Christian Krakau-Louis
101 lines
4.3 KiB
Python
101 lines
4.3 KiB
Python
#!/usr/bin/env python3
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import json
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import re
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from app.config import settings
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from app.tasks.retry_config import BaseTaskWithRetry
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from app.tasks.embed_metadata_into_pdf import embed_metadata_into_pdf
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# Import the shared Celery instance
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from app.celery_app import celery
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import openai
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# Initialize OpenAI client dynamically
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client = openai.OpenAI(
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api_key=settings.openai_api_key,
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base_url=settings.openai_base_url
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)
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def extract_json_from_text(text):
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"""
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Try to extract a JSON object from the text.
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- First, check for a JSON block inside triple backticks.
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- If not found, try to extract text from the first '{' to the last '}'.
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"""
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pattern = r"```(?:json)?\s*(\{.*?\})\s*```"
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match = re.search(pattern, text, re.DOTALL)
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if match:
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return match.group(1)
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else:
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start = text.find("{")
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end = text.rfind("}")
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if start != -1 and end != -1 and end > start:
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return text[start:end+1]
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return None
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@celery.task(base=BaseTaskWithRetry)
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def extract_metadata_with_gpt(filename: str, cleaned_text: str):
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"""Uses OpenAI to classify document metadata."""
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prompt = f"""
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You are a specialized document analyzer trained to extract structured metadata from documents.
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Your task is to analyze the given text and return a well-structured JSON object.
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Extract and return the following fields:
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1. **filename**: Machine-readable filename (YYYY-MM-DD_DescriptiveTitle, use only letters, numbers, periods, and underscores).
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2. **empfaenger**: The recipient, or "Unknown" if not found.
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3. **absender**: The sender, or "Unknown" if not found.
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4. **correspondent**: The entity or company that issued the document (shortest possible name, e.g., "Amazon" instead of "Amazon EU SARL, German branch").
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5. **kommunikationsart**: One of [Behoerdlicher_Brief, Rechnung, Kontoauszug, Vertrag, Quittung, Privater_Brief, Einladung, Gewerbliche_Korrespondenz, Newsletter, Werbung, Sonstiges].
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6. **kommunikationskategorie**: One of [Amtliche_Postbehoerdliche_Dokumente, Finanz_und_Vertragsdokumente, Geschaeftliche_Kommunikation, Private_Korrespondenz, Sonstige_Informationen].
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7. **document_type**: Precise classification (e.g., Invoice, Contract, Information, Unknown).
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8. **tags**: A list of up to 4 relevant thematic keywords.
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9. **language**: Detected document language (ISO 639-1 code, e.g., "de" or "en").
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10. **title**: A human-readable title summarizing the document content.
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11. **confidence_score**: A numeric value (0-100) indicating the confidence level of the extracted metadata.
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12. **reference_number**: Extracted invoice/order/reference number if available.
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13. **monetary_amounts**: A list of key monetary values detected in the document.
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### Important Rules:
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- **OCR Correction**: Assume the text has been corrected for OCR errors.
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- **Tagging**: Max 4 tags, avoiding generic or overly specific terms.
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- **Title**: Concise, no addresses, and contains key identifying features.
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- **Date Selection**: Use the most relevant date if multiple are found.
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- **Output Language**: Maintain the document's original language.
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Extracted text:
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{cleaned_text}
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Return only valid JSON with no additional commentary.
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"""
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try:
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print(f"[DEBUG] Sending classification request for {filename}...")
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completion = client.chat.completions.create(
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model=settings.openai_model,
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messages=[
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{"role": "system", "content": "You are an intelligent document classifier."},
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{"role": "user", "content": prompt}
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],
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temperature=0
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)
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content = completion.choices[0].message.content
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print(f"[DEBUG] Raw classification response for {filename}: {content}")
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json_text = extract_json_from_text(content)
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if not json_text:
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print(f"[ERROR] Could not find valid JSON in GPT response for {filename}.")
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return {}
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metadata = json.loads(json_text)
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print(f"[DEBUG] Extracted metadata: {metadata}")
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# Trigger the next step: embedding metadata into the PDF
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embed_metadata_into_pdf.delay(filename, cleaned_text, metadata)
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return {"s3_file": filename, "metadata": metadata}
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except Exception as e:
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print(f"[ERROR] OpenAI classification failed for {filename}: {e}")
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return {}
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