RAG-GPT: Chat with any documents and summarize long PDF files with Langchain | Gradio App

People & Blogs

Introduction

Introduction to RAG and its Functionality

In this article, we will explore the capabilities of RAG (Retrieval-Augmented Generation) through the design and implementation of a chatbot, named RAG-GPT. This chatbot will allow users to interact with various documents, retrieve answers to specific questions, and generate summaries from lengthy PDF files. To create this RAG chatbot, we will utilize several libraries, including Gradio for the user interface, OpenAI's embedding model and GPT-3.5 for language generation, and Langchain and Chroma for managing the retrieval aspect of our chatbot.

Demo of the RAG-GPT Chatbot

Before delving into the technical details, let's take a look at a demo of what the RAG-GPT chatbot can do. The chatbot has three primary functionalities:

  1. Question and Answer (Q&A) with Existing Documents: The bot can retrieve relevant information from a precompiled vector database containing various documents.

  2. Document Upload: Users can upload a document during the chat, allowing the system to create a new vector database from that uploaded document for immediate interaction.

  3. Summarization of PDF Files: The bot is capable of summarizing lengthy PDF documents, extracting key information from them, regardless of their length.

In the demo, we prepared three documents: a research paper on the CLIP model, another on Vision Transformers, and a lecture by Sam Altman. Users can ask specific questions about these documents, and the chatbot retrieves relevant chunks of text to formulate precise answers. The sidebar also displays the most relevant content retrieved to enhance user interaction.

How It Works

To implement the RAG system, we follow these main steps:

  1. Preparing the Vector Database: This includes loading, cleaning, chunking the documents, generating embeddings, and creating the vector database.

  2. Content Retrieval: This involves taking a user query, generating its embedding, searching through the vector database, and retrieving the most relevant chunks.

  3. Response Synthesis: The chatbot prepares an input for the language model that combines the user query, the retrieved content, and any chat history.

  4. Summarization Feature: This feature allows users to upload a PDF document, which the chatbot can then summarize by processing the document page by page.

Techniques for RAG Implementation

We utilized three notable techniques to enhance our RAG system:

  1. Basic RAG: This conventional technique creates chunks of text with overlaps, ensuring that adjacent chunks retain context for better comprehension by the model.

  2. Sentence Retrieval: This technique involves treating each sentence as a chunk and retaining context by including preceding and following sentences in the input provided to the language model.

  3. Auto-Merging Retrieval: This advanced technique takes into account parent and child nodes in the database structure. It retrieves both relevant and missing nodes based on the query to enrich the context provided to the language model.

Setting Up the Project

The project structure consists of a config folder for settings, a data folder containing the documents and resultant vector databases, and a source folder that includes all relevant code. By running specific Python scripts, we can set up the vector database and facilitate user interaction through the Gradio app.

Conclusion

The RAG-GPT chatbot stands out by enabling users to chat with any document and efficiently summarize long PDF files. By combining retrieval mechanisms with modern language models, we present a powerful tool for information access and summarization.

In the next project, we will extend the capabilities of the chatbot using Streamlit, introducing web search functionalities to improve its efficiency and response quality.

Keywords

  • RAG
  • Chatbot
  • Gradio
  • Langchain
  • Vector Database
  • Document Upload
  • Summarization
  • Q&A
  • Embeddings
  • GPT-3.5

FAQ

Q1: What libraries are used in the RAG-GPT project?
A1: The project utilizes Gradio for the user interface, OpenAI’s embedding model and GPT-3.5 for language generation, along with Langchain and Chroma for managing the retrieval component.

Q2: What are the main functionalities of the RAG-GPT chatbot?
A2: The chatbot allows users to perform Q&A with existing documents, upload new documents for real-time interaction, and summarize lengthy PDF files.

Q3: How does the RAG system retrieve information from documents?
A3: The RAG system retrieves information by first generating embeddings for both the user query and the documents, followed by a vector search to find the most relevant content.

Q4: What techniques improve the performance of the RAG chatbot?
A4: The chatbot employs basic RAG techniques, sentence retrieval, and auto-merging retrieval to enhance user experience and retrieval precision.

Q5: Can the chatbot summarize documents of any length?
A5: The chatbot effectively summarizes PDF files, though for extremely long documents, considerations around API limits and context lengths may need to be addressed.