Generate AI-powered responses by combining real-time web search with advanced language models, ideal for complex queries requiring reasoning and synthesis across multiple sources, with contextual memory for follow-up questions.
Search your knowledge graph memory using semantic vector embeddings to find entities similar to your query, with options for hybrid search, similarity thresholds, and entity type filtering.
Search the R2R knowledge base using semantic, hybrid, or graph methods to find relevant documents and information for development, research, or debugging tasks.
Update entities by adding observations to their data in the Elasticsearch Knowledge Graph, enhancing the memory-like storage and retrieval for AI models.
Enables Claude to perform hybrid search across local documents by combining semantic vector retrieval and BM25 keyword matching for optimal context recovery. It supports multiple file formats including PDF, CSV, and Markdown, leveraging local Ollama models for private and efficient document querying.
An advanced MCP server providing RAG-enabled memory through a knowledge graph with vector search capabilities, enabling intelligent information storage, semantic retrieval, and document processing.
Enables AI agents to build and query a persistent knowledge graph with entities, relationships, and observations. Features a core index system that ensures critical information is always accessible across all memory operations.