Inkeep RAG

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.

Ragie

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

Minima (Local RAG)

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

Apify RAG Web Browser

Utilize Apify's RAG Web Browser Actor, an open-source tool, to seamlessly conduct online searches, extract website links, and deliver information formatted in Markdown.

RAG Docs

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

RAG Documentation

Experience advanced knowledge access with seamless integration of Qdrant vector search and documentation retrieval in Model Context Protocol (MCP). Unlock context-aware responses and enable semantic querying for a richer user experience.

Qdrant Docs Rag

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

RAG Documentation Search

Enhances document search with semantic vectors for contextually relevant results from specified document repositories.

RagRabbit

Unlocks the power of RagRabbit integration for website crawling, vector embedding generation, and facilitating domain-specific information retrieval through advanced search and question answering capabilities.

DocuMCP (RAG Documentation Server)

Enhance your documentation system with a cutting-edge RAG-enabled server that seamlessly connects to vector databases. Enjoy advanced semantic search features for code, documentation, and diagrams while keeping all data secure within your environment.

RAG Memory

Enhance your information retrieval with a cutting-edge system integrating vector search and graph-based relationships, enriched by a knowledge graph. Access contextual information seamlessly from persistent memory with our advanced Model Context Protocol (MCP).

RAGDocs (Vector Documentation Search)

Unlock the ability to search and retrieve semantic documentation effortlessly through vector databases. Get URL extraction, source oversight, and index queuing, paired with diverse embedding providers such as Ollama and OpenAI.

Docs RAG

Utilizing a RAG framework developed with TypeScript, LlamaIndex, and Gemini embeddings, this protocol empowers artificial intelligence to search and assess native files and Git repositories effectively.

Shared Knowledge RAG

Facilitates AI applications to retrieve data from various vector storage systems like HNSWLib and Weaviate, offering a streamlined solution for knowledge retrieval in RAG processes without the need for extensive integration efforts.

OpenWebUI RAG

Integrate Claude seamlessly with OpenWebUI's API to empower document-based RAG functions. This integration facilitates seamless file uploads and enriched queries within the chat interface, enhancing user experience and workflow efficiency.