feat(flywheel): few-shot 在线检索注入打通风险规则编译链路
- 新增 FewShotStore：独立 Qdrant collection few_shot_samples，向量 upsert/search/delete， 全程失败降级不阻塞主链路 - 新增 FewShotIngestionService：RiskObservation confirmed/false_positive → FewShotSample + 向量，带 sample_key 幂等去重 - 新增 FewShotRetriever：按 case 特征检索相似历史样本，去重 + token 预算 + 单条字符上限裁剪 - risk_observations.create_feedback commit 后挂 hook 自动入库，带 feature flag 和 try/except 兜底 - risk_rule_generation_prompt 新增 few_shot_samples 可选参数，合并进 examples 并标 source=historical_confirmed；risk_rule_generation 构造 prompt 前调 retriever，失败降级为空
This commit is contained in:
caoxiaozhu
177
server/src/app/services/few_shot_ingestion.py
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177
server/src/app/services/few_shot_ingestion.py
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"""Few-shot 样本入库编排：RiskObservation → FewShotSample → Qdrant。
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只处理人工确认为 confirmed / false_positive 的观测，把它转成一条
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:class:`FewShotSample`，持久化到 DB，并同步向量到 Qdrant。
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入库动作由 :meth:`RiskObservationService.create_feedback` 在 commit 后触发，
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本服务全程吞异常（只记日志），保证反馈主流程不被 few-shot 链路拖崩。
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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 sqlalchemy import select
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from sqlalchemy.orm import Session
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from app.core.logging import get_logger
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from app.models.few_shot_sample import FewShotSample
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from app.models.risk_observation import RiskObservation, RiskObservationFeedback
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from app.services.embedding_provider import EmbeddingProvider
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from app.services.few_shot_store import FewShotStore
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logger = get_logger("app.services.few_shot_ingestion")
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# 仅这两个 feedback_status 视为已确认样本，会入库
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CONFIRMED_LABELS = {"confirmed", "false_positive"}
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# label → 自然语言结论（当 feedback.comment 缺失时兜底）
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LABEL_CONCLUSION_FALLBACK = {
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"confirmed": "经人工复核确认，该风险线索成立，需按规则拦截或补件。",
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"false_positive": "经人工复核判定为误报，相似情形不应触发该风险规则。",
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}
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class FewShotIngestionService:
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"""把已确认的风险观测沉淀为 few-shot 样本。"""
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def __init__(self, db: Session) -> None:
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self.db = db
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def ingest_observation_feedback(
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self,
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observation: RiskObservation,
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feedback: RiskObservationFeedback,
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) -> FewShotSample | None:
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"""人工确认/误报后调用，写入并同步向量。"""
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label = observation.feedback_status
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if label not in CONFIRMED_LABELS:
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return None
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sample_key = f"obs:{observation.id}"
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sample = self.db.scalar(
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select(FewShotSample).where(FewShotSample.sample_key == sample_key)
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)
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domain = self._extract_domain(observation)
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case_text = self._build_case_text(observation)
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conclusion_text = self._build_conclusion_text(observation, feedback, label)
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payload = self._build_payload(observation, feedback, label)
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if sample is None:
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sample = FewShotSample(
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sample_key=sample_key,
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source_observation_id=observation.id,
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scene="risk_rule_generation",
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domain=domain,
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risk_type=observation.risk_type or "",
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risk_level=observation.risk_level or "",
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label=label,
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case_text=case_text,
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conclusion_text=conclusion_text,
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payload_json=payload,
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status="active",
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)
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self.db.add(sample)
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else:
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sample.label = label
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sample.domain = domain
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sample.risk_type = observation.risk_type or ""
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sample.risk_level = observation.risk_level or ""
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sample.case_text = case_text
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sample.conclusion_text = conclusion_text
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sample.payload_json = payload
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sample.status = "active"
