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.

services/specialist/schemas.py

@@ -0,0 +1,56 @@
+"""Request/response schemas for the specialist inference service.
+
+Re-exports from models.py for discoverability, plus BatchConfig for
+deployment configuration.
+"""
+
+from __future__ import annotations
+
+from pydantic import BaseModel, Field
+
+from services.specialist.models import (
+    MODEL_VERSION,
+    SCHEMA_VERSION,
+    BatchResponse,
+    ClassificationRequest,
+    ClassificationResult,
+    EntityResult,
+    ExtractionRequest,
+    RelationResult,
+    StructuredResult,
+)
+
+__all__ = [
+    "BatchConfig",
+    "BatchResponse",
+    "ClassificationRequest",
+    "ClassificationResult",
+    "EntityResult",
+    "ExtractionRequest",
+    "MODEL_VERSION",
+    "RelationResult",
+    "SCHEMA_VERSION",
+    "SpanResult",
+    "StructuredResult",
+]
+
+
+class SpanResult(BaseModel):
+    """Generic span result used across extraction types."""
+
+    text: str
+    start_char: int
+    end_char: int
+    label: str
+    score: float = Field(..., ge=0.0, le=1.0)
+    model_version: str = MODEL_VERSION
+    schema_version: str = SCHEMA_VERSION
+
+
+class BatchConfig(BaseModel):
+    """Configuration for dynamic batching behavior."""
+
+    max_batch_size: int = Field(default=32, ge=1, le=512, description="Maximum items per batch")
+    max_wait_ms: float = Field(default=50.0, ge=1.0, le=5000.0, description="Maximum wait time before flushing a partial batch (ms)")
+    max_queue_size: int = Field(default=256, ge=1, le=10000, description="Maximum pending requests in queue before rejection")
+    warm_up_on_start: bool = Field(default=True, description="Whether to run a warm-up inference on startup")