init：项目初始化

.env

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+APP_ENV=development
+LLM_PROVIDER=deepseek
+DEEPSEEK_API_KEY=sk-xxxxxx
+DEEPSEEK_BASE_URL=https://api.deepseek.com
+DEEPSEEK_MODEL_NAME=deepseek-v4-flash
+DEEPSEEK_ANSWER_THINKING=true
+TAVILY_API_KEY=tvly-xxxxxx
+WEB_SEARCH_MAX_RESULTS=5
+
+EMBEDDING_PROVIDER=hash
+EMBEDDING_MODEL_NAME=sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+HASH_EMBEDDING_DIM=256
+
+MILVUS_URI=http://localhost:19530
+MILVUS_TOKEN=
+MILVUS_COLLECTION=agentic_rag_docs
+
+SQLITE_PATH=./data/shop.db
+ORDERS_SOURCE_PATH=./data/source/orders.csv
+DOCUMENTS_PATH=./data/documents
+MAX_RETRIEVAL_ROUNDS=2
+MIN_EVIDENCE_SCORE=0.35

.gitignore

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+.venv/
+__pycache__/
+*.py[cod]
+*.egg-info/
+.pytest_cache/
+.coverage
+htmlcov/
+.env
+data/*.db
+infra/milvus/volumes/

README.md

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+# 电商售后 Agentic RAG 课程项目
+
+技术栈：Python 3.11、FastAPI、LangGraph、Milvus、SQLite、Tavily、DeepSeek。
+
+项目默认使用确定性的 `demo` 决策引擎和 Hash Embedding。无需模型 Key 即可完成数据准备、意图路由、Milvus 检索、SQL 查询、证据评分和多源执行。
+
+## 1. 环境要求
+
+- Python 3.11
+- uv
+- 使用现有 Ubuntu Milvus 时，Windows 必须能访问其 `19530` 端口
+- 本地启动 Milvus 时，需要 Docker Desktop、WSL 2、至少 4 CPU 和 8 GB 内存
+
+## 2. 安装
+
+```powershell
+uv venv --python 3.11
+uv sync --extra test
+```
+
+项目直接读取根目录下的 `.env`。DeepSeek、Tavily、Milvus 和数据路径等运行配置统一在该文件中维护。
+
+按需安装可选能力：
+
+```powershell
+# DeepSeek 模型适配。
+uv sync --extra test --extra deepseek
+
+# Tavily 网页搜索。
+uv sync --extra test --extra web
+
+# 本地 Sentence Transformers。
+uv sync --extra test --extra embeddings
+```
+
+### 2.1 路径约定
+
+仓库中使用相对路径，运行时根据 `app/config.py` 的位置解析为绝对路径，不依赖当前工作目录。
+
+```env
+SQLITE_PATH=./data/shop.db
+ORDERS_SOURCE_PATH=./data/source/orders.csv
+DOCUMENTS_PATH=./data/documents
+```
+
+不要提交本机绝对路径和 `.env`。部署时如需使用仓库外的数据目录，可以在本地 `.env` 中配置绝对路径。
+
+## 3. 连接或启动 Milvus
+
+### 3.1 使用现有 Ubuntu Milvus
+
+Ubuntu Milvus 的 `19530` 已映射到 Windows 时：
+
+```env
+MILVUS_URI=http://localhost:19530
+```
+
+```powershell
+uv run python scripts/wait_for_milvus.py
+```
+
+Milvus 位于另一台主机时，将 `localhost` 替换为 Windows 可访问的 Ubuntu IP。
+
+### 3.2 使用项目内 Compose
+
+```powershell
+docker compose -f infra/milvus/docker-compose.yml config --quiet
+docker compose -f infra/milvus/docker-compose.yml up -d
+uv run python scripts/wait_for_milvus.py
+```
+
+## 4. 准备电商数据
+
+本地数据分为两组：
+
+- `data/source/orders.csv`：脱敏订单，写入 SQLite `orders` 表。
+- `data/documents/*.md`：退货、运费和价保政策，切分后写入 Milvus。
+
+```powershell
+uv run python scripts/prepare_data.py
+```
+
+准备脚本会重建课程数据库和 Milvus Collection。切换 Embedding 模型后也必须重新执行，不能混用不同模型或维度的向量。
+
+## 5. 示例问题
+
+| 类型 | 问题 | 预期工具 |
+|---|---|---|
+| 向量检索 | 耳机拆封后还能七日无理由退货吗？ | Milvus |
+| SQL 查询 | U1001 最近 30 天有几笔有效订单，实付总额是多少？ | SQLite |
+| 网页搜索 | 查询品牌官网关于 XPhone 15 Pro 的最新公告 | Tavily |
+| 双源组合 | 统计 U1001 最近 30 天订单，并结合内部运费规则说明是否包邮 | Milvus + SQLite |
+| 三源组合 | 统计 U1001 最近 30 天购买 XPhone 15 Pro 的订单金额，结合内部退货政策和品牌官网最新公告给出售后建议 | Milvus + SQLite + Tavily |
+
+订单用户编号在课程数据中使用 `U1001`。生产系统必须从登录态或服务端认证上下文注入，不能信任模型从自然语言生成的用户编号。
+
+## 6. 查看实际工具 Query
+
+使用 `--debug` 时，CLI 会先打印每个工具实际执行的 Query、参数和状态，再输出完整响应：
+
+```powershell
+uv run python -m app.cli `
+  "统计 U1001 最近 30 天订单，并结合内部运费规则说明是否包邮" `
+  --debug
+```
+
+输出结构：
+
+```text
+=== 实际执行的工具查询 ===
+- milvus_search: query='...' arguments={'policy_type': 'shipping_policy', 'top_k': 4}
+- sql_query: query='...' arguments={'user_id': 'U1001', 'days': 30, ...}
+```
+
+API 请求设置 `debug=true` 时，响应中的 `executed_queries` 返回相同信息。普通请求不返回内部查询轨迹。
+
+## 7. 自动化测试
+
+```powershell
+uv run pytest
+uv run python scripts/smoke_test.py
+```
+
+单元测试不访问真实模型或网页；Smoke Test 使用真实 Milvus 与 SQLite。
+
+## 8. 启动 API
+
+```powershell
+uv run uvicorn app.main:app --reload
+```
+
+```powershell
+$body = @{
+  query = "统计 U1001 最近 30 天订单，并结合内部运费规则说明是否包邮"
+  session_id = "demo"
+  debug = $true
+} | ConvertTo-Json
+
+Invoke-RestMethod `
+  -Method Post `
+  -Uri "http://127.0.0.1:8000/query" `
+  -ContentType "application/json" `
+  -Body $body
+```
+
+Swagger UI：`http://127.0.0.1:8000/docs`
+
+## 9. 配置 Web Search
+
+在 `.env` 中配置 Tavily：
+
+```env
+TAVILY_API_KEY=your-tavily-api-key
+WEB_SEARCH_MAX_RESULTS=5
+```
+
+未配置 Key 时，Milvus 和 SQLite 路由不受影响；`web_search` 返回 `WEB_SEARCH_NOT_CONFIGURED`。
+
+运行时代码只通过 `Settings` 读取 `TAVILY_API_KEY`，不会从代码常量、请求参数或 CLI 参数接收 Key。
+
+## 10. 切换到 DeepSeek
+
+```env
+LLM_PROVIDER=deepseek
+DEEPSEEK_API_KEY=your-deepseek-api-key
+DEEPSEEK_BASE_URL=https://api.deepseek.com
+DEEPSEEK_MODEL_NAME=deepseek-v4-flash
+DEEPSEEK_ANSWER_THINKING=true
+```
+
+`DeepSeekDecisionEngine` 使用 Function Calling 输出 `RouteDecision` 和 `QualityGrade`。这两个结构化调用固定关闭 Thinking，避免 Thinking 模式与强制 `tool_choice` 冲突；最终答案是否使用 Thinking 由 `DEEPSEEK_ANSWER_THINKING` 控制。
+
+`DEEPSEEK_API_KEY` 只通过 `Settings` 读取。所有 Key 使用 `SecretStr` 保存，打印配置对象时只显示掩码，不输出真实值。
+
+## 11. 停止本地 Compose 服务
+
+```powershell
+docker compose -f infra/milvus/docker-compose.yml down
+```
+
+数据目录位于 `infra/milvus/volumes`，已在 `.gitignore` 中排除。

