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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"""Seed migration helpers for the inference registry.
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Provides canonical endpoint profiles, model deployments, and agent
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provider-to-stage-binding conversion logic for migrating from the legacy
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model_provider/model_name fields to the v3 registry.
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Task 18.1-18.5: Migrate existing provider records.
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Requirements: 3.8, 3.9
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"""
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from __future__ import annotations
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from typing import Any
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from uuid import UUID
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# ─── Well-known IDs ────────────────────────────────────────────────────────────
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# These match the SQL seed migration (042_seed_inference_registry.sql)
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OLLAMA_ENDPOINT_ID = UUID("a0000000-0000-4000-8000-000000000001")
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VLLM_ENDPOINT_ID = UUID("a0000000-0000-4000-8000-000000000002")
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OLLAMA_DEPLOYMENT_ID = UUID("b0000000-0000-4000-8000-000000000001")
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VLLM_DEPLOYMENT_ID = UUID("b0000000-0000-4000-8000-000000000002")
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def get_initial_endpoints() -> list[dict[str, Any]]:
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"""Return the canonical endpoint profiles for the seed migration.
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Returns:
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List of endpoint dicts matching the inference_endpoints table schema.
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- stonks-ollama: Ollama native protocol at cluster-internal URL.
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- stonks-vllm: OpenAI-chat protocol (vLLM) at cluster-internal URL.
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"""
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return [
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{
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"id": OLLAMA_ENDPOINT_ID,
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"name": "stonks-ollama",
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"protocol": "ollama_native",
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"base_url": "http://ollama.ollama-service.svc.cluster.local:11434",
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"auth_secret_ref": None,
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"auth_scheme": "none",
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"default_headers": {},
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"health_path": "/api/tags",
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"enabled": True,
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},
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{
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"id": VLLM_ENDPOINT_ID,
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"name": "stonks-vllm",
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"protocol": "openai_chat",
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"base_url": "http://kube-vllm.stonks-oracle.svc.cluster.local:8000",
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"auth_secret_ref": None,
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"auth_scheme": "none",
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"default_headers": {},
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"health_path": "/health",
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"enabled": True,
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},
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]
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def get_initial_deployments() -> list[dict[str, Any]]:
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"""Return the initial model deployments for the seed migration.
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Returns:
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List of deployment dicts matching the model_deployments table schema.
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- Ollama: qwen3.5:9b with native JSON mode.
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- vLLM: AxionML/Qwen3.5-9B-NVFP4 on RTX 4070 Ti SUPER, strict JSON Schema.
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"""
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return [
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{
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"id": OLLAMA_DEPLOYMENT_ID,
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"endpoint_id": OLLAMA_ENDPOINT_ID,
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"served_model_name": "qwen3.5:9b",
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"display_name": "Qwen 3.5 9B (Ollama)",
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"capabilities": {
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"chat_completions": True,
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"json_schema": False,
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"json_object": True,
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"seed": False,
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"usage": False,
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"max_completion_tokens": False,
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"model_listing": True,
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},
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"context_window": 32768,
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"max_output_tokens": 32768,
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"quantization": None,
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"runtime_metadata": {
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"source": "ollama_native",
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"notes": "Ollama-served model with native JSON mode",
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},
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"enabled": True,
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},
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{
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"id": VLLM_DEPLOYMENT_ID,
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"endpoint_id": VLLM_ENDPOINT_ID,
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"served_model_name": "AxionML/Qwen3.5-9B-NVFP4",
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"display_name": "Qwen 3.5 9B NVFP4 (vLLM)",
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"capabilities": {
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"chat_completions": True,
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"json_schema": True,
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"json_object": True,
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"seed": True,
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"usage": True,
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"max_completion_tokens": True,
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"model_listing": True,
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},
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"context_window": 8192,
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"max_output_tokens": 2048,
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"quantization": "NVFP4",
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"runtime_metadata": {
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"gpu": "RTX 4070 Ti SUPER",
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"gpu_memory_utilization": 0.80,
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"max_num_seqs": 8,
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"vllm_structured_outputs": True,
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},
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"enabled": True,
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},
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]
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# ─── Provider mapping ──────────────────────────────────────────────────────────
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# Maps legacy model_provider values to endpoint/deployment IDs.
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_PROVIDER_TO_ENDPOINT: dict[str, UUID] = {
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"ollama": OLLAMA_ENDPOINT_ID,
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"vllm": VLLM_ENDPOINT_ID,
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}
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_PROVIDER_TO_DEPLOYMENT: dict[str, UUID] = {
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"ollama": OLLAMA_DEPLOYMENT_ID,
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"vllm": VLLM_DEPLOYMENT_ID,
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}
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class UnknownProviderError(ValueError):
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"""Raised when an agent record has an unrecognized model_provider value."""
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def __init__(self, provider: str, agent_id: str) -> None:
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self.provider = provider
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self.agent_id = agent_id
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super().__init__(
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f"Unknown model_provider '{provider}' for agent '{agent_id}'. "
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f"Supported providers: {sorted(_PROVIDER_TO_ENDPOINT.keys())}. "
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f"Cannot silently convert unknown providers."
