retrieval-augmented-generation and Retrieval-Augmented-Generation-RAG
These are complementary educational resources—one provides reference implementations from an official RAG bootcamp while the other offers a deeplearning.ai course implementation—designed to be studied together to understand RAG architectures from multiple pedagogical perspectives.
About retrieval-augmented-generation
VectorInstitute/retrieval-augmented-generation
Reference Implementations for the RAG bootcamp
Implements five distinct RAG pipeline examples—web search, document QA, SQL databases, cloud storage, and biomedical literature—using LangChain and LlamaIndex to demonstrate core techniques including chunking, embeddings, sparse/dense retrieval, and reranking. The PubMed implementation provides a complete end-to-end workflow with evaluation metrics via the Ragas framework, while the evaluation module focuses on assessing RAG pipeline quality through test set construction.
About Retrieval-Augmented-Generation-RAG
Aravinda89/Retrieval-Augmented-Generation-RAG
Retrieval Augmented Generation (RAG) deeplearning.ai
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work