Celes Renata
6179382d1e
- Recommendation worker now resolves thesis-rewriter config from DB and passes ollama_config to generate_recommendation. Thesis rewriting is now active when the thesis-rewriter agent exists in ai_agents. Refreshes config every 50 jobs. - Event classifier now resolves its own config separately from the document extractor via 'event-classifier' slug. Uses a separate OllamaClient when the model differs from the extractor. Refreshes alongside the extractor every 100 jobs. - Document extractor was already wired (existing code). - Added 8 unit tests for AgentConfigResolver covering: DB resolution, variant override, not-found, DB errors, TTL caching, cache refresh, and invalidation.
587 lines
24 KiB
Python
587 lines
24 KiB
Python
"""Extractor worker entrypoint - polls Redis for extraction jobs."""
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from __future__ import annotations
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import asyncio
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import json
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import logging
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import asyncpg
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import redis.asyncio as aioredis
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from minio import Minio
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from services.aggregation.interpolation import (
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build_default_profile,
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compute_macro_impact_with_sector,
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filter_low_confidence_events,
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persist_macro_impact_records,
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)
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from services.extractor.client import OllamaClient
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from services.extractor.event_classifier import classify_global_event
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from services.extractor.worker import persist_extraction
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from services.shared.agent_config import AgentConfigResolver, ResolvedAgentConfig
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from services.shared.config import OllamaConfig, load_config
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from services.shared.logging import inject_trace_context, setup_logging
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from services.shared.redis_keys import (
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QUEUE_AGGREGATION,
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QUEUE_EXTRACTION,
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QUEUE_MACRO_CLASSIFICATION,
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queue_key,
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)
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logger = logging.getLogger("extractor_main")
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def _build_ollama_config_from_resolved(
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resolved: ResolvedAgentConfig,
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base_config: OllamaConfig,
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) -> OllamaConfig:
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"""Build an OllamaConfig from a ResolvedAgentConfig, preserving base retry settings."""
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return OllamaConfig(
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base_url=base_config.base_url,
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model=resolved.model_name,
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timeout=resolved.timeout_seconds,
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max_retries=resolved.max_retries,
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retry_base_delay=base_config.retry_base_delay,
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retry_max_delay=base_config.retry_max_delay,
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retry_backoff_multiplier=base_config.retry_backoff_multiplier,
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max_tokens=resolved.max_tokens,
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stall_timeout=base_config.stall_timeout,
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loop_window=base_config.loop_window,
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loop_threshold=base_config.loop_threshold,
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context_window=resolved.context_window,
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)
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async def _check_token_budget(
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pool: asyncpg.Pool,
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variant_id: str,
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token_budget: int,
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) -> bool:
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"""Check if a variant has exceeded its hourly token budget.
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Returns True if the budget is exceeded and invocation should be skipped.
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"""
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row = await pool.fetchrow(
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"""SELECT COALESCE(SUM(input_tokens + output_tokens), 0) AS total_tokens
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FROM agent_performance_log
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WHERE variant_id = $1
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AND recorded_at >= NOW() - INTERVAL '1 hour'""",
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variant_id,
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)
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used = int(row["total_tokens"]) if row else 0
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if used >= token_budget:
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logger.warning(
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"Token budget exceeded for variant %s: used %d / budget %d — skipping invocation",
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variant_id, used, token_budget,
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)
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return True
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return False
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async def _log_agent_performance(
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pool: asyncpg.Pool,
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*,
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agent_id: str,
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variant_id: str | None = None,
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document_id: str = "",
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ticker: str = "",
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success: bool = False,
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duration_ms: int = 0,
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confidence: float = 0.0,
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retry_count: int = 0,
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input_tokens: int = 0,
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output_tokens: int = 0,
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error_message: str | None = None,
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) -> None:
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"""Insert a row into agent_performance_log with optional variant attribution."""
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try:
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await pool.execute(
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"""INSERT INTO agent_performance_log
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(agent_id, variant_id, document_id, ticker, success, duration_ms,
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confidence, retry_count, input_tokens, output_tokens, error_message)
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VALUES ($1::uuid, $2::uuid, $3::uuid, $4, $5, $6, $7, $8, $9, $10, $11)""",
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agent_id,
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variant_id,
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document_id if document_id else None,
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ticker,
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success,
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duration_ms,
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confidence,
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retry_count,
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input_tokens,
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output_tokens,
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error_message,
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)
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except Exception:
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logger.warning("Failed to log agent performance", exc_info=True)
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async def _build_company_id_map(pool: asyncpg.Pool) -> dict[str, str]:
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"""Build a ticker -> company_id mapping from the companies table."""
