Geeksfino_kb-mcp-server

Geeksfino_kb-mcp-server

0
research_and_dataknowledge_and_memory

Embedding MCP Server is a Model Context Protocol server that integrates txtai for semantic search and knowledge graph capabilities. It allows users to create, manage, and query knowledge bases efficiently, offering an all-in-one solution for embeddings databases and AI-driven text processing.

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, 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.

Build a Knowledge Base with kb_builder

  • Process documents from various sources (files, directories, JSON)
  • Extract text and create embeddings
  • Build knowledge graphs automatically
  • Export portable knowledge bases

Start the MCP Server

The MCP server provides a standardized interface to access the knowledge base:

  • Semantic search capabilities
  • Knowledge graph querying and visualization
  • Text processing pipelines (summarization, extraction, etc.)
  • Full compliance with the Model Context Protocol

MCP Server Configuration

Configure using environment variables or command-line arguments. Options include embedding paths, host settings, and transport methods. LLM clients can configure through JSON files.