gpt4-pdf-chatbot-langchain

GPT-4 & LangChain - Building a Chatbot for PDF Files

The "GPT-4 & LangChain" project offers an innovative approach to creating a chatbot that can handle large PDF files using the latest GPT-4 API. This project utilizes a powerful combination of technologies, including LangChain, Pinecone, Typescript, OpenAI, and Next.js, to facilitate the development of scalable AI/LLM apps and chatbots.

Overview of Technology Stack

• LangChain: A framework designed to simplify the creation of scalable AI applications, enabling developers to build sophisticated chatbots efficiently.
• Pinecone: A vector store where embeddings generated from your PDF files are stored, allowing similar documents to be retrieved quickly.
• Typescript and Next.js: These contribute to building the application's structure and user interface, providing a seamless experience.
• OpenAI: Leveraging the GPT-4 API, it offers the core intelligence that powers the chatbot's understanding and responsiveness.

Getting Started: Key Steps

Development Setup

1. Clone the Repository: Use the GitHub URL to clone the repo or download the ZIP file.

   git clone [github https url]
   

2. Install Required Packages:

   • First, ensure you have Yarn installed globally by running npm install yarn -g.
   • Then, execute yarn install to add all necessary packages.

3. Configuration:

   • Create a .env file by copying from .env.example, and fill in your API keys from OpenAI and Pinecone.
   • Modify the configuration file in the config folder to set your preferred namespace for storing embeddings in Pinecone.

4. Ingest PDFs:

   • Place your PDF files into the docs folder.
   • Run yarn run ingest to convert these files into embeddings that can be processed by the chatbot.

5. Launch the Application:

   • Once your PDF data is embedded and stored in Pinecone, start the application using npm run dev. This command launches the local development environment where you can interact with your chatbot.

Troubleshooting Tips

• Ensure you are using Node.js version 18 or later.
• Check the validity of your PDFs; issues may arise if they are scanned or require OCR.
• Log and verify environment variables, especially API keys and Pinecone settings.
• Keep API keys consistent across your environment to prevent configuration conflicts.

Specific Pinecone Issues

• Double-check that the Pinecone environment and index settings match those in your configuration files.
• Ensure vector dimensions are set correctly; the project specifies 1536 dimensions.
• Remember that Pinecone indexes can expire on the free tier if inactive for seven days. Regular API requests can reset this countdown.

Acknowledgments

The frontend inspiration for this project is credited to the langchain-chat-nextjs repository, which has influenced the design and development approach of this chatbot project.

By following the guidelines and using the robust technology stack this project offers, developers can effectively create a responsive chatbot tailored to handle large and complex PDF documents.