🌍 memories-dev

Test-Time Memory Framework: Control Hallucinations in Foundation Models

Real-time Contextual Memory Integration for Mission-Critical AI Applications

Deployment-ready • Space-hardened • 99.9% Reliability

📊 Overview

memories-dev provides a robust framework for eliminating hallucinations in foundation models through real-time contextual memory integration. Built for developers requiring absolute reliability, our system ensures AI outputs are verified against factual context before delivery to your applications.

Key benefits include:

Factual Grounding: Verify AI responses against contextual truth data in real-time
Minimal Latency: Framework adds less than 100ms overhead to inference
Deployment Flexibility: Horizontal scaling for high-throughput applications
Comprehensive Verification: Multi-stage validation ensures response accuracy

These capabilities are achieved through our memory verification framework that integrates seamlessly with any AI model, providing reliable operation even in challenging environments.

🔬 Memory Verification Framework

Our three-stage verification framework ensures reliable AI outputs:

Stage 1: Input Validation (EARTH)

Prevents corrupted or invalid data from entering the memory system using advanced validation rules and structured verification protocols.

Stage 2: Truth Verification (S-2)

Cross-validates information using multiple sources to establish reliable ground truth. Implements consistency checks and verification algorithms for data quality.

Stage 3: Response Validation (S-3)

Real-time verification of outputs against verified truth database. Applies confidence scoring to ensure response accuracy and validity.

Each stage works together to create a reliable memory system:

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graph TD
    classDef inputStage fill:#3b82f6,stroke:#2563eb,stroke-width:2px,color:white,font-weight:bold,rounded:true
    classDef truthStage fill:#ef4444,stroke:#dc2626,stroke-width:2px,color:white,font-weight:bold,rounded:true
    classDef responseStage fill:#10b981,stroke:#059669,stroke-width:2px,color:white,font-weight:bold,rounded:true
    classDef dataFlow fill:#8b5cf6,stroke:#7c3aed,stroke-width:2px,color:white,font-weight:bold,rounded:true
    
    A[Input Data] -->|"Feed"| B[Stage 1: Input Validation]
    B -->|"Process"| C[Validated Input]
    C -->|"Verify"| D[Stage 2: Truth Verification]
    D -->|"Confirm"| E[Verified Truth]
    E -->|"Validate"| F[Stage 3: Response Validation]
    F -->|"Deliver"| G[Verified Response]
    
    B:::inputStage
    D:::truthStage
    F:::responseStage
    A:::dataFlow
    C:::dataFlow
    E:::dataFlow
    G:::dataFlow

    linkStyle default stroke-width:2px,fill:none,stroke:#3A5D8C,curve:basis

📦 Installation

Choose the installation option that best fits your needs:

1. CPU-only Installation (Default)

pip install memories-dev

2. GPU Support Installation

For CUDA 11.8:

pip install memories-dev[gpu]

For different CUDA versions, install PyTorch manually first:

# For CUDA 12.1
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# Then install the package
pip install memories-dev[gpu]

3. Development Installation

For contributing to the project:

pip install memories-dev[dev]

4. Documentation Tools

For building documentation:

pip install memories-dev[docs]

5. Alternative Installation via Conda

conda install -c memories-dev

🔧 Common Issues and Solutions

Shapely Version Conflicts

For Python <3.13: Uses Shapely 1.7.0-1.8.5
For Python ≥3.13: Uses Shapely 2.0+

GPU Dependencies

CUDA toolkit must be installed separately
PyTorch Geometric packages are installed from wheels matching your CUDA version

Package Conflicts

If you encounter dependency conflicts:

pip install --upgrade pip
pip install memories-dev --no-deps
pip install -r requirements.txt

Missing Dependencies

For some specialized features, you may need to install:

# For spatial data processing
pip install geopandas rtree pyproj

# For advanced visualization
pip install matplotlib seaborn plotly

Memory Configuration Issues

If encountering memory-related errors:

from memories import Config

# Adjust memory tiers based on your hardware
config = Config(
    hot_memory_size=4,  # GB
    warm_memory_size=16,  # GB
    cold_memory_size=64,  # GB
    vector_store="faiss"  # Alternatives: milvus, qdrant, pgvector
)

