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.
372 lines
14 KiB
Python
372 lines
14 KiB
Python
"""Specialist extraction engine — wraps GLiNER2 or a mock for testing."""
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from __future__ import annotations
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import logging
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import os
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from typing import Any
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from services.specialist.batching import DynamicBatcher
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from services.specialist.models import (
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MODEL_VERSION,
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SCHEMA_VERSION,
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ClassificationResult,
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EntityResult,
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RelationResult,
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StructuredResult,
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)
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logger = logging.getLogger(__name__)
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class SpecialistEngine:
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"""CPU-first extraction engine backed by GLiNER2 Large or a mock.
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In test mode (SPECIALIST_TEST_MODE=1), uses a mock engine that produces
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deterministic results without downloading model weights.
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Integrates a DynamicBatcher for each extraction type to collect concurrent
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requests and process them as batches.
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"""
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def __init__(
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self,
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model_name: str = "urchade/gliner_large-v2.1",
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max_batch_size: int = 32,
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max_wait_ms: float = 50.0,
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max_queue_size: int = 256,
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) -> None:
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self.model_name = model_name
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self.max_batch_size = max_batch_size
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self.max_wait_ms = max_wait_ms
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self.max_queue_size = max_queue_size
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self._model: Any = None
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self._test_mode = os.environ.get("SPECIALIST_TEST_MODE", "1") == "1"
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self._ready = False
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# Dynamic batchers (created but not started until load_model)
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self._entity_batcher: DynamicBatcher | None = None
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self._classification_batcher: DynamicBatcher | None = None
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self._relation_batcher: DynamicBatcher | None = None
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self._structured_batcher: DynamicBatcher | None = None
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@property
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def is_ready(self) -> bool:
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return self._ready
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@property
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def batcher_metrics(self) -> dict[str, Any]:
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"""Return aggregated metrics from all batchers."""
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metrics: dict[str, Any] = {
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"total_batches": 0,
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"total_items": 0,
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"total_rejections": 0,
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"queue_depth": 0,
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}
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for batcher in [
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self._entity_batcher,
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self._classification_batcher,
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self._relation_batcher,
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self._structured_batcher,
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]:
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if batcher:
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metrics["total_batches"] += batcher.total_batches_processed
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metrics["total_items"] += batcher.total_items_processed
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metrics["total_rejections"] += batcher.total_rejections
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metrics["queue_depth"] += batcher.queue_size
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return metrics
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def load_model(self, model_name: str | None = None) -> None:
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"""Load the specialist model or mock engine."""
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if model_name:
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self.model_name = model_name
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if self._test_mode:
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logger.info("Specialist engine starting in TEST mode (mock)")
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self._model = _MockGLiNER()
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self._ready = True
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return
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try:
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from gliner import GLiNER # type: ignore[import-untyped]
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logger.info("Loading GLiNER model: %s", self.model_name)
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self._model = GLiNER.from_pretrained(self.model_name)
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self._ready = True
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logger.info("GLiNER model loaded successfully")
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except ImportError:
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logger.warning(
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"gliner package not available — falling back to mock engine"
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)
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self._model = _MockGLiNER()
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self._ready = True
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except Exception:
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logger.exception("Failed to load GLiNER model")
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self._model = _MockGLiNER()
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self._ready = True
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def warm_up(self) -> None:
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"""Run a dummy inference to warm up model weights and caches."""
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if not self._ready:
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self.load_model()
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dummy_text = "Apple Inc reported Q3 revenue of $81.4 billion."
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dummy_labels = ["company", "financial_metric", "date"]
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self.extract_entities([dummy_text], dummy_labels)
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logger.info("Specialist engine warm-up complete")
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def extract_entities(
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self, texts: list[str], labels: list[str]
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) -> list[list[EntityResult]]:
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"""Extract entities from a batch of texts."""
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if not self._ready:
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raise RuntimeError("Engine not initialized — call load_model() first")
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results: list[list[EntityResult]] = []
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for text in texts:
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entities = self._predict_entities(text, labels)
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results.append(entities)
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return results
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def classify_texts(
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self, texts: list[str], labels: list[str]
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) -> list[list[ClassificationResult]]:
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"""Classify texts against the given labels."""
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if not self._ready:
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raise RuntimeError("Engine not initialized — call load_model() first")
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results: list[list[ClassificationResult]] = []
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for text in texts:
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classifications = self._predict_classification(text, labels)
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results.append(classifications)
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return results
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def extract_relations(
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self, texts: list[str], labels: list[str]
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) -> list[list[RelationResult]]:
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"""Extract relations from a batch of texts."""
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if not self._ready:
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raise RuntimeError("Engine not initialized — call load_model() first")
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results: list[list[RelationResult]] = []
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for text in texts:
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relations = self._predict_relations(text, labels)
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results.append(relations)
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return results
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def extract_structured(
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self, texts: list[str], labels: list[str]
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) -> list[list[StructuredResult]]:
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"""Extract structured key-value facts from texts."""
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if not self._ready:
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raise RuntimeError("Engine not initialized — call load_model() first")
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results: list[list[StructuredResult]] = []
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for text in texts:
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structured = self._predict_structured(text, labels)
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results.append(structured)
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return results
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# ------------------------------------------------------------------
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# Internal prediction methods
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# ------------------------------------------------------------------
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def _predict_entities(self, text: str, labels: list[str]) -> list[EntityResult]:
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"""Run entity prediction for a single text."""
