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Warehouse Management ChatBot Project

This project is designed to assist with warehouse management through a ChatBot application. It leverages support from OpenAI and Amazon LLM models to respond to various user queries related to warehouse management.

Features

  • LLM Models: Supports both OpenAI and Amazon LLM models.
  • Database: Utilizes PostgreSQL for relational database management.
  • Embeddings: Pinecone is used for embedding operations.
  • Langchain Agents and Tools: Creates an agent to query both relational and unstructured datasets based on user queries.
  • Frontend: A Streamlit application provides the user interface.
  • History Mechanism: Keeps track of user interactions and query history.

Installation

  1. Clone the repository:

    git clone https://github.com/samitugal/WarehouseManagerAI.git
cd warehouse-management-chatbot

  2. Set up the Python environment:

    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 PostgreSQL using Docker:

    • Ensure Docker is installed and running on your system.
    • Use the provided docker-compose.yaml file to set up the PostgreSQL database with a sample Northwind database.
    • Run the following command to start the services: 
      docker-compose up -d
    • The database connection settings in the project configuration file should be updated to match those specified in the docker-compose.yaml file.

  5. Set up Pinecone:

    • Sign up for Pinecone and obtain the API key.
    • Update the Pinecone API settings in the project configuration file.

Usage

  1. Run the Streamlit application:

    streamlit run ui/streamlit_ui.py

  2. Interacting with the ChatBot:

    • Open the Streamlit application in your browser.
    • Use the ChatBot interface to query the warehouse management system.
    • The ChatBot will respond based on the combined power of OpenAI and Amazon LLM models, querying the PostgreSQL database and Pinecone embeddings as needed.

Project Structure

  • ui/streamlit_ui.py: The main Streamlit application file.
  • requirements.txt: List of required Python packages.
  • configs/: Directory for configuration definitions.
    • Database/: Directory for database configration definitons.
    • Embeddings/: Directory for embeddings configration definitons.
    • LLM/: Directory for large language model configration definitons.
  • data/: Directory for data-related files.
  • src/: Source code directory.
    • agents/: Agents used for querying data.
    • database/: Database-related modules.
    • embedding_providers/: Modules for embedding operations.
    • llm/: Large Language Model-related modules.
    • prompts/: Directory for prompt templates.
    • tools/: Tools used by the agents.
    • utils/: Utility functions and modules.
    • ui/: Streamlit UI components.

Contributing

We welcome contributions to improve the project. Please follow these steps to contribute:

  1. Fork the repository.
  2. Create a new branch (git checkout -b feature/your-feature).
  3. Make your changes.
  4. Commit your changes (git commit -m 'Add some feature').
  5. Push to the branch (git push origin feature/your-feature).
  6. Open a pull request.

Screenshots

image

License

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

Acknowledgements