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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"""OpenAI-compatible inference client.
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Uses httpx.AsyncClient directly (NOT the openai SDK). This keeps wire payloads
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explicit, permits provider-specific extra_body, and simplifies redacted request
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auditing.
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Requirements: 2.1, 2.2, 2.3, 2.4, 2.5, 2.7, 2.9
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"""
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from __future__ import annotations
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import json
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import logging
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import os
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import time
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from typing import Any
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import httpx
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from services.shared.inference.models import (
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ErrorCategory,
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InferenceResult,
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InferenceTarget,
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StructuredGenerationRequest,
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TokenUsage,
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)
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logger = logging.getLogger(__name__)
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# Status codes that trigger retry
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_RETRYABLE_STATUS_CODES = {429, 500, 502, 503, 504}
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# Sensitive keys that must never appear in logs
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_SENSITIVE_KEYS = frozenset({"authorization", "x-api-key", "api-key"})
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def _resolve_auth_secret(secret_ref: str | None) -> str | None:
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"""Resolve an authentication secret from environment variables.
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The secret_ref is treated as an environment variable name.
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Returns None if the ref is None or the env var is not set.
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"""
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if not secret_ref:
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return None
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return os.environ.get(secret_ref)
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def _redact_headers(headers: dict[str, str]) -> dict[str, str]:
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"""Return a copy of headers with sensitive values redacted."""
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redacted = {}
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for key, value in headers.items():
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if key.lower() in _SENSITIVE_KEYS:
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redacted[key] = "***REDACTED***"
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else:
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redacted[key] = value
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return redacted
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class OpenAICompatibleClient:
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"""Client for OpenAI-compatible /v1/chat/completions endpoints.
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Supports:
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- Bearer and configurable authentication via runtime secret resolution
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- Standard response_format.json_schema payloads
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- Configurable vLLM structured_outputs extra-body payloads
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- Explicit JSON-object and prompt-only fallback policies
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- Retry on transient errors (5xx, 429, timeout)
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- Local JSON schema revalidation
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- Metadata capture (request_id, usage, finish_reason, retries)
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"""
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def __init__(
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self,
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target: InferenceTarget,
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*,
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http_client: httpx.AsyncClient | None = None,
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) -> None:
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self._target = target
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self._owns_client = http_client is None
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self._http = http_client or httpx.AsyncClient(timeout=target.timeout_seconds)
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@property
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def target(self) -> InferenceTarget:
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"""The resolved inference target."""
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return self._target
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async def generate(
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self,
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request: StructuredGenerationRequest,
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) -> InferenceResult:
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"""Send a structured generation request and return the result.
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Chooses the strongest structured-output mode the target supports:
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1. json_schema — sends the actual schema with strict mode
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2. json_object — only if target explicitly declares json_object capability
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3. prompt_only — fallback when no structured output is available
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"""
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structured_mode = self._choose_structured_mode(request)
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headers = self._build_headers()
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body = self._build_body(request, structured_mode)
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url = f"{self._target.base_url.rstrip('/')}/v1/chat/completions"
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retries = 0
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max_retries = self._target.max_retries
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start_time = time.monotonic()
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last_error: str | None = None
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last_error_category: str | None = None
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while True:
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try:
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response = await self._http.post(
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url,
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json=body,
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headers=headers,
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timeout=request.timeout_seconds,
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)
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except httpx.TimeoutException:
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last_error = "Request timed out"
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last_error_category = ErrorCategory.TIMEOUT
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if retries < max_retries:
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retries += 1
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logger.warning(
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"Timeout on attempt %d/%d to %s",
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retries,
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max_retries,
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url,
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)
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continue
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return self._error_result(
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last_error,
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last_error_category,
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retries,
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start_time,
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structured_mode,
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)
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except httpx.ConnectError as exc:
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last_error = f"Connection error: {exc}"
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last_error_category = ErrorCategory.CONNECTION_ERROR
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if retries < max_retries:
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retries += 1
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logger.warning(
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"Connection error on attempt %d/%d to %s: %s",
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retries,
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max_retries,
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url,
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exc,
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)
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continue
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return self._error_result(
