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OPENRAG

Intelligent agent-powered document search

Apache-2.0

ABOUT

Enterprises struggle to ground LLMs in private data due to the complexity of setting up production RAG pipelines — connecting document parsers, vector databases, embedding models, rerankers, and LLMs requires stitching together multiple incompatible tools. OpenRAG solves this by delivering a single-package, open-source RAG platform that handles document ingestion, hybrid search, reranking, and LLM generation out of the box with a drag-and-drop workflow builder, backed by the Langflow orchestration engine and OpenSearch for production-grade retrieval.

INSTALL
uvx --python 3.13 openrag

INTEGRATION GUIDE

1. Build customer support assistants that search documentation and tickets for accurate, citation-backed answers 2. Deploy internal knowledge copilots answering employee questions using company documents and policies 3. Perform natural language analytics on large collections of reports, contracts, or regulatory documents 4. Create agentic RAG workflows with multi-agent coordination, re-ranking, and context-aware follow-ups 5. Enable drag-and-drop RAG pipeline construction for non-technical team members via Langflow visual interface

TAGS

ragretrieval-augmented-generationopensearchlangflowdoclingibmdocument-searchagentic-ragvector-search