Combine results from multiple vector queries (e.g., dense + sparse) using weighted or RRF re-ranking to find relevant documents across different embedding spaces.
Apache 2.0
460,818 tools. Updated 2026-08-18 06:19
"A local vector-based search engine for personal documents" matching MCP tools:
- Combine metadata filters with vector similarity to retrieve relevant documents. Pre-filter by tags, numeric ranges, or full-text before KNN ranking.MIT
- Search for similar documents by vector similarity, with optional scalar filters like age > 25.Apache 2.0
- Find relevant content across namespaces using natural language queries or vector similarity. Filter results by metadata or keywords for precise discovery.Apache 2.0
- Convert text into a numeric vector for semantic search, RAG, and similarity. Supports multiple languages.MIT
- Converts plain text documents into dense vector embeddings and upserts them into a Zvec collection for storage.Apache 2.0
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Congressional Documents — full-text search and retrieval over the official
- Perform a KNN vector similarity search on vectors stored in Redis hash data structures, returning the nearest neighbors for a given query vector.MIT
- Start a browser-based authorization, poll for user approval, and store the returned MCP personal token.MIT
- Search for similar vectors in Baidu Vector Database tables while filtering results by scalar attributes to find relevant data points efficiently.Apache 2.0
- Search personal memory layers using vector similarity and keyword fusion to retrieve relevant episodic, semantic, or procedural information.AGPL 3.0
- Search for documents using content-based retrieval and metadata filters. Specify search class and property conditions to narrow results.Apache 2.0
- Retrieve relevant personal memories by searching across episodic, semantic, and procedural layers using vector similarity and keyword matching fused via reciprocal rank fusion.AGPL 3.0
- Search FHIR documents for relevant information using vector embeddings. Retrieve answers from indexed notes and documents with a query and document ID.MIT
- Check the local Yggdrasil memory engine's health by confirming running status, stored-memory count, and semantic search availability. Useful for diagnosing engine failures before retrying other operations.AGPL 3.0
- Search markdown corpus with keyword or vector search on IP documents. Apply filters for energy, depth, project, or TODOs. Set result limit and minimum depth to retrieve targeted documents.MIT
- Displays personal agent configuration state while concealing baseline paths and local machine binding values for security.MIT
- Run DuckDB SQL queries on frontmatter metadata from markdown files, with optional semantic search to find similar documents based on content.MIT
- Index workspace documents into vector storage for search and memory. Use during initial setup or to rebuild the entire index from scratch.AGPL 3.0
- Create a new vector store to organize documents for semantic search and RAG. Attach files, set expiration, and configure chunking strategy.MIT
- Build, refresh, or check the status of a local hybrid-search index for Zotero library items, combining keyword and vector search. Run before semantic searches or after large library changes.MIT