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/intelligence_pipeline_v3/parsing/models.py

@@ -0,0 +1,47 @@
+"""Pydantic models for parsed financial candidates."""
+
+from __future__ import annotations
+
+from enum import Enum
+
+from pydantic import BaseModel, Field
+
+
+class CandidateType(str, Enum):
+    """Types of financial entities detected by the deterministic parser."""
+
+    TICKER = "ticker"
+    CURRENCY = "currency"
+    MONEY = "money"
+    PERCENTAGE = "percentage"
+    BASIS_POINTS = "basis_points"
+    RANGE = "range"
+    EPS = "eps"
+    REVENUE = "revenue"
+    DATE = "date"
+    FISCAL_PERIOD = "fiscal_period"
+
+
+class PeriodAnnotation(BaseModel):
+    """Optional period context for a parsed candidate (e.g., Q1, FY2025)."""
+
+    period_type: str = Field(description="Type: quarter, year, fiscal_year, half")
+    period_value: str = Field(description="Normalized period: Q1, Q2, H1, FY")
+    year: int | None = Field(default=None, description="Calendar or fiscal year")
+
+
+class ParsedCandidate(BaseModel):
+    """A single parsed financial entity with source offset and normalization.
+
+    Stores both the literal text as it appeared in the source document and the
+    normalized numeric value (if applicable). Exact character offsets allow
+    downstream evidence linking back to the source.
+    """
+
+    candidate_type: CandidateType = Field(description="Classification of the parsed entity")
+    literal_value: str = Field(min_length=1, description="Exact text as it appears in source")
+    normalized_value: float | None = Field(default=None, description="Normalized numeric value")
+    unit: str | None = Field(default=None, description="Unit: USD, EUR, %, bps, etc.")
+    start_char: int = Field(ge=0, description="Start character offset in source text")
+    end_char: int = Field(gt=0, description="End character offset in source text (exclusive)")
+    period: PeriodAnnotation | None = Field(default=None, description="Optional fiscal/calendar period")