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last_error,
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last_error_category,
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retries,
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start_time,
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structured_mode,
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)
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# Handle retryable HTTP status codes
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if response.status_code in _RETRYABLE_STATUS_CODES:
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if response.status_code == 429:
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last_error_category = ErrorCategory.RATE_LIMIT
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last_error = "Rate limited (429)"
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else:
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last_error_category = ErrorCategory.SERVER_ERROR
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last_error = f"Server error ({response.status_code})"
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if retries < max_retries:
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retries += 1
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logger.warning(
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"HTTP %d on attempt %d/%d to %s",
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response.status_code,
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retries,
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max_retries,
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url,
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)
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continue
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return self._error_result(
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last_error,
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last_error_category,
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retries,
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start_time,
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structured_mode,
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)
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# Handle non-retryable errors
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if response.status_code == 401 or response.status_code == 403:
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return self._error_result(
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f"Authentication failed ({response.status_code})",
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ErrorCategory.AUTHENTICATION,
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retries,
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start_time,
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structured_mode,
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)
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if response.status_code >= 400:
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return self._error_result(
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f"Client error ({response.status_code})",
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ErrorCategory.INVALID_RESPONSE,
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retries,
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start_time,
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structured_mode,
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)
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# Success path
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break
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latency_ms = int((time.monotonic() - start_time) * 1000)
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return self._parse_response(
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response,
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request,
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structured_mode,
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retries,
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latency_ms,
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)
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def _choose_structured_mode(
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self, request: StructuredGenerationRequest
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) -> str:
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"""Choose the strongest structured-output mode available."""
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caps = self._target.capabilities
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if request.json_schema and caps.json_schema:
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return "json_schema"
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if request.json_schema and caps.json_object:
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return "json_object"
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if request.json_schema:
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# Schema requested but endpoint supports neither — prompt-only fallback
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return "prompt_only"
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return "none"
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def _build_headers(self) -> dict[str, str]:
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"""Build request headers including authentication."""
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headers: dict[str, str] = {
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"Content-Type": "application/json",
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}
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# Add extra headers from target config
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headers.update(self._target.extra_headers)
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# Resolve auth secret
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secret = _resolve_auth_secret(self._target.auth_secret_ref)
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if secret:
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scheme = self._target.auth_scheme.lower()
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if scheme == "bearer":
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headers["Authorization"] = f"Bearer {secret}"
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else:
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# Custom auth scheme (e.g., "X-API-Key: <value>")
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headers[self._target.auth_scheme] = secret
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return headers
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def _build_body(
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self,
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request: StructuredGenerationRequest,
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structured_mode: str,
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) -> dict[str, Any]:
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"""Build the request body for /v1/chat/completions."""
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messages = [
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{"role": msg.role, "content": msg.content}
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for msg in request.messages
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]
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body: dict[str, Any] = {
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"model": self._target.model,
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"messages": messages,
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"temperature": request.temperature,
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"max_tokens": request.max_output_tokens,
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}
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# Add seed if the target supports it
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if self._target.capabilities.seed and request.seed is not None:
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body["seed"] = request.seed
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# Add response_format based on chosen mode
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if structured_mode == "json_schema" and request.json_schema:
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body["response_format"] = {
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"type": "json_schema",
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"json_schema": {
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"name": request.json_schema.get("title", "response"),
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"strict": True,
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"schema": request.json_schema,
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},
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}
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elif structured_mode == "json_object":
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body["response_format"] = {"type": "json_object"}
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# Add extra_body from target config (vLLM structured_outputs, etc.)
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if self._target.extra_body:
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body.update(self._target.extra_body)
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return body
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def _parse_response(
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self,
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response: httpx.Response,
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request: StructuredGenerationRequest,
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structured_mode: str,
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retries: int,
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latency_ms: int,
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) -> InferenceResult:
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"""Parse a successful response into InferenceResult."""
