SciPhi-AI/R2R

Features

• Agentic RAG Platforms - Operates as a comprehensive platform using LLMs and reasoning agents for multi-step retrieval-augmented generation.
• Agentic RAG Development - Provides a framework for building intelligent, self-correcting retrieval systems using agentic reasoning.
• Agentic Retrieval Frameworks - Employs reasoning agents to decompose complex queries into multiple retrieval steps and synthesize the results.
• Knowledge Graph Construction - Implements automated extraction of entities and relationships from ingested data to build connected knowledge bases.
• Retrieval Augmented Generation - Implements a system that grounds AI responses in verified factual knowledge through a retrieval-augmented generation pipeline.
• Retrieval-Augmented Agents - Combines reasoning agents with retrieval tools to perform multi-step data fetching for context-aware responses.
• Knowledge Graphs - Builds a knowledge graph that maps entity relationships to enable structured data traversal alongside vector search.
• Hybrid Search Engines - Integrates vector-based semantic retrieval with traditional keyword-based indexing for high-accuracy data discovery.
• Hybrid Vector-Keyword Indexing - Combines dense vector embeddings with traditional keyword matching to improve document retrieval accuracy.
• Knowledge Graph Indexing Engines - Extracts entities and relationships from unstructured data to build connected knowledge graph representations.
• Multimodal Search - Manages the ingestion and retrieval of multimodal content like text and images via hybrid search.
• Deep Research Execution - Uses multi-step reasoning and collaborative agents to fetch information and resolve complex research questions.
• AI Knowledge Management - Organizes internal multimodal content into searchable databases to support AI-driven factual grounding.
• Automated Research Platforms - Coordinates AI agents to conduct research across internal bases and the web to resolve complex questions.
• Multimodal Normalization - Parses diverse file formats into a normalized text representation for consistent indexing and retrieval.
• Multimodal Document Ingestion - Parses various file types, including PDFs and images, to prepare multimodal data for indexing.
• Hybrid Retrieval - Combines vector similarity and full-text keyword matching to enhance retrieval accuracy within large datasets.
• REST APIs - Provides a programmable REST API to integrate agentic retrieval and factual grounding into external applications.
• RESTful - Exposes retrieval and ingestion logic through a standardized RESTful API gateway for external integration.
• Application Data Isolation - Provides collection-based namespaces to enforce strict data separation and access control for multi-tenant environments.
• User Access Management - Provides administrative control of user identities and implements collection-based data isolation for multi-tenant access.
• Application Frameworks - Framework for rapid development of production-ready RAG systems.
• Retrieval Augmented Generation - Platform for building and deploying RAG applications.

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