app/__init__.py

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+"""Agentic RAG 课程实战应用。"""
+

app/cli.py

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+from __future__ import annotations
+
+import argparse
+import sys
+
+from app.config import get_settings
+from app.schemas import QueryRequest
+from app.service import AgenticRAGService
+
+
+def main() -> None:
+    # Windows 控制台可能使用 GBK；网页证据中的 € 等字符不能直接编码。
+    # 保留当前控制台编码，只将不支持的字符转义，避免输出阶段中断业务流程。
+    if hasattr(sys.stdout, "reconfigure"):
+        sys.stdout.reconfigure(errors="backslashreplace")
+
+    parser = argparse.ArgumentParser(description="Agentic RAG 命令行入口")
+    parser.add_argument("query", help="用户问题")
+    parser.add_argument("--debug", action="store_true", help="输出完整执行轨迹")
+    args = parser.parse_args()
+
+    response = AgenticRAGService(get_settings()).invoke(
+        QueryRequest(query=args.query, debug=args.debug)
+    )
+    if args.debug:
+        print("=== 实际执行的工具查询 ===")
+        for item in response.executed_queries:
+            print(
+                f"- {item['tool']}: query={item['query']!r}, "
+                f"arguments={item['arguments']}, status={item['status']}"
+            )
+        print("=== 完整响应 ===")
+    print(response.model_dump_json(indent=2))
+
+
+if __name__ == "__main__":
+    main()

app/config.py

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+from __future__ import annotations
+
+from functools import lru_cache
+from pathlib import Path
+
+from pydantic import Field, SecretStr
+from pydantic import field_validator
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+
+# 运行时通过源码位置确定项目根目录，不能依赖执行命令时的当前目录。
+# 仓库移动、CI Checkout 或其他开发者 Clone 后，该值会自动变化。
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+
+
+class Settings(BaseSettings):
+    model_config = SettingsConfigDict(
+        env_file=PROJECT_ROOT / ".env",
+        env_file_encoding="utf-8",
+        extra="ignore",
+    )
+
+    app_env: str = "development"
+
+    llm_provider: str = "demo"
+    # SecretStr 会在 repr、日志和校验错误中自动隐藏真实凭证。
+    deepseek_api_key: SecretStr = SecretStr("")
+    deepseek_base_url: str = "https://api.deepseek.com"
+    deepseek_model_name: str = "deepseek-v4-flash"
+    deepseek_answer_thinking: bool = True
+
+    tavily_api_key: SecretStr = SecretStr("")
+    web_search_max_results: int = Field(default=5, ge=1, le=10)
+
+    embedding_provider: str = "hash"
+    embedding_model_name: str = (
+        "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
+    )7
+    hash_embedding_dim: int = Field(default=256, ge=64, le=4096)
+
+    milvus_uri: str = "http://localhost:19530"
+    milvus_token: SecretStr = SecretStr("")
+    milvus_collection: str = "agentic_rag_docs"
+
+    sqlite_path: Path = Path("./data/shop.db")
+    orders_source_path: Path = Path("./data/source/orders.csv")
+    documents_path: Path = Path("./data/documents")
+    max_retrieval_rounds: int = Field(default=2, ge=1, le=5)
+    min_evidence_score: float = Field(default=0.35, ge=0.0, le=1.0)
+
+    @field_validator(
+        "sqlite_path",
+        "orders_source_path",
+        "documents_path",
+        mode="after",
+    )
+    @classmethod
+    def resolve_repository_path(cls, value: Path) -> Path:
+        """将仓库相对路径解析为绝对路径，同时保留外部绝对路径。"""
+        if value.is_absolute():
+            return value.resolve()
+        return (PROJECT_ROOT / value).resolve()
+
+
+@lru_cache(maxsize=1)
+def get_settings() -> Settings:
+    return Settings()

