README.md

---
title: CSRD GPT
emoji: 🌿
colorFrom: blue
colorTo: green
sdk: gradio
python_version: 3.10.0
sdk_version: 3.22.1
app_file: app.py
pinned: true
---

---
title: CSRD GPT
emoji: 📊
colorFrom: red
colorTo: green
sdk: gradio
sdk_version: 4.13.0
app_file: app.py
pinned: false
---

## Introduction

Python Version used is: 3.10.0

## Built With

- [Gradio](https://www.gradio.app/docs/interface) - Main server and interactive components
- [OpenAI API](https://platform.openai.com/docs/api-reference) - Main LLM engine used in the app
- [HuggingFace Sentence Transformers](https://huggingface.co/docs/hub/sentence-transformers) - Used as the default embedding model

## Requirements

> **_NOTE:_** Before installing the requirements, rename the file `.env.example` to `.env` and put your OpenAI API key there !

We suggest you to create a separate virtual environment running Python 3 for this app, and install all of the required dependencies there. Run in Terminal/Command Prompt:

```bash
git clone https://github.com/Nexialog/RegGPT.git
cd RegGPT/
python -m venv env
```

In UNIX system:

```bash
source venv/bin/activate
```

In Windows:

```bash
venv\Scripts\activate
```

To install all of the required packages to this environment, simply run:

```bash
pip install -r requirements.txt
```

and all of the required `pip` packages will be installed, and the app will be able to run.

## Usage of run_script.py

This script is used for processing PDF documents and generating text embeddings. You can specify different modes and parameters via command-line arguments.

### Process Documents
To process PDF documents and extract paragraphs and metadata, use the following command:

```bash
python run_script.py --type process_documents 
```

You can also use optional arguments to specify the folder containing PDFs, the output data folder, minimum paragraph length, and merge length.

### Generate Embeddings
To generate text embeddings from the processed paragraphs, use the following command:

```bash
python run_script.py --type generate_embeddings
```

This command will use the default embedding model, but you can specify another model using the `--embedding_model` argument.

### Process Documents and Generate Embeddings
To perform both document processing and embedding generation, use:

```bash
python run_script.py --type all
```

### Command Line Arguments

- `--type`: Specifies the operation type. Choices are `all`, `process_documents`, or `generate_embeddings`. (required)
- `--pdf_folder`: Path to the folder containing PDF documents. Default is `pdf_data/`. (optional)
- `--data_folder`: Path to the folder where processed data and embeddings will be saved. Default is `data/`. (optional)
- `--embedding_model`: Specifies the model to be used for generating embeddings. Default is `sentence-transformers/multi-qa-mpnet-base-dot-v1`. (optional)
- `--device`: Specifies the device to be used (CPU or GPU). Choices are `cpu` or `cuda`. Default is `cpu`. (optional)
- `--min_length`: Specifies the minimum paragraph length for inclusion. Default is `300`. (optional)
- `--merge_length`: Specifies the merge length for paragraphs. Default is `700`. (optional)

### Examples

```bash
python run_script.py --type process_documents --pdf_folder my_pdf_folder/ --merge_length 800
```

```bash
python run_script.py --type generate_embeddings --device cuda
```

### How to use Colab's GPU

1. Create your own [deploying key from github](https://github.com/Nexialog/RegGPT/settings/keys)
2. Upload the key to Google Drive on the path : `drive/MyDrive/ssh_key_github/`
3. Upload the notebook `notebooks/generate_embeddings.ipynb` into a colab session (or use this [link](https://colab.research.google.com/drive/1E7uHJF7gH_36O9ylIgWhiAjHpRJRyvnv?usp=sharing))
4. Upload the pdf files on the same colab session on the path : `pdf_data/`
5. Run the notebook on GPU mode and download the folder `data/` containing embeddings and chnukns

## How to Configure a New BOT

1. Put all pdf files in a folder at the same repository (We recommend using the folder name : 'pdf_data') 
2. Run the python sciprt 'run_script.py' as explained above. 3-Configure the BOT in config by following the steps bellow:

In order to configure the chatbot, you need to modify the config.py file that contains the CFG_APP class. Here's what each attribute in the class means:

### Basic Settings

- `DEBUG`: Debugging mode
- `K_TOTAL`: The total number of retrieved docs
- `THRESHOLD`: Threshold of retrieval by embeddings
- `DEVICE`: Device for computation
- `BOT_NAME`: The name of the bot
- `MODEL_NAME`: The name of the model

### Language and Data

- `DEFAULT_LANGUAGE`: Default language
- `DATA_FOLDER`: Path to the data folder
- `EMBEDDING_MODEL`: Embedding model

### Tokens and Prompts

- `MAX_TOKENS_REF_QUESTION`: Maximum tokens in the reformulated question
- `MAX_TOKENS_ANSWER`: Maximum tokens in answers
- `INIT_PROMPT`: Initial prompt
- `SOURCES_PROMPT`: Sources prompt for responses

### Default Questions

- `DEFAULT_QUESTIONS`: Tuple of default questions

### Reformulation Prompt

- `REFORMULATION_PROMPT`: Prompt for reformulating questions

### Metadata Path

- `DOC_METADATA_PATH`: Path to document metadata

## How to Use This BOT

Run this app locally by:

```bash
python app.py
```

Open [http://127.0.0.1:7860](http://127.0.0.1:7860) in your browser, and you will see the bot.