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Summarize private documents using RAG, LangChain, and LLMs

BeginnerGuided Project

Use Llama 3.3 (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.

4.7 (156 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Enrollment Count

  • 689

Skills You Will Learn

  • Artificial Intelligence, Generative AI, LLM, NLP, RAG, LangChain

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 45 minutes

Platform

  • SkillsNetwork

Last Update

  • March 14, 2025
About this Guided Project
In an age where information overload is a common challenge, the ability to efficiently process, understand, and summarize large volumes of text, especially your own private documents that you do not want external parties to access, is invaluable. Automating the process of reading, understanding, and summarizing text documents is a game-changer across various fields, from research and development to customer service and beyond. This project uses the cutting-edge capabilities of large language models (LLM), specifically from IBM watsonx.ai, along with the innovative retrieval and summarization processes provided by LangChain and Retrieval-Augmented Generation (RAG) technologies. By the end of this tutorial, you will not only grasp the theoretical knowledge of these technologies but also apply them practically by creating a chatbot. This chatbot will serve as a personal assistant that is capable of retrieving and summarizing documents based on your queries.



What you'll learn and achieve:

After completing this project, you are able to:
  • 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

To start on this journey, you need a basic understanding of Python programming and some familiarity with AI and machine learning concepts.

Instructors

Kang Wang

Data Scientist

I am a Data Scientist in the IBM. I am also a PhD Candidate in the University of Waterloo.

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Contributors

Sina Nazeri

Data Scientist at IBM

I am grateful to have had the opportunity to work as a Research Associate, Ph.D., and IBM Data Scientist. Through my work, I have gained experience in unraveling complex data structures to extract insights and provide valuable guidance.

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Wojciech "Victor" Fulmyk

Data Scientist at IBM

As a data scientist at the Ecosystems Skills Network at IBM and a Ph.D. candidate in Economics at the University of Calgary, I bring a wealth of experience in unraveling complex problems through the lens of data. What sets me apart is my ability to seamlessly merge technical expertise with effective communication, translating intricate data findings into actionable insights for stakeholders at all levels. Follow my projects to learn data science principles, machine learning algorithms, and artificial intelligence agent implementations.

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