solr-mcp
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Solr MCP is a Python package designed to access Apache Solr indexes using Model Context Protocol. It allows AI assistants to perform efficient search queries by integrating keyword and vector search capabilities. The project includes features like optimized vector search, Docker integration, and the ability to generate vector embeddings.
Solr MCP
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
- Optimized Vector Search: Efficiently handles combined vector and SQL queries by pushing down SQL filters to the vector search stage, ensuring optimal performance even with large result sets and pagination
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.