🛠️ Development Setup

Clone the repository:

git clone https://github.com/Vortx-AI/memories-dev.git
cd memories-dev

Create a virtual environment:

python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
.\venv\Scripts\activate  # Windows

Install development dependencies:

pip install -e .[dev]

Install pre-commit hooks:

pre-commit install

Run tests:

pytest tests/

Build documentation:

cd docs
make html

🏗️ Core Architecture

The Test-Time Memory Framework integrates with your AI systems through a simple, effective process:

AI model generates initial response
Memory framework retrieves contextual data
Response is verified against contextual information
Verified response delivered to application

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sequenceDiagram
    participant App as Application
    participant AI as AI Model
    participant MF as Memory Framework
    participant CD as Contextual Data
    
    rect rgba(64, 78, 103, 0.1)
    note right of App: Request Phase
    App->>+AI: Request response
    AI->>AI: Generate initial response
    end
    
    rect rgba(43, 155, 128, 0.1)
    note right of AI: Verification Phase
    AI->>+MF: Send for verification
    MF->>+CD: Retrieve contextual data
    CD-->>-MF: Return relevant context
    MF->>MF: Verify response against context
    MF-->>-AI: Return verified response
    end
    
    rect rgba(170, 110, 40, 0.1)
    note right of AI: Delivery Phase
    AI-->>-App: Deliver validated response
    end
    
    note over App,CD: Complete Verification Cycle

Memory System Architecture

Our multi-tiered memory system ensures optimal performance and reliability:

%%{init: { 'theme': 'default', 'themeVariables': { 'primaryColor': '#2c3e50', 'primaryTextColor': '#ecf0f1', 'primaryBorderColor': '#34495e', 'lineColor': '#3498db', 'secondaryColor': '#2980b9', 'tertiaryColor': '#1abc9c' } }}%%
graph TB
    classDef primary fill:#2c3e50,stroke:#34495e,stroke-width:2px,color:white,font-weight:bold,rounded:true
    classDef secondary fill:#3498db,stroke:#2980b9,stroke-width:2px,color:white,rounded:true
    classDef tertiary fill:#1abc9c,stroke:#16a085,stroke-width:2px,color:white,rounded:true
    
    A[Client Application]:::primary -->|"Requests"| B[Memory Manager]:::primary
    B -->|"Collects"| C[Data Acquisition]:::secondary
    B -->|"Stores"| D[Memory Store]:::secondary
    B -->|"Analyzes"| E[Earth Analyzers]:::secondary
    B -->|"Integrates"| F[AI Integration]:::secondary
    
    C -->|"Satellite"| C1[Satellite Data]:::tertiary
    C -->|"Vector"| C2[Vector Data]:::tertiary
    C -->|"IoT"| C3[Sensor Data]:::tertiary
    C -->|"External"| C4[Environmental APIs]:::tertiary
    
    D -->|"Fast Access"| D1[Hot Memory]:::tertiary
    D -->|"Regular Access"| D2[Warm Memory]:::tertiary
    D -->|"Infrequent Access"| D3[Cold Memory]:::tertiary
    D -->|"Archival"| D4[Glacier Storage]:::tertiary
    
    E -->|"Elevation"| E1[Terrain Analysis]:::tertiary
    E -->|"Weather"| E2[Climate Analysis]:::tertiary
    E -->|"Impact"| E3[Environmental Impact]:::tertiary
    E -->|"Development"| E4[Urban Development]:::tertiary
    
    F -->|"LLM"| F1[Model Connectors]:::tertiary
    F -->|"Context"| F2[Context Formation]:::tertiary
    F -->|"Prompts"| F3[Prompt Engineering]:::tertiary
    F -->|"Validation"| F4[Response Validation]:::tertiary
    
    linkStyle default stroke-width:2px,fill:none,stroke:#3498db,curve:basis

Data Processing Workflow

Our comprehensive data flow architecture transforms raw observation data into actionable intelligence:

