gptme-rag 0.1.1

RAG implementation for gptme context management

gptme-rag

RAG (Retrieval-Augmented Generation) implementation for gptme context management.

Features

• 📚 Document indexing with ChromaDB
  • Fast and efficient vector storage
  • Semantic search capabilities
  • Persistent storage
• 🔍 Semantic search with embeddings
  • Relevance scoring
  • Token-aware context assembly
  • Clean output formatting
• 🛠️ CLI interface for testing and development
  • Index management
  • Search functionality
  • Context assembly

Installation

# Using pip
pip install gptme-rag

# Using pipx (recommended for CLI tools)
pipx install gptme-rag

# From source (for development)
git clone https://github.com/ErikBjare/gptme-rag.git
cd gptme-rag
poetry install

After installation, the gptme-rag command will be available in your terminal.

Usage

Indexing Documents

# Index markdown files in a directory
poetry run python -m gptme_rag index /path/to/documents --pattern "**/*.md"

# Index with custom persist directory
poetry run python -m gptme_rag index /path/to/documents --persist-dir ./index

Searching

# Basic search
poetry run python -m gptme_rag search "your query here"

# Advanced search with options
poetry run python -m gptme_rag search "your query" \
  --n-results 5 \
  --persist-dir ./index \
  --max-tokens 4000 \
  --show-context

Example Output

Most Relevant Documents:

1. ARCHITECTURE.md (relevance: 0.82)
  The task system is designed to help track and manage work effectively across sessions. Components include task registry, status tracking, and journal integration.

2. TASKS.md (relevance: 0.75)
  Active tasks and their current status. Includes task categories, status indicators, and progress tracking.

3. docs/workflow.md (relevance: 0.65)
  Documentation about workflow integration and best practices for task management.

Full Context:
Total tokens: 1250
Documents included: 3
Truncated: False

Development

Running Tests

# Run all tests
poetry run pytest

# Run with coverage
poetry run pytest --cov=gptme_rag

Project Structure

gptme_rag/
├── __init__.py
├── cli.py               # CLI interface
├── indexing/           # Document indexing
│   ├── document.py    # Document model
│   └── indexer.py     # ChromaDB integration
├── query/             # Search functionality
│   └── context_assembler.py  # Context assembly
└── utils/             # Utility functions

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Run tests and linting
6. Submit a pull request

Integration with gptme

This package is designed to integrate with gptme as a plugin, providing:

• Automatic context enhancement
• Semantic search across project files
• Knowledge base integration
• Smart context assembly

License

MIT License. See LICENSE for details.