Journal 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.

Basic Memory

Transform your understanding with a cutting-edge knowledge system that creates a lasting semantic graph using markdown, right on your device.

Memory Bank

An advanced documentation system designed to create and manage interconnected Markdown files that cover a project's information from overarching objectives to specific technical aspects. It offers powerful search features across the entire document repository.

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.

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.

Markdown Library

Discover a unique protocol designed to efficiently organize and present Markdown knowledge repositories. Utilizing advanced features for tag-driven browsing, text exploration, and information extraction from extensive data sets.

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.

Better Qdrant

Enhance your AI systems with seamless integration to the Qdrant vector database for advanced semantic search functions using diverse embedding services. Streamline document handling and similarity assessments directly within the chat interface for an enhanced user experience.