In this video, we'll build a Corrective Retrieval Augmented Generation (C-RAG) workflow which integrates Web Search as a fallback knowledge base. We will go through the C-RAG paper which introduces the Retriever and a Generator module.
We will use the LangChain framework to implement C-RAG workflow from scratch in Python. In this video we will discuss:
1. Implementing the Retrieval Augmented Generation system from scratch.
2. Integrating Web Search to build a fallback Knowledge base.
3. Building the Retrieval module that retrieves essential documents and performs knowledge refinement.
4. Building the Generator module that generates answers for the question and the knowledge base.
Codebase: https://github.com/SauravP97/AI-Engineering-101/tree/main/corrective-rag
Corrective RAG paper: https://arxiv.org/pdf/2401.15884
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