app/decision_engine.py

@@ -0,0 +1,424 @@
+from __future__ import annotations
+
+import json
+import re
+from typing import Protocol
+
+from app.config import Settings
+from app.schemas import (
+    Evidence,
+    PlanStep,
+    QualityGrade,
+    RetrievalPlan,
+    RouteDecision,
+    RouteName,
+)
+
+
+USER_ID_PATTERN = re.compile(r"\bU\d{4}\b", re.IGNORECASE)
+PRODUCT_NAMES = ("XPhone 15 Pro", "AirBuds Pro", "USB-C Charger", "SmartWatch S")
+POLICY_KEYWORDS = {
+    "return_policy": ("退货", "退款", "七日无理由", "换货", "售后"),
+    "shipping_policy": ("运费", "包邮", "配送", "物流", "送达"),
+    "price_protection": ("价保", "保价", "差价", "降价"),
+}
+
+
+def extract_user_id(query: str) -> str:
+    match = USER_ID_PATTERN.search(query.upper())
+    return match.group(0) if match else ""
+
+
+def extract_product_name(query: str) -> str:
+    lowered = query.lower()
+    return next((name for name in PRODUCT_NAMES if name.lower() in lowered), "")
+
+
+def extract_policy_type(query: str) -> str:
+    for policy_type, keywords in POLICY_KEYWORDS.items():
+        if any(keyword in query for keyword in keywords):
+            return policy_type
+    return ""
+
+
+class DecisionEngine(Protocol):
+    def route(self, query: str, max_rounds: int) -> RouteDecision: ...
+
+    def plan(self, query: str, decision: RouteDecision) -> RetrievalPlan: ...
+
+    def grade(
+        self,
+        query: str,
+        decision: RouteDecision,
+        evidence: list[Evidence],
+        current_round: int,
+        max_rounds: int,
+        min_score: float,
+    ) -> QualityGrade: ...
+
+    def rewrite(self, query: str, grade: QualityGrade) -> str: ...
+
+    def answer(self, query: str, evidence: list[Evidence], partial: bool = False) -> str: ...
+
+
+class DemoDecisionEngine:
+    """确定性演示引擎：便于无 API Key 启动、调试和自动化测试。"""
+
+    SQL_TERMS = ("订单", "几单", "多少单", "实付", "总额", "金额", "统计", "最近")
+    DOC_TERMS = ("规则", "政策", "退货", "退款", "运费", "包邮", "价保", "售后")
+    WEB_TERMS = ("网页", "官网", "公开资料", "最新公告", "新闻", "互联网")
+    DIRECT_TERMS = ("改写", "润色", "翻译", "缩短", "转成")
+
+    def route(self, query: str, max_rounds: int) -> RouteDecision:
+        stripped = query.strip()
+        user_id = extract_user_id(stripped)
+        product_name = extract_product_name(stripped)
+        policy_type = extract_policy_type(stripped)
+
+        # 安全门控和缺失槽位检查必须先于数据源选择，避免先访问再拒绝。
+        if any(term in stripped for term in ("所有买家手机号", "密码", "支付密钥")):
+            return RouteDecision(
+                needs_retrieval=False,
+                intent="restricted_request",
+                routes=[RouteName.REFUSE],
+                confidence="high",
+                reason_code="SECURITY_POLICY",
+                max_rounds=max_rounds,
+            )
+
+        if any(term in stripped for term in self.DIRECT_TERMS):
+            return RouteDecision(
+                needs_retrieval=False,
+                intent="text_transformation",
+                routes=[RouteName.DIRECT_ANSWER],
+                confidence="high",
+                reason_code="NO_EXTERNAL_KNOWLEDGE_REQUIRED",
+                max_rounds=max_rounds,
+            )
+
+        needs_sql = any(term in stripped for term in self.SQL_TERMS)
+        needs_docs = any(term in stripped for term in self.DOC_TERMS)
+        needs_web = any(term in stripped for term in self.WEB_TERMS)
+        if needs_sql and not user_id:
+            return RouteDecision(
+                needs_retrieval=False,
+                intent="missing_authenticated_user",
+                routes=[RouteName.CLARIFY],
+                confidence="high",
+                reason_code="MISSING_USER_ID",
+                max_rounds=max_rounds,
+            )
+        if "这件商品" in stripped and not product_name:
+            return RouteDecision(
+                needs_retrieval=False,
+                intent="missing_product",
+                routes=[RouteName.CLARIFY],
+                confidence="high",
+                reason_code="MISSING_PRODUCT_NAME",
+                max_rounds=max_rounds,
+            )
+
+        filters = {
+            key: value
+            for key, value in {
+                "user_id": user_id,
+                "product_name": product_name,
+                "policy_type": policy_type,
+            }.items()
+            if value
+        }
+
+        # routes 是可并行的数据源集合，不使用单标签覆盖复合意图。
+        routes: list[RouteName] = []
+        if needs_docs:
+            routes.append(RouteName.MILVUS_SEARCH)
+        if needs_sql:
+            routes.append(RouteName.SQL_QUERY)
+        if needs_web:
+            routes.append(RouteName.WEB_SEARCH)
+
+        if len(routes) > 1:
+            return RouteDecision(
+                needs_retrieval=True,
+                intent="multi_source_research",
+                routes=routes,
+                requires_decomposition=True,
+                confidence="high" if not needs_sql or user_id else "medium",
+                reason_code="MULTIPLE_DATA_SOURCES_REQUIRED",
+                filters=filters,
+                max_rounds=max_rounds,
+            )
+        if needs_sql:
+            return RouteDecision(
+                needs_retrieval=True,
+                intent="order_statistics",
+                routes=[RouteName.SQL_QUERY],
+                confidence="high",
+                reason_code="EXACT_AGGREGATION_REQUIRED",
+                filters=filters,
+                max_rounds=max_rounds,
+            )
+        if needs_web:
+            return RouteDecision(
+                needs_retrieval=True,
+                intent="public_web_research",
+                routes=[RouteName.WEB_SEARCH],
+                confidence="high",
+                reason_code="CURRENT_PUBLIC_INFORMATION_REQUIRED",
+                filters=filters,
+                max_rounds=max_rounds,
+            )
+
+        return RouteDecision(
+            needs_retrieval=True,
+            intent="commerce_policy_qa",
+            routes=[RouteName.MILVUS_SEARCH],
+            confidence="high" if policy_type else "medium",
+            reason_code="INTERNAL_DOCUMENT_REQUIRED",
+            filters=filters,
+            max_rounds=max_rounds,
+        )
+
+    def plan(self, query: str, decision: RouteDecision) -> RetrievalPlan:
+        user_id = str(decision.filters.get("user_id", ""))
+        product_name = str(decision.filters.get("product_name", ""))
+        policy_type = str(decision.filters.get("policy_type", ""))
+        steps: list[PlanStep] = []
+        # 当前三类读取任务互不依赖，因此不填写 depends_on，由 Executor 并行执行。
+        if RouteName.MILVUS_SEARCH in decision.routes:
+            steps.append(
+                PlanStep(
+                    id="search_policy",
+                    tool=RouteName.MILVUS_SEARCH,
+                    query=query,
+                    arguments={"policy_type": policy_type, "top_k": 4},
+                )
+            )
+        if RouteName.SQL_QUERY in decision.routes:
+            steps.append(
+                PlanStep(
+                    id="query_statistics",
+                    tool=RouteName.SQL_QUERY,
+                    query=(
+                        f"统计用户 {user_id} 最近 30 天"
+                        f"{product_name or '全部商品'}的有效订单"
+                    ),
+                    arguments={
+                        "user_id": user_id,
+                        "days": 30,
+                        "product_keyword": product_name,
+                    },
+                )
+            )
+        if RouteName.WEB_SEARCH in decision.routes:
+            steps.append(
+                PlanStep(
+                    id="search_web",
+                    tool=RouteName.WEB_SEARCH,
+                    query=query,
+                    arguments={"max_results": 5},
+                )
+            )
+        return RetrievalPlan(goal=query, steps=steps)
+
+    def grade(
+        self,
+        query: str,
+        decision: RouteDecision,
+        evidence: list[Evidence],
+        current_round: int,
+        max_rounds: int,
+        min_score: float,
+    ) -> QualityGrade:
+        # 先做确定性的来源覆盖检查，再决定是否需要模型参与语义评分。
+        required_sources = set()
+        if RouteName.MILVUS_SEARCH in decision.routes:
+            required_sources.add("milvus")
+        if RouteName.SQL_QUERY in decision.routes:
+            required_sources.add("sql")
+        if RouteName.WEB_SEARCH in decision.routes:
+            required_sources.add("web")
+
+        available_sources = {item.source_type for item in evidence}
+        missing = sorted(required_sources - available_sources)
+        vector_evidence = [item for item in evidence if item.source_type == "milvus"]
+        vector_relevant = any(
+            item.score is None or item.score >= min_score for item in vector_evidence
+        )
+        if "milvus" not in required_sources:
+            vector_relevant = True
+
+        if not missing and vector_relevant:
+            return QualityGrade(
+                relevant=True,
+                sufficient=True,
+                recommended_action="accept",
+                reason="所需数据源均返回证据，向量结果达到最低质量门槛。",
+            )
+
+        # 只有预算尚未耗尽时才能 Rewrite；否则必须有界终止。
+        if current_round < max_rounds:
+            return QualityGrade(
+                relevant=bool(evidence),
+                sufficient=False,
+                missing_aspects=missing or ["高相关技术文档"],
+                recommended_action="rewrite_query",
+                reason="证据不完整或向量结果相关性不足，允许改写后重试。",
+            )
+
+        return QualityGrade(
+            relevant=bool(evidence),
+            sufficient=False,
+            missing_aspects=missing or ["高相关技术文档"],
+            recommended_action="stop",
+            reason="达到最大检索轮数，停止继续调用工具。",
+        )
+
+    def rewrite(self, query: str, grade: QualityGrade) -> str:
+        missing = "、".join(grade.missing_aspects)
+        suffix = f"电商平台内部政策 订单范围 官网公告 {missing}".strip()
+        return f"{query}；补充检索条件：{suffix}"
+
+    def answer(self, query: str, evidence: list[Evidence], partial: bool = False) -> str:
+        if not evidence:
+            if any(term in query for term in self.DIRECT_TERMS):
+                return "该请求不需要外部检索，应由文本处理模型直接完成。"
+            return "未获得能够支持回答的证据。"
+
+        prefix = "当前仅能给出部分结论。" if partial else "基于已检索证据，结论如下。"
+        lines = [prefix]
+        for index, item in enumerate(evidence, start=1):
+            lines.append(f"- {item.content} [{index}]")
+        return "\n".join(lines)
+
+
+class DeepSeekDecisionEngine(DemoDecisionEngine):
+    """使用 DeepSeek 的 OpenAI 兼容接口完成路由、评分和答案生成。"""
+
+    def __init__(self, settings: Settings) -> None:
+        # 凭证只从 Settings 读取，并在创建 SDK 客户端时短暂解密。
+        api_key = settings.deepseek_api_key.get_secret_value()
+        if not api_key:
+            raise RuntimeError("LLM_PROVIDER=deepseek 时必须配置 DEEPSEEK_API_KEY")
+        try:
+            from langchain_openai import ChatOpenAI
+        except ImportError as exc:
+            raise RuntimeError(
+                "缺少 DeepSeek 依赖，请执行 uv sync --extra test --extra deepseek"
+            ) from exc
+
+        common_kwargs = {
+            "model": settings.deepseek_model_name,
+            "api_key": api_key,
+            "base_url": settings.deepseek_base_url,
+            "max_retries": 2,
+        }
+
+        # 最终答案可以使用 Thinking；开关由 .env 控制。
+        answer_thinking = "enabled" if settings.deepseek_answer_thinking else "disabled"
+        self.llm = ChatOpenAI(
+            **common_kwargs,
+            extra_body={"thinking": {"type": answer_thinking}},
+        )
+
+        # with_structured_output(function_calling) 会发送强制 tool_choice。
+        # DeepSeek Thinking 模式不接受该组合，因此路由与评分必须关闭 Thinking。
+        self.structured_llm = ChatOpenAI(
+            **common_kwargs,
+            temperature=0,
+            extra_body={"thinking": {"type": "disabled"}},
+        )
+        self.router = self.structured_llm.with_structured_output(
+            RouteDecision,
+            method="function_calling",
+        )
+        self.grader = self.structured_llm.with_structured_output(
+            QualityGrade,
+            method="function_calling",
+        )
+
+    def route(self, query: str, max_rounds: int) -> RouteDecision:
+        prompt = f"""
+你是电商售后系统的路由器。只输出符合 Schema 的结果。
+可选路径：direct_answer、milvus_search、sql_query、web_search、clarify、refuse。
+内部退货、运费和价保政策使用 milvus_search；订单数量和金额使用 sql_query；
+官网、新闻、最新公告和公开资料使用 web_search；
+多类需求同时存在时返回对应的多条路径，并设置 requires_decomposition=true。
+订单统计缺少受控用户编号时选择 clarify，敏感越权请求选择 refuse。
+filters 中只填写已从原问题明确提取的 user_id、product_name、policy_type。
+最大检索轮数：{max_rounds}
+用户问题：{query}
+""".strip()
+        result = self.router.invoke(prompt)
+        # 部分兼容接口可能返回普通文本而非 Function Call，LangChain 此时返回 None。
+        # 路由属于关键控制决策，结构化输出缺失时回退到确定性规则，不允许继续空值。
+        if result is None:
+            return super().route(query, max_rounds)
+        # 关键业务槽位使用确定性解析覆盖模型结果，避免模型改写用户身份。
+        deterministic_filters = {
+            key: value
+            for key, value in {
+                "user_id": extract_user_id(query),
+                "product_name": extract_product_name(query),
+                "policy_type": extract_policy_type(query),
+            }.items()
+            if value
+        }
+        result.filters = {**result.filters, **deterministic_filters}
+        if RouteName.SQL_QUERY in result.routes and "user_id" not in result.filters:
+            return RouteDecision(
+                needs_retrieval=False,
+                intent="missing_authenticated_user",
+                routes=[RouteName.CLARIFY],
+                confidence="high",
+                reason_code="MISSING_USER_ID",
+                max_rounds=max_rounds,
+            )
+        result.max_rounds = max_rounds
+        return result
+
+    def grade(
+        self,
+        query: str,
+        decision: RouteDecision,
+        evidence: list[Evidence],
+        current_round: int,
+        max_rounds: int,
+        min_score: float,
+    ) -> QualityGrade:
+        prompt = f"""
+判断证据是否足以回答问题。recommended_action 只能是 accept、rewrite_query 或 stop。
+当前轮次：{current_round}/{max_rounds}
+问题：{query}
+路由：{decision.model_dump_json()}
+证据：{json.dumps([item.model_dump() for item in evidence], ensure_ascii=False)}
+""".strip()
+        result = self.grader.invoke(prompt)
+        # Grader 输出为空时使用确定性来源覆盖、相似度和轮次判断兜底。
+        if result is None:
+            return super().grade(
+                query=query,
+                decision=decision,
+                evidence=evidence,
+                current_round=current_round,
+                max_rounds=max_rounds,
+                min_score=min_score,
+            )
+        return result
+
+    def answer(self, query: str, evidence: list[Evidence], partial: bool = False) -> str:
+        prompt = f"""
+严格依据证据回答，不得补充证据之外的事实。每个关键结论使用 [序号] 引用。
+问题：{query}
+是否为部分证据：{partial}
+证据：{json.dumps([item.model_dump() for item in evidence], ensure_ascii=False)}
+""".strip()
+        return str(self.llm.invoke(prompt).content)
+
+
+def create_decision_engine(settings: Settings) -> DecisionEngine:
+    if settings.llm_provider == "demo":
+        return DemoDecisionEngine()
+    if settings.llm_provider == "deepseek":
+        return DeepSeekDecisionEngine(settings)
+    raise ValueError(f"不支持的 LLM_PROVIDER: {settings.llm_provider}")

