Solr Vector Search
About
Bridges Apache Solr search indexes with vector embeddings for hybrid keyword and semantic document retrieval, enabling contextual searches against structured data repositories without direct database access
Details
- Author
- allenday
- Repository
- allenday/solr-mcp
- GitHub stars
- 2
- License
- MIT License
- Categories
- Productivity, Developer Tools, Design, Workplace, File Management, AI, Search, Frontend, Database, Infrastructure
- Tags
- #integration
Jump to
- 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
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Solr Vector SearchCommand (node, npx, python, etc.)npxArguments-
Argument 1
-y -
Argument 2
@highlight/mcp-server
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
-
Argument 1
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
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.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"solr vector search": {
"env": {},
"args": [
"-y",
"@highlight/mcp-server"
],
"command": "npx"
}
}
}Linux
{
"env": [],
"args": [
"-y",
"@highlight/mcp-server"
],
"command": "npx"
}Macos
{
"env": [],
"args": [
"-y",
"@highlight/mcp-server"
],
"command": "npx"
}Windows
{
"env": [],
"args": [
"/c",
"npx",
"-y",
"@highlight/mcp-server"
],
"command": "cmd"
}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.
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