Build a Large Language Model AI Chatbot using Retrieval Augmented Generation

Introduction

In this article, we will explore the process of building a large language model (LLM) app that can chat using your own data. This technique, known as retrieval augmented generation, involves feeding chunks of your data into a prompt to generate responses from the LLM. We will use tools like Lang chain, Streamlit, and Watson to create a conversational chatbot that can interact with users based on the provided context.

To begin, we will import the necessary dependencies and set up a chat interface using Streamlit. Messages will be displayed in the app, and we will ensure that the chat history is preserved for a better user experience. We will then integrate a pre-trained large language model from Lang chain to provide responses in the chat. Additionally, we will look at how custom data, such as PDF documents, can be incorporated into the chatbot using retrieval techniques.

Keywords:

• Large Language Model
• Chatbot
• Retrieval Augmented Generation
• Lang chain
• Streamlit
• Watson
• PDF
• NLP

FAQ:

1. What is retrieval augmented generation? Retrieval augmented generation is a technique that involves feeding chunks of data into a prompt to generate responses from a large language model.

2. What tools are used to build the chatbot in the article? The article mentions the use of Lang chain, Streamlit, and Watson to create a conversational chatbot.

3. How can custom data like PDF documents be integrated into the chatbot? Custom data, such as PDF documents, can be loaded into the chatbot by using techniques like vector embeddings and retrieval QA chains.