• protoc Compiler: For Protocol Buffer definitions.
  • protoc-gen-go
  • protoc-gen-go-grpc

Clone the Repository

git clone [https://github.com/turtacn/huizhu.ai.git](https://github.com/turtacn/huizhu.ai.git)
cd huizhu.ai

Configuration

1. Each service loads its configuration from YAML files (e.g., configs/defaults.yaml, configs/user_service.example.yaml) and environment variables.
2. Copy default/example configuration files to your local setup (e.g., configs/user_service.dev.yaml) and update parameters like database DSNs, LLM API keys, etc.

Build

1. Generate gRPC Stubs: If .proto files are modified:

   ./scripts/gen_proto.sh

2. Build Services:

   ./scripts/build.sh

   Or an individual service (e.g., user_service):

   go build -o ./bin/user_service ./cmd/user_service/main.go

3. Build Docker Images (Example for user_service):

   docker build -f deployments/dockerfiles/Dockerfile.user_service -t turtacn/huizhu-user-service:latest .

Running Locally (Development)

1. Using Docker Compose (Recommended, requires a docker-compose.yml):

   docker-compose up -d

2. Running Individual Services Manually: Ensure dependencies are running. Set environment variables.

   ./bin/user_service
   # Or: go run ./cmd/user_service/main.go

Running on Kubernetes

1. Push Docker images to a registry accessible by your K8s cluster.
2. Apply manifests from deployments/kubernetes/services/:

   kubectl apply -f deployments/kubernetes/services/user-service.yaml
   # ... and so on for other services

Running Tests

./scripts/run_tests.sh
# Or:
# go test -v ./...

Project Structure

• api/proto/: Protocol Buffer definitions.
• cmd/: Main applications for each microservice.
• configs/: Configuration files and loading logic.
• deployments/: Dockerfiles, Kubernetes manifests.
• docs/: Project documentation, including architecture.
• internal/: Private application and library code.
• pkg/: Public library code (e.g., client SDK).
• scripts/: Utility scripts.
• test/: Integration and E2E tests.

For more details, see the architecture document.

Contributing

Contributions are welcome! Please read our Contributing Guidelines (CONTRIBUTING.md) (to be created) for details on our development process, coding standards, and how to submit pull requests.

Use the GitHub Issue Tracker to report bugs and suggest features.

License

This project is licensed under the Apache License 2.0. See the LICENSE file for details (to be created).

Contact & Support

• Issues: Open an issue on GitHub.
• Discussions: Use GitHub Discussions (if enabled).