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QA Bot with LangChain and LLM to Answer Questions from Doc

AdvancedGuided Project

Learn to build a question-answering bot using LangChain and large language models (LLMs). This project will guide you through loading documents, creating embeddings, and using vector databases for efficient information retrieval. You’ll integrate tools like document loaders, text splitters, and Gradio to construct a functional QA system capable of delivering accurate, context-aware answers. This hands-on project is perfect for applications in customer support, research, or any domain requiring quick and intelligent data access.

Language

  • English

Topic

  • Artificial Intelligence

Skills You Will Learn

  • Natural Language Processing, Information Retrieval, Large Language Models, Python, LangChain

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 60 minutes

Platform

  • SkillsNetwork

Last Update

  • August 26, 2025
About this Guided Project
In this guided project, discover the exciting world of building a question-answering (QA) bot using LangChain and large language models (LLMs). Harness the power of natural language processing (NLP) to create bots capable of delivering precise and contextually relevant responses. By integrating LangChain's framework with LLMs, you'll revolutionize how information is retrieved, making it perfect for environments where quick data access is crucial, such as customer support or research. This project, which you can complete in just 60 minutes, will elevate your applications by imbuing them with intelligent query-response capabilities, providing significant value to both users and organizations.

What You'll Learn

By the end of this project, you will be able to:
  • Understand how to load documents into the LangChain framework for natural language processing tasks.
  • Use LLMs to generate accurate and contextually appropriate responses.
  • Integrate and streamline information retrieval processes within your applications.
  • Wrap together multiple components like document loaders, text splitters, embedding models, and vector databases to construct a fully functional QA bot.
  • Leverage LangChain and LLMs to solve the problem of retrieving and answering questions based on content from large PDF documents.

What You'll Need

Before starting this project, you should have:
  • Familiarity with Python programming, as it will be used throughout the project.
  • Access to the IBM Skills Network Labs environment, where necessary tools like Docker are pre-installed.
  • A current version of a web browser such as Chrome, Edge, Firefox, Internet Explorer, or Safari to ensure full compatibility with the platform.

Instructors

Ricky Shi

Data Scientist at IBM

Ricky Shi is a Data Scientist at IBM, specializing in deep learning, computer vision, and Large Language Models. He applies advanced machine learning and generative AI techniques to solve complex challenges across various sectors. As an enthusiastic mentor, Ricky is committed to helping colleagues and peers master technical intricacies and drive innovation.

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Kang Wang

Data Scientist

I was a Data Scientist in the IBM. I also hold a PhD from the University of Waterloo.

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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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Kunal Makwana

Data Scientist

I’m a passionate Data Scientist and AI enthusiast, currently working at IBM on innovative projects in Generative AI and machine learning. My journey began with a deep interest in mathematics and coding, which inspired me to explore how data can solve real-world problems. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and leveraging cloud technologies to extract meaningful insights from complex datasets.

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Contributors

Hailey Quach

Data Scientist

Hi, I'm Hailey. I enjoy teaching others to build creative and impactful AI projects. By day, I’m a Data Scientist at IBM; by night, an Honors BSc student at Concordia University in Montreal, always exploring new ways to combine learning with innovation.

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