Summarize private documents using RAG, LangChain, and LLMs
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Learn how to summarize private documents using Retrieval-Augmented Generation (RAG), LangChain, and LLMs. Master techniques to extract, analyze, and condense key information from documents.
At a Glance
Use Llama 3 (on IBM watsonx.ai), LangChain, and RAG to enable LLMs to retrieve information from your own private document. Learn to split, embed, and summarize vast amounts of texts with advanced LLMs, crafting a smart agent that not only retrieves and condenses information, but also remembers your interactions. If you’re looking to revolutionize data handling, this tutorial offers hands-on experience in AI-driven document management, setting a new standard in efficiency.
What you’ll learn and achieve:
- Master document processing techniques: Learn to split and embed documents in formats that LLMs can efficiently process, making large volumes of text easily manageable.
- Utilize advanced LLMs: Gain hands-on experience with IBM watsonx.ai, choosing the best LLMs for your document processing needs to achieve high-quality outcomes.
- Customize information retrieval: Implement various retrieval chains from LangChain, adapting your document retrieval process for different purposes, and enhancing the precision of information extraction using promote templates.
- Build a smart agent: Develop an agent that integrates LLMs, LangChain, and RAG technologies for an interactive experience and is capable of efficiently retrieving and summarizing documents based on user queries.
What you’ll need
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