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)
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def convert_agent_providers(agent_records: list[dict[str, Any]]) -> list[dict[str, Any]]:
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"""Convert existing agent model_provider/model_name fields to stage bindings.
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For each agent record with a known model_provider (ollama or vllm), produces
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a stage binding record that maps the agent to the appropriate endpoint and
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model deployment. The original model_provider and model_name fields are
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retained for backward compatibility — this function supplements, not replaces.
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Args:
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agent_records: List of dicts with at least 'id' (or 'agent_id'),
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'model_provider', and optionally 'slug' fields.
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Returns:
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List of stage binding dicts suitable for insertion into
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agent_stage_bindings.
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Raises:
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UnknownProviderError: If a record has a model_provider that cannot be
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mapped. Unknown providers must NOT be silently converted.
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"""
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bindings: list[dict[str, Any]] = []
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for record in agent_records:
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agent_id = str(record.get("id") or record.get("agent_id", ""))
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provider = (record.get("model_provider") or "").strip().lower()
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if not provider:
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# No provider set — skip, nothing to convert
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continue
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if provider not in _PROVIDER_TO_ENDPOINT:
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raise UnknownProviderError(provider=provider, agent_id=agent_id)
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endpoint_id = _PROVIDER_TO_ENDPOINT[provider]
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deployment_id = _PROVIDER_TO_DEPLOYMENT[provider]
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# Determine stage from agent slug or default to 'extraction'
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slug = record.get("slug", "")
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stage = _infer_stage_from_slug(slug)
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bindings.append({
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"agent_id": agent_id,
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"stage": stage,
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"endpoint_id": endpoint_id,
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"model_deployment_id": str(deployment_id),
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"route_order": 0,
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"routing_config": {},
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"is_active": True,
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})
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return bindings
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def _infer_stage_from_slug(slug: str) -> str:
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"""Map an agent slug to a pipeline stage name.
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Known agent slugs and their corresponding stages:
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- document-extractor -> extraction
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- event-classifier -> classification
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- thesis-rewriter -> thesis_rewrite
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- report-summarizer -> summarization
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Falls back to 'extraction' for unrecognized slugs.
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"""
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slug_to_stage: dict[str, str] = {
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"document-extractor": "extraction",
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"event-classifier": "classification",
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"thesis-rewriter": "thesis_rewrite",
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"report-summarizer": "summarization",
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}
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return slug_to_stage.get(slug, "extraction")
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# ─── Conflicting defaults identification ──────────────────────────────────────
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# Known locations where model/provider defaults have historically conflicted.
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_KNOWN_CONFLICT_LOCATIONS: list[dict[str, str]] = [
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{
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"location": "services/shared/config.py",
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"field": "VLLMConfig.model",
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"description": "Python config default for vLLM model name",
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},
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{
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"location": "services/shared/config.py",
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"field": "VLLMConfig.base_url",
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"description": "Python config default for vLLM base URL (192.168.42.254:8000)",
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},
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{
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"location": "infra/migrations/026_ai_agents.sql",
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"field": "model_provider/model_name DEFAULT",
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"description": "Agent table DDL defaults to 'ollama'/'qwen3.5:9b-fast'",
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},
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{
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"location": "infra/migrations/031_fix_agent_defaults.sql",
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"field": "model_provider UPDATE",
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"description": "Migration updates agents to 'vllm'/'AxionML/Qwen3.5-9B-NVFP4'",
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},
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{
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"location": "infra/migrations/033_stop_hardcoding_agent_model.sql",
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"field": "model_provider UPDATE",
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"description": "Migration updates agents to 'vllm'/'AxionML/Qwen3.5-9B-NVFP4'",
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},
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{
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"location": "infra/helm/stonks-oracle/values.yaml",
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"field": "VLLM_BASE_URL / VLLM_MODEL",
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"description": "Helm values point to nuextract-external.vllm-service with NuExtract3",
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},
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{
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"location": "infra/helm/stonks-oracle/values.yaml",
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"field": "OLLAMA_BASE_URL / OLLAMA_MODEL",
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"description": "Helm values point to nuextract-external.vllm-service with NuExtract3",
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},
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{
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"location": "infra/kube-vllm/deployment.yaml",
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"field": "vLLM deployment model arg",
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"description": "Standalone kube-vllm deployment with AxionML/Qwen3.5-9B-NVFP4",
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},
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]
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def identify_conflicting_defaults() -> list[str]:
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"""List locations where model defaults historically conflict.
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Returns a list of human-readable strings identifying places where
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the model provider, model name, or base URL have conflicting
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defaults across config.py, migrations, Helm values, and the
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kube-vllm deployment.
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These conflicts should be resolved after the inference registry
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is established as the single source of truth.
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Returns:
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List of conflict description strings.
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"""
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conflicts: list[str] = []
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for entry in _KNOWN_CONFLICT_LOCATIONS:
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conflicts.append(
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f"{entry['location']} [{entry['field']}]: {entry['description']}"
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)
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return conflicts
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