https://github.com/eziodevio/rag-project

This project is a Retrieval-Augmented Generation (RAG) system that allows users to upload PDF documents and ask questions about the content, with responses generated using OpenAI's GPT model.
https://github.com/eziodevio/rag-project

chatgpt openai pyhon rag streamlit

Last synced: about 1 month ago
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This project is a Retrieval-Augmented Generation (RAG) system that allows users to upload PDF documents and ask questions about the content, with responses generated using OpenAI's GPT model.

• Host: GitHub
• URL: https://github.com/eziodevio/rag-project
• Owner: EzioDEVio
• Created: 2024-10-30T19:31:49.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-11-07T16:08:37.000Z (3 months ago)
• Last Synced: 2024-12-30T08:26:08.682Z (about 1 month ago)
• Topics: chatgpt, openai, pyhon, rag, streamlit
• Language: Python
• Homepage:
• Size: 80.1 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
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# EzioDevio RAG Project

This project is a Retrieval-Augmented Generation (RAG) system that allows users to upload PDF documents and ask questions about the content, with responses generated using OpenAI's GPT model.

## Features

- Upload PDF files and retrieve information from them.

- Ask questions based on the uploaded documents.

- Responses are generated in real-time using OpenAI’s `gpt-3.5-turbo` model.

## Setup Instructions

### Prerequisites

- Python 3.8+

- OpenAI API Key (instructions below on how to obtain one)

- Docker (optional for containerization)

### Installation

1. **Clone the repository**:

   ```bash

   git clone https://github.com/EzioDEVio/RAG-project.git

   cd RAG-project

2. **Create and activate a virtual environment**:

```bash

python -m venv venv

source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

```

## Project Directory Structure

![Project Directory Structure](images/directory_structure.png)

>>>>>>> fe0da40 (Save local changes before rebase)

1. **Install required packages**:

pip install -r requirements.txt

4. **Set up environment variables**:

Create a .env file in the root of your project:

```bash

OPENAI_API_KEY=your_openai_api_key_here

```

5. **Get your OpenAI API Key**:

Go to OpenAI API and create an API key.

Save this key in the .env file as shown above.

6. **Run the Application**:

```bash

streamlit run app.py

```

or 

```bash

python -m streamlit run app.py

```

The app will start on http://localhost:8501.

## Docker Instructions

1. **Build the Docker Image**:

```bash

docker build -t ezio_rag_app .

```

2. **Run the Docker Container**:

```bash

docker run -p 8501:8501 --env-file .env ezio_rag_app

```

> 💡 **Tip:** You can make notes more noticeable with emojis.

If you'd like to pass the API key directly as an environment variable when running the Docker container, you can omit the --env-file .env option and instead use the -e flag to specify the API key directly. Here’s how you can do it:

```bash

docker run -p 8501:8501 -e OPENAI_API_KEY=your_openai_api_key_here ezio_rag_app

```

> 🔥 **Important:** Don't forget to check your configuration settings!

**Ensure that .env is listed in .gitignore to avoid exposing the API key in the repository**.

**The app uses Streamlit for the UI and OpenAI for text generation**.

## Python

venv/ pycache/

## Environment variables

.env

## Streamlit cache

.streamlit/

## Docker

*.dockerignore