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---
title: "Paper-based RAG"
emoji: "📄"
colorFrom: "blue"
colorTo: "indigo"
sdk: gradio
sdk_version: 5.8.0
app_file: app.py
python_version: 3.11.0
models:
- sentence-transformers/all-mpnet-base-v2
tags:
- question-answering
- gradio
- LLM
- document-processing
---
# Document QA System
Document Question-Answering system that utilizes LlamaIndex for document indexing, generation, and retrieval and Gradio for the user interface.
## Technologies
- Data source
- [Paper about BERT](https://arxiv.org/pdf/1810.04805) located in the data directory are used as the data source for indexing.
- Chunking
- Document chunking is handled by [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- LLM
- The system utilizes the `gpt-4o-mini` for generating responses
- Retriever, Reranker
- gpt-4o-mini is used
- UI
- The user interface is built with Gradio
## Installation
### Prerequisites
1. **Docker**:
- [Install Docker](https://docs.docker.com/get-docker/)
2. **API keys**
- [OpenAI](https://platform.openai.com/api-keys)
- [LLamaParse](https://docs.cloud.llamaindex.ai/llamaparse/getting_started/get_an_api_key):
### Using HuggingFace Spaces
1. Follow the link to the [paper-based-rag](https://huggingface.co/spaces/Gepe55o/paper_based_rag) on Spaces.
2. Upload your paper for indexing or use the default [paper](https://arxiv.org/pdf/1810.04805) about BERT.
### Using Docker
1. **Build the Docker Image**:
```bash
docker build -t doc-qa-system .
```
2. **Run the Docker Container**:
```bash
docker run -p 7860:7860 doc-qa-system
```
4. **Access the Interface**:
- Open your browser and go to `http://localhost:7860`.
### Using Python
1. **Install Dependencies**:
```bash
pip install -r requirements.txt
```
2. **Add paper to the data directory**:
- Add the paper you want to index to the `data` directory or use default [paper](https://arxiv.org/pdf/1810.04805) about BERT.
2. **Run indexing data**:
```bash
python index.py
```
3. **Run the Application**:
```bash
python app.py
```
## Project structure
```bash
├── app.py # Gradio application
├── main.py # Main script for answering queries
├── utils/ # Utility functions and helpers
│ ├── constant.py # Constant values used in the project
│ ├── index.py # Handles document indexing
│ ├── retriever.py # Retrieves and ranks documents
│ ├── settings.py # Configuration settings
├── data/ # Directory containing documents to be indexed
├── index/ # Stores the generated index files
│ ├── default__vector_store.json
│ ├── docstore.json
│ ├── graph_store.json
│ ├── image__vector_store.json
│ ├── index_store.json
├── requirements.txt # Python dependencies
├── Dockerfile # Docker configuration
├── README.md # Project documentation
```
## Example questions
- What is the pre-training procedure for BERT, and how does it differ from traditional supervised learning?
- Can you describe how BERT can be fine-tuned for tasks like question answering or sentiment analysis? |