Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/sanikamal/gardening-pdf-rag
Implements a Retrieval-Augmented Generation (RAG) system for interactive question-answering based on PDF documents. Using LangChain, ChromaDB, Gradio, and OpenAI models, the system processes PDFs, extracts relevant information, and generates accurate answers to user queries.
https://github.com/sanikamal/gardening-pdf-rag
Last synced: about 11 hours ago
JSON representation
Implements a Retrieval-Augmented Generation (RAG) system for interactive question-answering based on PDF documents. Using LangChain, ChromaDB, Gradio, and OpenAI models, the system processes PDFs, extracts relevant information, and generates accurate answers to user queries.
- Host: GitHub
- URL: https://github.com/sanikamal/gardening-pdf-rag
- Owner: sanikamal
- License: mit
- Created: 2025-01-03T06:56:47.000Z (7 days ago)
- Default Branch: main
- Last Pushed: 2025-01-03T07:51:28.000Z (7 days ago)
- Last Synced: 2025-01-03T08:30:19.597Z (7 days ago)
- Language: Jupyter Notebook
- Size: 60.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Retrieval-Augmented Generation System for Interactive Question-Answering on Gardening Manual🤖
## Overview
Implements a **Retrieval-Augmented Generation (RAG)** system for interactive question-answering based on PDF documents. Using **LangChain**, **ChromaDB**, **Gradio**, and **OpenAI** models, the system processes PDFs, extracts relevant information, and generates accurate answers to user queries. Although it uses a gardening manual from [High Rocks](https://highrocks.org/) as an example, the system is flexible and can be adapted to any set of PDF documents. It automatically detects and processes all PDF files in the specified directory.
## Prerequisites and Setup Instructions 🚀
### **1. Install Python** 🐍
Ensure Python 3.11 or later is installed. If Python is not already installed, follow these steps:
- For a detailed guide on how to install Python on different operating systems, refer to the [Quick Guide for Installing Python](https://github.com/PackeTsar/Install-Python/blob/master/README.md#install-python-).
- The latest version of Python can be downloaded from the official website: [Download Python](https://www.python.org/downloads/).
Verify the installation:
```bash
python --version
```
### **2. Create a Virtual Environment** 💻
Creating a virtual environment is recommended to manage dependencies for the project.
- **For Windows**:
```bash
python -m venv venv
```
- **For MacOS/Linux**:
```bash
python3 -m venv venv
```
### **3. Activate the Virtual Environment** 🔑
After creating the virtual environment, it needs to be activated:
- **For Windows**:
```bash
venv\Scripts\activate
```
- **For MacOS/Linux**:
```bash
source venv/bin/activate
```
After activation, the terminal prompt should display `(venv)` at the beginning.
### **4. Install Project Dependencies** 📦
Install the required libraries listed in the `requirements.txt` file:
```bash
pip install -r requirements.txt
```
For MacOS/Linux, use `pip3` if necessary:
```bash
pip3 install -r requirements.txt
```
### **5. Obtain an OpenAI API Key** 🔑
An API key from OpenAI is required to access GPT models. Follow these steps:
1. Go to the [OpenAI API Keys](https://platform.openai.com/account/api-keys) page.
2. Log in or create an account.
3. Generate and copy the API key.
### **6. Configure the API Key** 🔧
Set up the API key for the project by creating a `.env` file in the root directory and adding the following line:
```
OPENAI_API_KEY=your_api_key_here
```
Alternatively, the API key can be exported in the terminal session:
- **For Windows**:
```bash
set OPENAI_API_KEY=your_api_key_here
```
- **For MacOS/Linux**:
```bash
export OPENAI_API_KEY='your_api_key_here'
```
## Start the Application ▶️
Run the chatbot application using Streamlit:
```bash
python app.py
```
The application will launch in the default web browser and can be accessed at `http://localhost:8501`.
## Contributing 💡
If you would like to contribute to this project, feel free to fork the repository, create a branch, and submit a pull request. Contributions, suggestions, and improvements are always welcome!
## License 📜
This project is open-source and licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.