Fetch the available thematic categories from the Open Knowledge Graphs catalog to filter searches and compare coverage across datasets.
MIT
384,444 tools. Last updated 2026-08-03 15:22
"Understanding Document Embeddings, Knowledge Graphs, and Vector Representations" matching MCP tools:
- 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
- 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
- Semantically search the SHOAL oracle and fleet vector index for patterns, crates, solutions, and knowledge. Returns ranked results with conservation metadata.MIT
- Delete a knowledge document from Anam AI using its unique ID. Removes the specified document from your knowledge base.MIT
Matching MCP Servers
- AlicenseBqualityAmaintenanceEnables building and querying vector-based knowledge graphs with node and edge management and semantic search.Last updated515MIT
- Alicense-qualityBmaintenanceEnables integrating RAG into AI agents via MCP server, supporting multiple vector database technologies.Last updated11MIT
Matching MCP Connectors
AI reasoning checks any document against known international standards before your agent acts on it.
Knowledge Base von designare.at – Michael Kanda, Web & KI aus Wien. Semantische Suche über RAG.
- Remove a knowledge document from the system by specifying its document ID.MIT
- Preview what will be removed (chunks, images) from the RAG knowledge base, then confirm to delete the document and all associated data.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
- 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
- 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 3.0
- Query relations valid at a specific date, enabling time-travel analysis on temporal knowledge graphs.MIT
- Add nodes to knowledge graphs for organizing components, events, requirements, or concepts. Supports multiple graph types including topology, timelines, and knowledge bases.MIT
- Rebuild vector embeddings for semantic and hybrid search. Use force=True to rebuild from scratch after embedding model changes, or converge to the full-text search chunk set without force.MIT
- Report local knowledge index sizes and document budget to monitor and manage storage capacity.MIT
- Compute local vector embeddings for indexed code nodes to enable semantic search. Incrementally updates only changed nodes.MIT
- Convert ephemeral insights into permanent long-term memory. Automatically chunks text, generates vector embeddings, and stores segments for semantic recall.Apache 2.0
- Search Fodda's expert-curated knowledge graphs using hybrid vector and keyword methods to find relevant trends and articles across industries like retail, beauty, and sports.Inno Setup
- Permanently delete a registered media asset by removing storage files, vector embeddings, and all associated metadata. This action cannot be undone.MIT