app/embeddings.py

@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+import hashlib
+import math
+import re
+from typing import Protocol
+
+from app.config import Settings
+
+
+class EmbeddingProvider(Protocol):
+    @property
+    def dimension(self) -> int: ...
+
+    def embed_query(self, text: str) -> list[float]: ...
+
+    def embed_documents(self, texts: list[str]) -> list[list[float]]: ...
+
+
+class HashEmbeddingProvider:
+    """无外部模型依赖的确定性向量，仅用于课程演示和自动化测试。"""
+
+    def __init__(self, dimension: int = 256) -> None:
+        self._dimension = dimension
+
+    @property
+    def dimension(self) -> int:
+        return self._dimension
+
+    @staticmethod
+    def _features(text: str) -> list[str]:
+        normalized = re.sub(r"\s+", "", text.lower())
+        chars = list(normalized)
+        bigrams = [normalized[index : index + 2] for index in range(len(normalized) - 1)]
+        words = re.findall(r"[a-z]+\d+|\d+|[a-z]+", normalized)
+        return chars + bigrams + words
+
+    def _embed(self, text: str) -> list[float]:
+        vector = [0.0] * self._dimension
+        for feature in self._features(text):
+            # 稳定哈希保证相同文本在不同进程中生成相同演示向量。
+            digest = hashlib.blake2b(feature.encode("utf-8"), digest_size=8).digest()
+            raw = int.from_bytes(digest, "big")
+            index = raw % self._dimension
+            sign = 1.0 if raw & 1 else -1.0
+            vector[index] += sign
+
+        norm = math.sqrt(sum(value * value for value in vector))
+        if norm == 0:
+            return vector
+        # 单位化后可直接使用 COSINE 距离进行课程检索演示。
+        return [value / norm for value in vector]
+
+    def embed_query(self, text: str) -> list[float]:
+        return self._embed(text)
+
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
+        return [self._embed(text) for text in texts]
+
+
+class SentenceTransformerEmbeddingProvider:
+    def __init__(self, model_name: str) -> None:
+        try:
+            from sentence_transformers import SentenceTransformer
+        except ImportError as exc:
+            raise RuntimeError(
+                "缺少 sentence-transformers，请执行 uv sync --extra embeddings"
+            ) from exc
+
+        self._model = SentenceTransformer(model_name)
+        self._dimension = self._model.get_sentence_embedding_dimension()
+
+    @property
+    def dimension(self) -> int:
+        return int(self._dimension)
+
+    def embed_query(self, text: str) -> list[float]:
+        return self._model.encode(text, normalize_embeddings=True).tolist()
+
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
+        return self._model.encode(texts, normalize_embeddings=True).tolist()
+
+
+def create_embedding_provider(settings: Settings) -> EmbeddingProvider:
+    # Provider 工厂隔离模型选择，Milvus Store 只依赖统一向量接口。
+    if settings.embedding_provider == "hash":
+        return HashEmbeddingProvider(settings.hash_embedding_dim)
+    if settings.embedding_provider == "sentence_transformers":
+        return SentenceTransformerEmbeddingProvider(settings.embedding_model_name)
+    raise ValueError(f"不支持的 EMBEDDING_PROVIDER: {settings.embedding_provider}")

