Zania AI Challenge

Overview

This project aims to create an AI agent that leverages the capabilities of a large language model. The agent can extract answers based on the content of a large PDF document and post the results on Slack using OpenAI's GPT-3.5-turbo model.

Features

• Extracts text from a PDF document.
• Splits the document text into manageable chunks.
• Uses OpenAI's GPT-3.5-turbo to answer questions based on the document content.
• Posts answers to a specified Slack channel.
• Returns structured JSON output with questions and corresponding answers.

Technologies Used

• Python
• OpenAI GPT-3.5-turbo
• PyPDF2
• Slack SDK
• Python-dotenv

Setup

Prerequisites

• Python 3.7 or higher
• pip (Python package installer)
• Git

Installation

1. Clone the repository:

   git clone https://github.com/LSRAO/submission.git
   cd submission

2. Create a Virtual Environment and activate it:

   python -m venv .venv
   source .venv/bin/activate  # On Windows use `.venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

4. Set up environment variables. Create a .env file in the root directory of the project and add the following:

   OPENAI_API_KEY=your-open-api-key
   SLACK_API_TOKEN=your-slack-api-key
   SLACK_CHANNEL=slack-channel-the-app-is-integrated-with

Usage

1. Ensure your virtual environment is activated:

   source .venv/bin/activate  # On Windows use `.venv\Scripts\activate`

2. Run the main script:

   python main.py

Example

To answer the question "What is the name of the company?" based on the content of handbook.pdf, run:

python main.py

Enter the File Path for the PDF, followed by the questions that needs to be answered. Output will be An updated JSON file with

Project Structure

submission/
│
├── data/
│   └── handbook.pdf
│
├── src/
│   ├── __init__.py
│   ├── pdf_extractor.py
│   ├── openai_agent.py
│   └── slack_client.py
│
├── .env
├── .gitignore
├── requirements.txt
├── README.md
└── main.py

Enhancements

Accuracy Improvements

• Fine-tune the OpenAI model with domain-specific data.
• Implement advanced text preprocessing techniques.
• Use a more sophisticated chunking strategy that considers context boundaries.
• Use RAG pipeline for efficient and accurate search.
• Using Hybrid search from vectordbs.

Code Quality Improvements

• Modularize the code further by separating concerns into different modules.
• Implement logging for better debugging and monitoring.
• Add more extensive error handling and input validation.

Contact

If you have any questions/suggestions, feel free to reach out to me at lsr22299@gmail.com.