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sample.vector_id = sample.vector_id
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try:
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self.db.commit()
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self.db.refresh(sample)
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except Exception:
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logger.exception("few-shot 样本持久化失败 observation_id=%s", observation.id)
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self.db.rollback()
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return None
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vector_id = self._store().upsert(sample)
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if vector_id:
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sample.vector_id = vector_id
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try:
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self.db.commit()
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except Exception:
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logger.warning("few-shot vector_id 回写失败 sample_id=%s", sample.id)
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return sample
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def retract_observation(self, observation_id: str) -> bool:
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"""观测被撤销时删掉对应样本及其向量。"""
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sample = self.db.scalar(
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select(FewShotSample).where(FewShotSample.source_observation_id == observation_id)
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)
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if sample is None:
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return False
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if sample.vector_id:
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self._store().delete_by_vector_id(sample.vector_id)
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try:
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self.db.delete(sample)
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self.db.commit()
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return True
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except Exception:
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logger.exception("few-shot 样本删除失败 observation_id=%s", observation_id)
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self.db.rollback()
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return False
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def _store(self) -> FewShotStore:
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provider = EmbeddingProvider.from_settings(self.db)
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return FewShotStore(provider)
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def _extract_domain(self, observation: RiskObservation) -> str:
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ontology = observation.ontology_json or {}
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return str(ontology.get("domain") or "")
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def _build_case_text(self, observation: RiskObservation) -> str:
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parts = [
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observation.title or "",
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observation.description or "",
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observation.risk_signal or "",
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observation.risk_type or "",
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]
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ontology = observation.ontology_json or {}
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scenario = ontology.get("scenario")
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if scenario:
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parts.append(f"场景:{scenario}")
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risk_signals = ontology.get("risk_signals")
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if isinstance(risk_signals, list) and risk_signals:
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parts.append("信号:" + "|".join(str(s) for s in risk_signals))
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return "\n".join(part for part in parts if part).strip()
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def _build_conclusion_text(
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self,
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observation: RiskObservation,
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feedback: RiskObservationFeedback,
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label: str,
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) -> str:
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comment = (feedback.comment or "").strip()
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if comment:
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return f"[{label}] {comment}"
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return LABEL_CONCLUSION_FALLBACK.get(label, label)
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def _build_payload(
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self,
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observation: RiskObservation,
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feedback: RiskObservationFeedback,
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label: str,
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) -> dict[str, Any]:
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return {
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"label": label,
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"risk_type": observation.risk_type,
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"risk_signal": observation.risk_signal,
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"risk_level": observation.risk_level,
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"feedback_type": feedback.feedback_type,
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"feedback_comment": feedback.comment or "",
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"feedback_actor": feedback.actor or "",
