Here's the rewritten README file for the project:

# ChatGPT Chatbot for PDF Files

Create a ChatGPT chatbot using GPT-4 API to answer questions from multiple large PDF files. This project uses LangChain, Pinecone, TypeScript, OpenAI, and Next.js to build a scalable AI chatbot.

## Table of Contents

- [Development](#development)
  - [Clone the Repository](#clone-the-repository)
  - [Install Dependencies](#install-dependencies)
  - [Set up Environment Variables](#set-up-environment-variables)
  - [Configure Pinecone Namespace](#configure-pinecone-namespace)
  - [Update Chat Model](#update-chat-model)
- [Ingest PDF Files](#ingest-pdf-files)
- [Run the App](#run-the-app)
- [Troubleshooting](#troubleshooting)
- [Credits](#credits)

## Development

### Clone the Repository

```bash
git clone [github https url]

Install Dependencies

pnpm install

Set up Environment Variables

1. Copy .env.example to create a new .env file.

2. Fill in the required API keys, environment, and index name from OpenAI and Pinecone.

Example .env file:

OPENAI_API_KEY=

PINECONE_API_KEY=
PINECONE_ENVIRONMENT=
PINECONE_INDEX_NAME=

Configure Pinecone Namespace

In the config folder, replace the PINECONE_NAME_SPACE with the desired namespace for storing your embeddings on Pinecone when you run pnpm run ingest. This namespace will be used for queries and retrieval later.

Update Chat Model

In utils/makechain.ts, change the QA_PROMPT for your own use case. Change modelName in new OpenAIChat to gpt-3.5-turbo if you don't have access to gpt-4. Please verify outside this repo that you have access to gpt-4, otherwise, the application will not work with it.

Ingest PDF Files

1. Add your PDF files or folders containing PDF files inside the docs folder.

2. Run the following script to ingest and embed your docs:

npm run ingest

3. Check the Pinecone dashboard to verify your namespace and vectors have been added.

Run the App

After successfully adding the embeddings and content to Pinecone, run the app:

pnpm run dev

Launch the local development environment and type a question in the chat interface.

Troubleshooting

Refer to the issues and discussions sections of this repo for solutions. If you encounter errors, follow the general troubleshooting steps provided in this README.

Credits

The frontend of this repo is inspired by langchain-chat-nextjs.