Activate vector search capabilities in Meilisearch to enable semantic similarity-based document retrieval using AI embeddings.
MIT
134,097 tools. Last updated 2026-05-16 00:57
"Understanding Vector Search" matching MCP tools:
- Activate vector search functionality in Meilisearch to enable semantic similarity-based document retrieval.
- Search for similar items in a Meilisearch index using vector embeddings to find content based on semantic similarity rather than exact text matches.
- Search Meilisearch indexes using vector embeddings to find semantically similar content, supporting hybrid text-vector searches and customizable filtering.MIT
- Search stored information using natural language queries to find semantically similar content in vector databases.
- Filter and retrieve Manticore Search documentation files using regex patterns to efficiently find multiple topics in a single call.Apache 2.0
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- Filter documents by metadata before ranking by vector similarity to enable production RAG and semantic search pipelines.MIT
- Retrieve a vector stored as a binary blob in a Redis hash and convert it to a list of floats.MIT
- Search memories by natural-language query with optional importance and similarity filters. Returns ranked results with similarity scores.MIT
- Auto-calibrates vector search threshold by analyzing random memory pairs and setting a statistical cutoff, adapting to embedding model and corpus without requiring labeled data.MIT
- Find similar vector embeddings in Zilliz Cloud collections using vector similarity search with optional filtering and result customization.Apache 2.0
- Find the K nearest neighbors to a query vector among vectors stored in Redis hash data structures.MIT
- Create a vector index on a vector field in Baidu Vector Database to enable efficient similarity searches for AI applications.Apache 2.0
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- Persist a numerical vector as a field in a Redis hash. Provide the hash key, field name (default 'vector'), and the vector to store.MIT
- 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
- Retrieve relevant memories for an agent using hybrid search across vector, FTS5, and keyword indices. Filter by channel and exclude known contents to avoid duplication.MIT
- Switch between databases in Baidu Cloud Vector Database to manage different datasets for vector search operations.Apache 2.0
- Retrieve all table names from the Baidu Vector Database to view available data collections for vector search operations.Apache 2.0