$ loading_
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.
Retrieve and process docs with vector search to enrich AI responses.
Build and query vector knowledge bases for semantic search and RAG workflows.
Turn unstructured documents into a searchable knowledge base for AI agents.
Search code and technical docs privately with local-first RAG for developers.
Intelligent RAG tool that chooses between private knowledge and web search.
Build production-grade RAG systems with hybrid retrieval and agentic reasoning.