保留RAG检索并修复相似案例调用

- 保留 pgvector 与混合检索能力，返回 TOP 5 相似案例\n- 将相似案例上下文传入 AI 预防建议生成\n- 修复风险评估调用缺少 similar_cases 参数导致的运行报错\n- 保留 Embedding 配置、数据库迁移、回填脚本及 RAG 测试\n- 恢复基础字符串数组格式的 AI 预防建议协议

.env.example

@@ -47,6 +47,16 @@ RISK_LLM_FALLBACK_MAX_RETRIES=1
 RISK_LLM_FALLBACK_REASONING_EFFORT=high
 RISK_LLM_FALLBACK_MAX_OUTPUT_TOKENS=16384
 
+# ==================== RAG 向量检索配置 ====================
+# 可选的 Embeddings 服务；留空 API Key 时复用主 LLM Key 和地址。
+# 当前数据库未安装 pgvector 时，系统自动使用业务字段 + 文本相关性混合检索。
+RISK_RAG_EMBEDDING_API_KEY=
+RISK_RAG_EMBEDDING_BASE_URL=https://api.openlux.ai/v1
+RISK_RAG_EMBEDDING_MODEL=gemini-embedding-2-preview
+RISK_RAG_EMBEDDING_DIMENSIONS=3072
+RISK_RAG_EMBEDDING_TIMEOUT_SECONDS=10
+RISK_RAG_TOP_K=5
+
 # ==================== 跨域配置 ====================
 # 允许访问后端接口的前端地址，多个地址用英文逗号分隔。
 RISK_CORS_ALLOW_ORIGINS=http://localhost:39158,http://127.0.0.1:39158

app/core/config.py

@@ -92,6 +92,37 @@ class Settings(BaseSettings):
         le=393_216,
         validation_alias=AliasChoices("RISK_LLM_FALLBACK_MAX_OUTPUT_TOKENS", "DEEPSEEK_MAX_OUTPUT_TOKENS"),
     )
+    rag_embedding_api_key: SecretStr | None = Field(
+        default=None,
+        repr=False,
+        validation_alias=AliasChoices("RISK_RAG_EMBEDDING_API_KEY", "EMBEDDING_API_KEY"),
+    )
+    rag_embedding_base_url: str = Field(
+        default="",
+        validation_alias=AliasChoices("RISK_RAG_EMBEDDING_BASE_URL", "EMBEDDING_BASE_URL"),
+    )
+    rag_embedding_model: str = Field(
+        default="bge-m3",
+        validation_alias=AliasChoices("RISK_RAG_EMBEDDING_MODEL", "EMBEDDING_MODEL"),
+    )
+    rag_embedding_dimensions: int = Field(
+        default=3072,
+        ge=1,
+        le=4096,
+        validation_alias=AliasChoices("RISK_RAG_EMBEDDING_DIMENSIONS", "EMBEDDING_DIMENSIONS"),
+    )
+    rag_embedding_timeout_seconds: int = Field(
+        default=10,
+        gt=0,
+        le=60,
+        validation_alias=AliasChoices("RISK_RAG_EMBEDDING_TIMEOUT_SECONDS", "EMBEDDING_TIMEOUT"),
+    )
+    rag_top_k: int = Field(
+        default=5,
+        ge=1,
+        le=20,
+        validation_alias=AliasChoices("RISK_RAG_TOP_K", "RAG_TOP_K"),
+    )
     cors_allow_origins: Annotated[tuple[str, ...], NoDecode] = (
         "http://localhost:39158",
         "http://127.0.0.1:39158",
@@ -121,6 +152,11 @@ class Settings(BaseSettings):
     def normalize_llm_fallback_base_url(cls, value: str) -> str:
         return value.rstrip("/")
 
