mirror of
https://github.com/tiennm99/DocsGPT.git
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Alex
7da46c2bea
- Ship tiktoken's cl100k_base inside the package and build the encoding from it, so token counting never downloads anything. - Default EMBEDDINGS_CACHE_DIR to <data home>/models instead of FastEmbed's temp dir, and read tokenizer.json and repo metadata from that cache, so a model downloads once and survives reboots. - TTS_PROVIDER=none and STT_PROVIDER=none switch the speech features off: the endpoints return 404, audio files fail to ingest with a clear message, /api/config reports tts_available/stt_available, and the UI hides the Speak and microphone buttons. - Drop the Google Fonts Roboto import from the web UI. - prefetch-models fills the cache the app reads; verify-offline checks the packaged encoding. - Docs: new Air-Gapped Deployment guide, settings and cache notes.
102 lines
4.8 KiB
Plaintext
102 lines
4.8 KiB
Plaintext
---
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title: Install with pip
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description: Run the DocsGPT backend from the PyPI package, inside your own Python environment.
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---
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import { Callout } from 'nextra/components'
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# Install with pip
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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).
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<Callout type="info">
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`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.
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</Callout>
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## Requirements
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- Python 3.12 or newer
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- [PostgreSQL](/Deploying/Postgres-Migration) for user data, with the `vector` extension if you set `VECTOR_STORE=pgvector`
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- Redis for the task queue and the cache
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- An LLM: an API key for a hosted provider, or a local model server
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## Install
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```bash
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python -m venv .venv && source .venv/bin/activate
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pip install docsgpt
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```
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Extras add the optional engines:
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```bash
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pip install "docsgpt[docling]" # DOC_PARSER_ENGINE=docling: OCR backend, structured output
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pip install "docsgpt[milvus]" # VECTOR_STORE=milvus
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```
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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:
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```bash
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pip install --index-url https://download.pytorch.org/whl/cpu torch torchvision
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pip install "docsgpt[docling]"
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```
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`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):
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```bash
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pipx runpip docsgpt install --force-reinstall --no-deps --index-url https://download.pytorch.org/whl/cpu torch torchvision
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```
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## Configure
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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 the directory you run the commands from, or the directory `DOCSGPT_HOME` points to. `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.
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Create a `.env` in the data home. The minimum for a hosted LLM:
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```ini
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# LLM provider and model: see App Configuration for the options
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LLM_PROVIDER=openai
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LLM_NAME=<model name>
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API_KEY=<provider API key>
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# User data
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POSTGRES_URI=postgresql://docsgpt:<password>@localhost:5432/docsgpt
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# Worker-to-API authentication (required for uploads)
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INTERNAL_KEY=<a long random string>
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```
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Redis defaults to `localhost:6379` (`CELERY_BROKER_URL`, `CELERY_RESULT_BACKEND`, `CACHE_REDIS_URL`). Every setting is listed in [App Configuration](/Deploying/DocsGPT-Settings).
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## Run
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```bash
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docsgpt migrate # create the database if it is missing and apply the migrations
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docsgpt api # the API and the web UI on http://127.0.0.1:7091
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docsgpt worker # in a second terminal: the Celery worker, with the scheduler
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```
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`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.
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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.
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Both commands print the data home they resolved on start-up. Run them from the same directory, or set `DOCSGPT_HOME` for both, so the worker finds the files the API stores and the API finds the indexes the worker builds.
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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.
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Other commands:
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- `docsgpt api --reload`: a development server with auto-reload.
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- `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)).
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- `docsgpt verify-offline`: check that a prepared install starts with networking off.
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- `docsgpt reembed`: re-embed every index after changing `EMBEDDINGS_NAME` (see [Upgrading](/upgrading)).
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## Upgrade
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```bash
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pip install -U docsgpt
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docsgpt migrate
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```
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Read the [upgrade notes](/upgrading) first when moving between minor versions.
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