Setting Up Your Own Private GPT Using Python

  • Wed, Aug 23, 2023
  • 3-minute read

AI Brain

A private GPT instance offers a range of benefits, including enhanced data privacy and security through localized data processing, compliance with industry regulations, and customization to tailor the model to specific needs. It provides low-latency responses for real-time applications, supports offline usage, and grants control over costs by eliminating reliance on external cloud services. With dedicated resources, a private instance ensures consistent performance and reduced dependencies on third-party APIs. It’s ideal for scenarios where sensitive data, customizations, compliance, and resource allocation are paramount, enabling experimentation and development while maintaining data control. However, setting up and maintaining a private instance demands technical expertise and resources to manage deployment, security, and ongoing maintenance effectively.

  1. Clone the Repository: If you have Git installed on your computer, navigate to a suitable folder (e.g., “Documents”) using the command line and clone the repository: 

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

cd privateGPT

  2. Create a Virtual Environment: Create a virtual environment specific to this build using either venv or conda. This setup requires Python 3.10 or higher. 

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

# For conda
conda create -n gpt python=3.10
conda activate gpt

  3. Install Required Packages: Install the required packages listed in requirements.txt into the “gpt” environment using pip: 

    pip install -r requirements.txt

  4. Download Language Model: In the README’s Environment Setup section, find the link to the Language Model (LLM). Currently, the LLM is ggml-gpt4all-j-v1.3-groovy.bin. Download the LLM file (approximately 3.5 GB).

  5. Move LLM File: Create a subfolder named “models” within the “privateGPT” folder. Move the downloaded LLM file to the “models” subfolder.

  6. Copy Environment File: In the “privateGPT” folder, copy the file named example.env to .env: 

    cp example.env .env

  7. Verify Copy: Use the following command to check that the copy was successful (you should see both example.env and .env listed): 

    ls -a

  8. Add Documents: Add your private documents to the “source_documents” subfolder within the “privateGPT” folder.

  9. Set Working Directory: Make sure 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
    Parsing may complete quickly for smaller documents or take longer for larger ones. A new subfolder named “db” will appear in “privateGPT” when the process is done.

  2. Start Querying: Run the following command to start querying your documents: 

    python privateGPT.py
    After loading, you will see the prompt “Enter a query:”. Input your question and press Enter.

  3. Get Responses: After a minute, PrivateGPT will answer your question along with a list of source documents it used for context. Keep in mind that the model’s knowledge extends beyond your provided documents.

  4. Exit the Interface: To finish querying, simply type exit.

Feel free to follow these steps to set up and use PrivateGPT as described.

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