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# Extract request ID from response headers
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request_id = response.headers.get("x-request-id")
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try:
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data = response.json()
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except (json.JSONDecodeError, ValueError):
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return InferenceResult(
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content="",
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endpoint_id=self._target.endpoint_id,
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deployment_id=self._target.deployment_id,
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model=self._target.model,
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protocol=self._target.protocol,
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structured_mode=structured_mode, # type: ignore[arg-type]
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latency_ms=latency_ms,
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retries=retries,
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request_id=request_id,
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error="Invalid JSON in response body",
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error_category=ErrorCategory.INVALID_RESPONSE,
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)
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# Extract content from choices
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choices = data.get("choices", [])
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if not choices:
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return InferenceResult(
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content="",
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endpoint_id=self._target.endpoint_id,
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deployment_id=self._target.deployment_id,
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model=self._target.model,
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protocol=self._target.protocol,
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structured_mode=structured_mode, # type: ignore[arg-type]
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latency_ms=latency_ms,
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retries=retries,
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request_id=request_id,
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error="Empty choices in response",
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error_category=ErrorCategory.INVALID_RESPONSE,
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)
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message = choices[0].get("message", {})
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content = message.get("content", "")
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finish_reason = choices[0].get("finish_reason")
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# Extract usage metadata
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usage_data = data.get("usage", {})
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input_tokens = usage_data.get("prompt_tokens")
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output_tokens = usage_data.get("completion_tokens")
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total_tokens = usage_data.get("total_tokens")
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# Parse JSON content if structured output was requested
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parsed: dict[str, Any] | None = None
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schema_valid: bool | None = None
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if structured_mode in ("json_schema", "json_object") and content:
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try:
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parsed = json.loads(content)
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except (json.JSONDecodeError, ValueError):
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# Content is not valid JSON
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schema_valid = False
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# Validate parsed JSON against supplied schema
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if parsed is not None and request.json_schema:
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schema_valid = self._validate_schema(parsed, request.json_schema)
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return InferenceResult(
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content=content,
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parsed=parsed,
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endpoint_id=self._target.endpoint_id,
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deployment_id=self._target.deployment_id,
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model=self._target.model,
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protocol=self._target.protocol,
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structured_mode=structured_mode, # type: ignore[arg-type]
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latency_ms=latency_ms,
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usage=TokenUsage(
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input_tokens=input_tokens,
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output_tokens=output_tokens,
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total_tokens=total_tokens,
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),
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request_id=request_id,
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finish_reason=finish_reason,
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retries=retries,
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schema_valid=schema_valid,
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)
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def _validate_schema(
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self, data: dict[str, Any], schema: dict[str, Any]
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) -> bool:
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"""Revalidate parsed JSON locally against the supplied schema.
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Returns True if valid, False otherwise.
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"""
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try:
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import jsonschema
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jsonschema.validate(instance=data, schema=schema)
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return True
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except Exception:
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logger.debug("Schema validation failed for response data")
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return False
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def _error_result(
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self,
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error: str,
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error_category: str,
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retries: int,
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start_time: float,
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structured_mode: str,
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) -> InferenceResult:
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"""Build an error InferenceResult."""
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latency_ms = int((time.monotonic() - start_time) * 1000)
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return InferenceResult(
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content="",
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endpoint_id=self._target.endpoint_id,
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deployment_id=self._target.deployment_id,
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model=self._target.model,
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protocol=self._target.protocol,
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structured_mode=structured_mode, # type: ignore[arg-type]
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latency_ms=latency_ms,
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retries=retries,
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error=error,
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error_category=error_category,
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)
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async def close(self) -> None:
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"""Close the underlying HTTP client if we own it."""
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if self._owns_client:
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await self._http.aclose()
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def __repr__(self) -> str:
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return (
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f"OpenAICompatibleClient("
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f"model={self._target.model!r}, "
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f"base_url={self._target.base_url!r})"
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)
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Block a user