At a Glance

Name: Summarize private documents using RAG, LangChain, and LLMs
Price: Free CAD
Rating: 4.7 (86 reviews)
Author: kang_wang, sina_nazeri, wojciech_fulmyk

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.

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.

Offered By: IBMSkillsNetwork

Summarize private documents using RAG, LangChain, and LLMs

At a Glance

What you'll learn and achieve:

What you'll need

This Guided Project is part of the following Learning Paths