Overview
What is the Embedding MCP Server?
The Embedding MCP Server is a Model Context Protocol (MCP) server implementation that utilizes txtai to provide semantic search, knowledge graph capabilities, and AI-driven text processing through a standardized interface.
How to use the Embedding MCP Server?
To use the server, you can build a knowledge base using the kb_builder command-line tool or directly through Python scripts. Once the knowledge base is created, you can start the MCP server and access it via a standardized interface.
Key features of the Embedding MCP Server?
- Unified vector database combining various data types.
- Semantic search capabilities that understand meaning beyond keywords.
- Automatic knowledge graph construction from data.
- Portable knowledge bases that can be easily shared.
- Extensible pipeline for processing various data formats.
- Local-first architecture ensuring data privacy.
Use cases of the Embedding MCP Server?
- Building and querying knowledge graphs for research.
- Semantic search for documents and data.
- AI-driven text processing for various applications.
- Creating portable knowledge bases for sharing and collaboration.
FAQ from the Embedding MCP Server?
- Can I use the MCP server without the knowledge base builder?
Yes, you can create a knowledge base using txtai's programming interface directly.
- Is the MCP server suitable for production use?
Yes, it is designed for extensibility and can be configured for production environments.
- How do I install the Embedding MCP Server?
You can install it via conda or from source, following the provided installation instructions.