RAG Documentation

Crawl4AI RAG

Enhance your knowledge access by leveraging a cutting-edge Model Context Protocol (MCP) that combines web crawling and RAG capabilities. This innovative approach allows for seamless retrieval and storage of website content in vector databases, paving the way for advanced semantic search functionalities across crawled data.

Qdrant

Enhance AI systems by storing and accessing vector-based memories efficiently.

Ragie

Enhances connectivity with Ragie's knowledge repository system for streamlined retrieval and extraction of data from extensive datasets, optimizing search and information access.

GraphRAG

Enhance your document search experience with a potent combination of Neo4j graph database and Qdrant vector database. Uncover semantic connections and expand structural context by seamlessly following relationships.

Minima (Local RAG)

Efficiently access and fetch contextual information from nearby documents for RAG applications.

RAG Docs

Enhances information retrieval through semantic search functionality and a vector database (Qdrant), facilitating streamlined access to extensive document repositories.

Qdrant with OpenAI Embeddings

Unlock the potential of AI applications by seamlessly integrating them with Qdrant vector databases through the innovative Model Context Protocol (MCP). This cutting-edge protocol leverages OpenAI embeddings to empower semantic search capabilities, facilitating contextual document retrieval and enhancing knowledge base query processes.

RDF Explorer

Discover the potential of delving into RDF Knowledge Graphs with the Model Context Protocol (MCP). Unleash the power of SPARQL queries, comprehensive full-text search capabilities, and advanced visualization tools in various operational modes, including local file and endpoint settings.

ChromaDB

Enhance your natural language processing and information retrieval projects with seamless integration of advanced capabilities from ChromaDB vector database. Experience optimized semantic document search, storage, and retrieval functionalities for enhanced efficiency.

Journal RAG

Easily search and retrieve personal notes and reflections from your markdown journal using advanced vector database technology, enhancing the way you recall past memories, ideas, and events.

Rust Local RAG

Discover a cutting-edge Model Context Protocol (MCP) offering swift local document access and management. Leveraging Rust for unparalleled PDF handling capabilities and semantic search powered by Ollama embeddings, this protocol efficiently indexes PDF files within designated folders. Enjoy rapid document retrieval sans reliance on external solutions.

Pinecone Vector DB

Utilize Pinecone's vector databases to enhance semantic search capabilities and RAG functionality.

Qdrant Knowledge Graph

Enhance your applications with seamless integration of a knowledge graph and advanced semantic search features. Streamline the storage, retrieval, and querying processes for structured data, enabling context-aware functionalities.

Qdrant Docs Rag

Efficiently capture and retrieve real-time contextual information using vector-based search with Qdrant technology.

QDrant RagDocs

Employs Qdrant vector database and embeddings for enhanced semantic search and documentation organization, facilitating Retrieval-Augmented Generation within the Model Context Protocol (MCP) framework.