Search conversations by conceptual meaning using vector embeddings to find related ideas beyond keyword matching.
MIT
112,291 tools. Last updated 2026-04-20 09:35
- Index project files for semantic search by creating vector embeddings, enabling AI-powered code and document retrieval within development tools.MIT
- Retrieve vector embeddings for documents to enable semantic search and AI-powered analysis within workspaces.MIT
- Manage vector embeddings for Cursor10x MCP by performing store, search, update, and delete operations to enhance AI memory retention and recall.MIT
- Regenerate vector embeddings for all published content to maintain search accuracy and relevance in LightCMS. Use after initial setup or when embeddings become outdated.MIT
- Generate numerical vector embeddings from text input using Ollama models for semantic analysis and similarity tasks.AGPL 3.0
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