memorix-ai 0.1.0

The memory layer for AI agents

🧠 Memorix AI

A flexible memory management system for AI applications with plug-in support for various vector stores and embedding models.

✨ Features

• 🗄️ Flexible Vector Stores: FAISS, Qdrant, and custom implementations
• 🤖 Multiple Embedding Models: OpenAI, Google Gemini, Sentence Transformers
• 📊 Metadata Management: Optional metadata storage with multiple backends
• ⚙️ YAML Configuration: Easy configuration management
• 🚀 Simple API: Clean and intuitive interface
• 🧪 Comprehensive Testing: Full test coverage and CI/CD
• 📚 Rich Documentation: API docs, examples, and guides

🚀 Quick Start

Installation

pip install memorix-ai

Basic Usage

from memorix import MemoryAPI, Config

# Initialize with configuration
config = Config('memorix.yaml')
memory = MemoryAPI(config)

# Store a memory
memory_id = memory.store(
    "Python is a high-level programming language.",
    metadata={"topic": "programming", "language": "python"}
)

# Retrieve relevant memories
results = memory.retrieve("programming languages", top_k=5)
for result in results:
    print(f"Content: {result['content']}")
    print(f"Similarity: {result['similarity']:.3f}")
    print(f"Metadata: {result['metadata']}")

⚙️ Configuration

Create a memorix.yaml file:

vector_store:
  type: faiss
  index_path: ./memorix_index
  dimension: 1536

embedder:
  type: openai
  model: text-embedding-ada-002
  api_key: ${OPENAI_API_KEY}

metadata_store:
  type: sqlite
  database_path: ./memorix_metadata.db

settings:
  max_memories: 10000
  similarity_threshold: 0.7

🔌 Supported Components

Vector Stores

• FAISS: Fast similarity search with CPU/GPU support
• Qdrant: Vector database with advanced features
• Custom: Implement your own vector store

Embedding Models

• OpenAI: text-embedding-ada-002, text-embedding-3-small, etc.
• Google Gemini: models/embedding-001
• Sentence Transformers: all-MiniLM-L6-v2, all-mpnet-base-v2, etc.

Metadata Stores

• SQLite: Persistent storage with SQL database
• In-Memory: Fast temporary storage
• JSON File: Simple file-based storage

📖 API Reference

MemoryAPI

Method	Description
store(content, metadata=None)	Store content with optional metadata
retrieve(query, top_k=5)	Retrieve relevant memories based on query
update(memory_id, content, metadata=None)	Update an existing memory
delete(memory_id)	Delete a memory by ID
list_memories(limit=100)	List all memories with basic info

🎯 Examples

Core Examples

See the examples/ directory for basic usage examples:

• basic_usage.py: Minimal usage example

🚀 Comprehensive Examples & Demos

For comprehensive examples, interactive demos, and real-world applications, check out our dedicated examples repository:

📚 Memorix Examples Repository

Featured Demos:

• Basic Usage Demo - Get started in 5 minutes
• Chatbot with Memory - Build conversational AI with persistent memory
• Vector Store Comparison - Compare FAISS vs Qdrant performance

Interactive Applications:

• Streamlit Web App - Interactive memory management interface
• Gradio Demo - Quick prototyping interface

Ready-to-Use Templates:

• Chatbot Template - Production-ready chatbot with memory

Quick Start:

# Clone the examples repository
git clone https://github.com/memorix-ai/memorix-examples.git
cd memorix-examples

# Install dependencies
pip install -r requirements.txt

# Run a demo
python demos/01_basic_usage.py

🛠️ Development

Setup

# Clone the repository
git clone https://github.com/memorix-ai/memorix-sdk.git
cd memorix-sdk

# Install in development mode
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

Common Commands

# Run tests
make test

# Run tests with coverage
make test-cov

# Format code
make format

# Run linting
make lint

# Build package
make build

# See all available commands
make help

Running Tests

python -m pytest tests/

Running Examples

python examples/basic_usage.py

🤝 Contributing

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

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

📄 License

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

🗺️ Roadmap

• Add more vector store backends (Pinecone, Weaviate, etc.)
• Add more embedding models (Cohere, Hugging Face, etc.)
• Add memory compression and summarization
• Add batch operations
• Add memory versioning
• Add memory expiration
• Add memory categories and tags
• Add memory search filters
• Add memory export/import
• Add memory analytics and insights

📚 Documentation

• 📖 Installation Guide - Complete setup instructions
• 🚀 Usage Guide - Comprehensive usage examples
• 🏗️ Architecture - System design and components
• 🎯 Vision - Project vision and roadmap
• 📋 Quick Reference - Essential commands and patterns
• 📝 Changelog - Version history and changes

📞 Support

• 📚 Documentation: docs.memorix.ai
• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions
• 📧 Email: [e-mail]

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Made with ❤️ by the Memorix Team