kb-mcp-server

# Embedding MCP Server A Model Context Protocol (MCP) server implementation powered by txtai, providing semantic search, knowledge graph capabilities, and AI-driven text processing through a standardized interface. ## The Power of txtai: All-in-one Embeddings Database This project leverages [txtai](https://github.com/neuml/txtai), an all-in-one embeddings database for RAG leveraging semantic search, knowledge graph construction, and language model workflows. txtai offers several key advantages: - **Unified Vector Database**: Combines vector indexes, graph networks, and relational databases in a single platform - **Semantic Search**: Find information based on meaning, not just keywords - **Knowledge Graph Integration**: Automatically build and query knowledge graphs from your data - **Portable Knowledge Bases**: Save entire knowledge bases as compressed archives (.tar.gz) that can be easily shared and loaded - **Extensible Pipeline System**: Process text, documents, audio, images, and video through a unified API - **Local-first Architecture**: Run everything locally without sending data to external services ## How It Works The project contains a knowledge base builder tool and a MCP server. The knowledge base builder tool is a command-line interface for creating and managing knowledge bases. The MCP server provides a standardized interface to access the knowledge base. It is not required to use the knowledge base builder tool to build a knowledge base. You can always build a knowledge base using txtai's programming interface by writing a Python script or even using a jupyter notebook. As long as the knowledge base is built using txtai, it can be loaded by the MCP server. Better yet, the knowledge base can be a folder on the file system or an exported .tar.gz file. Just give it to the MCP server and it will load it. ### 1. Build a Knowledge Base with kb_builder The `kb_builder` module provides a command-line interface for creating and managing knowledge bas…