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rows = await pool.fetch("SELECT id, ticker FROM companies WHERE active = TRUE")
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return {row["ticker"]: str(row["id"]) for row in rows}
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async def _fetch_document_type(pool: asyncpg.Pool, document_id: str) -> str | None:
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"""Fetch the document_type for a document."""
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row = await pool.fetchrow(
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"SELECT document_type FROM documents WHERE id = $1::uuid",
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document_id,
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)
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return row["document_type"] if row else None
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async def _fetch_company_info(pool: asyncpg.Pool) -> list[dict]:
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"""Fetch company info needed for exposure profile loading and interpolation."""
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rows = await pool.fetch(
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"""SELECT id, ticker, sector, industry, market_cap_bucket
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FROM companies WHERE active = TRUE"""
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)
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return [dict(r) for r in rows]
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async def _load_exposure_profile(pool: asyncpg.Pool, company_id: str, sector: str, industry: str, market_cap_bucket: str):
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"""Load exposure profile for a company: manual > inferred > default.
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Requirements: 4.1
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"""
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from services.shared.schemas import ExposureProfileSchema, MarketPositionTier
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# Try manual or inferred profile from DB
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row = await pool.fetchrow(
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"""SELECT company_id, geographic_revenue_mix, supply_chain_regions,
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key_input_commodities, regulatory_jurisdictions, market_position_tier,
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export_dependency_pct, source, confidence, version
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FROM exposure_profiles
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WHERE company_id = $1 AND active = TRUE
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ORDER BY version DESC LIMIT 1""",
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company_id,
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)
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if row:
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geo_mix = row["geographic_revenue_mix"]
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if isinstance(geo_mix, str):
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geo_mix = json.loads(geo_mix)
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tier_val = row["market_position_tier"]
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try:
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tier = MarketPositionTier(tier_val)
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except ValueError:
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tier = MarketPositionTier.REGIONAL
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return ExposureProfileSchema(
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company_id=str(row["company_id"]),
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geographic_revenue_mix=geo_mix or {},
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supply_chain_regions=list(row["supply_chain_regions"] or []),
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key_input_commodities=list(row["key_input_commodities"] or []),
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regulatory_jurisdictions=list(row["regulatory_jurisdictions"] or []),
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market_position_tier=tier,
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export_dependency_pct=float(row["export_dependency_pct"] or 0.0),
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source=row["source"] or "manual",
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confidence=float(row["confidence"] or 1.0),
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version=row["version"] or 1,
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)
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# Fall back to default profile
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profile = build_default_profile(sector or "", industry or "", market_cap_bucket or "small_cap")
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profile.company_id = str(company_id)
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return profile
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async def _compute_and_persist_macro_impacts(
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pool: asyncpg.Pool,
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event,
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companies: list[dict],
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confidence_threshold: float = 0.4,
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) -> list[str]:
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"""Compute MacroImpactRecords for all tracked companies and persist non-zero ones.
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Requirements: 4.1, 4.5
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"""
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# Filter low-confidence events
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filtered = filter_low_confidence_events([event], confidence_threshold)
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if not filtered:
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logger.info("Event %s excluded: confidence %.3f below threshold %.3f",
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event.event_id, event.confidence, confidence_threshold)
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return []
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records = []
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for company in companies:
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company_id = str(company["id"])
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ticker = company["ticker"]
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sector = company.get("sector") or ""
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industry = company.get("industry") or ""
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market_cap_bucket = company.get("market_cap_bucket") or "small_cap"
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profile = await _load_exposure_profile(pool, company_id, sector, industry, market_cap_bucket)
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record = compute_macro_impact_with_sector(event, profile, company_sector=sector)
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record.ticker = ticker
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record.company_id = company_id
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if record.macro_impact_score > 0.0:
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records.append(record)
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if records:
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ids = await persist_macro_impact_records(pool, records)
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logger.info(
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"Persisted %d macro impact records for event %s",
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len(ids), event.event_id,
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)
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return [r.ticker for r in records]
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return []
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# Track consecutive macro classification failures for alerting (Requirement 10.4)
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_macro_consecutive_failures = 0
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_MACRO_FAILURE_ALERT_THRESHOLD = 3
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async def _process_macro_classification(
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*,
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pool: asyncpg.Pool,
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minio_client: Minio,
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ollama: OllamaClient,
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redis_client: aioredis.Redis,
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document_id: str,
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text: str,
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company_id_map: dict[str, str],
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confidence_threshold: float = 0.4,
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) -> None:
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"""Route a macro_event document to event classification, compute interpolation,
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and trigger aggregation for affected tickers.