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"ontology": observation.ontology_json or {},
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"policy_refs": observation.policy_refs_json or [],
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"evidence": observation.evidence_json or [],
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"subject_label": observation.subject_label or "",
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"claim_no": observation.claim_no or "",
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}
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122
server/src/app/services/few_shot_retrieval.py
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122
server/src/app/services/few_shot_retrieval.py
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"""Few-shot 检索器：按当前 case 特征检索相似历史样本，拼成注入块。
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从 :class:`FewShotStore` 取相似样本，转成可供 prompt 构造函数直接使用的结构。
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带 token 预算裁剪和去重，确保不撑爆 prompt。
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典型用法（在构造 prompt 之前调用）::
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retriever = FewShotRetriever.from_session(session)
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samples = retriever.retrieve_for_risk_rule_generation(
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domain="travel", natural_language="票据城市与申报地不一致"
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)
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messages = build_risk_rule_compiler_messages(
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...,
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few_shot_samples=samples,
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)
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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 sqlalchemy.orm import Session
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from app.core.logging import get_logger
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from app.services.embedding_provider import EmbeddingProvider
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from app.services.few_shot_store import FewShotStore
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logger = get_logger("app.services.few_shot_retrieval")
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# 单条 few-shot 样本估算 token 数（用于预算裁剪）
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SAMPLE_TOKEN_BUDGET = 1200
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# 单条样本最大字符数，超长直接截断结论，避免撑爆 prompt
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SINGLE_SAMPLE_MAX_CHARS = 400
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# 历史样本最多注入条数（与原内置 examples 合并后总量受限）
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MAX_HISTORICAL_SAMPLES = 3
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class FewShotRetriever:
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"""按 case 特征检索已确认样本，返回 prompt 可直接消费的结构。"""
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def __init__(self, store: FewShotStore) -> None:
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self._store = store
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@classmethod
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def from_session(cls, session: Session) -> "FewShotRetriever":
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provider = EmbeddingProvider.from_settings(session)
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return cls(FewShotStore(provider))
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def retrieve_for_risk_rule_generation(
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self,
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*,
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domain: str = "",
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risk_type: str = "",
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natural_language: str,
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top_k: int = MAX_HISTORICAL_SAMPLES,
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) -> list[dict[str, Any]]:
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"""检索与当前规则需求相似的历史样本，返回注入块列表。"""
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case_text = self._build_case_text(
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natural_language=natural_language,
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domain=domain,
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risk_type=risk_type,
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)
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if not case_text:
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return []
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hits = self._store.search(
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case_text,
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scene="risk_rule_generation",
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labels=["confirmed", "false_positive"],
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top_k=top_k,
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)
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return self._hits_to_injection_blocks(hits)
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def _build_case_text(
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self,
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*,
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natural_language: str,
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domain: str = "",
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risk_type: str = "",
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) -> str:
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parts = [natural_language, domain, risk_type]
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return "\n".join(p for p in parts if p).strip()
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def _hits_to_injection_blocks(
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self,
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hits: list[dict[str, Any]],
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) -> list[dict[str, Any]]:
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"""把检索命中转成 prompt 可消费的块，做去重和预算裁剪。"""
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blocks: list[dict[str, Any]] = []
