README.md

metadata

title: Document QA System
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 Gradio for the interface and Docker for deployment.

Features

• Document Indexing: Efficiently processes and indexes documents for quick retrieval.
• Interactive Interface: Provides a user-friendly interface for querying documents.
• Dockerization: Easy to build and deploy using Docker.

Technologies

• Data source
  • Paper about Few-NERD dataset located in the data directory are used as the data source for indexing.
• Chunking
  • Document chunking is handled by all-mpnet-base-v2
• LLM
  • The system utilizes the Cohere Command R for generating responses
• Retriever, Reranker
  • Cohere Command R is used
• UI
  • The user interface is built with Gradio

Installation

Prerequisites

1. Docker:

   • Install Docker

2. Set path to the data directory, index directory:

   • Update the variables in utils/constant.py.

3. Set the API key for Cohere Command R and LLamaParse:

   • Update the CO_API_KEY and LLAMA_CLOUD_API_KEY in utils/settings.py in function configure_settings.

Using Docker

1. Clone the Repository:

   git clone <repository-url>
   cd <repository-folder>
   

2. Build the Docker Image:

   docker build -t doc-qa-system .
   

3. Run the Docker Container:

    docker run -p 7860:7860 doc-qa-system
   

4. Access the Interface:

   Open your browser and go to http://localhost:7860.

Using Python

1. Clone the Repository:

   git clone <repository-url>
   cd <repository-folder>
   

2. Install Dependencies:

   pip install -r requirements.txt
   

3. Run indexing data:

   python index.py
   

4. Run the Application:

   python app.py
   

Project structure

├── 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 Few-NERD?
• What is the Few-NERD dataset used for?
• What are NER types in dataset?
• What role does "transfer learning" play in the proposed few-shot learning system?
• What metric does the paper use to evaluate the effectiveness of the few-shot model?