app/graph.py

@@ -0,0 +1,302 @@
+from __future__ import annotations
+
+from concurrent.futures import ThreadPoolExecutor
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from typing import Literal
+
+from langgraph.graph import END, START, StateGraph
+
+from app.config import Settings
+from app.decision_engine import DecisionEngine
+from app.schemas import RouteName, ToolResult
+from app.state import AgentState
+from app.tools import SQLQueryTool, VectorSearchTool, WebSearchTool
+
+
+def trace_event(state: AgentState, node: str, detail: dict) -> list[dict]:
+    return [
+        *state.get("trace", []),
+        {
+            "node": node,
+            "at": datetime.now(timezone.utc).isoformat(),
+            "detail": detail,
+        },
+    ]
+
+
+@dataclass
+class GraphDependencies:
+    settings: Settings
+    engine: DecisionEngine
+    vector_tool: VectorSearchTool
+    sql_tool: SQLQueryTool
+    web_tool: WebSearchTool
+
+
+def build_graph(deps: GraphDependencies):
+    def normalize_query(state: AgentState) -> dict:
+        normalized = " ".join(state["original_query"].strip().split())
+        return {
+            "current_query": normalized,
+            "retrieval_round": state.get("retrieval_round", 0),
+            "trace": trace_event(state, "normalize_query", {"query": normalized}),
+        }
+
+    def route_query(state: AgentState) -> dict:
+        decision = deps.engine.route(
+            state["current_query"], deps.settings.max_retrieval_rounds
+        )
+        return {
+            "route_decision": decision,
+            "trace": trace_event(
+                state,
+                "route_query",
+                {
+                    "intent": decision.intent,
+                    "routes": [route.value for route in decision.routes],
+                    "reason_code": decision.reason_code,
+                },
+            ),
+        }
+
+    def after_route(
+        state: AgentState,
+    ) -> Literal["clarify", "refuse", "generate", "plan"]:
+        routes = set(state["route_decision"].routes)
+        # 退出类路由优先级高于检索，防止复合输出意外触发工具。
+        if RouteName.CLARIFY in routes:
+            return "clarify"
+        if RouteName.REFUSE in routes:
+            return "refuse"
+        if RouteName.DIRECT_ANSWER in routes:
+            return "generate"
+        return "plan"
+
+    def plan_query(state: AgentState) -> dict:
+        plan = deps.engine.plan(state["current_query"], state["route_decision"])
+        return {
+            "retrieval_plan": plan,
+            "trace": trace_event(
+                state,
+                "plan_query",
+                {"steps": [step.model_dump(mode="json") for step in plan.steps]},
+            ),
+        }
+
+    def execute_step(step) -> ToolResult:
+        # Executor 只识别注册过的枚举工具；模型不能构造任意函数名。
+        if step.tool == RouteName.MILVUS_SEARCH:
+            return deps.vector_tool.invoke(
+                query=step.query,
+                policy_type=str(step.arguments.get("policy_type", "")),
+                top_k=int(step.arguments.get("top_k", 5)),
+            )
+        if step.tool == RouteName.SQL_QUERY:
+            return deps.sql_tool.invoke(
+                user_id=str(step.arguments.get("user_id", "")),
+                days=int(step.arguments.get("days", 30)),
+                product_keyword=str(step.arguments.get("product_keyword", "")),
+            )
+        if step.tool == RouteName.WEB_SEARCH:
+            requested = int(step.arguments.get("max_results", 5))
+            # 即使模型给出更大的 max_results，也不能突破程序配置上限。
+            return deps.web_tool.invoke(
+                query=step.query,
+                max_results=min(max(requested, 1), deps.settings.web_search_max_results),
+            )
+        return ToolResult(
+            status="error",
+            tool=step.tool.value,
+            error_code="UNSUPPORTED_TOOL",
+            error_message=f"未注册工具：{step.tool.value}",
+        )
+
+    def execute_plan(state: AgentState) -> dict:
+        steps = state["retrieval_plan"].steps
+        if not steps:
+            results: list[ToolResult] = []
+        elif len(steps) == 1:
+            results = [execute_step(steps[0])]
+        else:
+            # 本项目的计划均为无依赖读任务；依赖型 DAG 需按拓扑批次调度。
+            with ThreadPoolExecutor(max_workers=min(len(steps), 4)) as executor:
+                results = list(executor.map(execute_step, steps))
+
+        # ToolResult 用于控制流，Evidence 用于答案生成，二者不能混为一体。
+        evidence = [item for result in results for item in result.evidence]
+        errors = [
+            {
+                "tool": result.tool,
+                "code": result.error_code,
+                "message": result.error_message,
+            }
+            for result in results
+            if result.status == "error"
+        ]
+        # 将规划 Query、受控参数和实际结果并列记录，便于观察真实调用。
+        executed_queries = [
+            {
+                "step_id": step.id,
+                "tool": result.tool,
+                "query": step.query,
+                "arguments": step.arguments,
+                "status": result.status,
+                "latency_ms": result.latency_ms,
+            }
+            for step, result in zip(steps, results, strict=True)
+        ]
+        return {
+            "tool_results": results,
+            "executed_queries": executed_queries,
+            "evidence": evidence,
+            "errors": [*state.get("errors", []), *errors],
+            "trace": trace_event(
+                state,
+                "execute_plan",
+                {
+                    "tools": [result.tool for result in results],
+                    "statuses": [result.status for result in results],
+                    "evidence_count": len(evidence),
+                    "executed_queries": executed_queries,
+                },
+            ),
+        }
+
+    def grade_evidence(state: AgentState) -> dict:
+        current_round = state.get("retrieval_round", 0)
+        max_rounds = state["route_decision"].max_rounds
+        grade = deps.engine.grade(
+            query=state["current_query"],
+            decision=state["route_decision"],
+            evidence=state.get("evidence", []),
+            current_round=current_round,
+            max_rounds=max_rounds,
+            min_score=deps.settings.min_evidence_score,
+        )
+        # 模型只能建议 Rewrite，是否还有预算必须由程序层决定。
+        # 达到上限后强制 Stop，避免模型持续 Rewrite 导致图无限循环。
+        if (
+            grade.recommended_action == "rewrite_query"
+            and current_round >= max_rounds
+        ):
+            grade = grade.model_copy(
+                update={
+                    "sufficient": False,
+                    "recommended_action": "stop",
+                    "reason": (
+                        f"{grade.reason}；已达到最大检索轮数 {max_rounds}，"
+                        "程序层强制停止。"
+                    ),
+                }
+            )
+        return {
+            "quality_grade": grade,
+            "trace": trace_event(
+                state,
+                "grade_evidence",
+                grade.model_dump(mode="json"),
+            ),
+        }
+
+    def after_grade(state: AgentState) -> Literal["generate", "rewrite", "generate_partial"]:
+        # 条件边只消费结构化动作，不解析 grader 的自然语言 reason。
+        action = state["quality_grade"].recommended_action
+        if action == "accept":
+            return "generate"
+        if action == "rewrite_query":
+            return "rewrite"
+        return "generate_partial"
+
+    def rewrite_query(state: AgentState) -> dict:
+        rewritten = deps.engine.rewrite(
+            state["current_query"], state["quality_grade"]
+        )
+        next_round = state.get("retrieval_round", 0) + 1
+        return {
+            "current_query": rewritten,
+            "retrieval_round": next_round,
+            "trace": trace_event(
+                state,
+                "rewrite_query",
+                {"round": next_round, "rewritten_query": rewritten},
+            ),
+        }
+
+    def generate_answer(state: AgentState) -> dict:
+        answer = deps.engine.answer(
+            state["original_query"], state.get("evidence", []), partial=False
+        )
+        return {
+            "final_answer": answer,
+            "termination_reason": "evidence_accepted"
+            if state.get("evidence")
+            else "direct_answer",
+            "trace": trace_event(state, "generate_answer", {"partial": False}),
+        }
+
+    def generate_partial_answer(state: AgentState) -> dict:
+        answer = deps.engine.answer(
+            state["original_query"], state.get("evidence", []), partial=True
+        )
+        return {
+            "final_answer": answer,
+            "termination_reason": "retrieval_budget_exhausted",
+            "trace": trace_event(state, "generate_partial_answer", {"partial": True}),
+        }
+
+    def clarify(state: AgentState) -> dict:
+        return {
+            "final_answer": "请补充用户编号、商品名称或订单范围后再查询。",
+            "termination_reason": "clarification_required",
+            "trace": trace_event(state, "clarify", {}),
+        }
+
+    def refuse(state: AgentState) -> dict:
+        return {
+            "final_answer": "当前请求涉及受限数据，系统拒绝执行。",
+            "termination_reason": "security_policy",
+            "trace": trace_event(state, "refuse", {}),
+        }
+
+    builder = StateGraph(AgentState)
+    builder.add_node("normalize_query", normalize_query)
+    builder.add_node("route_query", route_query)
+    builder.add_node("plan_query", plan_query)
+    builder.add_node("execute_plan", execute_plan)
+    builder.add_node("grade_evidence", grade_evidence)
+    builder.add_node("rewrite_query", rewrite_query)
+    builder.add_node("generate_answer", generate_answer)
+    builder.add_node("generate_partial_answer", generate_partial_answer)
+    builder.add_node("clarify", clarify)
+    builder.add_node("refuse", refuse)
+
+    builder.add_edge(START, "normalize_query")
+    builder.add_edge("normalize_query", "route_query")
+    builder.add_conditional_edges(
+        "route_query",
+        after_route,
+        {
+            "clarify": "clarify",
+            "refuse": "refuse",
+            "generate": "generate_answer",
+            "plan": "plan_query",
+        },
+    )
+    builder.add_edge("plan_query", "execute_plan")
+    builder.add_edge("execute_plan", "grade_evidence")
+    builder.add_conditional_edges(
+        "grade_evidence",
+        after_grade,
+        {
+            "generate": "generate_answer",
+            "rewrite": "rewrite_query",
+            "generate_partial": "generate_partial_answer",
+        },
+    )
+    builder.add_edge("rewrite_query", "plan_query")
+    builder.add_edge("generate_answer", END)
+    builder.add_edge("generate_partial_answer", END)
+    builder.add_edge("clarify", END)
+    builder.add_edge("refuse", END)
+    return builder.compile()

