43 lines
1.6 KiB
Python
43 lines
1.6 KiB
Python
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"""Transaction data extraction: LLM Vision + persistence."""
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from app.models import Transaction
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from app.models.database import async_session_maker
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from app.schemas.transaction import TransactionExtractItem, TransactionResponse
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from app.services.llm import get_llm_provider
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async def extract_and_save(
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case_id: int,
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screenshot_id: int,
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image_bytes: bytes,
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) -> list[TransactionResponse]:
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"""
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Run vision extraction on image and persist transactions to DB.
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Returns list of created transactions; low-confidence items are still saved but flagged.
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"""
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provider = get_llm_provider()
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items: list[TransactionExtractItem] = await provider.extract_from_image(image_bytes)
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results: list[TransactionResponse] = []
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async with async_session_maker() as session:
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for it in items:
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t = Transaction(
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case_id=case_id,
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screenshot_id=screenshot_id,
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app_source=it.app_source,
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transaction_type=it.transaction_type,
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amount=it.amount,
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currency=it.currency or "CNY",
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counterparty_name=it.counterparty_name,
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counterparty_account=it.counterparty_account,
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order_number=it.order_number,
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transaction_time=it.transaction_time,
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remark=it.remark,
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confidence=it.confidence if it.confidence in ("high", "medium", "low") else "medium",
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raw_text=None,
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)
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session.add(t)
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await session.flush()
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results.append(TransactionResponse.model_validate(t))
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await session.commit()
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return results
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