cognita and Controllable-RAG-Agent
These are **competitors** — both provide end-to-end RAG frameworks for production question-answering, with Cognita offering a modular, open-source platform while Controllable-RAG-Agent emphasizes graph-based algorithms for complex query handling, forcing users to choose one architectural approach over the other.
About cognita
truefoundry/cognita
RAG (Retrieval Augmented Generation) Framework for building modular, open source applications for production by TrueFoundry
This framework helps developers quickly build, organize, and deploy Retrieval Augmented Generation (RAG) applications that can answer questions based on specific documents or data. It takes in various document types (text, audio, video) and uses them to power a question-answering system. Data scientists and machine learning engineers who need to move RAG prototypes from notebooks to production-ready systems would use this.
About Controllable-RAG-Agent
NirDiamant/Controllable-RAG-Agent
This repository provides an advanced Retrieval-Augmented Generation (RAG) solution for complex question answering. It uses sophisticated graph based algorithm to handle the tasks.
Implements a deterministic graph-based agent that breaks down complex questions through multi-step reasoning—anonymizing queries to avoid pre-trained knowledge bias, decomposing tasks into retrieval or answer generation steps, and verifying outputs against source documents. Built on LangChain and FAISS with Streamlit visualization, it processes PDFs into chunked content, LLM-generated summaries, and quote databases to enable grounded, hallucination-resistant responses.
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