RagDocs MCP Server
MCP server for RAG-based document search and management
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.
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.