Haystack

Open Source RAG Framework designed to be 'Conventional but Configurable', streamlining the creation of RAG applications with efficient data management, embeddings generation, and vector storage.

Python toolkit for efficient RAG research providing 36 pre-processed benchmark datasets and 23 state-of-the-art RAG algorithms in a unified, modular framework for reproduction and development.

Programming framework for RAG and AI applications with cutting-edge optimization capabilities, featuring the lowest framework overhead and automatic improvement based on example data.

All-in-one open-source AI framework for semantic search, LLM orchestration and language model workflows. Combines vector indexes (sparse/dense), graph networks and relational databases. This is an OSS framework.

Document parsing platform delivering strong content fidelity and precision with low hallucination rates. Achieves 100% accuracy on simple tables and 75% on complex structures with comprehensive enterprise document support.

A premium vector database capability within the DataRobot Generative AI platform that stores chunked unstructured text and their embeddings for retrieval-augmented generation (RAG). Users can create vector database objects, connect supported data sources from the DataRobot Data Registry, configure embeddings and chunking, and attach these vector databases to LLM blueprints in the playground to ground model responses in proprietary data.