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RAG using NVIDIA NIM with LangChain

Overview

This project demonstrates the power and simplicity of NVIDIA NIM (NVIDIA Inference Model), a suite of optimized cloud-native microservices, by setting up and running a Retrieval-Augmented Generation (RAG) pipeline. NVIDIA NIM is designed to streamline the deployment and time-to-market of generative AI models across various environments, including cloud platforms, data centers, and GPU-accelerated workstations. By abstracting the complexities of AI model development and leveraging industry-standard APIs, NIM makes advanced AI technologies accessible to a broader range of developers.

Getting Started

Prerequisites

Installation

  1. Clone the repository

    git clone https://github.com/mickymultani/nvidia-NIM-RAG.git
cd nvidia-NIM-RAG

  2. Set up a virtual environment

    Create a virtual environment named nvidia:

    python -m venv nvidia

    Activate the virtual environment:

    • On Windows: 
      nvidia\Scripts\activate
    • On macOS/Linux: 
      source nvidia/bin/activate

  3. Install dependencies

    Install the required packages using pip:

    pip install -r requirements.txt

  4. Environment Variables

    Create a .env file in the root directory of the project, and add your NVIDIA API key:

    NVIDIA_API_KEY=your_nvidia_api_key_here

    Replace your_nvidia_api_key_here with your actual NVIDIA API key.

Usage

To run the project, execute the following command:

python nim.py

Contributing

Contributions to this project are welcome!

License

Distributed under the MIT License.