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/segmenter/models.py

@@ -0,0 +1,33 @@
+"""Pydantic models for document chunks produced by the segmenter."""
+
+from __future__ import annotations
+
+import hashlib
+
+from pydantic import BaseModel, Field, computed_field
+
+
+class DocumentChunk(BaseModel):
+    """A contiguous text segment of a source document with offset metadata.
+
+    The chunk preserves exact source character offsets so that downstream
+    evidence spans can always be mapped back to the original document text.
+    """
+
+    chunk_id: str = Field(description="Deterministic ID: {document_id}:{start_char}")
+    document_id: str = Field(description="Parent document identifier")
+    document_type: str = Field(description="Type of document: article, filing, transcript, macro_event")
+    section_path: list[str] = Field(default_factory=list, description="Hierarchical section/heading path")
+    speaker: str | None = Field(default=None, description="Speaker label for transcript chunks")
+    start_char: int = Field(ge=0, description="Start character offset in source document")
+    end_char: int = Field(gt=0, description="End character offset in source document (exclusive)")
+    text: str = Field(min_length=1, description="Chunk text content")
+    overlap_left: int = Field(default=0, ge=0, description="Characters of overlap with previous chunk")
+    overlap_right: int = Field(default=0, ge=0, description="Characters of overlap with next chunk")
+    boilerplate_score: float = Field(default=0.0, ge=0.0, le=1.0, description="Boilerplate likelihood 0.0-1.0")
+
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def checksum(self) -> str:
+        """SHA-256 hex digest of the chunk text."""
+        return hashlib.sha256(self.text.encode("utf-8")).hexdigest()