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 1.5 Smol adapter for on-demand CPU extraction.
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Provides an isolated interface for NuExtract inference:
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- Production mode: loads numind/NuExtract-1.5-smol on CPU
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- Test mode: deterministic schema-based mock extraction
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This adapter uses the shared InferenceGateway with a configurable
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target for CPU-only deployment. It is NOT always-resident — loaded
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on demand for benchmark or promoted document classes only.
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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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import re
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import time
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from typing import Any
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from services.intelligence_pipeline_v3.nuextract.models import (
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ExtractedField,
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NuExtractResult,
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)
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logger = logging.getLogger(__name__)
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# Pinned model configuration
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NUEXTRACT_MODEL_NAME = "numind/NuExtract-1.5-smol"
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NUEXTRACT_MODEL_VERSION = "numind/NuExtract-1.5-smol@v1.5"
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class NuExtractAdapter:
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"""Adapter for NuExtract 1.5 Smol hierarchical extraction.
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Designed for CPU/on-demand use, not always-resident GPU deployment.
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Parameters
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----------
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test_mode
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When True, uses deterministic schema-based extraction rather than
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loading the model. Useful for testing without model dependencies.
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max_length
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Maximum input text length in characters. Longer texts are processed
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in segments.
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"""
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def __init__(
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self,
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test_mode: bool = True,
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max_length: int = 16_000,
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) -> None:
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self._test_mode = test_mode
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self._max_length = max_length
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self._model = None
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self._tokenizer = None
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self._model_name = NUEXTRACT_MODEL_NAME
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self._model_version = NUEXTRACT_MODEL_VERSION
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self._loaded = False
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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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@property
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def is_loaded(self) -> bool:
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"""Return whether the model is currently loaded."""
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return self._loaded
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def _load_model(self) -> None:
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"""Load the NuExtract model and tokenizer for CPU inference."""
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try:
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from transformers import AutoModelForCausalLM, AutoTokenizer
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logger.info("Loading NuExtract model (CPU): %s", self._model_name)
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self._tokenizer = AutoTokenizer.from_pretrained(self._model_name)
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self._model = AutoModelForCausalLM.from_pretrained(
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self._model_name,
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device_map="cpu",
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torch_dtype="auto",
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)
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self._model.eval()
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self._loaded = True
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logger.info("NuExtract model loaded successfully on CPU")
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except ImportError:
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raise RuntimeError(
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"transformers and torch are required for NuExtract 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 NuExtract model: {e}") from e
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def unload(self) -> None:
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"""Unload model to free memory (on-demand lifecycle)."""
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if self._model is not None:
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del self._model
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del self._tokenizer
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self._model = None
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self._tokenizer = None
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self._loaded = False
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logger.info("NuExtract model unloaded")
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async def extract(
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self,
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text: str,
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schema: dict[str, Any],
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document_type: str = "",
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) -> NuExtractResult:
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"""Extract structured fields from text using the given schema.
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Parameters
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----------
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text
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Source document text.
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schema
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JSON schema defining the fields to extract.
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document_type
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Document type identifier for reporting.
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Returns
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-------
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NuExtractResult
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Extracted fields with confidence, latency, and memory metrics.
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"""
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start_time = time.perf_counter()
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try:
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if self._test_mode:
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fields = self._extract_test_mode(text, schema)
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else:
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fields = self._extract_production(text, schema)
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latency_ms = (time.perf_counter() - start_time) * 1000
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memory_mb = self._estimate_memory()
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# Compute overall confidence as mean of field confidences
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confidence = 0.0
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if fields:
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confidence = sum(f.confidence for f in fields) / len(fields)
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return NuExtractResult(
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fields=fields,
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spans=[
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{"start": f.start_char, "end": f.end_char, "field": f.name}
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for f in fields
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if f.start_char is not None
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],
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confidence=confidence,
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model_version=self._model_version,
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latency_ms=latency_ms,
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memory_mb=memory_mb,
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document_type=document_type,
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schema_used=schema,
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)
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except Exception as e:
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latency_ms = (time.perf_counter() - start_time) * 1000
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logger.error("NuExtract extraction failed: %s", e)
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return NuExtractResult(
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model_version=self._model_version,
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latency_ms=latency_ms,
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document_type=document_type,
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schema_used=schema,
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error=str(e),
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)
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def _extract_test_mode(
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self, text: str, schema: dict[str, Any]
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) -> list[ExtractedField]:
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"""Deterministic extraction for testing.
