Activate the experimental vector search feature in Meilisearch to enhance search capabilities with AI-driven semantic understanding through the MCP server interface.
Search Meilisearch indexes using vector embeddings to find semantically similar content, supporting hybrid text-vector searches and customizable filtering.
Enables semantic search across indexed documents using vector embeddings. Index GitHub repositories and URLs to perform natural language queries with AI-enhanced contextual results.
Enables comprehensive search and analysis of Claude Code conversation history using full-text search, optional semantic vector search, and conversation management tools. Provides fast SQLite-based indexing with role-based filtering, project organization, and hybrid search capabilities combining keyword and semantic matching.
Enables semantic search over local notes and documents using natural language queries. Supports multiple file types (Markdown, Python, HTML, JSON, CSV, text) with fast local embeddings and persistent ChromaDB vector storage.