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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"""Inference adapter for thesis rewriting via the InferenceGateway.
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Single implementation replacing the duplicate Ollama/vLLM branching in
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``thesis_llm.py``. Uses InferenceGateway.generate() with the appropriate
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target — no provider-specific code.
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Requirements: 2.12
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"""
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from __future__ import annotations
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import logging
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import time
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import asyncpg
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from services.recommendation.thesis_llm import (
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_log_thesis_performance,
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_strip_thinking_block,
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build_thesis_rewrite_prompt,
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)
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from services.shared.agent_config import AgentConfigResolver, ResolvedAgentConfig
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from services.shared.inference.gateway import InferenceGateway
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from services.shared.inference.lineage import ModelLineage, build_lineage_from_result
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from services.shared.inference.models import (
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ChatMessage,
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InferenceTarget,
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StructuredGenerationRequest,
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)
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from services.shared.schemas import TrendSummary
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logger = logging.getLogger("recommendation.inference_adapter")
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async def rewrite_thesis_via_gateway(
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deterministic_thesis: str,
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summary: TrendSummary,
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gateway: InferenceGateway,
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target: InferenceTarget,
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pool: asyncpg.Pool | None = None,
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) -> tuple[str, ModelLineage]:
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"""Rewrite a deterministic thesis using the InferenceGateway.
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This replaces the duplicate Ollama/vLLM branching in thesis_llm.py
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with a single implementation routed through the gateway.
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If the LLM call fails, returns the original deterministic thesis.
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The gateway handles protocol selection (Ollama, OpenAI-compatible, etc.)
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transparently.
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Args:
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deterministic_thesis: The rule-based thesis string.
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summary: The trend summary that produced the thesis.
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gateway: The shared InferenceGateway instance.
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target: Resolved inference target for thesis rewriting.
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pool: Optional asyncpg pool for performance logging.
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Returns:
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Tuple of (rewritten thesis, lineage). Falls back to deterministic
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thesis on failure.
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"""
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start_time = time.monotonic()
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# Resolve agent config for token budget check
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resolved: ResolvedAgentConfig | None = None
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if pool is not None:
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try:
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resolver = AgentConfigResolver(pool, ttl_seconds=60)
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resolved = await resolver.resolve("thesis-rewriter")
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except Exception:
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logger.warning(
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"Failed to resolve thesis-rewriter config — proceeding without budget check",
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exc_info=True,
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)
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# Token budget enforcement
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if (
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resolved is not None
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and resolved.token_budget > 0
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and resolved.variant_id is not None
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and pool is not None
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):
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try:
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row = await pool.fetchrow(
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"""SELECT COALESCE(SUM(input_tokens + output_tokens), 0) AS total_tokens
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FROM agent_performance_log
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WHERE variant_id = $1
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AND recorded_at >= NOW() - INTERVAL '1 hour'""",
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resolved.variant_id,
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)
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used = int(row["total_tokens"]) if row else 0
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if used >= resolved.token_budget:
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logger.warning(
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"Token budget exceeded for thesis-rewriter variant %s: used %d / budget %d",
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resolved.variant_id,
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used,
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resolved.token_budget,
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)
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return deterministic_thesis, ModelLineage(model=target.model, protocol=target.protocol)
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except Exception:
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logger.warning("Failed to check token budget for thesis-rewriter", exc_info=True)
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prompts = build_thesis_rewrite_prompt(deterministic_thesis, summary)
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# Override system prompt from resolved config
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if resolved is not None and resolved.system_prompt:
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prompts["system"] = resolved.system_prompt
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# Build the inference request — no JSON schema needed for thesis rewriting
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request = StructuredGenerationRequest(
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messages=[
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ChatMessage(role="system", content=prompts["system"]),
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ChatMessage(role="user", content=prompts["user"]),
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],
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json_schema=None,
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max_output_tokens=512,
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temperature=0.0,
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seed=None,
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timeout_seconds=target.timeout_seconds,
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trace_id=f"thesis-{summary.entity_id}",
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)
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try:
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result = await gateway.generate(target, request)
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lineage = build_lineage_from_result(result, trace_id=f"thesis-{summary.entity_id}")
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duration_ms = int((time.monotonic() - start_time) * 1000)
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if result.error:
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logger.warning(
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"LLM thesis rewrite failed for %s: %s — using deterministic thesis",
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summary.entity_id,
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result.error,
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)
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if pool is not None and resolved is not None:
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await _log_thesis_performance(
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pool,
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resolved=resolved,
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ticker=summary.entity_id,
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success=False,
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duration_ms=duration_ms,
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input_tokens=len(deterministic_thesis) // 4,
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output_tokens=0,
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error_message=result.error,
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)
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return deterministic_thesis, lineage
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content = result.content.strip()
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# Strip thinking blocks (Qwen models)
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content = _strip_thinking_block(content)
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if content:
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logger.info(
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"LLM thesis rewrite succeeded for %s (%d chars → %d chars)",
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summary.entity_id,
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len(deterministic_thesis),
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len(content),
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)
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if pool is not None and resolved is not None:
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await _log_thesis_performance(
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pool,
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resolved=resolved,
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ticker=summary.entity_id,
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success=True,
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duration_ms=duration_ms,
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input_tokens=len(deterministic_thesis) // 4,
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output_tokens=len(content) // 4,
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)
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return content, lineage
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logger.warning(
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"LLM thesis rewrite returned empty for %s — using deterministic thesis",
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summary.entity_id,
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)
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if pool is not None and resolved is not None:
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await _log_thesis_performance(
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pool,
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resolved=resolved,
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ticker=summary.entity_id,
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success=False,
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duration_ms=duration_ms,
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input_tokens=len(deterministic_thesis) // 4,
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output_tokens=0,
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error_message="empty_response",
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)
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return deterministic_thesis, lineage
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except Exception:
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duration_ms = int((time.monotonic() - start_time) * 1000)
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logger.exception(
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"LLM thesis rewrite failed for %s — using deterministic thesis",
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summary.entity_id,
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)
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lineage = ModelLineage(model=target.model, protocol=target.protocol)
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if pool is not None and resolved is not None:
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await _log_thesis_performance(
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pool,
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resolved=resolved,
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ticker=summary.entity_id,
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success=False,
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duration_ms=duration_ms,
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input_tokens=len(deterministic_thesis) // 4,
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output_tokens=0,
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error_message="exception",
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)
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return deterministic_thesis, lineage
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