SPARQL-LLM
Enhance Large Language Models' capabilities in generating and validating SPARQL queries for specific knowledge graph endpoints.
About
This project offers a comprehensive suite of tools designed to significantly improve Large Language Models' (LLMs) ability to generate accurate and relevant SPARQL queries for various endpoints. It encompasses a full chat web service, an MCP server exposing specialized tools, and a collection of reusable Python components. By integrating Retrieval-Augmented Generation (RAG) and SPARQL query validation against endpoint schemas, the system ensures high-quality query generation, especially for large-scale knowledge graphs. It requires endpoints to include metadata like SPARQL query examples and VoID descriptions for optimal performance.
Key Features
- Automatically parse and validate federated SPARQL queries against endpoint VoID descriptions.
- Extract and load relevant metadata from SPARQL endpoints, compatible with LangChain.
- MCP server exposing tools to assist LLMs in writing SPARQL queries for a set of endpoints.
- Deployable and containerized chat system with a web UI, API, and vector database for LLM-based SPARQL query generation.
- Generate complete ShEx shapes from VoID descriptions for SPARQL endpoints.
- 63 GitHub stars
Use Cases
- Building intelligent chat systems that can generate and validate SPARQL queries.
- Integrating LLMs with knowledge graphs to retrieve and analyze structured data.
- Developing tools for data scientists and developers to interact with complex SPARQL endpoints more effectively.
