PocketVeto/backend/app/workers/llm_processor.py

"""
LLM processor — generates AI briefs for fetched bill documents.
Triggered by document_fetcher after successful text retrieval.
"""
import logging
import time

from sqlalchemy import text

from app.database import get_sync_db
from app.models import Bill, BillBrief, BillDocument, Member
from app.services.llm_service import get_llm_provider
from app.workers.celery_app import celery_app

logger = logging.getLogger(__name__)


@celery_app.task(
    bind=True,
    max_retries=2,
    rate_limit="10/m",  # Respect LLM provider rate limits
    name="app.workers.llm_processor.process_document_with_llm",
)
def process_document_with_llm(self, document_id: int):
    """Generate an AI brief for a bill document. Full brief for first version, amendment brief for subsequent versions."""
    db = get_sync_db()
    try:
        # Idempotency: skip if brief already exists for this document
        existing = db.query(BillBrief).filter_by(document_id=document_id).first()
        if existing:
            return {"status": "already_processed", "brief_id": existing.id}

        doc = db.get(BillDocument, document_id)
        if not doc or not doc.raw_text:
            logger.warning(f"Document {document_id} not found or has no text")
            return {"status": "no_document"}

        bill = db.get(Bill, doc.bill_id)
        if not bill:
            return {"status": "no_bill"}

        sponsor = db.get(Member, bill.sponsor_id) if bill.sponsor_id else None

        bill_metadata = {
            "title": bill.title or "Unknown Title",
            "sponsor_name": sponsor.name if sponsor else "Unknown",
            "party": sponsor.party if sponsor else "Unknown",
            "state": sponsor.state if sponsor else "Unknown",
            "chamber": bill.chamber or "Unknown",
            "introduced_date": str(bill.introduced_date) if bill.introduced_date else "Unknown",
            "latest_action_text": bill.latest_action_text or "None",
            "latest_action_date": str(bill.latest_action_date) if bill.latest_action_date else "Unknown",
        }

        # Check if a full brief already exists for this bill (from an earlier document version)
        previous_full_brief = (
            db.query(BillBrief)
            .filter_by(bill_id=doc.bill_id, brief_type="full")
            .order_by(BillBrief.created_at.desc())
            .first()
        )

        provider = get_llm_provider()

        if previous_full_brief and previous_full_brief.document_id:
            # New version of a bill we've already analyzed — generate amendment brief
            previous_doc = db.get(BillDocument, previous_full_brief.document_id)
            if previous_doc and previous_doc.raw_text:
                logger.info(f"Generating amendment brief for document {document_id} (bill {doc.bill_id})")
                brief = provider.generate_amendment_brief(doc.raw_text, previous_doc.raw_text, bill_metadata)
                brief_type = "amendment"
            else:
                logger.info(f"Previous document unavailable, generating full brief for document {document_id}")
                brief = provider.generate_brief(doc.raw_text, bill_metadata)
                brief_type = "full"
        else:
            logger.info(f"Generating full brief for document {document_id} (bill {doc.bill_id})")
            brief = provider.generate_brief(doc.raw_text, bill_metadata)
            brief_type = "full"

        db_brief = BillBrief(
            bill_id=doc.bill_id,
            document_id=document_id,
            brief_type=brief_type,
            summary=brief.summary,
            key_points=brief.key_points,
            risks=brief.risks,
            deadlines=brief.deadlines,
            topic_tags=brief.topic_tags,
            llm_provider=brief.llm_provider,
            llm_model=brief.llm_model,
            govinfo_url=doc.govinfo_url,
        )
        db.add(db_brief)
        db.commit()
        db.refresh(db_brief)

        logger.info(f"{brief_type.capitalize()} brief {db_brief.id} created for bill {doc.bill_id} using {brief.llm_provider}/{brief.llm_model}")

        # Trigger news fetch now that we have topic tags
        from app.workers.news_fetcher import fetch_news_for_bill
        fetch_news_for_bill.delay(doc.bill_id)

        return {"status": "ok", "brief_id": db_brief.id, "brief_type": brief_type}

    except Exception as exc:
        db.rollback()
        logger.error(f"LLM processing failed for document {document_id}: {exc}")
        raise self.retry(exc=exc, countdown=300)  # 5 min backoff for LLM failures
    finally:
        db.close()


@celery_app.task(bind=True, name="app.workers.llm_processor.backfill_brief_citations")
def backfill_brief_citations(self):
    """
    Find briefs generated before citation support was added (key_points contains plain
    strings instead of {text, citation, quote} objects), delete them, and re-queue
    LLM processing against the already-stored document text.

    No Congress.gov or GovInfo calls — only LLM calls.
    """
    db = get_sync_db()
    try:
        uncited = db.execute(text("""
            SELECT id, document_id, bill_id
            FROM bill_briefs
            WHERE key_points IS NOT NULL
              AND jsonb_array_length(key_points) > 0
              AND jsonb_typeof(key_points->0) = 'string'
        """)).fetchall()

        total = len(uncited)
        queued = 0
        skipped = 0

        for row in uncited:
            if not row.document_id:
                skipped += 1
                continue

            # Confirm the document still has text before deleting the brief
            doc = db.get(BillDocument, row.document_id)
            if not doc or not doc.raw_text:
                skipped += 1
                continue

            brief = db.get(BillBrief, row.id)
            if brief:
                db.delete(brief)
                db.commit()

            process_document_with_llm.delay(row.document_id)
            queued += 1
            time.sleep(0.1)  # Avoid burst-queuing all LLM tasks at once

        logger.info(
            f"backfill_brief_citations: {total} uncited briefs found, "
            f"{queued} re-queued, {skipped} skipped (no document text)"
        )
        return {"total": total, "queued": queued, "skipped": skipped}
    finally:
        db.close()