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* feat: use default user folder * docs: update references to local paths * fix: windows * Potential fix for pull request finding Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> * fix: align make test and wipe with PGPT_HOME paths * fix: align wipe target with PGPT_HOME local_data * fix: folders --------- Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com> Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
133 lines
3.6 KiB
Plaintext
133 lines
3.6 KiB
Plaintext
---
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title: "Package Install"
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description: "Install PrivateGPT via Homebrew or uv — the fastest way to get started."
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---
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This is the recommended installation method for developers who want to run PrivateGPT against an existing LLM server (Ollama, LM Studio, etc.) without cloning the repository.
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<Note>
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PrivateGPT package files are hosted at `https://wheels.privategpt.dev/packages/`. `uv` uses that location to find the PrivateGPT release and continues to resolve public dependencies from PyPI by default.
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</Note>
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---
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## Install
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<Tabs>
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<Tab title="macOS (Homebrew)">
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```bash
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brew tap zylon-ai/tap
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brew install private-gpt
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```
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Upgrade:
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```bash
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brew upgrade private-gpt
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```
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</Tab>
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<Tab title="uv (recommended, any platform)">
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[`uv`](https://docs.astral.sh/uv/) installs PrivateGPT in an isolated environment and puts the `private-gpt` CLI on your path.
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```bash
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# Install the core package
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uv tool install --python 3.11 \
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--find-links https://wheels.privategpt.dev/packages/ \
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"private-gpt[core]"
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```
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Install `uv` first with the official standalone installer from `astral.sh/uv`.
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Add explicit extras only when needed, for example `private-gpt[core,queue]`.
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Upgrade:
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```bash
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uv tool upgrade \
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--find-links https://wheels.privategpt.dev/packages/ \
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private-gpt
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```
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</Tab>
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</Tabs>
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<Note>
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**Python 3.11 is required.** PrivateGPT does not support Python 3.10 or 3.12+.
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</Note>
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## Verify
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<Tabs>
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<Tab title="macOS / Linux">
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```bash
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command -v private-gpt
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```
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</Tab>
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<Tab title="Windows (PowerShell)">
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```powershell
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Get-Command private-gpt
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```
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</Tab>
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<Tab title="Windows (CMD)">
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```cmd
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where private-gpt
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```
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</Tab>
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</Tabs>
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## Run
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After installing, set `OPENAI_API_BASE` to your LLM server and start:
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<Tabs>
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<Tab title="macOS / Linux">
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```bash
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OPENAI_API_BASE=http://localhost:11434/v1 private-gpt serve
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```
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</Tab>
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<Tab title="Windows (PowerShell)">
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```powershell
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$env:OPENAI_API_BASE = "http://localhost:11434/v1"
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private-gpt serve
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```
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</Tab>
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<Tab title="Windows (CMD)">
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```cmd
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set OPENAI_API_BASE=http://localhost:11434/v1
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private-gpt serve
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```
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</Tab>
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</Tabs>
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PrivateGPT starts on port `8080` by default. Change it with `private-gpt serve --port <port>`.
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## Data storage
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All application data (vector store, ingested documents, models, caches) is stored under a single home directory:
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| Platform | Default path |
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| macOS / Linux | `~/.local/share/private-gpt/` |
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| Windows | `%LOCALAPPDATA%\private-gpt\` |
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| Docker | `/home/worker/app/` (fixed) |
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Override the location with `PGPT_HOME`:
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```bash
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PGPT_HOME=/data/private-gpt private-gpt serve
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```
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## Key environment variables
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| Variable | Default | Description |
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|---|---|---|
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| `OPENAI_API_BASE` | `https://api.openai.com/v1` | Base URL of your OpenAI-compatible LLM server |
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| `OPENAI_API_KEY` | _(empty)_ | API key, if your server requires one |
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| `OPENAI_EMBEDDING_API_BASE` | same as `OPENAI_API_BASE` | Override for the embeddings endpoint |
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| `PORT` | `8080` | Port the server listens on (also settable via `private-gpt serve --port`) |
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| `PGPT_HOME` | `~/.local/share/private-gpt` | Root directory for all local data (vector store, models, caches) |
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| `PGPT_LLM_AUTO_DISCOVER_MODELS` | `true` | Discover LLM models from `/v1/models` on startup |
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## What's next?
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- [Choose an LLM server](/providers/overview) — compare Ollama, LM Studio, LlamaCPP, vLLM
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- [Local with uv](/installation/local) — source install, hot reload, and detailed model configuration
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- [API Reference](/api-reference/api-reference) — start building
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