mirror of
https://github.com/tiennm99/DocsGPT.git
synced 2026-10-05 04:13:25 +00:00
Alex
ae9348bb2a
- docsgpt api binds 127.0.0.1 by default, like gunicorn and uvicorn do; --host 0.0.0.0 exposes it. The docs say so. - The embedded Milvus and LanceDB defaults derive from the data home, so they follow DOCSGPT_HOME like the faiss indexes and uploads do. A checkout run from its root and the Docker image resolve to the same paths as before. - The docs and the pyproject comment describe the CPU torch install as two steps (torch and torchvision from the PyTorch CPU index first, then the docling extra): pip picks the highest version across indexes, so --extra-index-url only yields the CPU build while that index keeps pace with PyPI. - AGENTS.md separates DOCSGPT_HOME (moves the data home) from DOCSGPT_ENV_FILE (selects the .env file); the docs example uses a password placeholder.
211 lines
6.8 KiB
TOML
211 lines
6.8 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. Build inputs
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# (Dockerfile, exported requirements), the sample index and local runtime data
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# stay out. The 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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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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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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