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seen_conclusions: set[str] = set()
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budget = SAMPLE_TOKEN_BUDGET
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for hit in hits:
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conclusion = (hit.get("conclusion_text") or "").strip()
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if not conclusion or conclusion in seen_conclusions:
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continue
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# 超长结论截断到上限，避免单条样本占用过多预算
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if len(conclusion) > SINGLE_SAMPLE_MAX_CHARS:
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conclusion = conclusion[:SINGLE_SAMPLE_MAX_CHARS]
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payload = hit.get("payload_json") or {}
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block = {
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"source": "historical_confirmed",
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"label": hit.get("label"),
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"domain": hit.get("domain") or "",
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"risk_type": hit.get("risk_type") or "",
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"score": round(float(hit.get("score") or 0.0), 4),
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"conclusion": conclusion,
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"context": {
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"risk_signal": payload.get("risk_signal") or "",
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"risk_level": payload.get("risk_level") or "",
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"ontology": payload.get("ontology") or {},
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"feedback_comment": payload.get("feedback_comment") or "",
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},
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}
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# 粗略 token 估算（按字符数 / 1.6 近似中文 token 比）
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estimated_tokens = int(len(conclusion) / 1.6) + 40
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if estimated_tokens > budget:
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break
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budget -= estimated_tokens
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blocks.append(block)
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seen_conclusions.add(conclusion)
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return blocks
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214
server/src/app/services/few_shot_store.py
Normal file
214
server/src/app/services/few_shot_store.py
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@@ -0,0 +1,214 @@
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"""Few-shot 样本的 Qdrant 向量存储。
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独立于 LightRAG 的 Qdrant 客户端，使用专用 collection ``few_shot_samples``，
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与知识库 RAG 的 collection 隔离。所有操作失败都不抛异常（记日志返回空），
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保证主链路不阻塞。
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向量来自 :class:`EmbeddingProvider`，payload 带业务过滤字段（scene/label/domain/risk_type），
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检索时按这些字段过滤 + 向量相似度排序。
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"""
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from __future__ import annotations
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import os
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import uuid
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from typing import Any
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from app.core.logging import get_logger
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from app.services.knowledge_rag import _resolve_default_qdrant_url
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logger = get_logger("app.services.few_shot_store")
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FEW_SHOT_COLLECTION = "few_shot_samples"
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def _resolve_qdrant_config() -> tuple[str, str]:
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"""复用 knowledge_rag 的 Qdrant URL/key 解析逻辑。"""
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url = os.environ.get("QDRANT_URL", "").strip() or _resolve_default_qdrant_url()
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api_key = os.environ.get("QDRANT_API_KEY", "").strip()
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return url, api_key
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class FewShotStore:
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"""对 Qdrant 的轻量封装，专供 few-shot 样本检索使用。
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设计要点：
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- 惰性创建 client 和 collection，首次操作时初始化。
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- 所有公共方法吞异常（返回空/False），主链路永远不被拖崩。
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- 向量写入和检索都依赖外部传入的 :class:`EmbeddingProvider`，
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由调用方保证与配置一致。
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"""
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def __init__(self, embedding_provider: Any) -> None:
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self._embedding_provider = embedding_provider
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self._client: Any = None
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self._ensured = False
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def _client_or_none(self) -> Any:
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"""惰性初始化 QdrantClient，失败返回 None。"""
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if self._client is not None:
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return self._client
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try:
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from qdrant_client import QdrantClient
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url, api_key = _resolve_qdrant_config()
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self._client = QdrantClient(url=url, api_key=api_key or None)
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except Exception:
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logger.warning("few-shot QdrantClient 初始化失败，本轮操作跳过", exc_info=True)
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self._client = None
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return self._client
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def _ensure_collection(self) -> bool:
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"""确保 collection 存在，成功返回 True。"""
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if self._ensured:
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return True
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client = self._client_or_none()
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if client is None:
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return False
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try:
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from qdrant_client.http.exceptions import UnexpectedResponse
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try:
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client.get_collection(FEW_SHOT_COLLECTION)
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self._ensured = True
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return True
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except UnexpectedResponse as exc:
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if exc.status_code != 404:
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raise
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# collection 不存在则创建
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dim = self._embedding_provider.dimension()
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from qdrant_client.http.models import (
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Distance,
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VectorParams,
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PayloadSchemaType,
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)
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client.create_collection(
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collection_name=FEW_SHOT_COLLECTION,
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vectors_config=VectorParams(size=dim, distance=Distance.COSINE),
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)
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for field, field_type in [
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("sample_id", PayloadSchemaType.KEYWORD),
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("scene", PayloadSchemaType.KEYWORD),
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("label", PayloadSchemaType.KEYWORD),
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("domain", PayloadSchemaType.KEYWORD),
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("risk_type", PayloadSchemaType.KEYWORD),
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("status", PayloadSchemaType.KEYWORD),
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]:
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try:
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client.create_payload_index(
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collection_name=FEW_SHOT_COLLECTION,
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field_name=field,
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field_schema=field_type,
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)
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except Exception:
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logger.debug("payload index 创建跳过 field=%s", field, exc_info=True)
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self._ensured = True
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logger.info("few-shot collection 创建成功 dim=%s", dim)
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return True
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except Exception:
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logger.warning("few-shot collection 初始化失败，本轮操作跳过", exc_info=True)
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return False
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def upsert(self, sample: Any) -> str | None:
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"""把一条样本向量化并写入 Qdrant，返回 vector_id，失败返回 None。"""
|
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if not self._ensure_collection():
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return None
|
||||
client = self._client
|
||||
try:
|
||||
vector = self._embedding_provider.embed([sample.case_text])[0]
|
||||
except Exception:
|
||||
logger.warning("few-shot embedding 失败 sample_key=%s", getattr(sample, "sample_key", ""), exc_info=True)
|
||||
return None
|
||||
vector_id = uuid.uuid4().hex
|
||||
payload = {
|
||||
"sample_id": sample.id,
|
||||
"scene": sample.scene,
|
||||
"label": sample.label,
|
||||
"domain": sample.domain,
|
||||
"risk_type": sample.risk_type,
|
||||
"risk_level": sample.risk_level,
|
||||
"status": getattr(sample, "status", "active"),
|
||||
"conclusion_text": sample.conclusion_text,
|
||||
"payload_json": sample.payload_json,
|
||||
}
|
||||
try:
|
||||
client.upsert(
|
||||
collection_name=FEW_SHOT_COLLECTION,
|
||||
points=[{"id": vector_id, "vector": vector, "payload": payload}],
|
||||
)
|
||||
return vector_id
|
||||
except Exception:
|
||||
logger.warning("few-shot upsert 失败 sample_key=%s", getattr(sample, "sample_key", ""), exc_info=True)
|
||||
return None
|
||||
|
||||
def search(
|
||||
self,
|
||||
case_text: str,
|
||||
*,
|
||||
scene: str | None = None,
|
||||
labels: list[str] | None = None,
|
||||
top_k: int = 3,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""按 case_text 检索相似样本，可按 scene/label 过滤。失败返回空列表。"""
|
||||
|
||||
if not case_text or not self._ensure_collection():
|
||||
return []
|
||||
client = self._client
|
||||
try:
|
||||
vector = self._embedding_provider.embed([case_text])[0]
|
||||
except Exception:
|
||||
logger.warning("few-shot 检索 embedding 失败", exc_info=True)
|
||||
return []
|
||||
must: list[dict[str, Any]] = [{"key": "status", "match": {"value": "active"}}]
|
||||
if scene:
|
||||
must.append({"key": "scene", "match": {"value": scene}})
|
||||
if labels:
|
||||
must.append({"key": "label", "match": {"any": labels}})
|
||||
try:
|
||||
from qdrant_client.http.models import Filter
|
||||
|
||||
