Enhancing Data Retrieval: IBM's LangChain RAG for Up-to-Date Responses
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In this riveting IBM Technology episode, we delve into the world of using LangChain for a simple RAG example in Python. The team highlights the common issue with large language models like the IBM granite model, which sometimes lack the latest info, only trained up to 2021. To combat this, they introduce the game-changer: RAG (retrieval augmented generation). By adding a knowledge base, setting up a retriever, feeding the LLM the freshest content, and creating a prompt for questions, they revolutionize the way we interact with these models.
To kick things off, the crew walks us through the process, starting with obtaining an API key and project ID, importing essential libraries, and saving credentials. They then move on to gather data from IBM.com URLs to build a knowledge base, load documents using LangChain, and clean up the content for optimal performance. By chunking the data, vectorizing it using IBM's Slate model, and setting up a vector store as a retriever, they ensure the system is primed for action.
Next up, the team focuses on setting up the generative LLM, selecting the IBM Granite model, configuring the model parameters, and instantiating the LLM using watsonx. They then craft a prompt combining instructions, search results, and questions to provide context to the LLM. Finally, they demonstrate how to ask questions about the knowledge base, where the generative model processes the augmented context and user queries to deliver accurate responses. The model impressively tackles inquiries about the UFC announcement and IBM's services watsonx.data and watsonx.ai, showcasing the power of this innovative approach.
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Image copyright Youtube
Image copyright Youtube
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