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
471,109 tools. Updated 2026-08-23 19:17
"Searching for technologies and tools related to embeddings and vector databases" 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
- Retrieve sessions related to a given session using keyword co-occurrence and semantic embeddings.Business Source 1.1
- 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
- Discover available API toolsets with descriptions, tool counts, and examples to identify relevant tools before searching.MIT
- List databases for a Hostinger account with pagination, and filter by domain or assigned status to find specific databases.MIT
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- Generate 1024-dimensional dense vector embeddings for multilingual and long text using BAAI BGE-Large. Accepts single text or array of texts for semantic search and NLP tasks.MIT
- Find similar vector embeddings in Zilliz Cloud collections using vector similarity search with optional filtering and result customization.Apache 2.0
- Retrieve all fields and their data types from a specified Fibery database and its related databases to understand data structure.MIT
- Search vector databases by combining semantic similarity with structured filters, then refine results using ranking strategies to retrieve relevant data.Apache 2.0
- Retrieve technographic tags to filter businesses by web technologies like CMS or analytics tools. Optionally narrow results with a keyword.MIT
- Retrieve memories related to an anchor memory, categorized as forward, backward, or lateral relationships based on vector similarity.MIT
- Create a Redis vector index using HNSW to enable approximate nearest neighbor search on hash-stored embeddings.MIT
- Find semantically similar thoughts to a text query via AI embeddings. Adjust result count and minimum similarity score to refine matches.MIT
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- Generate 768-dimensional dense vector embeddings from text to enable RAG and semantic search. Accepts single strings or batches, converting them into vector representations via BAAI BGE-Base.MIT
- Update the vector index for knowledge base documents. Re-embeds only changed files by default, or rebuild all embeddings from scratch when forcing a full reindex.Business Source 1.1
- List configured database connections to see available databases, drivers, and servers without exposing credentials. Use before other database tools.MIT
- Check embedding provider configuration and vector index status to diagnose why semantic search is unavailable. Compare chunk counts and rebuild missing embeddings.MIT
- Search within related memories to find solutions in specific problem contexts using semantic scoping without embeddings.MIT