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.
Remove specific entities from the Elasticsearch Knowledge Graph memory zone, optionally deleting associated relations for cleaner data management and improved graph accuracy.
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.
Enables algorithmic stock trading analysis by combining ARIMA, ARIMA-GARCH, and XGBoost models to generate buy/sell/hold signals with risk management, portfolio comparison, and volatility analysis for various market sectors.