Search personal journal entries using RAG to answer questions about past experiences, thoughts, and reflections.
MIT
212,100 tools. Last updated 2026-06-19 07:24
"Search for information about 'rag'" matching MCP tools:
- Generate text embeddings to enable semantic search and retrieval-augmented generation (RAG) for AI applications.MIT
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- Find relevant documents in the RAG system using semantic search with customizable similarity thresholds and result limits.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.
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- 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.
- Retrieve relevance-ranked web content with actual page text, tables, and code for AI grounding and RAG pipelines.
- Find diverse nearest neighbors by balancing relevance and diversity, reducing redundant results. Ideal for RAG pipelines needing broad coverage.Apache 2.0
- Execute a complete RAG workflow to answer questions using retrieved context documents. Handles embedding, semantic search, and answer generation with direct quotes.MIT
- Filter documents by metadata before ranking by vector similarity to enable production RAG and semantic search pipelines.MIT
- 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
- Retrieve a list of all knowledge folders in your organization, each containing documents for RAG capabilities.MIT
- Query Vectara's RAG system to retrieve search results and generate contextual responses using specified corpus keys and API parameters for accurate information extraction.Apache 2.0
- Search the web for current information, news, articles, and websites to find up-to-date content, research topics, or answer questions about recent events.Apache 2.0
- Combine BM25 keyword search with vector ANN search in a single pass. Use for RAG when either semantic or keyword search alone is insufficient.MIT
- Lists all available RAG categories indexed by RAGMap to help you identify suitable retrieval servers for your task.MIT
- Find relevant information from curated skills and documents using natural language queries. Semantic search leverages vector embeddings for more accurate results than keyword search.MIT