How to Build a Private, Offline GPT with Python – Step‑by‑Step Guide

PrivateGPT is a Python‑based technology that enables developers to run a GPT‑4‑like model locally, keeping all data private and avoiding any cloud communication.

It provides immediate access to large language model capabilities offline, ensuring 100% private deployment with no risk of data leakage.

PrivateGPT refers to a set of tools that protect user privacy when using generative AI, including a script that edits sensitive prompts before sending them to ChatGPT and restores the information in the answer, as well as services that connect to data sources like Notion, JIRA, Slack, and GitHub.

Environment Setup

Install the required components: pip install -r requirements.txt Rename example.env to .env and adjust the variables as needed:

MODEL_TYPE: supports LlamaCpp or GPT4All</code><code>PERSIST_DIRECTORY: is the folder you want your vectorstore in</code><code>LLAMA_EMBEDDINGS_MODEL: Path to your LlamaCpp supported embeddings model</code><code>MODEL_PATH: Path to your GPT4All or LlamaCpp supported LLM</code><code>MODEL_N_CTX: Maximum token limit for both embeddings and LLM models

Download LLM Files

Download the two model files and place them in a directory such as models, updating the paths in .env accordingly.

LLM: ggml-gpt4all-j-v1.3-groovy.bin (~10 GB) – download link

Embedding: ggml-model-q4_0.bin – download link

If you prefer other compatible models, download them and reference the new paths in .env.

Clone the Repository

Clone the PrivateGPT repository to your local machine:

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