+    @field_validator("rag_embedding_base_url")
+    @classmethod
+    def normalize_rag_embedding_base_url(cls, value: str) -> str:
+        return value.rstrip("/")
+
     @field_validator("cors_allow_origins", mode="before")
     @classmethod
     def split_cors_origins(cls, value: object) -> object:

app/schemas/risk_prediction.py

@@ -67,10 +67,18 @@ class RiskDimensionScore(ApiSchema):
 
 
 class SimilarCase(ApiSchema):
+    case_id: int | None = None
     date: str | None = None
     product: str | None = None
+    category: str | None = None
+    material: str | None = None
+    anomaly_type: str | None = None
+    description: str | None = None
+    root_cause: str | None = None
+    defect_level: str | None = None
     problem: str | None = None
     solution: str | None = None
+    similarity: float | None = None
 
 
 class RiskAssessmentResult(ApiSchema):

app/services/conversations.py

@@ -28,6 +28,7 @@ from app.schemas.risk_prediction import (
 )
 from app.services.database_risk_scoring import DatabaseRiskScoringService
 from app.services.llm_client import LLMClient
+from app.services.rag import AnomalyCaseRAG
 
 logger = logging.getLogger("dieya_risk_prediction.conversations")
 
@@ -233,11 +234,7 @@ class ConversationService:
 
         show_risk = self._is_risk_request(text, semantics)
         response_type = self._response_type(text, semantics)
-        response_type = await self._use_solution_card_without_history(
+        response_type = await self._use_solution_card_without_history(response_type, text, semantics)
-            response_type,
-            text,
-            semantics,
-        )
         risk_result = (
             await self._enrich_risk_result(text, semantics, self._predict(text))
             if show_risk and response_type == "risk_assessment"
@@ -657,6 +654,18 @@ class ConversationService:
         ) or "当前新品"
         direct_count = await self._count_category_anomalies(category)
         standards = await self._standards_for(category, text)
+        similar_cases = await self._similar_cases(
+            text,
+            category=category,
+            material=self._first_match(
+                text,
+                ("软管", "PET瓶", "PET", "铝瓶", "泵头", "彩盒", "瓶子", "喷头", "盖子"),
+            ),
+            supplier=self._semantic_text(semantics, "supplier"),
+            factory=self._semantic_text(semantics, "factory"),
+            stage=self._semantic_text(semantics, "stage"),
+            anomaly_type=self._semantic_text(semantics, "anomaly_type"),
+        )
         standard_fallback_enabled = direct_count == 0
         if standard_fallback_enabled and not standards:
             standards = await self._fallback_standards_for(text)
@@ -700,7 +709,10 @@ class ConversationService:
                     "standard_fallback_enabled": standard_fallback_enabled,
                     "matched_standards": [self._standard_dict(item) for item in standards[:5]],
                     "identified_risks": risks,
-                    "rag_similar_cases": [],
+                    "rag_similar_cases": [
+                        {"similarity": item.case.similarity, **item.case.model_dump(mode="json", by_alias=False)}
+                        for item in similar_cases
+                    ],
                 }
             )
         except Exception as exc:  # noqa: BLE001 - the solution card must remain available
@@ -722,6 +734,7 @@ class ConversationService:
             ),
             "standardFallbackEnabled": standard_fallback_enabled,
             "standards": [self._standard_dict(item) for item in standards[:4]],
+            "similarCases": [item.case.model_dump(mode="json", by_alias=False) for item in similar_cases],
             "risks": risks,
             "suggestions": suggestions,
             "warning": "以上方案由 AI 推理生成，实际执行前请咨询品控部门确认。",
@@ -908,6 +921,18 @@ class ConversationService:
         factory_count = await self._count_anomalies(AnomalyCase.factory, factory)
         anomaly_count = supplier_count if supplier else factory_count
         standard_rows = await self._standards_for(category, text)
+        similar_cases = await self._similar_cases(
+            text,
+            category=category,
+            material=self._first_match(
+                text,
+                ("软管", "PET瓶", "PET", "铝瓶", "泵头", "彩盒", "瓶子", "喷头", "盖子"),
+            ),
+            supplier=supplier,
+            factory=factory,
+            stage=self._semantic_text(semantics, "stage"),
+            anomaly_type=self._semantic_text(semantics, "anomaly_type"),
+        )
 
         alpha_count = supplier_count if supplier else 0
         alpha = round(min(1.5, 1 + (alpha_count / 100) * 0.3), 2) if alpha_count else 1.0
@@ -952,6 +977,7 @@ class ConversationService:
             database_score=database_score,
             dimensions=dimensions,
             standard_rows=standard_rows,
+            similar_cases=similar_cases,
         )
 
