cognita and oreilly-retrieval-augmented-gen-ai
The first tool is an open-source framework for building modular RAG applications, while the second is a demonstration of augmenting LLMs with real-time data using RAG, agents, and GraphRAG; thus, the second project could be considered an example or tutorial for concepts that might be implemented using the first framework.
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 oreilly-retrieval-augmented-gen-ai
sinanuozdemir/oreilly-retrieval-augmented-gen-ai
See how to augment LLMs with real-time data for dynamic, context-aware apps - Rag + Agents + GraphRAG.
This project helps AI developers build applications that can answer questions using up-to-date, external information. You'll learn how to feed real-time data into large language models (LLMs) to get more accurate and context-aware responses. It's designed for developers with Python skills and some background in machine learning and natural language processing who want to create dynamic, intelligent applications.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work