Shared Knowledge RAG

Knowledge Graph Memory

Create and search enduring semantic graphs for effective data organization.

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.

Knowledge Graph

Enhance natural language interactions by incorporating persistent memory and structured knowledge management using a local graph database, facilitating improved personalization and context retention.

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.

Cloudflare AutoRAG

Elevate your AI applications with precision by implementing comprehensive RAG pipelines that are fully managed for seamless operation.

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.

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 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.

Knowledge Graph Memory

Enhance your conversational memory with a cutting-edge system that manages knowledge graphs effectively. Store, retrieve, and query information seamlessly to enrich ongoing dialogues and foster lasting memory retention.

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.

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.