results = client.query_points(
|
||||
collection_name=FEW_SHOT_COLLECTION,
|
||||
query=vector,
|
||||
query_filter=Filter(must=must),
|
||||
limit=top_k,
|
||||
with_payload=True,
|
||||
).points
|
||||
except Exception:
|
||||
logger.warning("few-shot 检索失败", exc_info=True)
|
||||
return []
|
||||
hits: list[dict[str, Any]] = []
|
||||
for point in results:
|
||||
payload = getattr(point, "payload", None) or {}
|
||||
hits.append(
|
||||
{
|
||||
"sample_id": payload.get("sample_id"),
|
||||
"score": float(getattr(point, "score", 0.0)),
|
||||
"label": payload.get("label"),
|
||||
"domain": payload.get("domain"),
|
||||
"risk_type": payload.get("risk_type"),
|
||||
"conclusion_text": payload.get("conclusion_text") or "",
|
||||
"payload_json": payload.get("payload_json") or {},
|
||||
}
|
||||
)
|
||||
return hits
|
||||
|
||||
def delete_by_vector_id(self, vector_id: str) -> bool:
|
||||
"""按 vector_id 删除向量，失败返回 False。"""
|
||||
|
||||
if not vector_id or not self._ensure_collection():
|
||||
return False
|
||||
try:
|
||||
self._client.delete(
|
||||
collection_name=FEW_SHOT_COLLECTION,
|
||||
points_selector=[vector_id],
|
||||
)
|
||||
return True
|
||||
except Exception:
|
||||
logger.warning("few-shot 删除失败 vector_id=%s", vector_id, exc_info=True)
|
||||
return False
|
||||
@@ -1,5 +1,6 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
from datetime import UTC, datetime, timedelta
|
||||
from decimal import Decimal
|
||||
from typing import Any
|
||||
@@ -8,6 +9,7 @@ from sqlalchemy import func, select
|
||||
from sqlalchemy.orm import Session, joinedload
|
||||
|
||||
from app.algorithem.risk_graph import RiskHistoryStats, RiskObservationDraft
|
||||
from app.core.logging import get_logger
|
||||
from app.db.base import Base
|
||||
from app.models.financial_record import ExpenseClaim
|
||||
from app.models.risk_observation import RiskObservation, RiskObservationFeedback
|
||||
@@ -17,6 +19,8 @@ from app.schemas.risk_observation import (
|
||||
)
|
||||
from app.services.expense_claim_risk_stage import normalize_risk_business_stage
|
||||
|
||||
logger = get_logger("app.services.risk_observations")
|
||||
|
||||
HIGH_LEVELS = {"high", "critical"}
|
||||
SEVERITY_SCORE = {
|
||||
"low": 32,
|
||||
@@ -322,8 +326,27 @@ class RiskObservationService:
|
||||
observation.status, observation.feedback_status = mapped
|
||||
self.db.commit()
|
||||
self.db.refresh(feedback)
|
||||
self._maybe_ingest_few_shot(observation, feedback)
|
||||
return feedback
|
||||
|
||||
def _maybe_ingest_few_shot(
|
||||
self,
|
||||
observation: RiskObservation,
|
||||
feedback: RiskObservationFeedback,
|
||||
) -> None:
|
||||
"""人工确认/误报后把样本沉淀进 few-shot 池，任何失败都不影响主流程。"""
|
||||
|
||||
if os.environ.get("FEW_SHOT_INJECTION_ENABLED", "true").strip().lower() in {"0", "false", "no"}:
|
||||
return
|
||||
if observation.feedback_status not in {"confirmed", "false_positive"}:
|
||||
return
|
||||
try:
|
||||
from app.services.few_shot_ingestion import FewShotIngestionService
|
||||
|
||||
FewShotIngestionService(self.db).ingest_observation_feedback(observation, feedback)
|
||||
except Exception:
|
||||
logger.exception("few-shot ingestion failed for observation %s", observation.id)
|
||||
|
||||
def summarize_dashboard(
|
||||
self,
|
||||
*,
|
||||
|
||||
@@ -234,6 +234,10 @@ class RiskRuleGenerationService:
|
||||
}
|
||||
for item in fields
|
||||
]
|
||||
few_shot_samples = self._retrieve_few_shot_samples(
|
||||
domain=domain,
|
||||
natural_language=natural_language,
|
||||
)
|
||||
messages = build_risk_rule_compiler_messages(
|
||||
domain=domain,
|
||||
domain_label=BUSINESS_DOMAIN_LABELS[domain],
|
||||
@@ -243,6 +247,7 @@ class RiskRuleGenerationService:
|
||||
expense_category_label=expense_category_label,
|
||||
natural_language=natural_language,
|
||||
available_fields=field_payload,
|
||||
few_shot_samples=few_shot_samples,
|
||||
)
|
||||
answer = self.runtime_chat_service.complete(
|
||||
messages,
|
||||
@@ -263,6 +268,29 @@ class RiskRuleGenerationService:
|
||||
payload = unwrap_semantic_plan_payload(payload)
|
||||
return self._sanitize_model_draft(payload, fields=fields)
|
||||
|
||||
def _retrieve_few_shot_samples(
|
||||
self,
|
||||
*,
|
||||
domain: str,
|
||||
natural_language: str,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""检索已确认历史样本，失败降级为空列表。"""
|
||||
|
||||
import os
|
||||
|
||||
if os.environ.get("FEW_SHOT_INJECTION_ENABLED", "true").strip().lower() in {"0", "false", "no"}:
|
||||
return []
|
||||
try:
|
||||
from app.services.few_shot_retrieval import FewShotRetriever
|
||||
|
||||
retriever = FewShotRetriever.from_session(self.db)
|
||||
return retriever.retrieve_for_risk_rule_generation(
|
||||
domain=domain,
|
||||
natural_language=natural_language,
|
||||
)
|
||||
except Exception:
|
||||
return []
|
||||
|
||||
def _sanitize_model_draft(
|
||||
self,
|
||||
payload: dict[str, Any],
|
||||
|
||||
@@ -14,10 +14,15 @@ def build_risk_rule_compiler_messages(
|
||||
expense_category_label: str,
|
||||
natural_language: str,
|
||||
available_fields: list[dict[str, Any]],
|
||||
few_shot_samples: list[dict[str, Any]] | None = None,
|
||||
) -> list[dict[str, str]]:
|
||||
"""构造自然语言规则编译提示词。
|
||||
|
||||
大模型只负责把业务语言拆成“语义计划”，后端会校验字段、操作符和模板。
|
||||
|
||||
``few_shot_samples`` 是从已确认历史样本中检索出来的相似案例，会被合并进
|
||||
``examples`` 字段并标注 ``source: "historical_confirmed"``，让编译器参考
|
||||
过往人工结论。传 ``None`` 或空列表时行为与历史完全一致（向后兼容）。
|
||||
"""
|
||||
|
||||
schema = {
|
||||
@@ -161,6 +166,20 @@ def build_risk_rule_compiler_messages(
|
||||
},
|
||||
}
|
||||
]
|
||||
historical_examples: list[dict[str, Any]] = []
|
||||
if few_shot_samples:
|
||||
for sample in few_shot_samples:
|
||||
historical_examples.append(
|
||||
{
|
||||
"source": "historical_confirmed",
|
||||
"label": sample.get("label"),
|
||||
"domain": sample.get("domain") or "",
|
||||
"risk_type": sample.get("risk_type") or "",
|
||||
"conclusion": sample.get("conclusion") or "",
|
||||
"context": sample.get("context") or {},
|
||||
}
|
||||
)
|
||||
merged_examples = historical_examples + examples
|
||||
return [
|
||||
{
|
||||
"role": "system",
|
||||
@@ -186,7 +205,7 @@ def build_risk_rule_compiler_messages(
|
||||
"natural_language": natural_language,
|
||||
"available_fields": available_fields,
|
||||
"required_json_shape": response_schema,
|
||||
"examples": examples,
|
||||
"examples": merged_examples,
|
||||
},
|
||||
ensure_ascii=False,
|
||||
),
|
||||
|
||||
Reference in New Issue
Block a user