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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"""9B adjudicator deployment configuration for Intelligence Pipeline v3.
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Manages the approved model/version pins, VRAM gating, concurrency
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semaphore configuration, and alerting thresholds for the 9B adjudicator
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running on RTX 4070 Ti SUPER via vLLM.
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"""
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from __future__ import annotations
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import asyncio
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from typing import Any
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from pydantic import BaseModel, Field
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# --- Pinned model and version constants ---
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APPROVED_MODEL: str = "AxionML/Qwen3.5-9B-NVFP4"
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"""Approved 9B model for adjudication. NVFP4 quantization for 4070 Ti SUPER."""
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APPROVED_VLLM_VERSION: str = "0.8.5"
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"""Approved vLLM version matching the cluster deployment."""
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APPROVED_SERVED_NAME: str = "stonks-adjudicator-9b"
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"""The served model name exposed by the vLLM deployment."""
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MAX_MODEL_LEN: int = 8192
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"""Maximum model context length configured for the deployment."""
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MAX_NUM_SEQS: int = 8
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"""Maximum concurrent sequences for the vLLM deployment."""
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GPU_MEMORY_UTILIZATION: float = 0.80
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"""Target GPU memory utilization fraction."""
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VRAM_GATE_PERCENT: float = 5.0
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"""Maximum allowed VRAM increase over baseline (percentage)."""
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# --- Structured output verification ---
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def verify_structured_output(target: dict[str, Any]) -> bool:
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"""Verify that structured output works with the given deployment target.
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Checks that the target deployment declares json_schema support in its
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capabilities and that the required configuration fields are present.
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Args:
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target: Deployment target configuration dict containing at minimum:
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- capabilities: dict with json_schema boolean
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- served_model_name: str matching APPROVED_SERVED_NAME
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- vllm_version: str for version verification
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Returns:
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True if strict schema output is expected to work, False otherwise.
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"""
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capabilities = target.get("capabilities", {})
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if not capabilities.get("json_schema", False):
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return False
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# Verify model matches approved deployment
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served_name = target.get("served_model_name", "")
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if served_name and served_name != APPROVED_SERVED_NAME:
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return False
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# Verify vLLM version compatibility
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vllm_version = target.get("vllm_version", "")
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if vllm_version and vllm_version != APPROVED_VLLM_VERSION:
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return False
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# Verify the model is the approved one
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model = target.get("model", "")
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if model and model != APPROVED_MODEL:
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return False
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return True
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# --- VRAM gate ---
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def check_vram_gate(peak_mb: float, baseline_mb: float) -> bool:
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"""Check whether peak VRAM usage is within the +5% gate of baseline.
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The gate ensures that no deployment update exceeds the measured current
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9B deployment VRAM by more than 5 percent.
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Args:
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peak_mb: Measured peak VRAM in megabytes during test.
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baseline_mb: Baseline VRAM measurement in megabytes.
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Returns:
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True if peak is within acceptable range, False if it exceeds the gate.
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"""
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if baseline_mb <= 0:
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return False
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if peak_mb <= 0:
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return False
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max_allowed_mb = baseline_mb * (1.0 + VRAM_GATE_PERCENT / 100.0)
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return peak_mb <= max_allowed_mb
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# --- Concurrency semaphore ---
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class ConcurrencySemaphore(BaseModel):
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"""Configuration for the adjudication concurrency semaphore.
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Limits concurrent adjudication requests to protect vLLM from
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overload. Aligned with max-num-seqs and KV-cache behavior.
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"""
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max_concurrent: int = Field(
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default=MAX_NUM_SEQS,
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gt=0,
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description="Maximum concurrent adjudication requests",
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)
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queue_timeout_seconds: float = Field(
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default=120.0,
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gt=0,
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description="Maximum time to wait for semaphore acquisition",
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)
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backpressure_threshold: int = Field(
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default=MAX_NUM_SEQS * 4,
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ge=0,
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description="Queue depth at which backpressure signals are emitted",
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)
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def create_semaphore(self) -> asyncio.Semaphore:
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"""Create an asyncio.Semaphore with the configured max_concurrent."""
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return asyncio.Semaphore(self.max_concurrent)
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# --- Alert configuration ---
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class AlertConfig(BaseModel):
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"""Alert thresholds for adjudicator monitoring.
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Defines queue-depth and availability thresholds that trigger alerts
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when the adjudicator is overloaded or unavailable.
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"""
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queue_depth_warning: int = Field(
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default=16,
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ge=1,
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description="Queue depth that triggers a warning alert",
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)
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queue_depth_critical: int = Field(
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default=32,
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ge=1,
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description="Queue depth that triggers a critical alert",
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)
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availability_threshold_percent: float = Field(
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default=95.0,
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gt=0.0,
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le=100.0,
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description="Minimum availability percentage before alerting",
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)
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latency_p95_warning_ms: int = Field(
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default=5000,
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gt=0,
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description="p95 latency (ms) that triggers a warning",
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)
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latency_p95_critical_ms: int = Field(
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default=15000,
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gt=0,
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description="p95 latency (ms) that triggers a critical alert",
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)
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consecutive_failures_alert: int = Field(
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default=3,
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ge=1,
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description="Number of consecutive failures before alerting",
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)
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health_check_interval_seconds: float = Field(
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default=30.0,
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gt=0,
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description="Interval between health checks in seconds",
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)
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