app/main.py

@@ -0,0 +1,31 @@
+from __future__ import annotations
+
+from functools import lru_cache
+
+from fastapi import FastAPI
+
+from app.config import get_settings
+from app.schemas import QueryRequest, QueryResponse
+from app.service import AgenticRAGService
+
+
+app = FastAPI(
+    title="Agentic RAG Course Project",
+    version="1.0.0",
+)
+
+
+@lru_cache(maxsize=1)
+def get_service() -> AgenticRAGService:
+    return AgenticRAGService(get_settings())
+
+
+@app.get("/health")
+def health() -> dict[str, str]:
+    return {"status": "ok"}
+
+
+@app.post("/query", response_model=QueryResponse)
+def query(request: QueryRequest) -> QueryResponse:
+    return get_service().invoke(request)
+

app/milvus_store.py

@@ -0,0 +1,163 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+from pymilvus import DataType, MilvusClient
+
+from app.embeddings import EmbeddingProvider
+from app.schemas import Evidence
+
+
+@dataclass(frozen=True)
+class IndexedDocument:
+    content: str
+    source: str
+    doc_type: str
+    chunk_index: int
+    policy_type: str
+    category: str
+    metadata: dict[str, Any]
+
+
+class MilvusVectorStore:
+    def __init__(
+        self,
+        uri: str,
+        token: str,
+        collection_name: str,
+        embeddings: EmbeddingProvider,
+    ) -> None:
+        kwargs: dict[str, Any] = {"uri": uri}
+        if token:
+            kwargs["token"] = token
+        self.client = MilvusClient(**kwargs)
+        self.collection_name = collection_name
+        self.embeddings = embeddings
+
+    def is_ready(self) -> bool:
+        self.client.list_collections()
+        return True
+
+    def create_collection(self, recreate: bool = False) -> None:
+        exists = self.client.has_collection(collection_name=self.collection_name)
+        # 课程数据允许显式重建；生产环境应创建新 Collection 后切换 Alias。
+        if exists and recreate:
+            self.client.drop_collection(collection_name=self.collection_name)
+            exists = False
+        if exists:
+            self.client.load_collection(collection_name=self.collection_name)
+            return
+
+        schema = MilvusClient.create_schema(
+            auto_id=True,
+            enable_dynamic_field=False,
+        )
+        schema.add_field("id", DataType.INT64, is_primary=True)
+        schema.add_field("content", DataType.VARCHAR, max_length=8192)
+        schema.add_field("source", DataType.VARCHAR, max_length=1024)
+        schema.add_field("doc_type", DataType.VARCHAR, max_length=64)
+        schema.add_field("chunk_index", DataType.INT64)
+        schema.add_field("policy_type", DataType.VARCHAR, max_length=64)
+        schema.add_field("category", DataType.VARCHAR, max_length=64)
+        schema.add_field("metadata", DataType.JSON)
+        schema.add_field(
+            "embedding",
+            DataType.FLOAT_VECTOR,
+            # Schema 维度必须与当前 Embedding Provider 完全一致。
+            dim=self.embeddings.dimension,
+        )
+
+        index_params = self.client.prepare_index_params()
+        index_params.add_index(
+            field_name="embedding",
+            index_name="embedding_hnsw",
+            index_type="HNSW",
+            metric_type="COSINE",
+            params={"M": 16, "efConstruction": 200},
+        )
+        index_params.add_index(
+            field_name="policy_type",
+            index_name="policy_type_inverted",
+            index_type="INVERTED",
+        )
+
+        self.client.create_collection(
+            collection_name=self.collection_name,
+            schema=schema,
+            index_params=index_params,
+        )
+        self.client.load_collection(collection_name=self.collection_name)
+
+    def insert_documents(self, documents: list[IndexedDocument]) -> int:
+        if not documents:
+            return 0
+        # 先批量生成向量，再用 strict zip 防止文档与向量静默错位。
+        vectors = self.embeddings.embed_documents([item.content for item in documents])
+        rows = []
+        for item, vector in zip(documents, vectors, strict=True):
+            rows.append(
+                {
+                    "content": item.content,
+                    "source": item.source,
+                    "doc_type": item.doc_type,
+                    "chunk_index": item.chunk_index,
+                    "policy_type": item.policy_type,
+                    "category": item.category,
+                    "metadata": item.metadata,
+                    "embedding": vector,
+                }
+            )
+        self.client.insert(collection_name=self.collection_name, data=rows)
+        self.client.flush(collection_name=self.collection_name)
+        return len(rows)
+
+    def search(
+        self,
+        query: str,
+        top_k: int = 5,
+        policy_type: str = "",
+    ) -> list[Evidence]:
+        query_vector = self.embeddings.embed_query(query)
+        # Filter 只由已校验的结构化字段生成，不接收任意 Milvus 表达式。
+        if policy_type and not policy_type.replace("_", "").isalnum():
+            raise ValueError("policy_type 非法")
+        filter_expression = f'policy_type == "{policy_type}"' if policy_type else ""
+        results = self.client.search(
+            collection_name=self.collection_name,
+            data=[query_vector],
+            anns_field="embedding",
+            filter=filter_expression,
+            limit=top_k,
+            output_fields=[
+                "content",
+                "source",
+                "doc_type",
+                "chunk_index",
+                "policy_type",
+                "category",
+                "metadata",
+            ],
+            search_params={"metric_type": "COSINE", "params": {"ef": 64}},
+        )
+        evidence: list[Evidence] = []
+        # Store 层统一转换 Evidence，Graph 不依赖 PyMilvus 的原始返回结构。
+        for hit in results[0]:
+            entity = hit.get("entity", {})
+            evidence.append(
+                Evidence(
+                    source_type="milvus",
+                    source=entity.get("source", "unknown"),
+                    content=entity.get("content", ""),
+                    score=float(hit.get("distance", 0.0)),
+                    metadata={
+                        "id": hit.get("id"),
+                        "doc_type": entity.get("doc_type"),
+                        "chunk_index": entity.get("chunk_index"),
+                        "policy_type": entity.get("policy_type"),
+                        "category": entity.get("category"),
+                        **(entity.get("metadata") or {}),
+                    },
+                )
+            )
+        return evidence

