Fetch the available thematic categories from the Open Knowledge Graphs catalog to filter searches and compare coverage across datasets.
MIT
632,540 tools. Updated 2026-10-03 07:47
"Understanding Document Embeddings, Knowledge Graphs, and Vector Representations" matching MCP tools:
- Generate vector representations for texts via Forge's hosted API to power semantic search, RAG, clustering, or similarity; use query/document input types.MIT
- Save web pages or raw notes into a persistent memory vault using automated text extraction, OpenGraph metadata parsing, and vector embeddings for reliable semantic recall.MIT
- 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
- Generate text embeddings (vector representations) using OpenAI-compatible models, with support for x402 wallet or API key authentication.MIT
Matching MCP Servers
- AlicenseBqualityBmaintenanceEnables building and querying vector-based knowledge graphs with node and edge management and semantic search.510 npm29 PyPIMIT
- AlicenseNot gradedqualityCmaintenanceServes a central playbook hub that provides situational guidance to LLM agents, lets clients list available playbooks, fetch procedural guidance, and apply graph mutations for persistent self-evolution.MIT
Matching MCP Connectors
Expert-curated knowledge, brand, research & earnings intelligence — 46 tools, 220+ graphs.
AI reasoning checks any document against known international standards before your agent acts on it.
- Semantically search the SHOAL oracle and fleet vector index for patterns, crates, solutions, and knowledge. Returns ranked results with conservation metadata.MIT
- Refresh embeddings and reindex content by synchronizing a knowledge base. Provide the unique ID to update its state.MIT
- Delete a knowledge document from Anam AI using its unique ID. Removes the specified document from your knowledge base.MIT
- Remove a knowledge document from the system by specifying its document ID.MIT
- Retrieve the download URL for a knowledge document by providing its unique document ID.MIT
- 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
- Store typed knowledge (observation, decision, learning, error, pattern) with tags, importance, and optional relation. Embeddings enable semantic retrieval.MIT
- Preview what will be removed (chunks, images) from the RAG knowledge base, then confirm to delete the document and all associated data.MIT
- Retrieve verified firearms information from Woody's knowledge base using vector search. Get raw document chunks with relevance scores for fact-checking or finding specifications.MIT
- Store precomputed embedding vectors for multiple requirements in one call, keeping requirement text private and data local. Ensures consistent vector dimensions across all stored embeddings.MIT
- Updates the vector index with current knowledge base docs. Re-embeds changed files by default; use force to rebuild all embeddings from scratch after changing the embedding model.Business Source 1.1
- Retrieve and filter knowledge graphs to obtain graph IDs and node information for subsequent operations like adding edges or querying node details.MIT
- Create and manage per-team knowledge bases with vector-indexed documents. Agents search via hybrid cosine similarity and keyword fallback at runtime.AGPL 3.0Destructive
- Ingest long-form text as a document to split it into chunks, generate embeddings, and add graph nodes, replacing any existing document with the same title.Apache 2.0
- Rebuild vector embeddings for semantic and hybrid search. Default converges the index to the current vault; force=true rebuilds from scratch after changing the embedding model.MIT