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
510,001 tools. Updated 2026-09-03 15:39
"A vector database for efficient similarity search and AI applications" matching MCP tools:
- Initialize a new vector collection at a specified path, define its vector and scalar fields with optional indexes, and open it for use.Apache 2.0
- Search by meaning, not exact wording. Embed a query locally, rank rows by vector similarity, and return paraphrases and synonyms with relevance scores.Apache 2.0
- Combine metadata filters with vector similarity to retrieve relevant documents. Pre-filter by tags, numeric ranges, or full-text before KNN ranking.MIT
- Set up an HNSW vector index for fast similarity search on graph nodes. Requires label, property, dimensions, and metric to index existing and future vectors.Apache 2.0
- Search for similar documents by vector similarity, with optional scalar filters like age > 25.Apache 2.0
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- Search Zvec vector stores by converting natural language queries into embeddings and retrieving similar items from a specified collection.Apache 2.0
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- Find relevant content across namespaces using natural language queries or vector similarity. Filter results by metadata or keywords for precise discovery.Apache 2.0
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