Convert text into a numeric vector for semantic search, RAG, and similarity. Supports multiple languages.
MIT
466,285 tools. Updated 2026-08-19 12:09
"Search for information about 'rag'" matching MCP tools:
- Get precise answers to any question about Lamatic.ai documentation by searching all indexed docs with RAG.MIT
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- Manage and search opencode session archives: list, inspect, archive, restore, rename, delete, full-text search, semantic RAG, and code-recall. Reads SQLite depot directly.MIT
- Generate vector embeddings for RAG and semantic search. Convert text into 4096-dimensional vectors using the NaN API.MIT
- Retrieve relevant information from RAG and Wiki sources using keyword, semantic, hybrid, or auto search strategies.MIT
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Fetch URLs and return clean Markdown for RAG — nav, ads and boilerplate stripped.
- Search personal journal entries using RAG to answer questions about past experiences, thoughts, and reflections.MIT
- Convert text into vector embeddings for semantic search, RAG, clustering, and similarity comparisons using NVIDIA NIM models.MIT
- Generate text embeddings to enable semantic search and retrieval-augmented generation (RAG) for AI applications.MIT
- Search Vega-Lite documentation to find information about charts, encodings, marks, and visualization specifications.MIT
- Retrieve detailed information about a PDF search session using the session ID from a previous search operation.MIT
- Find relevant documents in the RAG system using semantic search with customizable similarity thresholds and result limits.MIT
- Obtain statistics about a RAG vector store collection to understand its size and composition.MIT
- Convert text into semantic embeddings for similarity search, clustering, and RAG applications using Saptiva AI's embedding model.
- Initialize a new vector-based RAG project for semantic book search and organization within your Calibre ebook library.
- Generate text embeddings using BGE-M3 model to convert texts into vector arrays for RAG and search.
- Retrieve detailed information about a specific RAG project within the Calibre ebook library, including its configuration, contents, and organization for semantic search and contextual conversations.
- Add books to a RAG project for vectorization and context search, enabling semantic search and retrieval from your Calibre ebook library.
- Generate search results optimized for RAG context by providing relevant web content based on your query, with adjustable depth and result limits.
- Keep RAG search current by re-indexing catalog schema after changes. Uses schema hashing to skip unchanged schemas.Apache 2.0