Implementing Retrieval Augmented Generation by integrating Keras NLP with LangChain and HuggingFace

Описание: In this tutorial, we will dive into the implementation of Retrieval Augmented Generation (RAG) by integrating Keras NLP, LangChain, and HuggingFace. You'll learn how to use these tools to build efficient retrieval-based systems that combine large language models (LLMs) with powerful retrieval mechanisms to generate more accurate and contextually relevant responses.

🔍 Topics Covered:
Introduction to Retrieval Augmented Generation (RAG)
Installing required packages to start with
Setting up Keras NLP for LLM integration
Overriding base class from LangChain to integrate keras_nlp model
Defining global context
Leveraging LangChain for document indexing and retrieval
Using HuggingFace model to generate vector embeddings
Defining LangChain RetrievalQA chain with components like LLM, chain type and retriever.
Generating response with created RetrievalQA chain.

References:
https://python.langchain.com/docs/how...

The script used in the video is available at:
https://gitlab.com/Nayan.1989/youtube...

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