MCP for semantic hybrid search on knowledge base

MCP for Semantic Hybrid Search on Knowledge Base

This MCP tool provides a robust solution for implementing semantic hybrid search capabilities directly within your knowledge base. Leveraging the power of sqlite-vec for vector embeddings and FTS5 for full-text search, it enables sophisticated querying of your textual data. The system is designed for developers seeking to integrate advanced search functionalities into their AI applications, offering a flexible and efficient approach to information retrieval.

What it Does

The core functionality of this MCP is to create a searchable index of your text-based knowledge base. It achieves this by combining two powerful indexing techniques: semantic search using vector embeddings generated by sqlite-vec , and traditional full-text search powered by FTS5 . This hybrid approach allows for queries that understand the meaning and context of your data (semantic) while also being precise with keyword matching (full-text). The optional inclusion of a cross-encoder reranking step further refines search results by providing a more nuanced understanding of query-document relevance.

Key Features

• Hybrid Search: Combines semantic vector search with FTS5 full-text indexing for comprehensive querying.
• SQLite-Native: Built upon SQLite, offering a lightweight and embedded database solution.
• Vector Embeddings: Integrates with sqlite-vec for efficient storage and retrieval of vector representations.
• FTS5 Integration: Utilizes SQLite's powerful FTS5 extension for fast and flexible text searching.
• Optional Reranking: Supports cross-encoder reranking for enhanced result accuracy.
• Developer-Focused: Designed for integration into custom AI development workflows.

Who it's For

This tool is specifically designed for AI developers and engineers working on projects that require advanced knowledge base search. It is ideal for those building applications that need to understand user intent beyond simple keyword matching, such as:

• Developers creating AI-powered chatbots and virtual assistants.
• Teams building internal documentation search systems.
• Researchers and analysts needing to query large datasets of text.
• Anyone looking to implement a sophisticated, semantic-aware search layer within their existing infrastructure.