app/schemas.py

@@ -0,0 +1,83 @@
+from __future__ import annotations
+
+from enum import StrEnum
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+
+class RouteName(StrEnum):
+    DIRECT_ANSWER = "direct_answer"
+    MILVUS_SEARCH = "milvus_search"
+    SQL_QUERY = "sql_query"
+    WEB_SEARCH = "web_search"
+    MULTI_SOURCE = "multi_source"
+    CLARIFY = "clarify"
+    REFUSE = "refuse"
+
+
+class RouteDecision(BaseModel):
+    needs_retrieval: bool
+    intent: str
+    routes: list[RouteName] = Field(default_factory=list)
+    requires_decomposition: bool = False
+    confidence: str = "medium"
+    reason_code: str
+    filters: dict[str, Any] = Field(default_factory=dict)
+    max_rounds: int = 2
+    fallback: str = "clarify"
+
+
+class PlanStep(BaseModel):
+    id: str
+    tool: RouteName
+    query: str
+    arguments: dict[str, Any] = Field(default_factory=dict)
+    depends_on: list[str] = Field(default_factory=list)
+
+
+class RetrievalPlan(BaseModel):
+    goal: str
+    steps: list[PlanStep]
+
+
+class Evidence(BaseModel):
+    source_type: str
+    source: str
+    content: str
+    score: float | None = None
+    metadata: dict[str, Any] = Field(default_factory=dict)
+
+
+class ToolResult(BaseModel):
+    status: str
+    tool: str
+    data: list[dict[str, Any]] = Field(default_factory=list)
+    evidence: list[Evidence] = Field(default_factory=list)
+    latency_ms: int = 0
+    retryable: bool = False
+    error_code: str | None = None
+    error_message: str | None = None
+
+
+class QualityGrade(BaseModel):
+    relevant: bool
+    sufficient: bool
+    missing_aspects: list[str] = Field(default_factory=list)
+    recommended_action: str
+    reason: str
+
+
+class QueryRequest(BaseModel):
+    query: str = Field(min_length=1, max_length=4000)
+    session_id: str = "default"
+    debug: bool = False
+
+
+class QueryResponse(BaseModel):
+    answer: str
+    citations: list[Evidence]
+    route: RouteDecision
+    executed_queries: list[dict[str, Any]] = Field(default_factory=list)
+    trace: list[dict[str, Any]] = Field(default_factory=list)
+    termination_reason: str

app/service.py

@@ -0,0 +1,82 @@
+from __future__ import annotations
+
+from app.config import Settings
+from app.decision_engine import create_decision_engine
+from app.embeddings import create_embedding_provider
+from app.graph import GraphDependencies, build_graph
+from app.milvus_store import MilvusVectorStore
+from app.schemas import QueryRequest, QueryResponse, RouteDecision
+from app.sql_store import OrderRepository
+from app.tools import (
+    SQLQueryTool,
+    TavilyWebSearchProvider,
+    VectorSearchTool,
+    VectorStore,
+    WebSearchProvider,
+    WebSearchTool,
+)
+
+
+class AgenticRAGService:
+    def __init__(
+        self,
+        settings: Settings,
+        vector_store: VectorStore | None = None,
+        web_search_provider: WebSearchProvider | None = None,
+    ) -> None:
+        self.settings = settings
+        # 测试可注入 Fake Store；生产路径才创建真实 Milvus 客户端。
+        if vector_store is None:
+            embeddings = create_embedding_provider(settings)
+            vector_store = MilvusVectorStore(
+                uri=settings.milvus_uri,
+                token=settings.milvus_token.get_secret_value(),
+                collection_name=settings.milvus_collection,
+                embeddings=embeddings,
+            )
+        if web_search_provider is None:
+            # Tavily Provider 延迟到真正调用 web_search 时才校验 Key 和依赖。
+            web_search_provider = TavilyWebSearchProvider(
+                settings.tavily_api_key.get_secret_value()
+            )
+        dependencies = GraphDependencies(
+            settings=settings,
+            engine=create_decision_engine(settings),
+            vector_tool=VectorSearchTool(vector_store),
+            sql_tool=SQLQueryTool(OrderRepository(settings.sqlite_path)),
+            web_tool=WebSearchTool(web_search_provider),
+        )
+        self.graph = build_graph(dependencies)
+
+    def invoke(self, request: QueryRequest) -> QueryResponse:
+        # 每次请求创建独立初始状态，避免跨会话共享 Evidence 或错误信息。
+        state = self.graph.invoke(
+            {
+                "original_query": request.query,
+                "current_query": request.query,
+                "session_id": request.session_id,
+                "debug": request.debug,
+                "evidence": [],
+                "tool_results": [],
+                "executed_queries": [],
+                "retrieval_round": 0,
+                "errors": [],
+                "trace": [],
+            },
+            config={"recursion_limit": 20},
+        )
+        route = state.get("route_decision")
+        if route is None:
+            route = RouteDecision(
+                needs_retrieval=False,
+                intent="internal_error",
+                reason_code="MISSING_ROUTE_DECISION",
+            )
+        return QueryResponse(
+            answer=state.get("final_answer", "系统未生成答案。"),
+            citations=state.get("evidence", []),
+            route=route,
+            executed_queries=state.get("executed_queries", []) if request.debug else [],
+            trace=state.get("trace", []) if request.debug else [],
+            termination_reason=state.get("termination_reason", "unknown"),
+        )