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Matches schema field names against text content using simple
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pattern matching to simulate extraction behavior.
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"""
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fields: list[ExtractedField] = []
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properties = schema.get("properties", schema)
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for field_name, field_spec in properties.items():
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# Simple pattern: look for the field name or related keywords in text
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pattern = re.compile(
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rf"\b{re.escape(field_name.replace('_', ' '))}[:\s]+([^\n.;]+)",
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re.IGNORECASE,
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)
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match = pattern.search(text)
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if match:
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value = match.group(1).strip()
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fields.append(
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ExtractedField(
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name=field_name,
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value=value,
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start_char=match.start(1),
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end_char=match.end(1),
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confidence=0.85,
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)
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)
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else:
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# Try extracting from nearby context for hierarchical schemas
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if isinstance(field_spec, dict) and "properties" in field_spec:
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# Nested schema — attempt hierarchical extraction
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nested = self._extract_test_mode(text, field_spec)
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if nested:
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fields.append(
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ExtractedField(
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name=field_name,
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value={f.name: f.value for f in nested},
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confidence=sum(f.confidence for f in nested) / len(nested),
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)
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)
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else:
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# Field not found in text
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fields.append(
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ExtractedField(
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name=field_name,
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value=None,
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confidence=0.0,
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)
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)
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return fields
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def _extract_production(
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self, text: str, schema: dict[str, Any]
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) -> list[ExtractedField]:
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"""Run NuExtract inference on text using the loaded model."""
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import json
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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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# Format input in NuExtract's expected format
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schema_str = json.dumps(schema, indent=2)
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prompt = f"<|input|>\n### Template:\n{schema_str}\n### Text:\n{text[:self._max_length]}\n<|output|>\n"
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inputs = self._tokenizer(
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prompt,
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return_tensors="pt",
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truncation=True,
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max_length=4096,
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)
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with torch.no_grad():
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outputs = self._model.generate(
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**inputs,
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max_new_tokens=1024,
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temperature=0.0,
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do_sample=False,
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)
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# Decode and parse the output
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generated = self._tokenizer.decode(
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outputs[0][inputs["input_ids"].shape[1]:],
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skip_special_tokens=True,
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)
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return self._parse_output(generated, text, schema)
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def _parse_output(
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self, output: str, source_text: str, schema: dict[str, Any]
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) -> list[ExtractedField]:
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"""Parse model output into structured fields with spans."""
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import json
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fields: list[ExtractedField] = []
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try:
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parsed = json.loads(output)
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except json.JSONDecodeError:
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logger.warning("Failed to parse NuExtract output as JSON")
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return fields
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properties = schema.get("properties", schema)
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for field_name in properties:
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if field_name in parsed:
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value = parsed[field_name]
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# Try to find the value in source text for span
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start_char = None
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end_char = None
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if isinstance(value, str) and value:
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idx = source_text.find(value)
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if idx >= 0:
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start_char = idx
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end_char = idx + len(value)
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fields.append(
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ExtractedField(
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name=field_name,
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value=value,
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start_char=start_char,
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end_char=end_char,
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confidence=0.8,
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)
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)
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return fields
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def _estimate_memory(self) -> float:
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"""Estimate current memory usage in MB."""
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if self._test_mode:
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return 0.0
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try:
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import torch
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if torch.cuda.is_available():
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return torch.cuda.memory_allocated() / (1024 * 1024)
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# For CPU, estimate from model parameters
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if self._model is not None:
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param_bytes = sum(
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p.nelement() * p.element_size() for p in self._model.parameters()
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)
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return param_bytes / (1024 * 1024)
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except Exception:
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pass
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return 0.0
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