langflow-ai/openrag
Openrag
OpenRAG is an agentic retrieval-augmented generation framework and containerized stack. It provides a vector search engine for indexing unstructured documents and a Model Context Protocol server that exposes ingestion and semantic search tools to external AI assistants.
The system distinguishes itself through a visual AI orchestration interface, allowing users to build retrieval pipelines via a drag-and-drop designer rather than manual code. It utilizes agentic workflows that coordinate multiple agents and re-ranking steps to improve response accuracy, and it allows the definition of agent skills through a standardized markdown format.
The platform covers comprehensive document ingestion pipelines to parse unstructured data, enterprise semantic search capabilities, and containerized deployment options with GPU acceleration support. It also includes server interface authentication and user role synchronization for access control.
Features
- Agentic Workflow Orchestration - Coordinates multiple AI agents and re-ranking steps to refine document retrieval and improve response accuracy.
- Agentic RAG Platforms - Provides an end-to-end platform combining reasoning agents and retrieval-augmented generation for complex data fetching.
- Model Context Protocol Servers - Implements a Model Context Protocol server to expose document ingestion and semantic search tools to external AI assistants.
- Model Context Protocol Integrations - Provides a Model Context Protocol implementation to expose search and ingestion tools to external AI assistants.
- Semantic Vector Search - Indexes unstructured documents into embeddings to enable high-performance semantic retrieval via a vector search engine.
- Vector Search Engines - Integrates a production-grade vector search engine for indexing unstructured documents and performing semantic queries.
- Visual AI Programming - Provides a node-based visual interface for designing and iterating on complex AI retrieval pipelines.
- Visual Pipeline Designers - Ships a drag-and-drop visual designer for building retrieval-augmented generation workflows without manual code.
- Document Ingestion Pipelines - Parses unstructured real-world data into a searchable format for use in retrieval-augmented generation pipelines.
- Containerized RAG Services - Ships a full-stack RAG deployment consisting of a backend, search index, and dashboard packaged for Docker.
- Agent Skill Definitions - Defines AI agent capabilities and task-specific operations using a standardized markdown format.
- Enterprise AI Search - Provides AI-powered semantic search capabilities for indexing and retrieving information from large internal document datasets.
- GPU Acceleration - Configures containerized environments to utilize GPUs for accelerated computation and processing of AI models.
- Vector Search Indexes - Implements high-performance vector search indexes to enable efficient retrieval of document embeddings.
- Docker Compose Deployments - Orchestrates the build and launch of services using containerized configurations tailored for different hardware.
- Containerized Application Deployments - Packages the application into a minimal runtime image using a multi-stage build process for consistent deployment.
- Containerized Service Deployments - Packages backend and search services into minimal runtime images using a multi-stage build process.
- AI and Agents - Platform for Retrieval-Augmented Generation.