         return result.model_copy(
@@ -986,7 +1012,7 @@ class ConversationService:
                 ),
                 "standard_judgment": standard_rows,
                 "dimension_scores": dimensions,
-                "similar_cases": [],
+                "similar_cases": [item.case.model_dump(mode="json", by_alias=False) for item in similar_cases],
                 "ai_suggestions": suggestions,
             }
         )
@@ -1012,6 +1038,7 @@ class ConversationService:
         database_score,
         dimensions: Sequence[RiskDimensionScore],
         standard_rows: Sequence[StandardJudgment],
+        similar_cases: Sequence[Any],
     ) -> Sequence[str]:  # noqa: ANN001
         """Generate suggestions from live scoring data, with a deterministic fallback."""
 
@@ -1042,7 +1069,13 @@ class ConversationService:
                 for item in dimensions
             ],
             "matched_standards": [self._standard_dict(item) for item in standard_rows[:5]],
-            "rag_similar_cases": [],
+            "rag_similar_cases": [
+                {
+                    "similarity": item.case.similarity,
+                    **item.case.model_dump(mode="json", by_alias=False),
+                }
+                for item in similar_cases
+            ],
         }
         try:
             suggestions = await self.llm.generate_prevention_suggestions(context)
@@ -1055,6 +1088,33 @@ class ConversationService:
             )
             return self._suggestions(text, supplier_or_factory)
 
+    async def _similar_cases(
+        self,
+        query: str,
+        *,
+        category: str | None = None,
+        material: str | None = None,
+        supplier: str | None = None,
+        factory: str | None = None,
+        stage: str | None = None,
+        anomaly_type: str | None = None,
+    ) -> Sequence[Any]:
+        """Return historical cases for both the card and the AI context."""
+
+        try:
+            return await AnomalyCaseRAG(self.session).search(
+                query=query,
+                category=category,
+                material=material,
+                supplier=supplier,
+                factory=factory,
+                stage=stage,
+                anomaly_type=anomaly_type,
+            )
+        except Exception as exc:  # noqa: BLE001 - RAG must not block risk scoring
+            logger.warning("RAG historical case search failed error=%s", exc.__class__.__name__)
+            return []
+
     async def _count_category_anomalies(self, category: str) -> int:
         """The imported workbook has sparse product_category values.
 

