RagDocs MCP Server

what is RagDocs MCP Server? RagDocs MCP Server is a Model Context Protocol (MCP) server that provides RAG (Retrieval-Augmented Generation) capabilities for semantic search and management of documentation using a Qdrant vector database and Ollama/OpenAI embeddings. how to use RagDocs MCP Server? To use RagDocs, install it via npm, configure the server with your Qdrant instance and embedding provider, and then utilize the available tools to add, search, list, and delete documents. key features of RagDocs MCP Server? Add documentation with metadata Perform semantic searches through documents List and organize documentation Delete documents Support for both Ollama (free) and OpenAI (paid) embeddings Automatic text chunking and embedding generation Vector storage with Qdrant use cases of RagDocs MCP Server? Managing large sets of documents with semantic search capabilities. Enhancing document retrieval processes in research and data management. Integrating with applications that require advanced document management features. FAQ from RagDocs MCP Server? What are the prerequisites for using RagDocs? You need Node.js 16 or higher and a Qdrant setup (local or cloud). Can I use RagDocs with OpenAI embeddings? Yes, RagDocs supports both Ollama and OpenAI embeddings. Is there a cost associated with using RagDocs? Ollama is free, while OpenAI requires a paid API key.

As an MCP (Model Context Protocol) server, RagDocs MCP Server enables AI agents to communicate effectively through standardized interfaces. The Model Context Protocol simplifies integration between different AI models and agent systems.

To use RagDocs, install it via npm, configure the server with your Qdrant instance and embedding provider, and then utilize the available tools to add, search, list, and delete documents. key features of RagDocs MCP Server? Add documentation with metadata Perform semantic searches through documents List and organize documentation Delete documents Support for both Ollama (free) and OpenAI (paid) embeddings Automatic text chunking and embedding generation Vector storage with Qdrant use cases of RagDocs MCP Server? Managing large sets of documents with semantic search capabilities. Enhancing document retrieval processes in research and data management. Integrating with applications that require advanced document management features. FAQ from RagDocs MCP Server? What are the prerequisites for using RagDocs? You need Node.js 16 or higher and a Qdrant setup (local or cloud). Can I use RagDocs with OpenAI embeddings? Yes, RagDocs supports both Ollama and OpenAI embeddings. Is there a cost associated with using RagDocs? Ollama is free, while OpenAI requires a paid API key.

Learn how to integrate this MCP server with your AI agents and leverage the Model Context Protocol for enhanced capabilities.