Filter documents by metadata before ranking by vector similarity to enable production RAG and semantic search pipelines.
MIT
127,274 tools. Last updated 2026-05-05 13:07
"A vector database for efficient similarity search and AI applications" matching MCP tools:
- Diagnose embedding setup or perform manual similarity checks by creating vector embeddings for short texts. Returns the vector and dimension without indexing.MIT
- Create a vector index on a vector field in Baidu Vector Database to enable efficient similarity searches for AI applications.Apache 2.0
- Retrieve relevant AI agent memories using hybrid vector, full-text, and keyword search with optional deep search and channel filtering.MIT
- Find similar vector embeddings in Zilliz Cloud collections using vector similarity search with optional filtering and result customization.Apache 2.0
- Resume a paused Zilliz Cloud vector database cluster to restore data access and processing capabilities for AI applications.Apache 2.0
Matching MCP Servers
- AlicenseBqualityCmaintenanceMCP server that allows Claude AI to interact directly with MySQL databases, enabling query execution and table information retrieval through natural language.Last updated6164MIT
- Alicense-qualityCmaintenanceProvides AI assistants with a standardized interface to interact with the Todo for AI task management system. It enables users to retrieve project tasks, create new entries, and submit completion feedback through natural language.Last updated39Apache 2.0
Matching MCP Connectors
Search for local businesses worldwide. Structured data optimized for AI agents. • Search Millions of businesses over 49 countries (Europe, Northamerica, Southamerica, Asia, Oceania) • Quality & demand scoring for every business • Ranking based on real user click-through data
4 web-search tiers (x402 USDC on Base) - simple/medium/deep/cached. Free health.
- Search vector databases by combining semantic similarity with structured filters, then refine results using ranking strategies to retrieve relevant data.Apache 2.0
- Set up a Redis 8 vector similarity index using HNSW to perform approximate nearest neighbor search on hash-stored documents with float32 vectors.MIT
- Search text content in Baidu Vector Database tables using BM25 similarity with attribute filtering to retrieve relevant results.Apache 2.0
- Search for similar vectors in Baidu Vector Database tables while filtering results by scalar attributes to find relevant data points efficiently.Apache 2.0
- Remove a vector index from a table in Baidu Vector Database to manage storage or modify search structures.Apache 2.0
- Rebuild a vector index in Baidu Vector Database to optimize search performance and maintain data integrity for AI applications.Apache 2.0
- Create a new vector database in Baidu Cloud to store and organize vector embeddings for AI applications.Apache 2.0
- Convert files into vector embeddings to enable AI-powered semantic search and content analysis.MIT
- Search Neo4j vector indexes using text queries to find similar nodes with sanitized properties and similarity scores for GraphRAG applications.MIT
- Index content artifacts defined in .socraticodecontextartifacts.json into a vector database for semantic search. Re-indexes existing artifacts to update embeddings.AGPL 3.0
- Execute Cypher queries with vector and fulltext search capabilities to retrieve and analyze graph data for GraphRAG applications.MIT
- Adds document files (PDF, DOCX, TXT, MD) to a local vector database for semantic search, enabling private document retrieval without cloud services.
- Add multiple context entries to a vector database in one batch operation for efficient bulk indexing and storage of semantic information.MIT
- View all documents stored in your local vector database, showing file paths and chunk counts for each ingested file.