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Store and retrieve text semantically with local vector memory for conversations.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-rag-local" yet — see the docs or source repo.
Store the following project document snippets in the local knowledge base and optimize them for semantic retrieval. I will ask questions later:\n1. Release process: requirement review, development scheduling, testing acceptance, canary release.\n2. Incident escalation: P1 respond within 15 minutes, P2 within 1 hour.
The text is stored in the local vector database and can be accurately recalled in later conversations.
Search local memory for content most related to 'high-priority incident response time' and return the original passage with a brief summary.
Returns the most relevant knowledge passages, such as P1/P2 response rules, with a concise summary.
Remember these preferences: give the conclusion first, then steps; prefer Python for code examples; keep answers concise. Follow these preferences in future responses.
The preferences are saved in retrievable memory, and future conversations automatically reference this long-term context.
Search code and technical docs privately with local-first RAG for developers.
Search and retrieve local documents semantically for faster AI-powered knowledge access.
Provide persistent local semantic memory for MCP tools to store and search notes.
Search local markdown docs and meeting transcripts semantically without cloud or API keys.
Search local documents with vector similarity for RAG answers.
Build and query vector knowledge bases for semantic search and RAG workflows.