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graph LR
    classDef ingestion fill:#1d4ed8,stroke:#1e40af,stroke-width:2px,color:white,font-weight:bold,rounded:true
    classDef processing fill:#b91c1c,stroke:#991b1b,stroke-width:2px,color:white,font-weight:bold,rounded:true
    classDef storage fill:#047857,stroke:#065f46,stroke-width:2px,color:white,font-weight:bold,rounded:true
    classDef analytics fill:#7c3aed,stroke:#6d28d9,stroke-width:2px,color:white,font-weight:bold,rounded:true
    classDef delivery fill:#9a3412,stroke:#9a3412,stroke-width:2px,color:white,font-weight:bold,rounded:true
    
    %% Data Ingestion Nodes
    A1[Satellite Imagery] -.->|"Raw Data"| A
    A2[Vector Databases] -.->|"Spatial"| A
    A3[Sensor Networks] -.->|"IoT"| A
    A4[Environmental APIs] -.->|"External"| A
    A[Data Ingestion Engine] ==>|"Input"| B
    
    %% Data Processing Nodes
    B ==>|"Process"| B1[Data Cleaning]
    B ==>|"Extract"| B2[Feature Extraction]
    B ==>|"Time Align"| B3[Temporal Alignment]
    B ==>|"Geo Register"| B4[Spatial Registration]
    B[Multi-Modal Processing] ==>|"Transform"| C
    
    %% Storage Nodes
    C ==>|"Immediate"| C1[Hot Memory Cache]
    C ==>|"Regular"| C2[Warm Vector Store]
    C ==>|"Archive"| C3[Cold Object Storage]
    C ==>|"Deep Archive"| C4[Glacier Archive]
    C[Adaptive Memory System] ==>|"Store"| D
    
    %% Analytics Nodes
    D ==>|"Spatial"| D1[Geospatial Analytics]
    D ==>|"Temporal"| D2[Time Series Analytics]
    D ==>|"Evolution"| D3[Change Detection]
    D ==>|"Patterns"| D4[Correlation Engine]
    D[Earth Intelligence Suite] ==>|"Analyze"| E
    
    %% Delivery Nodes
    E ==>|"Models"| E1[AI Model Integration]
    E ==>|"Services"| E2[Application APIs]
    E ==>|"Visual"| E3[Visualization Tools]
    E ==>|"Export"| E4[Export Services]
    E[Insight Delivery] ==>|"Decide"| F
    
    F[Decision Intelligence]
    
    %% Classifications
    A1:::ingestion
    A2:::ingestion
    A3:::ingestion
    A4:::ingestion
    A:::ingestion
    
    B1:::processing
    B2:::processing
    B3:::processing
    B4:::processing
    B:::processing
    
    C1:::storage
    C2:::storage
    C3:::storage
    C4:::storage
    C:::storage
    
    D1:::analytics
    D2:::analytics
    D3:::analytics
    D4:::analytics
    D:::analytics
    
    E1:::delivery
    E2:::delivery
    E3:::delivery
    E4:::delivery
    E:::delivery
    
    F:::delivery
    
    linkStyle 0,1,2,3 stroke:#1d4ed8,stroke-width:1.5px,stroke-dasharray:3,curve:basis;
    linkStyle 4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24 stroke-width:3px,fill:none,curve:basis;

🚀 Advanced Applications

Our satellite-verified memory system powers a wide range of cutting-edge AI applications where factual grounding and reliability are mission-critical:

Space-Based Applications

Upstream Ground Systems

Enhance pre-launch verification with AI that can validate mission parameters against physical constraints, preventing costly errors before they reach orbit.

In-Orbit Decision Making

Enable autonomous spacecraft to make reliable decisions during communication blackouts by maintaining factual context about their environment and mission parameters.

Downstream Data Processing

Process satellite telemetry and science data with context-aware AI that can detect anomalies, classify observations, and prioritize findings without hallucinations.

Robotics & Physical AI

Autonomous Systems

Ground exploration robots maintain accurate terrain understanding and mission objectives even with delayed or limited communication with control systems.