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Requirements: 2.1, 2.2, 2.3, 4.1, 4.5, 10.4
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"""
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global _macro_consecutive_failures
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agg_queue = queue_key(QUEUE_AGGREGATION)
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try:
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event = await classify_global_event(
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normalized_text=text,
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document_id=document_id,
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ollama_client=ollama,
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pool=pool,
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minio_client=minio_client,
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)
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logger.info(
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"Classified macro event %s for doc %s: severity=%s types=%s",
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event.event_id, document_id, event.severity, event.event_types,
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)
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# Reset failure counter on success
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_macro_consecutive_failures = 0
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# Load all tracked companies and compute macro impacts
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companies = await _fetch_company_info(pool)
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affected_tickers = await _compute_and_persist_macro_impacts(
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pool, event, companies, confidence_threshold,
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)
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# Trigger aggregation for affected tickers (those with non-zero impact)
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enqueued_tickers = set()
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for ticker in affected_tickers:
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if ticker not in enqueued_tickers:
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await redis_client.rpush(
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agg_queue,
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json.dumps(inject_trace_context({
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"ticker": ticker,
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"macro_event_id": event.event_id,
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})),
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)
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enqueued_tickers.add(ticker)
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logger.info(
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"Enqueued aggregation jobs for %d affected tickers after macro event %s",
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len(enqueued_tickers), event.event_id,
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)
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except ValueError as e:
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_macro_consecutive_failures += 1
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logger.error("Macro event classification failed for doc %s: %s", document_id, e)
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if _macro_consecutive_failures >= _MACRO_FAILURE_ALERT_THRESHOLD:
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logger.critical(
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"ALERT: Sustained macro classification failures (%d consecutive). "
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"Continuing with company-only signals. Operator action required.",
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_macro_consecutive_failures,
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)
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except Exception:
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_macro_consecutive_failures += 1
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logger.exception("Unexpected error classifying macro event for doc %s", document_id)
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if _macro_consecutive_failures >= _MACRO_FAILURE_ALERT_THRESHOLD:
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logger.critical(
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"ALERT: Sustained macro classification failures (%d consecutive). "
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"Continuing with company-only signals. Operator action required.",
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_macro_consecutive_failures,
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)
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async def main() -> None:
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config = load_config()
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setup_logging("extractor", level=config.log_level, json_output=config.json_logs)
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pool = await asyncpg.create_pool(dsn=config.postgres.dsn, min_size=2, max_size=8)
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minio_client = Minio(
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config.minio.endpoint,
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access_key=config.minio.access_key,
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secret_key=config.minio.secret_key,
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secure=config.minio.secure,
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)
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# Resolve extractor config from DB (active variant override + TTL cache)
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resolver = AgentConfigResolver(pool, ttl_seconds=60)
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resolved_config: ResolvedAgentConfig | None = None
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extractor_ollama_config = config.ollama
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try:
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resolved_config = await resolver.resolve("document-extractor")
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if resolved_config is not None:
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extractor_ollama_config = _build_ollama_config_from_resolved(
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resolved_config, config.ollama,
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)
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logger.info(
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"Extractor using resolved config: model=%s variant=%s",
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resolved_config.model_name, resolved_config.variant_id,
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)
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else:
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logger.info("No DB config for document-extractor — using env defaults")