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if self._test_mode or isinstance(self._model, _MockGLiNER):
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return self._model.predict_entities(text, labels)
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# Real GLiNER inference
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raw_entities = self._model.predict_entities(text, labels)
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return [
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EntityResult(
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text=ent["text"],
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entity_type=ent["label"],
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start_char=ent["start"],
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end_char=ent["end"],
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score=round(float(ent["score"]), 4),
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model_version=MODEL_VERSION,
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schema_version=SCHEMA_VERSION,
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)
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for ent in raw_entities
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]
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def _predict_classification(
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self, text: str, labels: list[str]
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) -> list[ClassificationResult]:
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"""Run classification for a single text using zero-shot NER heuristic."""
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if self._test_mode or isinstance(self._model, _MockGLiNER):
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return self._model.predict_classification(text, labels)
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# Use entity extraction as a proxy for classification
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raw_entities = self._model.predict_entities(text, labels)
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# Group by label and take the highest score per label
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label_scores: dict[str, float] = {}
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for ent in raw_entities:
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lbl = ent["label"]
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score = float(ent["score"])
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if lbl not in label_scores or score > label_scores[lbl]:
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label_scores[lbl] = score
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return [
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ClassificationResult(
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text=text[:200],
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label=lbl,
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score=round(score, 4),
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model_version=MODEL_VERSION,
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schema_version=SCHEMA_VERSION,
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)
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for lbl, score in label_scores.items()
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]
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def _predict_relations(
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self, text: str, labels: list[str]
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) -> list[RelationResult]:
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"""Run relation extraction for a single text."""
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if self._test_mode or isinstance(self._model, _MockGLiNER):
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return self._model.predict_relations(text, labels)
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# Real GLiNER does not natively support relations — use mock pattern
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return self._model.predict_relations(text, labels)
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def _predict_structured(
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self, text: str, labels: list[str]
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) -> list[StructuredResult]:
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"""Run structured fact extraction for a single text."""
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if self._test_mode or isinstance(self._model, _MockGLiNER):
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return self._model.predict_structured(text, labels)
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# Real GLiNER structured extraction uses entity spans as key-value pairs
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raw_entities = self._model.predict_entities(text, labels)
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return [
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StructuredResult(
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text=ent["text"],
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field=ent["label"],
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value=ent["text"],
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start_char=ent["start"],
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end_char=ent["end"],
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score=round(float(ent["score"]), 4),
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model_version=MODEL_VERSION,
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schema_version=SCHEMA_VERSION,
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)
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for ent in raw_entities
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]
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class _MockGLiNER:
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"""Mock GLiNER model for testing without downloading weights."""
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def predict_entities(self, text: str, labels: list[str]) -> list[EntityResult]:
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"""Produce deterministic mock entities based on simple heuristics."""
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results: list[EntityResult] = []
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# Simple keyword-based mock extraction
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_MOCK_ENTITIES = {
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"company": ["Apple", "Google", "Microsoft", "Tesla", "Amazon"],
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"person": ["Elon Musk", "Tim Cook", "Satya Nadella"],
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"financial_metric": ["revenue", "earnings", "EPS", "profit"],
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"date": ["Q1", "Q2", "Q3", "Q4", "2024", "2025"],
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"currency": ["$", "€", "£"],
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"percentage": ["%"],
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}
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for label in labels:
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keywords = _MOCK_ENTITIES.get(label, [])
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for keyword in keywords:
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start = text.find(keyword)
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if start >= 0:
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results.append(
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EntityResult(
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text=keyword,
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entity_type=label,
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start_char=start,
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end_char=start + len(keyword),
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score=0.85,
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model_version=MODEL_VERSION,
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schema_version=SCHEMA_VERSION,
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)
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)
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return results
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def predict_classification(
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self, text: str, labels: list[str]
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) -> list[ClassificationResult]:
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"""Produce deterministic mock classification results."""
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results: list[ClassificationResult] = []
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# Assign first label with high score, rest with decreasing
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for i, label in enumerate(labels):
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score = max(0.3, 0.9 - i * 0.2)
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results.append(
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ClassificationResult(
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text=text[:200],
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label=label,
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score=round(score, 4),
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model_version=MODEL_VERSION,
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schema_version=SCHEMA_VERSION,
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)
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)
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return results
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def predict_relations(
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self, text: str, labels: list[str]
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) -> list[RelationResult]:
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"""Produce deterministic mock relation results."""
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results: list[RelationResult] = []
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# Simple mock: if text mentions two companies, create a relation
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companies = ["Apple", "Google", "Microsoft", "Tesla", "Amazon"]
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found: list[tuple[str, int]] = []
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for company in companies:
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idx = text.find(company)
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if idx >= 0:
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found.append((company, idx))
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if len(found) >= 2 and labels:
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subj_name, subj_start = found[0]
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obj_name, obj_start = found[1]
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results.append(
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RelationResult(
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subject=subj_name,
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subject_type="company",
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subject_start=subj_start,
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subject_end=subj_start + len(subj_name),
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relation=labels[0],
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object=obj_name,
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object_type="company",
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object_start=obj_start,
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object_end=obj_start + len(obj_name),
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score=0.78,
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model_version=MODEL_VERSION,
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schema_version=SCHEMA_VERSION,
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)
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)
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return results
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def predict_structured(
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self, text: str, labels: list[str]
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) -> list[StructuredResult]:
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"""Produce deterministic mock structured results."""
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results: list[StructuredResult] = []
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# Extract number-like patterns as structured facts
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import re
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for label in labels:
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# Find dollar amounts
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pattern = r"\$[\d,]+\.?\d*\s*(?:billion|million|thousand)?"
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matches = list(re.finditer(pattern, text))
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for match in matches:
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results.append(
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StructuredResult(
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text=match.group(),
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field=label,
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value=match.group(),
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start_char=match.start(),
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end_char=match.end(),
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score=0.82,
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model_version=MODEL_VERSION,
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schema_version=SCHEMA_VERSION,
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)
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)
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break # one per label for mock
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return results
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