Embedding MCP Server

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?

1. Building and querying knowledge graphs for research.
2. Semantic search for documents and data.
3. AI-driven text processing for various applications.
4. 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.