mirror of
https://github.com/tiennm99/DocsGPT.git
synced 2026-10-12 18:15:33 +00:00
Alex
43f493b926
pip install docsgpt now brings the web UI with it: `docsgpt api` serves the API and the UI on one port. - scripts/build_frontend.sh builds the frontend into docsgpt/static (gitignored) the way the frontend image does: .env.development as the production baseline, and index.html loading /config.js ahead of the bundle. hatch admits the directory into the wheel and the sdist through `artifacts`; the package workflows run the script before `uv build` and fail if the wheel lacks the UI. The backend image keeps ignoring it. - docsgpt/ui.py serves the build in front of Flask: files as they are, hashed assets immutable, Flask's own path prefixes (taken from its URL map, so new blueprints need no registration) passed through, every other GET rendered as index.html for the client-side router. /config.js is generated per request with VITE_API_HOST and VITE_BASE_URL set to the page's origin, VITE_* environment variables winning. SERVE_UI=false leaves the API alone. - docsgpt api configures gunicorn in code (gunicorn.app.base.Application) instead of rewriting sys.argv, so the SIGUSR2 re-exec that gunicorn uses for zero-downtime upgrades runs the docsgpt console script again and works; verified with a live handover. - Docs: the pip page says the UI is included, that DOCSGPT_HOME and DOCSGPT_ENV_FILE are process environment variables rather than .env entries, and the settings page describes SERVE_UI.
215 lines
7.0 KiB
TOML
215 lines
7.0 KiB
TOML
[build-system]
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requires = ["hatchling>=1.27,<2"]
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build-backend = "hatchling.build"
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[project]
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name = "docsgpt"
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# The version is read from docsgpt/version.py (the release workflow reads the
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# same file); bump it there.
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dynamic = ["version"]
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description = "DocsGPT backend: chat with your documents, agents, and tools."
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readme = "README.md"
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requires-python = ">=3.12"
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license = "MIT"
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license-files = ["LICENSE"]
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authors = [{ name = "Arc53" }]
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keywords = ["rag", "llm", "agents", "documents", "chat", "search"]
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classifiers = [
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"Development Status :: 5 - Production/Stable",
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"Environment :: Web Environment",
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"Framework :: Flask",
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"Intended Audience :: Developers",
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"Operating System :: OS Independent",
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"Programming Language :: Python :: 3",
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"Programming Language :: Python :: 3.12",
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"Programming Language :: Python :: 3.13",
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"Topic :: Scientific/Engineering :: Artificial Intelligence",
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]
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# Direct dependencies only, as compatible ranges: the floor is the version
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# uv.lock pins, the ceiling the next major (next minor below 1.0), so the
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# PyPI package installs next to other packages. The pip-facing files under
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# docsgpt/ (requirements*.txt) are exported from the lock by
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# scripts/export_requirements.sh and must not be edited by hand; Docker, CI
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# and the checkout install those exact versions.
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dependencies = [
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"a2wsgi>=1.10.10,<2",
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"alembic>=1.13,<2",
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"anthropic>=0.121.0,<0.122",
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"beautifulsoup4>=4.15.0,<5",
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"boto3>=1.43.67,<2",
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"cel-python>=0.5.0,<0.6",
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"celery>=5.6.3,<6",
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"celery-redbeat>=2.4.2,<3",
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"croniter>=6.2.4,<7",
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"cryptography>=50.0.0,<51",
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"dataclasses-json>=0.6.7,<0.7",
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"daytona>=0.205.1,<0.206",
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"ddgs>=8.0.0,<10",
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"defusedxml>=0.7.1,<0.8",
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"docx2txt>=0.9,<0.10",
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"elevenlabs>=2.62.0,<3",
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"faiss-cpu>=1.15.0,<2",
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"fast-ebook>=0.2.0,<0.3",
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# Default document converter (DOC_PARSER_ENGINE=anydoc): a Rust extension
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# with no model downloads. The docling engine is the `docling` extra.
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"firecrawl-anydoc>=0.2.3,<0.3",
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"fastembed>=0.8.0,<0.9",
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"fastmcp>=3.4.6,<4",
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"Flask>=3.1.3,<4",
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"flask-restx>=1.3.2,<2",
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"google-api-python-client>=2.198.0,<3",
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"google-auth-oauthlib>=1.4.0,<2",
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"google-genai>=2.17.0,<3",
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"gTTS>=2.5.4,<3",
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"gunicorn>=26.0.0,<27",
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"jinja2>=3.1.6,<4",
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"kombu>=5.6.2,<6",
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"markdownify>=1.2.3,<2",
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"msal>=1.37.0,<2",
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"networkx>=3.6.1,<4",
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"numpy>=2.5.1,<3",
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# fastembed's runtime: local embeddings execute on it.
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"onnxruntime>=1.28.0,<2",
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"openai>=2.53.0,<3",
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"openapi3-parser>=1.1.22,<2",
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# pandas reads .xlsx through openpyxl but does not depend on it.
