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https://github.com/younis-ali/rag-pipeline-app

Implement RAG using LangChain and HuggingFace embedding models
https://github.com/younis-ali/rag-pipeline-app

fastapi huggingface-embeddings langchain question-answering rag streamlit

Last synced: 3 months ago
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Implement RAG using LangChain and HuggingFace embedding models

Host: GitHub
URL: https://github.com/younis-ali/rag-pipeline-app
Owner: younis-ali
License: mit
Created: 2024-09-14T05:31:02.000Z (5 months ago)
Default Branch: main
Last Pushed: 2024-09-14T15:09:45.000Z (5 months ago)
Last Synced: 2024-10-17T03:26:20.040Z (4 months ago)
Topics: fastapi, huggingface-embeddings, langchain, question-answering, rag, streamlit
Language: Jupyter Notebook
Homepage:
Size: 8.99 MB
Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

# RAG QA Bot

This application allows users to upload PDF files, create a vector database from the document using open-source HuggingFace embeddings, and ask questions related to the PDF content using a Retrieval-Augmented Generation approach. The app integrates with LangChain Framework, OpenAI's LLM and HuggingFace embeddings.

## Features
- Upload a PDF file and save it locally. Later we can create the API to delete the old files.
- Create a vector database from the PDF's content using HuggingFace model `sentence-transformers/all-mpnet-base-v2`
- Ask questions about the PDF content.
- View the context used for answering the questions that is toggleable via a checkbox.
- The POC of RAG pipeline is tested in using `rag_pipeline.ipynb`

## Screenshots of the RAG app

![first](https://github.com/user-attachments/assets/4c720ae0-14ba-4829-9dff-e0ec37621aa2)

![third](https://github.com/user-attachments/assets/00a9baaa-5a10-4274-9348-16d3e42ea53e)

![Screenshot from 2024-09-14 20-05-29](https://github.com/user-attachments/assets/077b3d5b-60d1-4c1e-b9bb-b7d698362c29)

## Installation
### Clone the Repository
```bash
git clone https://github.com/yourusername/rag-qa-bot.git
cd rag-qa-bot
```
### Set up a Virtual Environment (optional but recommended)
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```

### Install Dependencies
```bash
pip install -r requirements.txt
```

### The main packages are:
- `streamlit`: For the web UI.
- `PyPDFLoader`: To extract content from PDF files.
- `langchain`: For embeddings, document chunking, and question-answering.
- `faiss-cpu`: For vector store creation and retrieval.
- `openai`: To integrate with OpenAI's language models.

## Configuration

We provide a `config.json` file in the root directory, this will allow you to select the models at your choice, with the following details:

```json
{
"embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
"openai_api_key": "your_openai_api_key",
"openai_model": "gpt-3.5-turbo",
"vector_db_path": "./vector_store"
}
```
## Usage

### Running the App
To launch the Streamlit web app, run the following command in your terminal:

```bash
streamlit run main.py
```
browse the url `http://localhost:8501/`

## How It Works

1. **PDF Upload**: The user uploads a PDF file using the Streamlit file uploader.
2. **Document Chunking**: The PDF content is split into manageable chunks using the `RecursiveCharacterTextSplitter` api fo LangChain.
3. **Embeddings Generation**: The chunks are passed through a HuggingFace embedding model to generate embeddings.
4. **Vector Store Creation**: The embeddings are stored in a FAISS-based vector store, which is then saved locally.
5. **Question Answering**: When a user asks a question, the system retrieves the relevant context from the vector store and generates an answer using OpenAI's LLM.

## Project Structure