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.
39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
"""Company-specific financial sentiment analysis.
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This package provides FinBERT-based sentiment classification
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on company-linked evidence groups, producing per-company probability
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distributions with full evidence provenance.
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"""
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from services.intelligence_pipeline_v3.sentiment.aggregation import (
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aggregate_evidence_sentiments,
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)
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from services.intelligence_pipeline_v3.sentiment.calibrator import SentimentCalibrator
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from services.intelligence_pipeline_v3.sentiment.evidence_groups import build_evidence_groups
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from services.intelligence_pipeline_v3.sentiment.finbert_adapter import FinBERTAdapter
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from services.intelligence_pipeline_v3.sentiment.mixed_sentiment import compute_mixed_sentiment
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from services.intelligence_pipeline_v3.sentiment.models import (
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CompanySentimentResult,
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EvidenceGroup,
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SentimentBatchResult,
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TextSentiment,
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)
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from services.intelligence_pipeline_v3.sentiment.sentiment_scorer import (
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SentimentModel,
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SentimentScorer,
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)
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__all__ = [
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"SentimentCalibrator",
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"SentimentModel",
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"SentimentScorer",
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"CompanySentimentResult",
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"EvidenceGroup",
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"FinBERTAdapter",
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"SentimentBatchResult",
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"TextSentiment",
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"aggregate_evidence_sentiments",
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"build_evidence_groups",
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"compute_mixed_sentiment",
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]
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