IntelliExtract-AI

Harnessing LLM for Intelligent Information Extraction.

IntelliExtract-AI is an innovative application designed to facilitate interactive data analysis and visualization through natural language conversations with various sources,i.e. csv, text, pdf, database, web url.

• Introduction
• Prerequisites
• Installation
• Getting Started
• Usage
• Features
• Contributing
• License

Before getting started with the Multiple-CSV ChatApp with LLM, ensure you have the following prerequisites installed on your system:

• Python 3.10
• Streamlit
• Pandasai for data manipulation
• Matplotlib for data visualization
• The python-dotenv package for environment variable management
• An active OpenAI account with access to API services

To set up the Multiple-CSV ChatApp with LLM on your local machine, follow these steps:

1. Clone the repository from GitHub:

   git clone https://github.com/vivifyhealthcare/IntelliExtract-AI.git
   cd IntelliExtract-AI
   

    

2. Create a conda environment with Python 3.10:

   conda create -p venv python==3.10 -y
   

    

3. Activate the created virtual environment:

   conda activate venv/
   

    

4. Install the required dependencies using pip:

   pip install -r requirements.txt
   

    

5. Create a .env file at the root of the project directory and add your OpenAI API key:

   OPENAI_API_KEY='YourOpenAIKeyHere'
   

    

After completing the installation, you can start the Multiple-CSV ChatApp with LLM by running the Streamlit application: streamlit run app.py

The Multiple-CSV ChatApp with LLM enables users to perform various data analysis tasks through an intuitive chat interface:

• CSV File Upload: Users can upload multiple CSV files and select one for analysis through a dropdown menu.
• Natural Language Queries: Engage in a chat-like interaction with the selected CSV file to ask questions, request summaries, or generate visualizations.
• Data Visualization: The application supports on-the-fly generation of charts and graphs based on the user’s queries.

• Multiple CSV Support: Seamlessly switch between multiple uploaded CSV files for analysis.
• LLM-Powered Insights: Utilizes the OpenAI language model for interpreting natural language queries and generating meaningful responses.
• Interactive Visualizations: Generates dynamic matplotlib charts in response to user queries for a more engaging data analysis experience.
• Streamlit Integration: Offers a clean and responsive web interface for uploading files, entering queries, and displaying results.

We welcome contributions to the Multiple-CSV ChatApp with LLM project. To contribute:

1. Fork the repository.
2. Create a new branch for your feature or fix.
3. Make your changes and commit them.
4. Push your changes to your fork and submit a pull request.

Ensure your contributions are well-documented and follow the project’s coding standards.