how to use privategpt in vertex ai

2 min read 23-01-2025

Meta Description: Learn how to deploy PrivateGPT on Google Vertex AI for secure and efficient large language model (LLM) interactions. This guide provides a step-by-step tutorial, covering setup, deployment, and usage. Unlock the power of PrivateGPT's secure processing within the robust Vertex AI environment.

Introduction:

PrivateGPT, a revolutionary tool, allows you to interact with large language models (LLMs) like GPT-4 locally and securely. This eliminates the need to send your sensitive data to external APIs. This guide shows you how to deploy PrivateGPT within the scalable and reliable environment of Google Vertex AI. This combination provides both privacy and efficiency for your LLM applications.

Setting Up Your Vertex AI Environment

Before deploying PrivateGPT, you need a functioning Google Cloud Platform (GCP) account and a Vertex AI instance ready.

1. GCP Project and Billing

Create or select a GCP project. Ensure billing is enabled.
Enable the necessary APIs: Vertex AI, Cloud Storage, etc. The exact APIs may vary depending on your PrivateGPT setup.

2. Vertex AI Notebook Instance

Create a new Vertex AI notebook instance. Choose a machine type with sufficient resources (RAM and CPU) to handle the PrivateGPT model. Larger models will require more resources.

3. Clone the PrivateGPT Repository

Within your notebook instance, clone the PrivateGPT GitHub repository:

git clone https://github.com/imartinez1986/PrivateGPT.git

4. Install Dependencies

Navigate to the PrivateGPT directory and install the necessary Python packages using pip:

pip install -r requirements.txt

Deploying and Running PrivateGPT on Vertex AI

Now, let's deploy and run PrivateGPT within your Vertex AI environment.

1. Data Preparation

PrivateGPT operates on local data. Prepare your data in a suitable format (e.g., a .txt file, a CSV file) and upload it to your Google Cloud Storage bucket. Note the path to your data.

2. Configuration

Modify the config.py file to match your setup. Crucially, update the paths to your data and any necessary model weights. Also, configure the appropriate LLM parameters.

3. Execution

Run the PrivateGPT script using the following command. Remember to replace placeholders with your actual values:

python main.py --data_path gs://your-bucket-name/your-data.txt  --model_name gpt-3.5-turbo --use_local_model False

--data_path: Path to your data in Google Cloud Storage.
--model_name: Specify the desired LLM. Options depend on your PrivateGPT setup and available models.
--use_local_model: Set to False for cloud-based model usage. If using a local model adjust accordingly.

Securely Interacting with PrivateGPT

PrivateGPT offers enhanced security, ensuring your data remains within your control.

1. Data Privacy

Remember that your data remains local within your Vertex AI environment. It’s never transmitted to an external LLM provider.

2. Access Control

Utilize Google Cloud’s robust access control mechanisms (IAM) to manage permissions and restrict access to your PrivateGPT deployment.

3. Model Selection

Choose an appropriate model for your needs. Balancing accuracy with resource requirements is vital.

Conclusion

Deploying PrivateGPT on Vertex AI combines the power of secure local LLM processing with the scalability and reliability of Google's cloud infrastructure. This approach safeguards sensitive data while enabling efficient large language model interactions. By following these steps, you can leverage the full potential of PrivateGPT in a secure and optimized environment. Remember to consult the official PrivateGPT documentation for the most up-to-date instructions and potential modifications.