app/services/rag.py

@@ -0,0 +1,311 @@
+"""Hybrid retrieval for historical anomaly cases."""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import re
+from dataclasses import dataclass
+from typing import Any
+
+import httpx
+from sqlalchemy import select
+from sqlalchemy import text as sql_text
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.core.config import get_settings
+from app.models.entities import AnomalyCase
+from app.schemas.risk_prediction import SimilarCase
+
+logger = logging.getLogger("dieya_risk_prediction.rag")
+_embedding_semaphore = asyncio.Semaphore(1)
+
+
+@dataclass(frozen=True, slots=True)
+class RetrievedCase:
+    case: SimilarCase
+    score: float
+    case_id: int
+
+
+class AnomalyCaseRAG:
+    """Retrieve historical cases with business, text, and optional vector signals."""
+
+    def __init__(
+        self,
+        session: AsyncSession,
+        *,
+        top_k: int | None = None,
+        transport: httpx.AsyncBaseTransport | None = None,
+    ) -> None:
+        self.session = session
+        self.top_k = top_k or get_settings().rag_top_k
+        self.transport = transport
+
+    async def search(
+        self,
+        *,
+        query: str,
+        category: str | None = None,
+        material: str | None = None,
+        supplier: str | None = None,
+        factory: str | None = None,
+        stage: str | None = None,
+        anomaly_type: str | None = None,
+    ) -> list[RetrievedCase]:
+        terms = self._terms(query, category, material, supplier, factory, stage, anomaly_type)
+        rows = await self._load_rows(
+            query,
+            category=category,
+            material=material,
+            supplier=supplier,
+            factory=factory,
+            stage=stage,
+            anomaly_type=anomaly_type,
+        )
+        ranked = [
+            RetrievedCase(
+                case=self._to_case(row),
+                score=self._score(
+                    row,
+                    query=query,
+                    category=category,
+                    material=material,
+                    supplier=supplier,
+                    factory=factory,
+                    stage=stage,
+                    anomaly_type=anomaly_type,
+                    terms=terms,
+                ),
+                case_id=int(row["id"]),
+            )
+            for row in rows
+        ]
+        ranked = [item for item in ranked if item.score > 0]
+        ranked.sort(key=lambda item: (item.score, item.case_id), reverse=True)
+        for item in ranked[: self.top_k]:
+            vector_similarity = next(
+                (
+                    float(row["vector_score"])
+                    for row in rows
+                    if int(row["id"]) == item.case_id and row.get("vector_score") is not None
+                ),
+                None,
+            )
+            item.case.similarity = round(
+                max(0.0, min(1.0, vector_similarity if vector_similarity is not None else item.score / 100)),
+                3,
+            )
+        return ranked[: self.top_k]
+
+    async def _load_rows(
+        self,
+        query: str,
+        *,
+        category: str | None,
+        material: str | None,
+        supplier: str | None,
+        factory: str | None,
+        stage: str | None,
+        anomaly_type: str | None,
+    ) -> list[dict[str, Any]]:
+        fields = (
+            AnomalyCase.id,
+            AnomalyCase.date,
+            AnomalyCase.product_name,
+            AnomalyCase.product_category,
+            AnomalyCase.material_type,
+            AnomalyCase.supplier,
+            AnomalyCase.factory,
+            AnomalyCase.stage_new,
+            AnomalyCase.anomaly_type,
+            AnomalyCase.description,
+            AnomalyCase.solution,
+            AnomalyCase.root_cause,
+            AnomalyCase.defect_level,
+        )
+        vector_ready = await self._vector_column_ready()
+        has_vectors = await self._has_stored_vectors() if vector_ready else False
+        vector = await self._query_embedding(query) if has_vectors else None
+        if vector is None:
+            return [dict(row) for row in (await self.session.execute(select(*fields).limit(5000))).mappings().all()]
+
+        vector_literal = "[" + ",".join(str(float(value)) for value in vector) + "]"
+        statement = sql_text(
+            """
+            SELECT id, date, product_name, product_category, material_type, supplier,
+                   factory, stage_new, anomaly_type, description, solution, root_cause,
+                   defect_level, 1 - (embedding_vector <=> CAST(:embedding AS halfvec)) AS vector_score
+            FROM anomaly_cases
+            WHERE embedding_vector IS NOT NULL
+            ORDER BY embedding_vector <=> CAST(:embedding AS halfvec)
+            LIMIT 5000
+            """
+        )
+        result = await self.session.execute(statement, {"embedding": vector_literal})
+        rows = [dict(row) for row in result.mappings().all()]
+        if rows:
+            return rows
+        return [dict(row) for row in (await self.session.execute(select(*fields).limit(5000))).mappings().all()]
+
+    async def _vector_column_ready(self) -> bool:
+        try:
+            result = await self.session.execute(
+                sql_text(
+                    """
+                    SELECT EXISTS (
+                        SELECT 1
+                        FROM information_schema.columns
+                        WHERE table_name = 'anomaly_cases'
+                          AND column_name = 'embedding_vector'
+                          AND udt_name IN ('vector', 'halfvec')
+                    )
+                    """
+                )
+            )
+            return bool(result.scalar())
+        except Exception:  # noqa: BLE001 - a missing optional extension uses lexical retrieval
+            await self.session.rollback()
+            return False
+
+    async def _query_embedding(self, query: str) -> list[float] | None:
+        settings = get_settings()
