#!/usr/bin/env bash
# Regenerate the pip-facing requirements files from uv.lock.
#
# pyproject.toml declares the direct dependencies and the optional extras;
# uv.lock pins everything. pip users, the Dockerfile and CI install from the
# exported files, so run this after any change to pyproject.toml or uv.lock:
#
#   uv lock                      # or: uv lock --upgrade-package <name>
#   bash scripts/export_requirements.sh
#
# Each exported file is a complete environment (core plus the named extra),
# so `pip install -r docsgpt/requirements-docling.txt` on its own works,
# and installing it on top of requirements.txt only adds the extra's packages.
set -euo pipefail

cd "$(dirname "$0")/.."

UV=(uv)
if ! uv --version 2>/dev/null | grep -qE '^uv 0\.([89]|[1-9][0-9])\.'; then
  # `uv export` needs a current uv; run one through uvx without touching the
  # machine's install.
  UV=(uv tool run --from 'uv>=0.8' uv)
fi

export_file() {
  local out="$1"; shift
  local header="$1"; shift
  local index_url="${INDEX_URL:-}"
  {
    echo "# GENERATED by scripts/export_requirements.sh from uv.lock -- do not edit."
    echo "#"
    # shellcheck disable=SC2001
    echo "$header" | sed 's/^/# /'
    echo
    # uv export records the wheel's origin in uv.lock but writes no index
    # directive; pip needs one to find the +cpu torch build.
    if [ -n "$index_url" ]; then echo "--extra-index-url $index_url"; echo; fi
    "${UV[@]}" export --frozen --no-hashes --no-dev --no-emit-project --no-header --quiet "$@"
  } > "$out"
  echo "wrote $out ($(grep -cE '^[A-Za-z0-9]' "$out") packages)"
}

export_file docsgpt/requirements.txt \
"Core runtime. Optional extras live in requirements-<extra>.txt:
  docling  DOC_PARSER_ENGINE=docling, docling OCR backend, read_document structured output
  milvus   VECTOR_STORE=milvus"

INDEX_URL=https://download.pytorch.org/whl/cpu export_file docsgpt/requirements-docling.txt \
"Core runtime plus the docling extra: DOC_PARSER_ENGINE=docling, the docling
OCR backend (layout-model hybrid OCR, ocrmac/rapidocr engines), .adoc/.vtt/.xml
attachment parsing, and read_document's 'structured' output. The default
anydoc engine needs none of this, and OCR itself does not either:
OCR_ENABLED=true with the tesseract binary (or a DeepSeek-OCR endpoint) runs
through docsgpt/parser/file/ocr_parser.py.
On Linux torch comes from the CPU-only PyTorch index (no CUDA stack); a GPU
deployment can reinstall torch from PyPI on top.
pip resolves the extra index as expected. uv only takes a package from the
first index that lists it, and the PyTorch index carries stale copies of
common packages, so with uv either run 'uv sync --extra docling' (the lock
pins the index per package) or set UV_INDEX_STRATEGY=unsafe-best-match.
Docker: --build-arg EXTRAS=docling" \
  --extra docling

export_file docsgpt/requirements-milvus.txt \
"Core runtime plus the milvus extra (VECTOR_STORE=milvus): pymilvus and the
embedded milvus-lite server, which pulls pyarrow.
Docker: --build-arg EXTRAS=milvus" \
  --extra milvus