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except Exception:
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logger.warning("Failed to resolve extractor config — using env defaults", exc_info=True)
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ollama = OllamaClient(extractor_ollama_config)
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# Resolve event classifier config separately (may use different model)
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classifier_resolved: ResolvedAgentConfig | None = None
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classifier_ollama_config = config.ollama
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try:
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classifier_resolved = await resolver.resolve("event-classifier")
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if classifier_resolved is not None:
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classifier_ollama_config = _build_ollama_config_from_resolved(
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classifier_resolved, config.ollama,
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)
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logger.info(
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"Event classifier using resolved config: model=%s variant=%s",
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classifier_resolved.model_name, classifier_resolved.variant_id,
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)
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else:
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logger.info("No DB config for event-classifier — using extractor config")
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except Exception:
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logger.warning("Failed to resolve event-classifier config — using extractor config", exc_info=True)
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# Use a separate OllamaClient for the classifier if it has a different model
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classifier_ollama: OllamaClient
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if classifier_ollama_config.model != extractor_ollama_config.model:
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classifier_ollama = OllamaClient(classifier_ollama_config)
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else:
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classifier_ollama = ollama
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redis_client = aioredis.from_url(config.redis.url)
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queue = queue_key(QUEUE_EXTRACTION)
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macro_queue = queue_key(QUEUE_MACRO_CLASSIFICATION)
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agg_queue = queue_key(QUEUE_AGGREGATION)
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confidence_threshold = config.macro.macro_confidence_threshold
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logger.info("Extractor worker started, polling %s and %s", queue, macro_queue)
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# Pre-load company ID map (refreshed periodically)
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company_id_map = await _build_company_id_map(pool)
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refresh_counter = 0
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# Alternate between queues to prevent starvation: process 1 macro then 2 extractions
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macro_turn_counter = 0
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try:
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while True:
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# Alternate: every 3rd job from macro queue, rest from extraction
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# This prevents macro events from starving regular extractions
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raw = None
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is_macro_job = False
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if macro_turn_counter % 3 == 0:
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# Try macro first
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raw = await redis_client.lpop(macro_queue)
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is_macro_job = raw is not None
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if raw is None:
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raw = await redis_client.lpop(queue)
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else:
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# Try extraction first
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raw = await redis_client.lpop(queue)
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if raw is None:
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raw = await redis_client.lpop(macro_queue)
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is_macro_job = raw is not None
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macro_turn_counter += 1
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if raw is None:
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await asyncio.sleep(1)
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continue
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job = json.loads(raw)
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document_id = job.get("document_id", "")
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ticker = job.get("ticker", "")
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text = job.get("text", "") or job.get("normalized_text", "")
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# If no text in job, try to fetch from MinIO via the document's normalized_storage_ref
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if not text:
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ref_row = await pool.fetchrow(
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"SELECT normalized_storage_ref FROM documents WHERE id = $1::uuid",
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document_id,
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)
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if ref_row and ref_row["normalized_storage_ref"]:
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try:
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ref = ref_row["normalized_storage_ref"]
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# ref format: s3://bucket/path
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parts = ref.replace("s3://", "").split("/", 1)
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if len(parts) == 2:
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obj = minio_client.get_object(parts[0], parts[1])
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text = obj.read().decode("utf-8")
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obj.close()
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obj.release_conn()
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except Exception as e:
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logger.warning("Could not fetch normalized text for doc %s: %s", document_id, e)
