DeltaX Data Professor

This project integrates multiple large language models (LLMs) like PandasAI, LangChain, OpenAI, Google Gemini, Anthropic, and Groq to allow users to interact with their data using natural language. Users can upload files in CSV, TSV, Excel formats or connect to databases like MySQL, SQLite, and PostgreSQL to perform data analysis, querying, and exploration. The chatbot supports both text and voice input, making it versatile for different interaction preferences.

Table of Contents

• DeltaX Data Professor
  • Table of Contents
  • Features
  • Installation
    • Windows Setup
    • Linux Setup
  • Environment Setup
    • Windows Environment
    • Linux Environment
  • Usage
    • File Upload
    • Database Connection
    • Text Input Interaction
    • Voice Input Interaction
  • Contribution
  • License

Features

• Multi-LLM Integration: Supports PandasAI, LangChain, OpenAI, Google Gemini, Anthropic, and Groq models.
• Data Formats: Upload and analyze CSV, TSV, and Excel files.
• Database Connections: Connect to MySQL, SQLite, and PostgreSQL databases for analysis.
• Text and Voice Input: Users can interact with the chatbot via text or voice for a seamless experience.
• Natural Language Data Analysis: Perform data exploration, querying, and analysis using conversational language.

Installation

Windows Setup

1. Clone the repository:

   git clone https://github.com/mrqadeer/DeltaX-Data-Professor.git
   cd DeltaX-Data-Professor

2. Install dependencies using pip:

   pip install -r requirements.txt

3. Alternatively, set up a virtual environment:

   python -m venv venv
   .\venv\Scripts\activate
   pip install -r requirements.txt

Linux Setup

1. Clone the repository:

   git clone https://github.com/mrqadeer/DeltaX-Data-Professor.git
   cd DeltaX-Data-Professor

2. Install dependencies using pip:

   pip install -r requirements.txt

3. Alternatively, set up a virtual environment:

   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

Environment Setup

Windows Environment

1. Install Anaconda from official site.
2. Create a new environment:

   conda create --name chatbot_env python=3.12
   conda activate chatbot_env
   pip install -r requirements.txt

Linux Environment

1. Install Anaconda:

   wget https://repo.anaconda.com/archive/Anaconda3-2023.07-Linux-x86_64.sh
   bash Anaconda3-2023.07-Linux-x86_64.sh

2. Create and activate a new environment:

   conda create --name chatbot_env python=3.12
   conda activate chatbot_env
   pip install -r requirements.txt

Usage

File Upload

1. Upload a CSV, TSV, or Excel file via the interface.
2. The chatbot will automatically parse the file and allow you to query the data in natural language.

Database Connection

1. Connect to MySQL, SQLite, or PostgreSQL by providing the connection details.
2. Once connected, you can use natural language to query and analyze data from the database.

Text Input Interaction

• Type your query related to data analysis or exploration, and the chatbot will respond using the connected LLM models.

Voice Input Interaction

• Speak your query into the microphone, and the chatbot will process the speech and provide results based on the analysis of the data.

Contribution

We welcome contributions to enhance the functionality of this chatbot. To contribute: For detail please refer to CONTRIBUTE.md.

License

This project is licensed under the MIT License. See the LICENSE file for more details.