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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"""NuExtract benchmark comparing against GLiNER2 + deterministic parsing.
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Evaluates NuExtract 1.5 Smol on hierarchical extraction for long filings
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and transcripts, measuring incremental correctness, CPU latency, and memory.
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Reports per-document-type incremental value to determine which document
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classes benefit from NuExtract supplementation.
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Requirement: 6.6
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"""
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from __future__ import annotations
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import logging
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from collections import defaultdict
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from typing import Any
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from services.intelligence_pipeline_v3.nuextract.adapter import NuExtractAdapter
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from services.intelligence_pipeline_v3.nuextract.models import (
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BenchmarkReport,
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IncrementalValueReport,
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NuExtractResult,
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PromotionGate,
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)
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from services.intelligence_pipeline_v3.nuextract.promotion import PromotionEvaluator
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logger = logging.getLogger(__name__)
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class GoldDocument:
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"""A document from the gold corpus with ground-truth labels."""
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def __init__(
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self,
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text: str,
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document_type: str,
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gold_fields: dict[str, Any],
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schema: dict[str, Any],
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document_id: str = "",
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) -> None:
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self.text = text
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self.document_type = document_type
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self.gold_fields = gold_fields
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self.schema = schema
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self.document_id = document_id
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class GLiNERResult:
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"""Simulated result from GLiNER2 + deterministic parsing."""
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def __init__(
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self,
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fields: dict[str, Any],
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latency_ms: float = 0.0,
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memory_mb: float = 0.0,
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) -> None:
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self.fields = fields
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self.latency_ms = latency_ms
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self.memory_mb = memory_mb
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class NuExtractBenchmark:
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"""Benchmark comparing NuExtract vs GLiNER2 + deterministic parsing.
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Evaluates per-document-type to determine where NuExtract adds value.
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Parameters
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----------
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adapter
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NuExtractAdapter instance (test_mode or production).
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gate
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Promotion gate thresholds for deciding promotion.
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"""
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def __init__(
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self,
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adapter: NuExtractAdapter | None = None,
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gate: PromotionGate | None = None,
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) -> None:
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self._adapter = adapter or NuExtractAdapter(test_mode=True)
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self._gate = gate or PromotionGate()
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self._evaluator = PromotionEvaluator(self._gate)
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async def evaluate_against_gliner(
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self,
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documents: list[GoldDocument],
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gliner_results: list[GLiNERResult],
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) -> BenchmarkReport:
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"""Run full benchmark comparing NuExtract vs GLiNER2 + deterministic parsing.
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Parameters
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----------
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documents
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Gold corpus documents with ground-truth labels.
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gliner_results
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Pre-computed GLiNER2 + deterministic parsing results for each document.
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Returns
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-------
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BenchmarkReport
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Full benchmark report with per-type results and promotion decisions.
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"""
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if len(documents) != len(gliner_results):
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raise ValueError(
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f"Document count ({len(documents)}) must match "
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f"GLiNER result count ({len(gliner_results)})"
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)
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# Group by document type
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by_type: dict[str, list[tuple[GoldDocument, GLiNERResult]]] = defaultdict(list)
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for doc, gliner in zip(documents, gliner_results):
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by_type[doc.document_type].append((doc, gliner))
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# Evaluate each document type
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reports: list[IncrementalValueReport] = []
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for doc_type, pairs in by_type.items():
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report = await self._evaluate_type(doc_type, pairs)
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reports.append(report)
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# Determine promotions
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promoted_types: list[str] = []
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for report in reports:
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if self._evaluator.evaluate(report):
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report.promoted = True
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promoted_types.append(report.document_type)
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# Compute overall metrics
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total_docs = len(documents)
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overall_nuextract_f1 = 0.0
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overall_gliner_f1 = 0.0
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if reports:
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weighted_nu = sum(r.nuextract_f1 * r.sample_count for r in reports)
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weighted_gl = sum(r.gliner_f1 * r.sample_count for r in reports)
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overall_nuextract_f1 = weighted_nu / total_docs if total_docs > 0 else 0.0
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overall_gliner_f1 = weighted_gl / total_docs if total_docs > 0 else 0.0
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return BenchmarkReport(
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reports=reports,
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gate=self._gate,
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promoted_types=promoted_types,
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overall_nuextract_f1=overall_nuextract_f1,
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overall_gliner_f1=overall_gliner_f1,
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overall_delta=overall_nuextract_f1 - overall_gliner_f1,
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total_documents=total_docs,
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)
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async def _evaluate_type(
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self,
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doc_type: str,
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pairs: list[tuple[GoldDocument, GLiNERResult]],
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) -> IncrementalValueReport:
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"""Evaluate NuExtract vs GLiNER for a single document type."""