+        api_key = (
+            settings.rag_embedding_api_key.get_secret_value()
+            if settings.rag_embedding_api_key
+            else settings.llm_api_key.get_secret_value() if settings.llm_api_key else ""
+        )
+        base_url = settings.rag_embedding_base_url or settings.llm_base_url
+        if not api_key or not base_url:
+            return None
+        endpoint = f"{base_url.rstrip('/')}/embeddings"
+        if not base_url.rstrip("/").endswith("/v1"):
+            endpoint = f"{base_url.rstrip('/')}/v1/embeddings"
+        timeout = httpx.Timeout(
+            settings.rag_embedding_timeout_seconds,
+            connect=min(5, settings.rag_embedding_timeout_seconds),
+        )
+        try:
+            async with _embedding_semaphore:
+                async with httpx.AsyncClient(timeout=timeout, transport=self.transport) as client:
+                    response = await client.post(
+                        endpoint,
+                        headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
+                        json={"model": settings.rag_embedding_model, "input": query[:2000]},
+                    )
+                    response.raise_for_status()
+                    body = response.json()
+            embedding = (body.get("data") or [{}])[0].get("embedding")
+            if not isinstance(embedding, list) or len(embedding) != settings.rag_embedding_dimensions:
+                return None
+            return [float(value) for value in embedding]
+        except (httpx.HTTPError, ValueError, TypeError, KeyError) as exc:
+            logger.warning(
+                "RAG embedding request failed error=%s; using lexical retrieval",
+                exc.__class__.__name__,
+            )
+            return None
+
+    async def _has_stored_vectors(self) -> bool:
+        """Avoid a paid/network embedding call until case vectors exist."""
+
+        try:
+            result = await self.session.execute(
+                sql_text(
+                    "SELECT EXISTS ("
+                    "SELECT 1 FROM anomaly_cases WHERE embedding_vector IS NOT NULL LIMIT 1"
+                    ")"
+                )
+            )
+            return bool(result.scalar())
+        except Exception:  # noqa: BLE001 - lexical retrieval is the safe fallback
+            await self.session.rollback()
+            return False
+
+    @staticmethod
+    def _terms(*values: str | None) -> list[str]:
+        terms: list[str] = []
+        for value in values:
+            if not value:
+                continue
+            cleaned = " ".join(str(value).strip().lower().split())
+            runs = re.findall(r"[a-z0-9]+|[\u4e00-\u9fff]{2,}", cleaned)
+            terms.extend(runs)
+            for run in runs:
+                if re.fullmatch(r"[\u4e00-\u9fff]+", run) and len(run) > 3:
+                    terms.extend(
+                        run[index : index + size]
+                        for size in (2, 3, 4)
+                        for index in range(len(run) - size + 1)
+                    )
+        return list(dict.fromkeys(term for term in terms if len(term) > 1))
+
+    @classmethod
+    def _score(
+        cls,
+        row: dict[str, Any],
+        *,
+        query: str,
+        category: str | None,
+        material: str | None,
+        supplier: str | None,
+        factory: str | None,
+        stage: str | None,
+        anomaly_type: str | None,
+        terms: list[str],
+    ) -> float:
+        score = 0.0
+        score += cls._field_score(row.get("product_category"), category, 7)
+        score += cls._field_score(row.get("product_name"), category, 5)
+        score += cls._field_score(row.get("material_type"), material, 7)
+        score += cls._field_score(row.get("supplier"), supplier, 6)
+        score += cls._field_score(row.get("factory"), factory, 6)
+        score += cls._field_score(row.get("stage_new"), stage, 3)
+        score += cls._field_score(row.get("anomaly_type"), anomaly_type, 7)
+        vector_score = row.get("vector_score")
+        if vector_score is not None:
+            score += max(0.0, float(vector_score)) * 40
+        searchable = " ".join(str(row.get(key) or "") for key in (
+            "product_name",
+            "product_category",
+            "material_type",
+            "anomaly_type",
+            "description",
+            "root_cause",
+            "solution",
+        )).lower()
+        query_lower = query.lower().strip()
+        if query_lower and query_lower in searchable:
+            score += 8
+        score += min(12.0, sum(1 for term in terms if term in searchable) * 1.5)
+        if str(row.get("defect_level") or "").upper() == "A":
+            score += 1.0
+        return round(score, 2)
+
+    @staticmethod
+    def _field_score(actual: Any, expected: str | None, weight: float) -> float:
+        if not actual or not expected:
+            return 0.0
+        actual_text = str(actual).lower()
+        expected_text = str(expected).lower()
+        if actual_text == expected_text:
+            return weight
+        if expected_text in actual_text or actual_text in expected_text:
+            return weight * 0.7
+        return 0.0
+
+    @staticmethod
+    def _to_case(row: dict[str, Any]) -> SimilarCase:
+        return SimilarCase(
+            case_id=int(row["id"]) if row.get("id") is not None else None,
+            date=row.get("date").isoformat() if row.get("date") else None,
+            product=row.get("product_name") or row.get("product_category") or "未命名产品",
+            category=row.get("product_category"),
+            material=row.get("material_type"),
+            anomaly_type=row.get("anomaly_type"),
+            description=row.get("description"),
+            root_cause=row.get("root_cause"),
+            defect_level=row.get("defect_level"),
+            problem=(row.get("anomaly_type") or row.get("description") or "历史异常未填写"),
+            solution=(row.get("solution") or row.get("root_cause") or "请查看原始案例记录"),
+        )

