Setting Up and Running a Private GPT Instance

GPT Logo Deploying a private GPT instance offers significant advantages, such as enhanced data privacy, compliance with industry regulations, and the ability to tailor the model to specific needs. In this comprehensive guide, we will delve into the process of setting up a private GPT instance, complete with code examples and a detailed step-by-step walkthrough.

Benefits of a Private GPT Instance

  • Enhanced Data Privacy and Security: Processing data locally ensures that sensitive information remains within your control.
  • Compliance with Industry Regulations: Customizing the deployment to adhere to specific regulatory requirements.
  • Customization: Fine-tuning the model for specific use cases and applications.
  • Low-Latency Responses: Reduced dependency on external servers, making it ideal for real-time applications.
  • Offline Usage: Capability to operate without an internet connection.
  • Cost Control: Eliminates dependence on external cloud services, offering a cost-effective solution.
  • Consistent Performance: Dedicated resources ensure reliable performance and minimize third-party API dependencies.
  • Critical Data Scenarios: Especially beneficial in contexts requiring data sensitivity, customizations, regulatory compliance, and resource allocation.

Setting up a private GPT instance necessitates technical expertise, particularly in areas such as deployment management, security, and ongoing maintenance.

Step-by-Step Setup Guide

1. Clone the Repository

First, navigate to a suitable directory on your computer (e.g., “Documents”) and clone the repository:

git clone https://github.com/imartinez/privateGPT.git
cd privateGPT

2. Create a Virtual Environment

To ensure that dependencies remain isolated from other projects, create a virtual environment using venv or conda. This setup should be performed with Python 3.10 or higher.

Using venv:

python3 -m venv gpt-env
source gpt-env/bin/activate

Using conda:

conda create -n gpt python=3.10
conda activate gpt

3. Install Required Packages

Install the necessary packages as specified in requirements.txt:

pip install -r requirements.txt

4. Download the Language Model

Refer to the repository’s README under the Environment Setup section for the link to the Language Model (LLM). As of the latest update, the recommended LLM is ggml-gpt4all-j-v1.3-groovy.bin.

Download the LLM file (approximately 3.5 GB) and move it to a new subfolder named models within the privateGPT directory:

mkdir models
mv /path/to/downloaded/ggml-gpt4all-j-v1.3-groovy.bin models/

5. Configure the Environment

Copy the example environment settings file to set up your environment configuration:

cp example.env .env

Verify the copy:

ls -a

You should see both example.env and .env listed.

6. Add Documents

Place your private documents into the source_documents subfolder within the privateGPT directory.

7. Set Working Directory

Ensure your current working directory is the privateGPT folder:

cd /path/to/privateGPT

Running PrivateGPT

1. Parse Documents

Run the following command to parse the documents:

python ingest.py

This process may take some time. Upon completion, a new subfolder named db will appear in the privateGPT directory.

2. Start Querying

To launch the PrivateGPT querying interface, execute:

python privateGPT.py

You will now see the prompt Enter a query:. Enter your question and press Enter.

3. Receive Responses

PrivateGPT will process your query and provide an answer, along with a list of source documents used for context.

4. Exit the Interface

To exit the querying interface, type exit and press Enter.

Enter a query: exit