Industrial Automation

Enable robotic construction and manufacturing with AI that maintains accurate spatial awareness and operation plans verified against physical constraints.

Earth Applications

Healthcare

In medical diagnostics and treatment planning, our memory framework ensures AI systems provide accurate recommendations by verifying outputs against real-time patient data and established medical protocols.

Transportation & Logistics

For autonomous vehicles, air traffic control, and logistics management, our framework helps reduce decision errors by incorporating real-time environmental data into AI decision processes.

Financial Services

For algorithmic trading, fraud detection, and risk assessment, our technology helps prevent costly errors by verifying AI decisions against current market conditions and regulatory requirements.

🚢 Deployment Patterns

memories.dev supports three powerful deployment patterns to meet diverse operational needs:

1. Standalone Deployment

Optimized for single-tenant applications requiring maximum performance:

%%{init: { 'theme': 'default', 'themeVariables': { 'primaryColor': '#0F4C81', 'primaryTextColor': '#fff', 'primaryBorderColor': '#0D3E69', 'lineColor': '#0D3E69', 'secondaryColor': '#41B883', 'tertiaryColor': '#F7A922' } }}%%
graph TD
    subgraph Architecture["Standalone Architecture"]
        direction TB
        
        Client[Client Applications] -->|"Requests"| API[API Gateway]
        API -->|"Process"| Server[Memories Server]
        Server -->|"Inference"| Models[Model System]
        Server -->|"Data"| DataAcq[Data Acquisition]
        Models -->|"Local"| LocalModels[Local Models]
        Models -->|"External"| APIModels[API-based Models]
        DataAcq -->|"Vector"| VectorData[Vector Data Sources]
        DataAcq -->|"Earth"| SatelliteData[Satellite Data]
        Server -->|"Persist"| Storage[Persistent Storage]
    end
    
    classDef client fill:#4C78B5,stroke:#3A5D8C,color:white,font-weight:bold,rounded:true
    classDef server fill:#41B883,stroke:#2D8A64,color:white,font-weight:bold,rounded:true
    classDef model fill:#F7A922,stroke:#BF821A,color:white,rounded:true
    classDef data fill:#F06292,stroke:#C2185B,color:white,rounded:true
    classDef storage fill:#7E57C2,stroke:#5E35B1,color:white,rounded:true
    
    class Client client
    class API,Server server
    class Models,LocalModels,APIModels model
    class DataAcq,VectorData,SatelliteData data
    class Storage storage
    
    linkStyle default stroke-width:2px,fill:none,curve:basis

Best suited for:

High-performance computing workloads
Machine learning model inference
Real-time data processing
Direct hardware access

2. Consensus Deployment

Perfect for distributed systems requiring strong consistency:

%%{init: { 'theme': 'default', 'themeVariables': { 'primaryColor': '#0F4C81', 'primaryTextColor': '#fff', 'primaryBorderColor': '#0D3E69', 'lineColor': '#0D3E69', 'secondaryColor': '#41B883', 'tertiaryColor': '#F7A922' } }}%%
graph TD
    subgraph ConsensusArch["Consensus Architecture"]
        direction TB
        
        Client[Client Applications] -->|"Load Balanced"| LB[Load Balancer]
        LB -->|"Route"| Node1[Node 1]
        LB -->|"Route"| Node2[Node 2]
        LB -->|"Route"| Node3[Node 3]
        
        subgraph "Consensus Group"
            direction LR
            Node1 <-->|"Sync"| Node2
            Node2 <-->|"Sync"| Node3
            Node3 <-->|"Sync"| Node1
        end
        
        Node1 -->|"Inference"| Models1[Model System]
        Node2 -->|"Inference"| Models2[Model System]
        Node3 -->|"Inference"| Models3[Model System]
        
