---
title: Install with pip
description: Run the DocsGPT backend from the PyPI package, inside your own Python environment.
---
import { Callout } from 'nextra/components'
# Install with pip
DocsGPT is on PyPI as [`docsgpt`](https://pypi.org/project/docsgpt/): the API server, the web UI, the Celery worker and the maintenance scripts in one package, behind a single `docsgpt` command. Use it when you want DocsGPT inside your own Python environment or process manager rather than the [Docker images](/Deploying/Docker-Deploying).
`docsgpt api` serves the web UI on the same port as the API. Set `SERVE_UI=false` to run the API alone, for example behind the frontend Docker image or a UI you host yourself.
With Docker available, the same package can run the whole stack for you, including Postgres and Redis: `docsgpt up`. See [Run it with `docsgpt up`](/Deploying/Docker-Deploying#run-it-with-docsgpt-up).
## Requirements
- Python 3.12 or newer
- [PostgreSQL](/Deploying/Postgres-Migration) for user data, with the `vector` extension if you set `VECTOR_STORE=pgvector`
- Redis for the task queue and the cache
- An LLM: an API key for a hosted provider, or a local model server
## Install
```bash
python -m venv .venv && source .venv/bin/activate
pip install docsgpt
```
Extras add the optional engines:
```bash
pip install "docsgpt[docling]" # DOC_PARSER_ENGINE=docling: OCR backend, structured output
pip install "docsgpt[milvus]" # VECTOR_STORE=milvus
```
The `docling` extra pulls in PyTorch, and on Linux the PyPI torch wheels bring the CUDA stack with them. On a CPU-only machine install the CPU build first, then the extra; pip keeps the torch it already has:
```bash
pip install --index-url https://download.pytorch.org/whl/cpu torch torchvision
pip install "docsgpt[docling]"
```
`uv pip install` accepts the same two commands. With `pipx`, install `docsgpt[docling]` first, then replace the CUDA build inside its environment (`--no-deps` keeps pip from touching torch's dependencies, which the PyTorch index carries in older copies):
```bash
pipx runpip docsgpt install --force-reinstall --no-deps --index-url https://download.pytorch.org/whl/cpu torch torchvision
```
## Configure
DocsGPT keeps its runtime files in a **data home**: the `.env` file it reads settings from, uploaded files under `inputs/`, vector indexes under `indexes/` and downloaded embedding models under `models/`. The data home is `~/.docsgpt/server` (`/opt/docsgpt` when you run as root on Linux), whatever directory you run the commands from. `DOCSGPT_HOME` moves it, and `DOCSGPT_ENV_FILE` points at a `.env` kept somewhere else. Both variables must be set in the process environment, not in `.env`: they decide where `.env` is read from. In a source checkout the data home is the checkout.
Up to 0.20 the data home of an installed package was the directory you ran the command from. If you kept `.env` and your data there, move them to `~/.docsgpt/server` or set `DOCSGPT_HOME` to that directory. `docsgpt api` and `docsgpt worker` point out a `.env` in the working directory that they no longer read.
Create a `.env` in the data home. The minimum for a hosted LLM:
```ini
# LLM provider and model: see App Configuration for the options
LLM_PROVIDER=openai
LLM_NAME=
API_KEY=
# User data
POSTGRES_URI=postgresql://docsgpt:@localhost:5432/docsgpt
# Worker-to-API authentication (required for uploads)
INTERNAL_KEY=
```
Redis defaults to `localhost:6379` (`CELERY_BROKER_URL`, `CELERY_RESULT_BACKEND`, `CACHE_REDIS_URL`). Every setting is listed in [App Configuration](/Deploying/DocsGPT-Settings).
## Run
```bash
docsgpt migrate # create the database if it is missing and apply the migrations
docsgpt api # the API and the web UI on http://127.0.0.1:7091
docsgpt worker # in a second terminal: the Celery worker, with the scheduler
```
`docsgpt api` listens on localhost only. Pass `--host 0.0.0.0` to accept connections from other machines or containers, and put a reverse proxy with TLS in front of it for anything public.
The API applies pending migrations when it starts (`AUTO_MIGRATE`), so `docsgpt migrate` is the explicit step for deployments that want the schema in place before the first request or that run the API with a restricted database role.
Both commands print the data home they resolved on start-up. They share it as long as `DOCSGPT_HOME` is the same for both (or unset), so the worker finds the files the API stores and the API finds the indexes the worker builds.
The worker is not optional: query embedding runs on it, so search fails without one. `docsgpt worker --help` lists the queue, concurrency and pool options; `--no-beat` starts a worker without the scheduler when another worker already runs it. On Windows the scheduler cannot be embedded, so run `docsgpt beat` in a third terminal.
Other commands:
- `docsgpt api --reload`: a development server with auto-reload.
- `docsgpt prefetch-models`: download the embedding models and their tokenizers ahead of time, for machines that go offline (see [Air-Gapped Deployment](/Deploying/Air-Gapped)).
- `docsgpt verify-offline`: check that a prepared install starts with networking off.
- `docsgpt reembed`: re-embed every index after changing `EMBEDDINGS_NAME` (see [Upgrading](/upgrading)).
## Upgrade
```bash
pip install -U docsgpt
docsgpt migrate
```
Read the [upgrade notes](/upgrading) first when moving between minor versions.