feat: Intelligence Pipeline v3 — full implementation
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.
This commit is contained in:
Celes Renata
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"""FinBERT adapter for financial sentiment classification.
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Provides a unified interface for FinBERT inference with:
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- Production mode: loads ProsusAI/finbert and runs real inference
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- Test mode: deterministic keyword-based mock probabilities
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Model version is pinned and exposed for lineage tracking.
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"""
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from __future__ import annotations
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import logging
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logger = logging.getLogger(__name__)
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# Pinned model configuration
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FINBERT_MODEL_NAME = "ProsusAI/finbert"
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FINBERT_MODEL_VERSION = "ProsusAI/finbert@v1.0"
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# Keywords for deterministic test mode
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_POSITIVE_KEYWORDS = frozenset({
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"growth", "profit", "beat", "raised", "upgrade", "strong",
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"surge", "gain", "bullish", "outperform", "exceeded", "record",
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"positive", "optimistic", "rally", "upside",
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})
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_NEGATIVE_KEYWORDS = frozenset({
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"loss", "decline", "miss", "cut", "downgrade", "weak",
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"plunge", "drop", "bearish", "underperform", "fell", "crash",
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"negative", "pessimistic", "risk", "downside", "slump",
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})
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class FinBERTAdapter:
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"""Adapter for FinBERT financial sentiment classification.
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Parameters
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----------
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test_mode
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When True, uses deterministic keyword-based classification
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instead of loading the actual FinBERT model. Useful for testing
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without GPU/large model dependencies.
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"""
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def __init__(self, test_mode: bool = True) -> None:
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self._test_mode = test_mode
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self._model = None
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self._tokenizer = None
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self._model_name = FINBERT_MODEL_NAME
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self._model_version = FINBERT_MODEL_VERSION
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if not test_mode:
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self._load_model()
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@property
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def model_version(self) -> str:
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"""Return the pinned model version string."""
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return self._model_version
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@property
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def model_name(self) -> str:
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"""Return the model name."""
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return self._model_name
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def _load_model(self) -> None:
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"""Load the FinBERT model and tokenizer for production inference."""
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try:
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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logger.info("Loading FinBERT model: %s", self._model_name)
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self._tokenizer = AutoTokenizer.from_pretrained(self._model_name)
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self._model = AutoModelForSequenceClassification.from_pretrained(self._model_name)
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self._model.eval()
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logger.info("FinBERT model loaded successfully")
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except ImportError:
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raise RuntimeError(
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"transformers and torch are required for production FinBERT inference. "
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"Install with: pip install transformers torch"
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)
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except Exception as e:
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raise RuntimeError(f"Failed to load FinBERT model: {e}") from e
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def classify(self, texts: list[str]) -> list[tuple[float, float, float]]:
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"""Classify texts and return probability distributions.
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Parameters
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----------
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texts
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List of text strings to classify.
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Returns
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-------
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list[tuple[float, float, float]]
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List of (positive_prob, negative_prob, neutral_prob) tuples.
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Probabilities sum to 1.0 for each text.
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"""
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if not texts:
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return []
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if self._test_mode:
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return self._classify_test_mode(texts)
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return self._classify_production(texts)
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def _classify_test_mode(self, texts: list[str]) -> list[tuple[float, float, float]]:
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"""Deterministic keyword-based classification for testing.
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Returns consistent probabilities based on keyword presence:
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- Positive keywords dominant -> (0.75, 0.10, 0.15)
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- Negative keywords dominant -> (0.10, 0.75, 0.15)
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- Both present (mixed signals) -> (0.40, 0.40, 0.20)
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- Neither present -> (0.15, 0.15, 0.70)
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"""
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results: list[tuple[float, float, float]] = []
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for text in texts:
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lower_text = text.lower()
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words = set(lower_text.split())
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has_positive = bool(words & _POSITIVE_KEYWORDS)
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has_negative = bool(words & _NEGATIVE_KEYWORDS)
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if has_positive and has_negative:
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# Mixed signals
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results.append((0.40, 0.40, 0.20))
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elif has_positive:
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results.append((0.75, 0.10, 0.15))
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elif has_negative:
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results.append((0.10, 0.75, 0.15))
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else:
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# Neutral — no sentiment keywords
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results.append((0.15, 0.15, 0.70))
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return results
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def _classify_production(self, texts: list[str]) -> list[tuple[float, float, float]]:
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"""Run FinBERT inference on texts using the loaded model."""
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import torch
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if self._model is None or self._tokenizer is None:
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raise RuntimeError("Model not loaded. Initialize with test_mode=False.")
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results: list[tuple[float, float, float]] = []
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# Process in batches to manage memory
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batch_size = 16
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for i in range(0, len(texts), batch_size):
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batch = texts[i : i + batch_size]
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inputs = self._tokenizer(
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batch,
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padding=True,
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truncation=True,
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max_length=512,
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return_tensors="pt",
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)
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with torch.no_grad():
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outputs = self._model(**inputs)
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# FinBERT output order: positive, negative, neutral
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probs = torch.softmax(outputs.logits, dim=-1)
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for prob in probs:
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pos = float(prob[0])
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neg = float(prob[1])
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neu = float(prob[2])
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results.append((pos, neg, neu))
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return results
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