        Node1 -->|"Data"| DataAcq1[Data Acquisition]
        Node2 -->|"Data"| DataAcq2[Data Acquisition]
        Node3 -->|"Data"| DataAcq3[Data Acquisition]
        
        subgraph "Shared Storage"
            Storage[Distributed Storage]
        end
        
        Node1 -->|"Write"| Storage
        Node2 -->|"Write"| Storage
        Node3 -->|"Write"| Storage
    end
    
    classDef client fill:#4C78B5,stroke:#3A5D8C,color:white,font-weight:bold,rounded:true
    classDef loadbal fill:#F7A922,stroke:#BF821A,color:white,font-weight:bold,rounded:true
    classDef node fill:#41B883,stroke:#2D8A64,color:white,font-weight:bold,rounded:true
    classDef model fill:#7E57C2,stroke:#5E35B1,color:white,rounded:true
    classDef data fill:#F06292,stroke:#C2185B,color:white,rounded:true
    classDef storage fill:#FF8A65,stroke:#E64A19,color:white,font-weight:bold,rounded:true
    
    class Client client
    class LB loadbal
    class Node1,Node2,Node3 node
    class Models1,Models2,Models3 model
    class DataAcq1,DataAcq2,DataAcq3 data
    class Storage storage
    
    linkStyle default stroke-width:2px,fill:none,curve:basis
    linkStyle 3,4,5 stroke:#41B883,stroke-width:3px,stroke-dasharray:5 5

Best suited for:

Distributed databases
Blockchain networks
Distributed caching systems
Mission-critical applications

3. Swarmed Deployment

Ideal for globally distributed applications:

%%{init: { 'theme': 'default', 'themeVariables': { 'primaryColor': '#0F4C81', 'primaryTextColor': '#fff', 'primaryBorderColor': '#0D3E69', 'lineColor': '#0D3E69', 'secondaryColor': '#41B883', 'tertiaryColor': '#F7A922' } }}%%
graph TD
    subgraph SwarmArch["Swarmed Architecture"]
        direction TB
        
        Client[Client Applications] -->|"Load Balanced"| LB[Load Balancer]
        LB -->|"Route"| API1[API Gateway 1]
        LB -->|"Route"| API2[API Gateway 2]
        LB -->|"Route"| API3[API Gateway 3]
        
        subgraph "Manager Nodes"
            direction LR
            Manager1[Manager 1]
            Manager2[Manager 2]
            Manager3[Manager 3]
            
            Manager1 <-->|"Orchestrate"| Manager2
            Manager2 <-->|"Orchestrate"| Manager3
            Manager3 <-->|"Orchestrate"| Manager1
        end
        
        API1 -->|"Direct"| Manager1
        API2 -->|"Direct"| Manager2
        API3 -->|"Direct"| Manager3
        
        subgraph "Worker Nodes"
            direction TB
            Worker1[Worker 1]
            Worker2[Worker 2]
            Worker3[Worker 3]
            Worker4[Worker 4]
            Worker5[Worker 5]
        end
        
        Manager1 -->|"Dispatch"| Worker1
        Manager1 -->|"Dispatch"| Worker2
        Manager2 -->|"Dispatch"| Worker3
        Manager2 -->|"Dispatch"| Worker4
        Manager3 -->|"Dispatch"| Worker5
        
        subgraph "Shared Services"
            direction LR
            Registry[Container Registry]
            Config[Configuration Store]
            Secrets[Secrets Management]
            Monitoring[Monitoring & Logging]
        end
        
        Manager1 -->|"Utilize"| Registry
        Manager1 -->|"Configure"| Config
        Manager1 -->|"Secure"| Secrets
        Manager1 -->|"Monitor"| Monitoring
    end
    
    classDef client fill:#4C78B5,stroke:#3A5D8C,color:white,font-weight:bold,rounded:true
    classDef loadbal fill:#F7A922,stroke:#BF821A,color:white,font-weight:bold,rounded:true
    classDef gateway fill:#9CCC65,stroke:#7CB342,color:white,font-weight:bold,rounded:true
    classDef manager fill:#42A5F5,stroke:#1E88E5,color:white,font-weight:bold,rounded:true
    classDef worker fill:#7E57C2,stroke:#5E35B1,color:white,rounded:true
    classDef service fill:#F06292,stroke:#EC407A,color:white,font-weight:bold,rounded:true
    
    class Client client
    class LB loadbal
    class API1,API2,API3 gateway
    class Manager1,Manager2,Manager3 manager
    class Worker1,Worker2,Worker3,Worker4,Worker5 worker
    class Registry,Config,Secrets,Monitoring service
    
    linkStyle default stroke-width:2px,fill:none,curve:basis
    linkStyle 7,8,9 stroke:#42A5F5,stroke-width:3px,stroke-dasharray:5 5

