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Build private local RAG search and Q&A over personal documents.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "OpenRAG MCP Server" yet — see the docs or source repo.
Connect to the OpenRAG MCP Server and answer: “What conclusions did I record over the past year about choosing a vector database?” based on my local PDFs, notes, and Markdown files, with citations.
A concise answer grounded in local documents, with relevant citations or file references.
Use Traditional, Contextual, and Graph RAG on my project documents to answer: “What are the dependencies between services in the system architecture?” Then compare the differences in the three results.
Responses from all three retrieval strategies, plus a comparison of strengths, weaknesses, and best-fit scenarios.
Using contracts and meeting notes stored locally in OpenRAG MCP Server, summarize the key milestones, owners, and action items for the current collaboration project without relying on external cloud services.
A structured project summary listing milestones, owners, and next action items.
Let AI securely query private local documents with persistent memory.
Index documents and retrieve relevant context for better LLM responses.
Turn unstructured documents into a searchable knowledge base for AI agents.
Enable MCP apps to process, retrieve, and query multimodal documents with RAG.
Search, retrieve, and answer questions from PDF documents with RAG.
Retrieve relevant context and metadata from Qdrant using natural language queries.