Embedding MCP Server
by Geeksfino
The Embedding MCP Server is a Model Context Protocol (MCP) server implementation powered by txtai. It provides semantic search, knowledge graph capabilities, and AI-driven text processing through a standardized interface.
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What is Embedding MCP Server?
The Embedding MCP Server is a server that provides a standardized interface to access a knowledge base built using txtai. It leverages txtai's capabilities for semantic search, knowledge graph construction, and language model workflows.
How to use Embedding MCP Server?
First, build a knowledge base using the provided kb_builder tool or directly with txtai's Python API. Then, start the MCP server, pointing it to the knowledge base. Configure your LLM client to use the MCP server by providing the server's command and arguments in a configuration file.
Key features of Embedding MCP Server
Semantic search capabilities
Knowledge graph querying and visualization
Text processing pipelines (summarization, extraction, etc.)
Full compliance with the Model Context Protocol
Portable knowledge bases
Causal boosting mechanism for enhanced relevance scoring
Use cases of Embedding MCP Server
Question answering systems
Chatbots with knowledge base integration
Semantic search applications
Knowledge discovery and exploration
FAQ from Embedding MCP Server
What is txtai?
What is txtai?
txtai is an all-in-one embeddings database for RAG leveraging semantic search, knowledge graph construction, and language model workflows.
How do I build a knowledge base?
How do I build a knowledge base?
You can use the kb_builder tool or txtai's programming interface to create a knowledge base from various data sources.
Can I use a knowledge base built with txtai directly?
Can I use a knowledge base built with txtai directly?
Yes, as long as the knowledge base is built using txtai, it can be loaded by the MCP server.
How do I configure the MCP server?
How do I configure the MCP server?
The MCP server is configured using environment variables or command-line arguments. YAML files are only used for configuring txtai components during knowledge base building.
What is the causal boosting mechanism?
What is the causal boosting mechanism?
The causal boosting mechanism enhances search relevance by identifying and prioritizing causal relationships in queries and documents.