Best suited for:

Edge computing applications
Content delivery networks
IoT device networks
Global data distribution

Cloud Provider Support

Each deployment pattern is supported across major cloud providers with:

Cloud Provider	Features	Deployment Models	Hardware Support
AWS	Auto-scaling, S3 integration, Lambda functions	All	NVIDIA GPUs, Graviton (ARM)
GCP	Kubernetes, TPU support, Cloud Storage	All	NVIDIA GPUs, TPUs
Azure	AKS, Container Apps, Blob Storage	All	NVIDIA GPUs, AMD MI
On-premises	Custom hardware support, airgapped operation	All	NVIDIA GPUs, AMD MI, Intel GPUs

👩‍💻 Developer-Centric Reliability

Our memory framework provides a simple API that lets your AI systems cross-check responses against environmental facts and context, reducing hallucinations while maintaining the flexibility developers need.

For ML Engineers

Simple API integration with any LLM
Minimal latency overhead (< 100ms typical)
Production-ready with comprehensive logging

For System Architects

Horizontal scaling for high-throughput needs
Distributed verification architecture
On-premise or cloud deployment options

For Safety Teams

Comprehensive audit trails
Real-time monitoring dashboards
Configurable verification thresholds

🔧 Technical Principles

Principle	Feature	Description
Context	Environmental Awareness	Integration of real-time situational data into inference processes
Verification	Multi-source Validation	Cross-checking outputs against multiple reliable data sources
Latency	Minimal Processing Overhead	Optimized for fast response times in time-sensitive applications
Reliability	Fault-Tolerant Design	Resilient architecture for operation in challenging environments

💻 Usage Examples

Multi-Model Integration

from memories.models.load_model import LoadModel
from memories.models.multi_model import MultiModelInference

# Initialize multiple models for ensemble analysis
models = {
    "openai": LoadModel(model_provider="openai", model_name="gpt-4"),
    "anthropic": LoadModel(model_provider="anthropic", model_name="claude-3-opus"),
    "deepseek": LoadModel(model_provider="deepseek-ai", model_name="deepseek-coder")
}

# Create multi-model inference engine
multi_model = MultiModelInference(models=models)

# Analyze property with Earth memory integration
responses = multi_model.get_responses_with_earth_memory(
    query="Analyze environmental risks for this property",
    location={"lat": 37.7749, "lon": -122.4194},
    earth_memory_analyzers=["terrain", "climate", "water"]
)

# Compare model assessments
for provider, response in responses.items():
    print(f"\n--- {provider.upper()} ASSESSMENT ---")
    print(response["analysis"])

Earth Analyzers

from memories.core.analyzers import TerrainAnalyzer, ClimateAnalyzer, WaterResourceAnalyzer

# Initialize analyzers
terrain = TerrainAnalyzer()
climate = ClimateAnalyzer()
water = WaterResourceAnalyzer()

# Analyze location
terrain_analysis = await terrain.analyze(
    location={"lat": 37.7749, "lon": -122.4194},
    resolution="high"
)

climate_analysis = await climate.analyze(
    location={"lat": 37.7749, "lon": -122.4194},
    time_range={"start": "2020-01-01", "end": "2023-01-01"}
)

water_analysis = await water.analyze(
    location={"lat": 37.7749, "lon": -122.4194},
    include_forecast=True
)

Real Estate Analysis

from memories import MemoryStore, Config
from examples.real_estate_agent import RealEstateAgent

