🧠 Universal Memory Layer

Seamless conversation continuity across multiple AI models

Switch between ChatGPT, Claude, Gemini, and Groq models while maintaining perfect conversation context

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✨ Features

🔄 Multi-LLM Support - Seamlessly switch between OpenAI, Google, Anthropic, and Groq models
💾 Persistent Memory - Store conversation history with metadata across different providers
🔍 Semantic Search - Find relevant messages using vector embeddings for intelligent context retrieval
🎯 Unified Interface - Consistent API for interacting with different LLM providers
💬 Interactive CLI - Easy-to-use command-line interface for conversations
⚙️ Configurable Embeddings - Support for OpenAI and Hugging Face embedding providers
🧩 Context Strategies - Multiple strategies for retrieving conversation context (recent, relevant, hybrid)
🛡️ Error Handling - Graceful handling of API failures and rate limits

🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/CodeGovindz/MindLayer.git
cd MindLayer

# Install dependencies
pip install -r requirements.txt

# Install the package
pip install -e .

API Keys Setup

Set up your API keys (at least one required):

# OpenAI (for ChatGPT, GPT-4)
export OPENAI_API_KEY="sk-your-openai-key"

# Google (for Gemini)
export GOOGLE_API_KEY="AIza-your-google-key"

# Anthropic (for Claude)
export ANTHROPIC_API_KEY="sk-ant-your-anthropic-key"

# Groq (for Llama, Mixtral)
export GROQ_API_KEY="gsk-your-groq-key"

Launch

# Interactive CLI
python -m universal_memory_layer.cli.main

# Or use the console script
uml

🎯 Supported Models

Provider	Models	Status
OpenAI	GPT-4, GPT-3.5-turbo, GPT-5-nano	✅ Supported
Google	Gemini Pro, Gemini-2.0-flash	✅ Supported
Anthropic	Claude-3 Sonnet	✅ Supported
Groq	Llama-3.1-8B, Mixtral-8x7B	✅ Supported

💡 Usage Examples

Interactive CLI

# Start the CLI
python -m universal_memory_layer.cli.main

# Available commands:
/help              # Show all commands
/models            # List available models  
/switch            # Switch between models
/recent 5          # Show last 5 messages
/search "topic"    # Search conversation history
/stats             # Show memory statistics
/quit              # Exit

Programmatic Usage

from universal_memory_layer.conversation_manager import ConversationManager

# Initialize
manager = ConversationManager()
manager.initialize()

# Chat with GPT-4
manager.switch_model("gpt-4")
response = manager.chat("Hello! How are you?")

# Switch to Gemini while maintaining context
manager.switch_model("gemini-2.0-flash")
response = manager.chat("What did we just talk about?")

# Use different context strategies
response = manager.chat(
    "Tell me more about that topic",
    context_strategy="relevant",  # or "recent", "hybrid"
    context_count=5
)

Model Switching Demo

# Available models
models = manager.get_available_models()
# ['chatgpt', 'gpt-4', 'gemini', 'gemini-2.0-flash', 'claude', 'llama-3.1-8b', 'mixtral-8x7b']

# Seamless switching with memory retention
manager.switch_model("llama-3.1-8b")  # Groq
response1 = manager.chat("I love machine learning")

manager.switch_model("gemini-2.0-flash")  # Google  
response2 = manager.chat("What do I love?")  # Remembers context!

⚙️ Configuration

Environment Variables

Variable	Description	Default	Example
OPENAI_API_KEY	OpenAI API key	-	sk-proj-...
GOOGLE_API_KEY	Google AI API key	-	AIza...
ANTHROPIC_API_KEY	Anthropic API key	-	sk-ant-...
GROQ_API_KEY	Groq API key	-	gsk_...
UML_EMBEDDING_PROVIDER	Embedding provider	openai	huggingface
UML_DATABASE_PATH	Database file path	memory.db	./data/memory.db
UML_LOG_LEVEL	Logging level	WARNING	INFO

Configuration File

Create a .env file for easy setup:

# API Keys
OPENAI_API_KEY=sk-your-openai-key
GOOGLE_API_KEY=AIza-your-google-key
GROQ_API_KEY=gsk-your-groq-key

# Settings
UML_EMBEDDING_PROVIDER=huggingface
UML_LOG_LEVEL=INFO

🏗️ Architecture

graph TB
    CLI[🖥️ CLI Interface] --> CM[🧠 Conversation Manager]
    API[🔌 Python API] --> CM
    
    CM --> LLM1[🤖 OpenAI Client]
    CM --> LLM2[🤖 Google Client] 
    CM --> LLM3[🤖 Anthropic Client]
    CM --> LLM4[🤖 Groq Client]
    
    CM --> MS[💾 Memory Store]
    MS --> DB[(🗄️ SQLite Database)]
    MS --> VS[🔍 Vector Store]
    MS --> EMB[📊 Embeddings]
    
    EMB --> OPENAI[OpenAI Embeddings]
    EMB --> HF[🤗 Hugging Face]

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📁 Project Structure

universal_memory_layer/
├── 🧠 models/              # Data models (Message, Config)
├── 💾 storage/             # Database & vector storage
├── 🤖 clients/             # LLM client implementations  
├── 📊 embeddings/          # Embedding providers
├── 🖥️ cli/                # Command-line interface
├── 🔧 config_loader.py    # Configuration management
├── 📝 logging_config.py   # Logging setup
└── ⚡ conversation_manager.py  # Main orchestrator

🧪 Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=universal_memory_layer

# Run integration tests
python tests/run_integration_tests.py

# Test specific components
pytest tests/test_conversation_manager.py
pytest tests/storage/
pytest tests/clients/

📊 Performance

Operation	Time	Memory
Model switching	<100ms	~50MB
Message storage	<50ms	~10MB
Context retrieval	<200ms	~20MB
Semantic search	<500ms	~100MB

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📋 Requirements

• Python: 3.8+
• Dependencies: See requirements.txt
• API Keys: At least one LLM provider
• Storage: 100MB+ free space

🔒 Security

• API keys are never logged or stored
• Local database encryption available
• Secure memory handling
• Rate limiting and error handling

🐛 Troubleshooting

🚫 No Models Available

Problem: CLI shows "No models available"

Solutions:

• ✅ Set at least one API key in environment variables
• ✅ Verify API key format and validity
• ✅ Check network connectivity

# Check if keys are set
echo $OPENAI_API_KEY
echo $GOOGLE_API_KEY

💾 Database Errors

Problem: SQLite database errors

Solutions:

• ✅ Check file permissions for database directory
• ✅ Ensure sufficient disk space
• ✅ Delete database file to recreate: rm memory.db

🔍 Embedding Issues

Problem: Embedding generation fails

Solutions:

• ✅ OpenAI: Check API key and quota
• ✅ Hugging Face: Ensure model can be downloaded
• ✅ Switch providers: export UML_EMBEDDING_PROVIDER=huggingface

⚡ Performance Issues

Problem: Slow responses or high memory usage

Solutions:

• ✅ Use context_strategy="recent" for speed
• ✅ Reduce context_count parameter
• ✅ Set UML_MAX_CONTEXT_LENGTH=2000

📚 Documentation

Document	Description
📖 API Reference	Complete API documentation
💡 Examples	Usage examples and patterns
⚙️ Configuration	Configuration options
🔧 Troubleshooting	Detailed troubleshooting

🙏 Acknowledgments

• OpenAI for GPT models and embeddings
• Google for Gemini models
• Anthropic for Claude models
• Groq for fast inference
• Hugging Face for open-source embeddings
• FAISS for vector search

📄 License

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

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Made with ❤️ by CodeGovindz