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nuextract_scores: list[float] = []
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gliner_scores: list[float] = []
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nuextract_latencies: list[float] = []
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nuextract_memories: list[float] = []
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gliner_latencies: list[float] = []
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gliner_memories: list[float] = []
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for doc, gliner_result in pairs:
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# Run NuExtract extraction
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nu_result = await self._adapter.extract(
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text=doc.text,
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schema=doc.schema,
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document_type=doc.document_type,
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)
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# Compute F1 for NuExtract
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nu_f1 = self._compute_field_f1(nu_result, doc.gold_fields)
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nuextract_scores.append(nu_f1)
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nuextract_latencies.append(nu_result.latency_ms)
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nuextract_memories.append(nu_result.memory_mb)
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# Compute F1 for GLiNER
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gl_f1 = self._compute_extraction_f1(gliner_result.fields, doc.gold_fields)
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gliner_scores.append(gl_f1)
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gliner_latencies.append(gliner_result.latency_ms)
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gliner_memories.append(gliner_result.memory_mb)
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# Aggregate metrics
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n = len(pairs)
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avg_nu_f1 = sum(nuextract_scores) / n if n > 0 else 0.0
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avg_gl_f1 = sum(gliner_scores) / n if n > 0 else 0.0
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p95_nu_latency = _percentile(nuextract_latencies, 95)
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p95_gl_latency = _percentile(gliner_latencies, 95)
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max_nu_memory = max(nuextract_memories) if nuextract_memories else 0.0
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max_gl_memory = max(gliner_memories) if gliner_memories else 0.0
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return IncrementalValueReport(
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document_type=doc_type,
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gliner_f1=avg_gl_f1,
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nuextract_f1=avg_nu_f1,
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delta=avg_nu_f1 - avg_gl_f1,
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nuextract_latency_ms=p95_nu_latency,
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nuextract_memory_mb=max_nu_memory,
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gliner_latency_ms=p95_gl_latency,
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gliner_memory_mb=max_gl_memory,
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sample_count=n,
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promoted=False,
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)
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def _compute_field_f1(
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self, result: NuExtractResult, gold: dict[str, Any]
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) -> float:
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"""Compute F1 score for NuExtract result against gold labels."""
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if not gold:
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return 1.0 if not result.fields else 0.0
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extracted_fields = {
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f.name: f.value for f in result.fields if f.value is not None
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}
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return self._compute_extraction_f1(extracted_fields, gold)
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def _compute_extraction_f1(
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self, predicted: dict[str, Any], gold: dict[str, Any]
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) -> float:
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"""Compute field-level F1 between predicted and gold extractions."""
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if not gold and not predicted:
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return 1.0
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if not gold or not predicted:
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return 0.0
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gold_set = set(gold.keys())
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pred_set = set(predicted.keys())
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# True positives: predicted fields that match gold (key present AND value matches)
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tp = 0
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for key in gold_set & pred_set:
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if self._values_match(predicted[key], gold[key]):
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tp += 1
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precision = tp / len(pred_set) if pred_set else 0.0
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recall = tp / len(gold_set) if gold_set else 0.0
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if precision + recall == 0:
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return 0.0
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return 2 * precision * recall / (precision + recall)
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def _values_match(self, predicted: Any, gold: Any) -> bool:
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"""Check if a predicted value matches gold (with tolerance)."""
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if predicted is None:
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return gold is None
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if gold is None:
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return False
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# String comparison (case-insensitive, trimmed)
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if isinstance(gold, str) and isinstance(predicted, str):
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return predicted.strip().lower() == gold.strip().lower()
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# Numeric comparison with tolerance
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if isinstance(gold, (int, float)) and isinstance(predicted, (int, float)):
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if gold == 0:
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return abs(predicted) < 1e-6
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return abs(predicted - gold) / abs(gold) < 0.05
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# Dict comparison (recursive for hierarchical)
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if isinstance(gold, dict) and isinstance(predicted, dict):
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if not gold:
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return not predicted
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matches = sum(
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1
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for k in gold
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if k in predicted and self._values_match(predicted[k], gold[k])
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)
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return matches / len(gold) >= 0.5
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# Fallback: equality
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return predicted == gold
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def _percentile(values: list[float], pct: int) -> float:
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"""Compute a percentile from a list of values."""
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if not values:
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return 0.0
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sorted_vals = sorted(values)
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idx = int(len(sorted_vals) * pct / 100)
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idx = min(idx, len(sorted_vals) - 1)
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return sorted_vals[idx]
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