migrations/versions/20260905_0014_add_optional_pgvector.py

@@ -0,0 +1,51 @@
+"""Add the optional pgvector column for anomaly-case retrieval."""
+
+from collections.abc import Sequence
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "20260905_0014"
+down_revision: str | None = "20260902_0013"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def upgrade() -> None:
+    connection = op.get_bind()
+    available = connection.execute(
+        sa.text("SELECT EXISTS (SELECT 1 FROM pg_available_extensions WHERE name = 'vector')")
+    ).scalar()
+    if not available:
+        # The application has a structured/text retrieval fallback. Keep the
+        # migration non-blocking on managed PostgreSQL instances without the
+        # optional extension; install pgvector and rerun this revision later.
+        return
+    is_superuser = connection.execute(
+        sa.text("SELECT rolsuper FROM pg_roles WHERE rolname = current_user")
+    ).scalar()
+    if not is_superuser:
+        # Managed PostgreSQL may expose the package but reserve CREATE
+        # EXTENSION for an administrator. Lexical RAG remains usable.
+        return
+    connection.execute(sa.text("CREATE EXTENSION IF NOT EXISTS vector"))
+    connection.execute(
+        sa.text(
+            "ALTER TABLE anomaly_cases "
+            "ADD COLUMN IF NOT EXISTS embedding_vector halfvec(3072)"
+        )
+    )
+    connection.execute(
+        sa.text(
+            "CREATE INDEX IF NOT EXISTS ix_anomaly_cases_embedding_vector "
+            "ON anomaly_cases USING hnsw (embedding_vector halfvec_cosine_ops)"
+        )
+    )
+
+
+def downgrade() -> None:
+    connection = op.get_bind()
+    connection.execute(sa.text("DROP INDEX IF EXISTS ix_anomaly_cases_embedding_vector"))
+    connection.execute(
+        sa.text("ALTER TABLE anomaly_cases DROP COLUMN IF EXISTS embedding_vector")
+    )

