Activate vector search capabilities in Meilisearch to enable semantic similarity-based document retrieval using AI embeddings.
MIT
510,248 tools. Updated 2026-09-03 22:52
"Understanding Vector Search" matching MCP tools:
- Get a list of Vector Search endpoints from your Databricks workspace.MIT
- Activate vector search functionality in Meilisearch to enable semantic similarity-based document retrieval.
- Apache 2.0
- Search a Qdrant collection by providing a raw vector to find similar items based on vector similarity, without needing a text query.Apache 2.0
- Search Meilisearch indexes using vector embeddings to find semantically similar content, supporting hybrid text-vector searches and customizable filtering.MIT
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Search Vascue's public healthcare-ops, insurance-claims and booking docs. Public content only.
Search Vascue's public healthcare-operations, insurance-claims and clinic-booking documentation. Public content only; never send patient data, credentials or booking requests.
- Search for similar items in a Meilisearch index using vector embeddings to find content based on semantic similarity rather than exact text matches.
- Query a Databricks Vector Search index using text or vector input to retrieve similar results with optional filters and scoring.MIT
- Search OpenMetadata entities using natural language queries with vector-based semantic matching.MIT
- Search stored information using natural language queries to find semantically similar content in vector databases.
- Retrieve the k nearest nodes to a query embedding using HNSW vector similarity. Use for semantic search after creating a vector index on a specific label and property.Apache 2.0
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- Combine metadata filters with vector similarity to retrieve relevant documents. Pre-filter by tags, numeric ranges, or full-text before KNN ranking.MIT
- Search digital assets using vector or keyword queries. Use natural language filters to locate specific assets.ISC
- Search for similar documents by vector similarity, with optional scalar filters like age > 25.Apache 2.0
- Search Zvec vector stores by converting natural language queries into embeddings and retrieving similar items from a specified collection.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
- Lists available Solr 9.x environments with dense vector and hybrid query support, so you can choose where to run vector search workloads.MIT
- Create a vector index on a vector field in Baidu Vector Database to enable efficient similarity searches for AI applications.Apache 2.0