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"openpyxl>=3.1.5,<4",
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"opentelemetry-distro>=0.50b0,<1",
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"opentelemetry-exporter-otlp>=1.29.0,<2",
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"opentelemetry-instrumentation-celery>=0.50b0,<1",
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"opentelemetry-instrumentation-flask>=0.50b0,<1",
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"opentelemetry-instrumentation-logging>=0.50b0,<1",
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"opentelemetry-instrumentation-psycopg>=0.50b0,<1",
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"opentelemetry-instrumentation-redis>=0.50b0,<1",
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"opentelemetry-instrumentation-requests>=0.50b0,<1",
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"opentelemetry-instrumentation-sqlalchemy>=0.50b0,<1",
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"opentelemetry-instrumentation-starlette>=0.50b0,<1",
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"pandas>=3.0.5,<4",
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"pdf2image>=1.17.0,<2",
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"pgvector>=0.5,<1",
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"pillow>=12.3.0,<13",
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"praw>=8.0.2,<9",
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"psycopg[binary,pool]>=3.1,<4",
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"pydantic>=2.13.5,<3",
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"pydantic-settings>=2.15.0,<3",
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"pypdf>=6.15.0,<7",
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"pypdfium2>=5.12.1,<6",
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"python-dateutil>=2.9.0,<3",
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"python-dotenv>=1.2.3,<2",
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"python-jose>=3.5.0,<4",
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"python-pptx>=1.0.2,<2",
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"PyYAML>=6.0.3,<7",
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"qdrant-client>=1.19.0,<2",
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"redis>=7.4.0,<8",
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"requests>=2.34.2,<3",
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"retry>=0.9.2,<0.10",
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"sqlalchemy>=2.0,<3",
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"starlette>=1.0,<2",
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"tiktoken>=0.13.0,<0.14",
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"tldextract>=5.3.2,<6",
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"tokenizers>=0.22.2,<0.23",
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"tqdm>=4.67.3,<5",
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"uvicorn[standard]>=0.30,<1",
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"uvicorn-worker>=0.4,<1",
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"websocket-client>=1.9.0,<2",
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"werkzeug>=3.1.0,<4",
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]
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[project.optional-dependencies]
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# Docling parser engine: DOC_PARSER_ENGINE=docling, the docling OCR backend
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# (layout-model hybrid OCR, ocrmac/rapidocr engines), .adoc/.vtt/.xml
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# attachment parsing, and read_document's `structured` output. Pulls torch and
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# transformers; with uv on Linux torch resolves from the CPU-only PyTorch index
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# (see [tool.uv.sources]) so the extra does not drag the CUDA stack in. pip
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# users on Linux install torch and torchvision from that index first
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# (--index-url https://download.pytorch.org/whl/cpu), then the extra; pip keeps
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# the torch it already has.
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docling = [
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"docling>=2.119.0,<3",
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"rapidocr>=3.9.2,<4",
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# docling's model stack. Declared here (not left transitive) so the floors
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# hold and the CPU index source below applies. transformers is capped by
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# docling-core at <5.9: 5.9+ breaks the PDF layout model on Apple Silicon.
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"torch>=2.11.0,<3",
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"torchvision>=0.26.0,<0.27",
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"transformers>=5.8.1,<5.9",
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]
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# VECTOR_STORE=milvus. milvus-lite (the embedded server) pulls pyarrow.
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milvus = [
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"pymilvus>=3.0.1,<4",
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"milvus-lite>=3.2.0,<4; sys_platform != 'win32'",
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]
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[project.scripts]
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docsgpt = "docsgpt.cli:main"
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[project.urls]
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Homepage = "https://www.docsgpt.cloud/"
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Documentation = "https://docs.docsgpt.cloud/"
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Repository = "https://github.com/arc53/DocsGPT"
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Issues = "https://github.com/arc53/DocsGPT/issues"
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Changelog = "https://github.com/arc53/DocsGPT/releases"
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[dependency-groups]
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# Mirrors tests/requirements.txt for `uv sync`; pip users install that file.
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dev = [
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"pytest>=8.0.0",
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"pytest-asyncio>=0.23",
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"pytest-cov>=4.1.0",
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"pytest-xdist>=3.5",
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"coverage>=7.4.0",
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"pytest-postgresql>=6.0.0",
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"jupyter-client>=8.0",
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"python-docx>=1.1",
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"reportlab>=4.0,<5",
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"ruff",
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]
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[tool.hatch.version]
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path = "docsgpt/version.py"
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# The wheel is the docsgpt package with the data it reads at runtime: prompts,
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# model catalogs, the seed config, alembic.ini and the migrations, plus the web
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# UI build under docsgpt/static (gitignored; scripts/build_frontend.sh makes
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# it, and `artifacts` admits it). Build inputs (Dockerfile, exported
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# requirements), the sample index and local runtime data stay out. The
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# one-release `application` import alias is checkout-only.
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[tool.hatch.build.targets.wheel]
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packages = ["docsgpt"]
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artifacts = ["docsgpt/static/**"]
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exclude = [
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"docsgpt/Dockerfile",
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"docsgpt/requirements*.txt",
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"docsgpt/index.faiss",
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"docsgpt/index.pkl",
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"docsgpt/indexes/",
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"docsgpt/inputs/",
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"docsgpt/vectors/",
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]
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[tool.hatch.build.targets.sdist]
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include = ["/docsgpt"]
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artifacts = ["docsgpt/static/**"]
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exclude = [
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"docsgpt/Dockerfile",
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"docsgpt/requirements*.txt",
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"docsgpt/index.faiss",
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"docsgpt/index.pkl",
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"docsgpt/indexes/",
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"docsgpt/inputs/",
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"docsgpt/vectors/",
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]
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[[tool.uv.index]]
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name = "pytorch-cpu"
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url = "https://download.pytorch.org/whl/cpu"
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explicit = true
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[tool.uv.sources]
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# PyPI's Linux torch wheels depend on the full CUDA 13 stack (~2.7 GB of
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# wheels). The docling extra runs its models on CPU, so take torch from the
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# CPU index there. macOS and Windows PyPI wheels are CPU-only already.
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torch = [{ index = "pytorch-cpu", marker = "sys_platform == 'linux'" }]
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torchvision = [{ index = "pytorch-cpu", marker = "sys_platform == 'linux'" }]
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