Setting Up Your ChatGPT with Custom PDF Data using LangChain

Welcome to the detailed guide for setting up your own ChatGPT with custom PDF data using LangChain. This README will walk you through the process of creating a chatbot system that allows users to upload PDF files, convert them into text, and use the text to create embeddings for semantic search in a vector database. This system utilizes a large language model for chat completion and can be customized with different models and libraries.

Features

• PDF Upload: Users can upload PDF files, which are then converted into text for further processing.
• Semantic Search: The system creates embeddings for the text and uses them for efficient semantic search in a vector database.
• Customization: The chatbot system can be customized with different models and libraries to suit specific requirements.
• Vector Store Generation: Learn how to generate a vector store from text chunks using embeddings and FAISS for efficient retrieval.
• Conversational Chain: The system initializes a conversational chain for handling chat interactions.
• Information Extraction: The application is capable of extracting information from documents based on user queries.
• Use Cases: Understand the potential use cases for this application, including extracting information from large PDF documents and creating personalized chatbots based on specific data sets.

Prerequisites

Before getting started, ensure that you have the following prerequisites installed:

• Python 3.x
• LangChain framework
• OpenAI GPT-3 or other large language models
• FAISS library for efficient similarity search

Installation

1. Clone the repository to your local machine:

   git clone https://github.com/pik1989/pdfGPT.git
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

Usage

1. PDF Upload and Text Conversion:

   • Provide clear instructions on how users can upload PDF files and convert them into text for processing.

2. Customization:

   • Explain how users can customize the chatbot system with different models and libraries to suit their specific requirements.

3. Vector Store Generation:

   • Detail the process of generating a vector store from text chunks using embeddings and FAISS for efficient retrieval.

4. Conversational Chain Initialization:

   • Provide instructions for initializing the conversational chain for handling chat interactions.

5. Information Extraction:

   • Explain how the application can extract information from documents based on user queries.

6. Use Cases:

   • Highlight the potential use cases for this application, such as extracting information from large PDF documents and creating personalized chatbots based on specific data sets.

Contribution

We welcome contributions to improve this project. If you have any suggestions, bug reports, or feature requests, please feel free to open an issue or submit a pull request.

Video

Acknowledgements

• Mention any individuals or organizations you'd like to acknowledge for their contributions or support.

Support

For any questions or assistance, please reach out to us at pattnaiksatyajit89@gmail.com

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Now you're all set to share your ChatGPT with custom PDF data using LangChain on GitHub! Happy coding!