A Python package for accessing Apache Solr indexes via Model Context Protocol (MCP). This integration allows AI assistants like Claude to perform powerful search queries against your Solr indexes, combining both keyword and vector search capabilities.
Features
- MCP Server: Implements the Model Context Protocol for integration with AI assistants
- Hybrid Search: Combines keyword search precision with vector search semantic understanding
- Vector Embeddings: Generates embeddings for documents using Ollama with nomic-embed-text
- Unified Collections: Store both document content and vector embeddings in the same collection
- Docker Integration: Easy setup with Docker and docker-compose
Quick Start
- Clone this repository
- Start SolrCloud with Docker:
docker-compose up -d
- Install dependencies:
python -m venv venv
source venv/bin/activate # On Windows: venvScriptsactivate
pip install poetry
poetry install
- Process and index the sample document:
python scripts/process_markdown.py data/bitcoin-whitepaper.md --output data/processed/bitcoin_sections.json
python scripts/create_unified_collection.py unified
python scripts/unified_index.py data/processed/bitcoin_sections.json --collection unified
- Run the MCP server:
poetry run python -m solr_mcp.server
For more detailed setup and usage instructions, see the QUICKSTART.md guide.
Requirements
- Python 3.10 or higher
- Docker and Docker Compose
- SolrCloud 9.x
- Ollama (for embedding generation)
License
This project is licensed under the MIT License - see the LICENSE file for details.
Contributing
Contributions are welcome! Please see CONTRIBUTING.md for guidelines.