Celes Renata
a72f336ad1
Multi-stage evidence-grounded inference architecture replacing the monolithic 9B model extraction pipeline. CPU-first specialist services handle routine extraction while the 9B vLLM model is preserved for semantic adjudication of ambiguous cases. Key components: - Capability-aware inference gateway (OpenAI-compatible + Ollama) - Endpoint registry with DB migrations and REST API - Sentence-aware document segmenter (property tests) - Deterministic financial parsing with offset integrity - Symbol resolution with ambiguity detection - Specialist service (GLiNER2, dynamic batching, K8s deployment) - Company-specific sentiment (FinBERT, calibration) - Retrieval-based novelty and duplicate detection - Confidence calibration pipeline - Deterministic routing engine (property tests) - 9B adjudication layer with VRAM gating - Stock-specific impact model (features, labels, baseline, trained) - Pipeline orchestrator (state machine, queues, leases, feature flags) - Bounded parallelism (async workers, semaphore, load shedding) - Observability (tracing, metrics, alerts) - Compatibility adapter (v3→v2 golden mapping tests) - Shadow/canary promotion framework - Active learning and fine-tuning pipeline Test results: 1,161 tests pass, ruff lint clean. All 282 spec tasks completed.
137 lines
4.3 KiB
Python
137 lines
4.3 KiB
Python
"""Sentiment aggregation logic.
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Aggregates per-text sentiment scores into a single company result,
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with mixed sentiment detection when evidence groups disagree.
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Mixed sentiment is NOT an unconstrained fourth softmax label — it is
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computed from disagreement between evidence texts (some positive,
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some negative with margin > threshold).
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"""
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from __future__ import annotations
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from services.intelligence_pipeline_v3.sentiment.models import (
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CompanySentimentResult,
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TextSentiment,
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)
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# Disagreement threshold: both positive and negative probabilities
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# must be >= this value across evidence texts to signal disagreement.
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# A text with positive_prob >= threshold AND another text with
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# negative_prob >= threshold indicates conflicting evidence.
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MIXED_DISAGREEMENT_THRESHOLD = 0.3
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def aggregate_evidence_sentiments(
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company_id: str,
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per_text_scores: list[TextSentiment],
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model_version: str,
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calibration_version: str = "uncalibrated",
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) -> CompanySentimentResult:
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"""Aggregate per-text sentiment scores into a company-level result.
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Computes weighted average probabilities and detects mixed sentiment
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from evidence-group disagreement. Mixed is triggered when at least
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one text has positive_prob >= threshold AND at least one other text
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has negative_prob >= threshold.
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Parameters
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----------
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company_id
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Resolved company identifier.
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per_text_scores
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List of TextSentiment objects, one per evidence text.
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model_version
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Sentiment model version string for lineage.
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calibration_version
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Calibration artifact version.
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Returns
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-------
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CompanySentimentResult
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Aggregated result with label, probabilities, per-text scores,
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is_mixed flag, and evidence IDs.
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"""
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if not per_text_scores:
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return CompanySentimentResult(
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company_id=company_id,
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label="neutral",
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positive_prob=0.0,
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negative_prob=0.0,
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neutral_prob=1.0,
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evidence_ids=[],
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is_mixed=False,
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per_text_scores=[],
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model_version=model_version,
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calibration_version=calibration_version,
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)
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# Detect mixed sentiment from disagreement
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is_mixed = _detect_mixed_from_disagreement(per_text_scores)
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# Compute average probabilities across texts
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n = len(per_text_scores)
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avg_pos = sum(s.positive_prob for s in per_text_scores) / n
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avg_neg = sum(s.negative_prob for s in per_text_scores) / n
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avg_neu = sum(s.neutral_prob for s in per_text_scores) / n
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# Normalize to ensure probabilities sum to 1.0
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total = avg_pos + avg_neg + avg_neu
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if total > 0:
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avg_pos /= total
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avg_neg /= total
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avg_neu /= total
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else:
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avg_pos = 0.0
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avg_neg = 0.0
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avg_neu = 1.0
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# Determine label
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if is_mixed:
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label = "mixed"
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else:
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label = _argmax_label(avg_pos, avg_neg, avg_neu)
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evidence_ids = [s.evidence_id for s in per_text_scores]
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return CompanySentimentResult(
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company_id=company_id,
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label=label,
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positive_prob=round(avg_pos, 6),
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negative_prob=round(avg_neg, 6),
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neutral_prob=round(avg_neu, 6),
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evidence_ids=evidence_ids,
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is_mixed=is_mixed,
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per_text_scores=per_text_scores,
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model_version=model_version,
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calibration_version=calibration_version,
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)
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def _detect_mixed_from_disagreement(per_text_scores: list[TextSentiment]) -> bool:
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"""Detect mixed sentiment from evidence-group disagreement.
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Returns True when at least one text has positive_prob >= threshold
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AND at least one (different) text has negative_prob >= threshold.
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This indicates conflicting evidence directions.
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A single text cannot trigger mixed on its own (we need disagreement
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between at least 2 texts).
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"""
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if len(per_text_scores) < 2:
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return False
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max_pos = max(s.positive_prob for s in per_text_scores)
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max_neg = max(s.negative_prob for s in per_text_scores)
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return max_pos >= MIXED_DISAGREEMENT_THRESHOLD and max_neg >= MIXED_DISAGREEMENT_THRESHOLD
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def _argmax_label(pos: float, neg: float, neu: float) -> str:
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"""Return the label with the highest probability."""
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if pos >= neg and pos >= neu:
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return "positive"
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elif neg >= pos and neg >= neu:
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return "negative"
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return "neutral"
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