Natural Language to MongoDB Query Converter

A Streamlit-powered application leveraging LangChain, OpenAI GPT, and MongoDB to dynamically convert natural language queries into MongoDB commands.

link to canva presentation

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🚀 Features

• Natural Language Query Processing: Converts user input into optimized MongoDB queries using GPT models.
• LangChain Integration: Uses the LangGraph Agent for enhanced multi-step query workflows and decision-making.
• User-Friendly Interface: Built with Streamlit for a seamless and interactive user experience.
• Robust Backend: Handles dynamic query execution and ensures accurate database responses.
• Dockerized Deployment: Easy-to-use containerized setup for consistent and scalable deployment.

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🛠️ Architecture Overview

The system comprises the following components:

1. Frontend:

   • Streamlit App: A lightweight interface for users to input natural language queries.

2. Backend:

   • LangChain with OpenAI GPT: Converts natural language into MongoDB queries.
   • Database Handler: Executes the queries and retrieves results from MongoDB.

3. Database:

   • MongoDB: Stores data and supports complex NoSQL queries.

4. Deployment:

   • Dockerized Environment: Ensures portability and consistency across systems.

The Agent's workflow:

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📦 Installation and Setup

Prerequisites

• Docker installed on your system.
• A running instance of MongoDB.
• OpenAI API Key for GPT access.

Steps

1. Clone the Repository

   git clone https://github.com/username/NL2MongoDB.git
   cd NL2MongoDB

2. Configure Environment Variables Create a .env file in the root directory containing

OPENAI_API_KEY=
MONGO_URI=
PROMPT_PATH=
AGENT_PROMPT_PATH=
LOG_FILE=
DATABASE_NAME=
COLLECTION_NAME=

3. Build and Run the Docker Container

   docker build -t nl2mongodb .
   docker run -p 8080:8080 nl2mongodb

4. Access the Application Open your browser and navigate to http://localhost:8080

🛠️ How to Use

1. Input a natural language query into the Streamlit app.
2. Review the generated MongoDB query.
3. View results dynamically fetched from the MongoDB database.

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🤖 Technologies Used

• Programming Language: Python
• Framework: Streamlit
• AI Model: GPT-4o (via OpenAI API)
• Integration: LangChain (LangGraph Agent,and LLM integration)
• Database: MongoDB
• Containerization: Docker

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📷 Demo

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🤝 Contributing

Contributions are welcome! Please follow these steps:

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