What is Solr Mcp

Solr-MCP is a Python package designed for accessing Apache Solr indexes using the Model Context Protocol (MCP). It enables AI assistants like Claude to execute advanced search queries that leverage both keyword and vector search functionalities.

Use cases

Use cases for Solr-MCP include enhancing search functionalities in e-commerce platforms, improving content discovery in digital libraries, enabling semantic search in knowledge bases, and supporting AI-driven applications that require efficient and accurate search results.

How to use

To use Solr-MCP, clone the repository, set up SolrCloud using Docker, install the necessary dependencies, and process your documents for indexing. Detailed steps are provided in the README.

Key features

Key features of Solr-MCP include: implementation of the MCP for AI integration, hybrid search capabilities combining keyword and vector searches, generation of vector embeddings, unified collections for storing documents and embeddings, easy Docker integration, and optimized vector search for improved performance.

Where to use

Solr-MCP can be used in various fields that require advanced search capabilities, such as e-commerce, content management systems, data analytics, and any application that benefits from AI-driven search functionalities.

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:

1. SQL filters (WHERE clauses) are pushed down to the vector search stage
2. This ensures that vector similarity calculations are only performed on documents that will match the final SQL criteria
3. 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

1. Clone this repository
2. Start SolrCloud with Docker:

   docker-compose up -d
   

3. Install dependencies:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install poetry
   poetry install
   

4. 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
   

5. 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.