scripts/backfill_embeddings.py

@@ -0,0 +1,175 @@
+"""Generate and store anomaly-case embeddings after pgvector is available.
+
+Usage:
+    .venv/Scripts/python -m scripts.backfill_embeddings
+
+The script is intentionally separate from application startup. Embedding 2,448
+rows can take time and should never make the API unavailable.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import logging
+import random
+from typing import Any
+
+import httpx
+from app.core.config import get_settings
+from app.database.session import SessionFactory
+from sqlalchemy import text
+
+logger = logging.getLogger("dieya_risk_prediction.embedding_backfill")
+
+
+def _case_text(row: dict[str, Any]) -> str:
+    labels = (
+        ("产品", row.get("product_name")),
+        ("品类", row.get("product_category")),
+        ("包材", row.get("material_type")),
+        ("供应商", row.get("supplier")),
+        ("加工厂", row.get("factory")),
+        ("阶段", row.get("stage_new")),
+        ("异常类型", row.get("anomaly_type")),
+        ("异常描述", row.get("description")),
+        ("原因", row.get("root_cause")),
+        ("解决方案", row.get("solution")),
+    )
+    return "；".join(f"{label}：{value}" for label, value in labels if value)
+
+
+async def _embedding(
+    client: httpx.AsyncClient,
+    endpoint: str,
+    api_key: str,
+    texts: list[str],
+    model: str,
+) -> list[list[float]]:
+    for attempt in range(4):
+        response = await client.post(
+            endpoint,
+            headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
+            json={"model": model, "input": texts},
+        )
+        if response.status_code not in {429, 500, 502, 503, 504}:
+            response.raise_for_status()
+            data = response.json().get("data") or []
+            ordered = sorted(data, key=lambda item: item.get("index", 0))
+            return [list(map(float, item["embedding"])) for item in ordered]
+        if attempt == 3:
+            response.raise_for_status()
+        retry_after = response.headers.get("retry-after")
+        try:
+            delay = min(60.0, max(3.0, float(retry_after))) if retry_after else 3.0 * (2**attempt)
+        except ValueError:
+            delay = 3.0 * (2**attempt)
+        delay += random.uniform(0, 1)
+        logger.warning(
+            "Embedding API 限流或暂时不可用 status=%s attempt=%s delay=%.1fs",
+            response.status_code,
+            attempt + 1,
+            delay,
+        )
+        await asyncio.sleep(delay)
+    raise RuntimeError("Embedding 请求重试次数已用尽")
+
+
+async def main(batch_size: int) -> None:
+    settings = get_settings()
+    api_key = (
+        settings.rag_embedding_api_key.get_secret_value()
+        if settings.rag_embedding_api_key
+        else settings.llm_api_key.get_secret_value() if settings.llm_api_key else ""
+    )
+    base_url = settings.rag_embedding_base_url or settings.llm_base_url
+    if not api_key or not base_url:
+        raise RuntimeError("未配置 Embeddings API Key 或 Base URL")
+    endpoint = f"{base_url.rstrip('/')}/embeddings"
+    if not base_url.rstrip("/").endswith("/v1"):
+        endpoint = f"{base_url.rstrip('/')}/v1/embeddings"
+
+    async with SessionFactory() as session:
+        column_exists = await session.scalar(
+            text(
+                """
+                SELECT EXISTS (
+                    SELECT 1 FROM information_schema.columns
+                    WHERE table_name = 'anomaly_cases'
+                      AND column_name = 'embedding_vector'
+                      AND udt_name = 'halfvec'
+                )
+                """
+            )
+        )
+        if not column_exists:
+            raise RuntimeError("当前数据库没有 embedding_vector，请先安装 pgvector 并执行 alembic upgrade head")
+        rows = (
+            await session.execute(
+                text(
+                    """
+                    SELECT id, product_name, product_category, material_type, supplier,
+                           factory, stage_new, anomaly_type, description, root_cause, solution
+                    FROM anomaly_cases
+                    WHERE embedding_vector IS NULL
+                    ORDER BY id
+                    """
+                )
+            )
+        ).mappings().all()
+
+    logger.info("待生成案例向量数量=%s", len(rows))
+    print(f"待生成案例向量数量：{len(rows)}，批次大小：{batch_size}", flush=True)
+    timeout = httpx.Timeout(
+        settings.rag_embedding_timeout_seconds,
+        connect=min(5, settings.rag_embedding_timeout_seconds),
+    )
+    async with httpx.AsyncClient(timeout=timeout) as client:
+        success_count = 0
+        failure_count = 0
+        for start in range(0, len(rows), batch_size):
+            batch = rows[start : start + batch_size]
+            try:
+                vectors = await _embedding(
+                    client,
+                    endpoint,
+                    api_key,
+                    [_case_text(dict(row)) for row in batch],
+                    settings.rag_embedding_model,
+                )
+            except (httpx.HTTPError, ValueError, TypeError, KeyError) as exc:
+                failure_count += len(batch)
+                logger.exception("案例向量生成失败 case_id=%s", batch[0]["id"])
+                print(f"案例 {batch[0]['id']} 失败：{exc.__class__.__name__}，已跳过", flush=True)
+                continue
+            if len(vectors) != len(batch):
+                raise RuntimeError("Embeddings 返回数量与请求数量不一致")
+            async with SessionFactory() as session:
+                for row, vector in zip(batch, vectors, strict=True):
+                    literal = "[" + ",".join(str(value) for value in vector) + "]"
+                    await session.execute(
+                        text(
+                            "UPDATE anomaly_cases "
+                            "SET embedding_vector = CAST(:embedding AS halfvec) "
+                            "WHERE id = :id"
+                        ),
+                        {"embedding": literal, "id": row["id"]},
+                )
+                await session.commit()
+            success_count += len(batch)
+            logger.info("已完成案例向量回填 %s/%s", min(start + batch_size, len(rows)), len(rows))
+            print(
+                f"已完成：{min(start + batch_size, len(rows))}/{len(rows)}，"
+                f"成功：{success_count}，失败：{failure_count}，当前案例：{batch[-1]['id']}",
+                flush=True,
+            )
+            await asyncio.sleep(1.5)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--batch-size", type=int, default=1)
+    args = parser.parse_args()
+    if not 1 <= args.batch_size <= 8:
+        raise SystemExit("--batch-size 必须在 1 到 8 之间")
+    asyncio.run(main(args.batch_size))

