Introduction
This article delves into the creation of an AI – powered Retrieval – Augmented Generation (RAG) and Streamlit chatbot. This chatbot is designed to answer user questions based on custom – uploaded documents. By leveraging open – source Large Language Model (LLM) models, it offers a cost – free solution. The interface is built using Streamlit, making it user – friendly and accessible. Similar to popular AI applications like ChatGPT and Gemini, but with the ability to work with custom knowledge bases, let’s explore how to develop this innovative chatbot.
Learning Objectives
Understand the concept of LLM and Retrieval – Augmented Generation in the context of chatbots. Learn the step – by – step process of performing RAG in a Jupyter Notebook, including document splitting, embedding, storing, answer retrieval, and generation. Experiment with different open – source LLM models, along with parameters like temperature and max_length, to optimize chatbot performance. Gain proficiency in developing a Streamlit application as the user interface, and understand how to use LangChain memory for better conversation continuity. Also, develop skills in integrating new documents into the chatbot’s knowledge base through Streamlit.
RAG and Streamlit Chatbot
Retrieval – Augmented Generation (RAG) is a technique that augments large language models by providing additional context. This context is typically sourced from a custom – built knowledge base. Streamlit, on the other hand, is a Python framework for quickly creating web apps. Together, they offer several advantages. RAG ensures more accurate and relevant answers by utilizing a custom knowledge base, while Streamlit provides a user – friendly interface for interacting with the chatbot.
Implementing RAG in Jupyter Notebook
The process of developing RAG can be summarized in three main steps: splitting documents, embedding and storing, and answer retrieval and generation. In the document – splitting step, text is divided into chunks. For example, using two source documents (one about a manga and another about snakes from Wikipedia), the code reads pdf and txt files and splits them into chunks with specified sizes and overlaps. Embedding and storing involve capturing the semantic information of the text chunks and saving them as high – dimensional vectors in a vector store like FAISS. Finally, in answer retrieval and generation, when a user asks a question, the system searches for similar text chunks in the vector store and sends them to the LLM to generate an answer.
LangChain Memory
When conversing with a chatbot, it’s desirable for the chatbot to remember previous chats. LangChain offers different types of memory, such as Conversation Buffer Memory, Conversation Buffer Window Memory, Conversation Token Buffer Memory, and Conversation Summary Buffer Memory. For example, Conversation Buffer Window Memory can be used to save a specified number of the latest chats, enabling the chatbot to better understand follow – up questions.
Streamlit Experiment: Developing the User Interface
After completing the RAG experiment in a Jupyter Notebook, a Streamlit – based user interface is created. The application has important files like rag_chatbot.py (the main file for running the app and containing the chatbot page), document_embeddings.py (for processing document embeddings), and rag_functions.py (containing utility functions). The interface allows users to set LLM parameters, upload documents, and have conversations with the chatbot. It also provides options for creating or merging vector stores.
Conclusion
Large Language Models and Retrieval – Augment Generation have opened up new possibilities for answering questions based on specific documents. Through this article, we’ve learned the step – by – step process of developing a RAG and Streamlit chatbot, from document processing in Jupyter Notebook to creating a user – friendly interface with Streamlit. We’ve also explored the importance of memory in chatbots and how to use various libraries and tools effectively.
