Project: Generative AI Applications with RAG and LangChain
My last module in my IBM course!
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What I learned!
- Exploring essential techniques for loading, preparing, and structuring documents to build effective retrieval-augmented generation (RAG) applications using LangChain. You will learn how to use LangChain’s document loaders to import content from various sources, apply best practices for document ingestion, and implement text-splitting strategies to enhance model responsiveness;
- Learning how to embed documents using watsonx’s embedding model and store these embeddings using vector databases, such as Chroma DB and FAISS. You will explore the role of embeddings in RAG pipelines, configure vector stores...
- Learning how to implement RAG to improve information retrieval, set up user interfaces using Gradio, and construct a question-answering bot that leverages LLMs and LangChain to respond to queries from loaded documents.
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