scripts/enable_pgvector.py

@@ -0,0 +1,38 @@
+"""Enable pgvector schema objects with a PostgreSQL administrator account.
+
+Run once after the pgvector package is installed on the server:
+
+    .venv/Scripts/python -m scripts.enable_pgvector
+
+Then the normal application account can run ``scripts.backfill_embeddings``.
+"""
+
+from __future__ import annotations
+
+import asyncio
+
+from app.database.session import SessionFactory
+from sqlalchemy import text
+
+
+async def main() -> None:
+    async with SessionFactory() as session:
+        await session.execute(text("CREATE EXTENSION IF NOT EXISTS vector"))
+        await session.execute(
+            text(
+                "ALTER TABLE anomaly_cases "
+                "ADD COLUMN IF NOT EXISTS embedding_vector halfvec(3072)"
+            )
+        )
+        await session.execute(
+            text(
+                "CREATE INDEX IF NOT EXISTS ix_anomaly_cases_embedding_vector "
+                "ON anomaly_cases USING hnsw (embedding_vector halfvec_cosine_ops)"
+            )
+        )
+        await session.commit()
+    print("pgvector 已启用，embedding_vector 和 HNSW 索引已准备完成。")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

tests/test_rag.py

@@ -0,0 +1,87 @@
+from datetime import date
+
+import pytest
+from app.services.rag import AnomalyCaseRAG
+
+
+def _row(**values):
+    return {
+        "id": 1,
+        "date": date(2026, 1, 10),
+        "product_name": "洗发水 500ml",
+        "product_category": "洗护类",
+        "material_type": "软管",
+        "supplier": "祥印",
+        "factory": "晶品",
+        "stage_new": "产品开发",
+        "anomaly_type": "密封性异常",
+        "description": "软管漏液",
+        "solution": "增加密封性测试",
+        "root_cause": "封口参数不稳定",
+        "defect_level": "B",
+        **values,
+    }
+
+
+def test_rag_terms_and_structured_text_score() -> None:
+    terms = AnomalyCaseRAG._terms("软管密封性异常", "洗发水", "软管")
+    score = AnomalyCaseRAG._score(
+        _row(),
+        query="洗发水软管密封性异常",
+        category="洗发水",
+        material="软管",
+        supplier="祥印",
+        factory=None,
+        stage="产品开发",
+        anomaly_type="密封性异常",
+        terms=terms,
+    )
+
+    assert "密封性" in terms
+    assert score > 20
+    case = AnomalyCaseRAG._to_case(_row())
+    assert case.product == "洗发水 500ml"
+    assert case.problem == "密封性异常"
+    assert case.solution == "增加密封性测试"
+
+
+@pytest.mark.asyncio
+async def test_rag_does_not_call_embedding_until_vectors_are_stored() -> None:
+    class FakeMappings:
+        def all(self):
+            return [_row()]
+
+    class FakeResult:
+        def mappings(self):
+            return FakeMappings()
+
+    class FakeSession:
+        async def execute(self, _statement):
+            return FakeResult()
+
+    rag = AnomalyCaseRAG(FakeSession(), top_k=1)
+
+    async def vector_column_ready():
+        return True
+
+    async def has_stored_vectors():
+        return False
+
+    async def embedding_must_not_run(_query):
+        raise AssertionError("没有已回填向量时不应调用 Embedding")
+
+    rag._vector_column_ready = vector_column_ready
+    rag._has_stored_vectors = has_stored_vectors
+    rag._query_embedding = embedding_must_not_run
+
+    rows = await rag._load_rows(
+        "洗发水软管密封性异常",
+        category="洗发水",
+        material="软管",
+        supplier=None,
+        factory=None,
+        stage=None,
+        anomaly_type=None,
+    )
+
+    assert len(rows) == 1