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246834fac0
The row→domain mapper now names all 93 constructor arguments explicitly
instead of splatting a lowercased dict, takes a plain Mapping (a
DataFrame.to_dict("records") row) instead of a pandas Series, and ignores
columns the domain type doesn't know. A missing/renamed CSV column fails
loudly as a KeyError at the row. Both iterrows() call sites move to
to_dict("records") — pandas-stubs types iterrows' Series unparameterized,
which strict mode rejects. pandas-stubs + boto3-stubs[s3] make the stack
check clean: pyright strict is now 0 errors across the PR's files.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
36 lines
799 B
Text
36 lines
799 B
Text
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fastapi==0.115.2
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sqlalchemy==2.0.36
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pydantic-settings==2.6.0
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psycopg2-binary==2.9.10
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python-jose==3.3.0
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cryptography==43.0.3
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mangum==0.19.0
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playwright==1.58.0
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pymupdf
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# AWS
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boto3==1.35.44
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# Data
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openpyxl==3.1.5
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# Basic
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pytz
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msal
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uvicorn[standard]
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sqlmodel
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# Testing
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pytest==9.0.2
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pytest-cov==7.0.0
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ipykernel>=6.25,<7
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dotenv
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psycopg[binary]
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pytest-postgresql
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moto[s3,sqs]==5.0.28 # mock_aws (moto 5.x) for S3/SQS in orchestration tests
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# Formatting
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black==26.1.0
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boto3-stubs[s3] # typed boto3.client("s3") for the S3 repositories
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openai
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# Type checking — strict pyright gate (CLAUDE.md). The pip `pyright` wrapper uses
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# the container's Node. pandas-stubs lets pandas-typed modules check cleanly
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# (CLAUDE.md: add pandas-stubs when introducing pandas to a module).
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pyright==1.1.411
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pandas-stubs
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