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

License

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

Acknowledgements

• OpenAI
• Amazon LLM
• Langchain
• Pinecone
• Streamlit