Provides access to Apache Solr indexes, allowing hybrid search capabilities that combine keyword search precision with vector search semantic understanding across document collections.
Uses Ollama with nomic-embed-text to generate vector embeddings for documents, enabling semantic search capabilities in Solr collections.
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
Architecture
Vector Search Optimization
The system employs an important optimization for combined vector and SQL queries. When executing a query that includes both vector similarity search and SQL filters:
- SQL filters (WHERE clauses) are pushed down to the vector search stage
- This ensures that vector similarity calculations are only performed on documents that will match the final SQL criteria
- Significantly improves performance for queries with:
- Selective WHERE clauses
- Pagination (LIMIT/OFFSET)
- Large result sets
This optimization reduces computational overhead and network transfer by minimizing the number of vector similarity calculations needed.
Quick Start
- Clone this repository
- Start SolrCloud with Docker:
- Install dependencies:
- Process and index the sample document:
- Run the 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.
This server cannot be installed
hybrid server
The server is able to function both locally and remotely, depending on the configuration or use case.
A Python server that enables AI assistants to perform hybrid search queries against Apache Solr indexes through the Model Context Protocol, combining keyword precision with vector-based semantic understanding.
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