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# Refresh company map every 100 jobs
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refresh_counter += 1
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if refresh_counter % 100 == 0:
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company_id_map = await _build_company_id_map(pool)
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# Re-resolve extractor config (picks up active variant swaps)
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try:
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resolved_config = await resolver.resolve("document-extractor")
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if resolved_config is not None:
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new_ollama_cfg = _build_ollama_config_from_resolved(
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resolved_config, config.ollama,
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)
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if new_ollama_cfg.model != ollama._config.model:
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logger.info(
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"Extractor config changed: model=%s variant=%s",
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resolved_config.model_name, resolved_config.variant_id,
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)
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await ollama.close()
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ollama = OllamaClient(new_ollama_cfg)
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else:
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ollama._config = new_ollama_cfg
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except Exception:
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logger.warning("Failed to refresh extractor config", exc_info=True)
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# Re-resolve event classifier config
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try:
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classifier_resolved = await resolver.resolve("event-classifier")
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if classifier_resolved is not None:
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new_cls_cfg = _build_ollama_config_from_resolved(
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classifier_resolved, config.ollama,
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)
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if new_cls_cfg.model != classifier_ollama._config.model:
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logger.info(
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"Event classifier config changed: model=%s variant=%s",
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classifier_resolved.model_name, classifier_resolved.variant_id,
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)
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if classifier_ollama is not ollama:
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await classifier_ollama.close()
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classifier_ollama = OllamaClient(new_cls_cfg)
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elif classifier_ollama is ollama and new_cls_cfg.model != ollama._config.model:
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classifier_ollama = OllamaClient(new_cls_cfg)
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except Exception:
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logger.warning("Failed to refresh event-classifier config", exc_info=True)
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# Route macro_event documents to event classification (Requirement 2.1)
|
|
doc_type = None
|
|
if is_macro_job:
|
|
doc_type = "macro_event"
|
|
else:
|
|
doc_type = await _fetch_document_type(pool, document_id)
|
|
|
|
if doc_type == "macro_event":
|
|
logger.info("Routing macro_event doc %s to event classifier", document_id)
|
|
await _process_macro_classification(
|
|
pool=pool,
|
|
minio_client=minio_client,
|
|
ollama=classifier_ollama,
|
|
redis_client=redis_client,
|
|
document_id=document_id,
|
|
text=text,
|
|
company_id_map=company_id_map,
|
|
confidence_threshold=confidence_threshold,
|
|
)
|
|
continue
|
|
|
|
# Standard extraction pipeline for non-macro documents
|
|
logger.info("Processing extraction job for doc %s / %s", document_id, ticker)
|
|
|
|
try:
|
|
# Token budget enforcement (Requirement 10.6)
|
|
if (
|
|
resolved_config is not None
|
|
and resolved_config.token_budget > 0
|
|
and resolved_config.variant_id is not None
|
|
):
|
|
budget_exceeded = await _check_token_budget(
|
|
pool, resolved_config.variant_id, resolved_config.token_budget,
|
|
)
|
|
if budget_exceeded:
|
|
continue
|
|
|
|
# Input token limit truncation (Requirement 10.5)
|
|
extraction_text = text
|
|
if resolved_config is not None and resolved_config.input_token_limit > 0:
|
|
# Rough estimate: ~4 chars per token
|
|
max_chars = resolved_config.input_token_limit * 4
|
|
if len(extraction_text) > max_chars:
|
|
extraction_text = extraction_text[:max_chars]
|
|
logger.info(
|
|
"Truncated input for doc %s from %d to %d chars (token limit %d)",
|
|
document_id, len(text), max_chars, resolved_config.input_token_limit,
|
|
)
|
|
|
|
# Pass all tracked tickers so the model can identify any mentioned companies
|
|
all_tickers = list(company_id_map.keys()) if company_id_map else ([ticker] if ticker else None)
|
|
extraction_response = await ollama.extract(
|
|
extraction_text,
|
|
document_id=document_id,
|
|
known_tickers=all_tickers,
|
|
)
|
|
result = await persist_extraction(
|
|
pool=pool,
|
|
minio_client=minio_client,
|
|
document_id=document_id,
|
|
ticker=ticker,
|
|
extraction_response=extraction_response,
|
|
company_id_map=company_id_map,
|
|
document_text_length=len(extraction_text),
|
|
)
|
|
|
|
# Log to agent_performance_log with variant attribution
|
|
if resolved_config is not None:
|
|
output_tokens = 0
|
|
if extraction_response.attempts:
|
|
final = extraction_response.attempts[-1]
|
|
output_tokens = len(final.raw_output) // 4 if final.raw_output else 0
|
|
await _log_agent_performance(
|
|
pool,
|
|
agent_id=resolved_config.agent_id,
|
|
variant_id=resolved_config.variant_id,
|
|
document_id=document_id,
|
|
ticker=ticker,
|
|
success=extraction_response.success,
|
|
duration_ms=extraction_response.total_duration_ms,
|
|
confidence=extraction_response.result.confidence if extraction_response.result else 0.0,
|
|
retry_count=max(0, len(extraction_response.attempts) - 1),
|
|
input_tokens=len(extraction_text) // 4,
|
|
output_tokens=output_tokens,
|
|
error_message=(
|
|
extraction_response.attempts[-1].error
|
|
if extraction_response.attempts and extraction_response.attempts[-1].error
|
|
else None
|
|
),
|
|
)
|
|
|
|
# Enqueue aggregation job for the ticker on success
|
|
if result.success and ticker:
|
|
await redis_client.rpush(
|
|
agg_queue,
|
|
json.dumps(inject_trace_context({"ticker": ticker})),
|
|
)
|
|
except Exception:
|
|
logger.exception("Extraction failed for doc %s", document_id)
|
|
finally:
|
|
await pool.close()
|
|
await redis_client.close()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main())
|