# Initialize memory store
config = Config(
    storage_path="./real_estate_data",
    hot_memory_size=50,
    warm_memory_size=200,
    cold_memory_size=1000
)
memory_store = MemoryStore(config)

# Initialize agent with earth memory
agent = RealEstateAgent(
    memory_store,
    enable_earth_memory=True,
    analyzers=["terrain", "climate", "water", "environmental"]
)

# Add property and analyze
property_id = await agent.add_property(property_data)
analysis = await agent.analyze_property_environment(property_id)

print(f"Property added: {property_id}")
print(f"Environmental analysis: {analysis}")

Environmental Monitoring

from memories.analyzers import ChangeDetector
from datetime import datetime, timedelta

# Initialize change detector
detector = ChangeDetector(
    baseline_date=datetime(2020, 1, 1),
    comparison_dates=[
        datetime(2021, 1, 1),
        datetime(2022, 1, 1),
        datetime(2023, 1, 1),
        datetime(2024, 1, 1)
    ]
)

# Detect environmental changes
changes = await detector.analyze_changes(
    location={"lat": 37.7749, "lon": -122.4194, "radius": 5000},
    indicators=["vegetation", "water_bodies", "urban_development"],
    visualization=True
)

# Present findings
detector.visualize_changes(changes)
detector.generate_report(changes, format="pdf")

🤝 Contributing

We welcome contributions to the memories-dev project! Please see our CONTRIBUTING.md file for guidelines on how to contribute.

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Built with 💜 by the memories-dev team

Name		Name	Last commit message	Last commit date
Latest commit History 1,007 Commits
.github/workflows		.github/workflows
config		config
deployments		deployments
diffusers		diffusers
docs		docs
examples		examples
memories		memories
tests		tests
.env.example		.env.example
.gitignore		.gitignore
.readthedocs.yaml		.readthedocs.yaml
CHANGELOG.md		CHANGELOG.md
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
INSTALL.md		INSTALL.md
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
buildrequirements.txt		buildrequirements.txt
buildtestrequirements.txt		buildtestrequirements.txt
check_mocks.py		check_mocks.py
cuda_setup.py		cuda_setup.py
download_nltk_data.py		download_nltk_data.py
gpu-requirements.txt		gpu-requirements.txt
install_gpu.py		install_gpu.py
memories_install_report.txt		memories_install_report.txt
mock_diffusers.py		mock_diffusers.py
pyproject.toml		pyproject.toml
pytest.ini		pytest.ini
requirements.txt		requirements.txt
setup.cfg		setup.cfg
setup.py		setup.py
setup_mocks.py		setup_mocks.py
verify_install.py		verify_install.py

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Vortx-AI/memories-dev

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🌍 memories-dev

Real-time Contextual Memory Integration for Mission-Critical AI Applications

📊 Overview

📝 Table of Contents

🔬 Memory Verification Framework

Stage 1: Input Validation (EARTH)

Stage 2: Truth Verification (S-2)

Stage 3: Response Validation (S-3)

📦 Installation

1. CPU-only Installation (Default)

2. GPU Support Installation

3. Development Installation

4. Documentation Tools

5. Alternative Installation via Conda

🔧 Common Issues and Solutions

Shapely Version Conflicts

GPU Dependencies

Package Conflicts

Missing Dependencies

Memory Configuration Issues

🛠️ Development Setup

🏗️ Core Architecture

Memory System Architecture

Data Processing Workflow

🚀 Advanced Applications

Space-Based Applications

Upstream Ground Systems

In-Orbit Decision Making

Downstream Data Processing

Robotics & Physical AI

Autonomous Systems

Industrial Automation

Earth Applications

Healthcare

Transportation & Logistics

Financial Services

🚢 Deployment Patterns

1. Standalone Deployment

2. Consensus Deployment

3. Swarmed Deployment

Cloud Provider Support

👩‍💻 Developer-Centric Reliability

For ML Engineers

For System Architects

For Safety Teams

🔧 Technical Principles

💻 Usage Examples

Multi-Model Integration

Earth Analyzers

Real Estate Analysis

Environmental Monitoring

🤝 Contributing

📄 License

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