Log time-stamped events such as ideas, notes, or tasks, and index them for vector search to build a chronological memory of occurrences.
AGPL 3.0
270,963 tools. Last updated 2026-07-07 23:23
"An open-source vector database for similarity search and AI applications" matching MCP tools:
- Search your personal memory layers by combining vector similarity with keyword matching to retrieve relevant episodic, semantic, and procedural memories.AGPL 3.0
- Search across screen, voice, and clipboard entries to find content semantically related to any query. Returns a unified ranked list with source tags for open-ended recall spanning multiple data types.AGPL 3.0
- 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
- 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
- Open a saved vector collection from disk for read-write or read-only use, enabling vector search and document retrieval.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
- 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 Redis documentation and knowledge base to find information on concepts, data structures, features, and use cases including caching, session management, and semantic search.MIT
- 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
- Search indexed source and documentation using a blended BM25 and vector relevance score. Filter results by source type with include flags like git, papertrail, or generated.MIT
- Retrieves all supported vector database providers, including required and optional config fields and setup notes. Use this to prepare the configuration needed for syncing data to a vector database.MIT
- Find relevant content across namespaces using natural language queries or vector similarity. Filter results by metadata or keywords for precise discovery.Apache 2.0
- Generate vector embeddings from text for semantic search, RAG, clustering, or similarity tasks. Choose between query or document input type and adjust model quality and dimensionality.MIT
- Search memories by natural-language query with optional importance and similarity filters. Returns ranked results with similarity scores.MIT
- Generate dense embedding vectors from text for similarity search using OpenAI's embedding API.Apache 2.0
- Search persistent memory using semantic similarity to retrieve relevant memories and related knowledge graph facts.MIT
- Find past experiences that match your current task by searching episodic memory with vector similarity.MIT
- Search US patent applications and grants to find titles, inventors, applicants, status, and classification codes.MIT