app/sql_store.py

@@ -0,0 +1,207 @@
+from __future__ import annotations
+
+import csv
+import re
+import sqlite3
+from datetime import date, timedelta
+from pathlib import Path
+
+from app.config import PROJECT_ROOT
+from app.schemas import Evidence
+
+
+SCHEMA_SQL = """
+CREATE TABLE IF NOT EXISTS orders (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    ordered_at TEXT NOT NULL,
+    order_id TEXT NOT NULL UNIQUE,
+    user_id TEXT NOT NULL,
+    product_name TEXT NOT NULL,
+    category TEXT NOT NULL,
+    amount REAL NOT NULL,
+    status TEXT NOT NULL
+);
+CREATE INDEX IF NOT EXISTS idx_orders_user_time
+ON orders(user_id, ordered_at);
+CREATE INDEX IF NOT EXISTS idx_orders_product
+ON orders(product_name);
+"""
+
+DEFAULT_SOURCE_PATH = PROJECT_ROOT / "data" / "source" / "orders.csv"
+ORDER_ID_PATTERN = re.compile(r"^O\d{7}$")
+USER_ID_PATTERN = re.compile(r"^U\d{4}$")
+ALLOWED_STATUSES = {"paid", "shipped", "completed", "cancelled", "refunded"}
+ACTIVE_STATUSES = ("paid", "shipped", "completed")
+
+
+def load_orders(source_path: Path) -> list[tuple[date, str, str, str, str, float, str]]:
+    if not source_path.exists():
+        raise FileNotFoundError(f"SQLite 原始订单不存在：{source_path}")
+
+    # 相对天数在导入时转换，保证“最近 30 天”样例不会随课程日期过期。
+    today = date.today()
+    rows: list[tuple[date, str, str, str, str, float, str]] = []
+    with source_path.open("r", encoding="utf-8-sig", newline="") as file:
+        reader = csv.DictReader(file)
+        required_fields = {
+            "days_ago",
+            "order_id",
+            "user_id",
+            "product_name",
+            "category",
+            "amount",
+            "status",
+        }
+        if not reader.fieldnames or not required_fields <= set(reader.fieldnames):
+            raise ValueError("orders.csv 缺少必需字段")
+
+        for line_number, item in enumerate(reader, start=2):
+            try:
+                days_ago = int(item["days_ago"])
+                amount = float(item["amount"])
+            except (TypeError, ValueError) as exc:
+                raise ValueError(f"第 {line_number} 行数值字段非法") from exc
+            if not 0 <= days_ago <= 3650:
+                raise ValueError(f"第 {line_number} 行 days_ago 超出范围")
+            if amount < 0:
+                raise ValueError(f"第 {line_number} 行 amount 不能为负数")
+
+            order_id = item["order_id"].strip().upper()
+            user_id = item["user_id"].strip().upper()
+            product_name = item["product_name"].strip()
+            category = item["category"].strip().lower()
+            status = item["status"].strip().lower()
+            if not ORDER_ID_PATTERN.fullmatch(order_id):
+                raise ValueError(f"第 {line_number} 行 order_id 非法")
+            if not USER_ID_PATTERN.fullmatch(user_id):
+                raise ValueError(f"第 {line_number} 行 user_id 非法")
+            if not product_name or not category:
+                raise ValueError(f"第 {line_number} 行商品信息为空")
+            if status not in ALLOWED_STATUSES:
+                raise ValueError(f"第 {line_number} 行 status 非法")
+
+            rows.append(
+                (
+                    today - timedelta(days=days_ago),
+                    order_id,
+                    user_id,
+                    product_name,
+                    category,
+                    amount,
+                    status,
+                )
+            )
+
+    if not rows:
+        raise ValueError("orders.csv 没有数据")
+    return rows
+
+
+def initialize_database(
+    path: Path,
+    source_path: Path | None = None,
+    reset: bool = False,
+) -> int:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    if reset and path.exists():
+        path.unlink()
+
+    # CSV 是可审计真源，SQLite 只负责课程中的精确过滤和聚合。
+    rows = load_orders(source_path or DEFAULT_SOURCE_PATH)
+    with sqlite3.connect(path) as connection:
+        connection.executescript(SCHEMA_SQL)
+        current = connection.execute("SELECT COUNT(*) FROM orders").fetchone()[0]
+        if current == 0:
+            connection.executemany(
+                """
+                INSERT INTO orders(
+                    ordered_at, order_id, user_id, product_name,
+                    category, amount, status
+                )
+                VALUES (?, ?, ?, ?, ?, ?, ?)
+                """,
+                [
+                    (
+                        item[0].isoformat(),
+                        item[1],
+                        item[2],
+                        item[3],
+                        item[4],
+                        item[5],
+                        item[6],
+                    )
+                    for item in rows
+                ],
+            )
+        return connection.execute("SELECT COUNT(*) FROM orders").fetchone()[0]
+
+
+class OrderRepository:
+    def __init__(self, path: Path) -> None:
+        self.path = path
+
+    def summarize_recent(
+        self,
+        user_id: str,
+        days: int = 30,
+        product_keyword: str = "",
+    ) -> tuple[dict, Evidence]:
+        if not self.path.exists():
+            raise RuntimeError(
+                "业务数据库不存在，请先执行 uv run python scripts/prepare_data.py"
+            )
+        normalized_user_id = user_id.strip().upper()
+        if not USER_ID_PATTERN.fullmatch(normalized_user_id):
+            raise ValueError("user_id 必须是 U 加四位数字")
+        if not 1 <= days <= 365:
+            raise ValueError("days 必须在 1 到 365 之间")
+        if len(product_keyword) > 100:
+            raise ValueError("product_keyword 过长")
+
+        start_date = (date.today() - timedelta(days=days)).isoformat()
+        normalized_product = product_keyword.strip()
+        # SQL 模板固定并使用占位符；模型不能生成 SQL 或拼接 WHERE 表达式。
+        sql = """
+            SELECT COUNT(*) AS order_count,
+                   COALESCE(SUM(amount), 0) AS total_paid,
+                   SUM(CASE WHEN amount < 99 THEN 1 ELSE 0 END) AS below_99_count,
+                   GROUP_CONCAT(order_id) AS order_ids
+            FROM orders
+            WHERE user_id = ?
+              AND ordered_at >= ?
+              AND status IN (?, ?, ?)
+              AND (? = '' OR product_name LIKE ?)
+        """
+        product_pattern = f"%{normalized_product}%" if normalized_product else ""
+        parameters = (
+            normalized_user_id,
+            start_date,
+            *ACTIVE_STATUSES,
+            normalized_product,
+            product_pattern,
+        )
+        with sqlite3.connect(self.path) as connection:
+            connection.row_factory = sqlite3.Row
+            row = connection.execute(sql, parameters).fetchone()
+
+        result = {
+            "user_id": normalized_user_id,
+            "days": days,
+            "product_keyword": normalized_product,
+            "order_count": int(row["order_count"]),
+            "total_paid": round(float(row["total_paid"]), 2),
+            "below_99_count": int(row["below_99_count"] or 0),
+            "order_ids": row["order_ids"].split(",") if row["order_ids"] else [],
+        }
+        product_scope = f"、商品包含“{normalized_product}”" if normalized_product else ""
+        evidence = Evidence(
+            source_type="sql",
+            source="orders",
+            content=(
+                f"用户 {normalized_user_id} 最近 {days} 天{product_scope}的有效订单共 "
+                f"{result['order_count']} 单，实付总额 {result['total_paid']:.2f} 元，"
+                f"其中低于 99 元 {result['below_99_count']} 单。"
+            ),
+            metadata={"query_name": "summarize_recent_orders", **result},
+        )
+        return result, evidence

app/state.py

@@ -0,0 +1,23 @@
+from __future__ import annotations
+
+from typing_extensions import TypedDict
+
+from app.schemas import Evidence, QualityGrade, RetrievalPlan, RouteDecision, ToolResult
+
+
+class AgentState(TypedDict, total=False):
+    original_query: str
+    current_query: str
+    session_id: str
+    debug: bool
+    route_decision: RouteDecision
+    retrieval_plan: RetrievalPlan
+    evidence: list[Evidence]
+    tool_results: list[ToolResult]
+    executed_queries: list[dict]
+    quality_grade: QualityGrade
+    retrieval_round: int
+    errors: list[dict]
+    trace: list[dict]
+    final_answer: str
+    termination_reason: str

app/tools.py

@@ -0,0 +1,172 @@
+from __future__ import annotations
+
+from time import perf_counter
+from typing import Protocol
+
+from app.schemas import Evidence, ToolResult
+from app.sql_store import OrderRepository
+
+
+class VectorStore(Protocol):
+    def search(
+        self,
+        query: str,
+        top_k: int = 5,
+        policy_type: str = "",
+    ) -> list[Evidence]: ...
+
+
+class WebSearchProvider(Protocol):
+    def search(self, query: str, max_results: int = 5) -> list[Evidence]: ...
+
+
+class TavilyWebSearchProvider:
+    """把 Tavily 搜索结果转换为项目统一的 Evidence。"""
+
+    def __init__(self, api_key: str) -> None:
+        self.api_key = api_key
+
+    def search(self, query: str, max_results: int = 5) -> list[Evidence]:
+        if not self.api_key:
+            raise RuntimeError("网页搜索未配置 TAVILY_API_KEY")
+        try:
+            from tavily import TavilyClient
+        except ImportError as exc:
+            raise RuntimeError(
+                "缺少网页搜索依赖，请执行 uv sync --extra test --extra web"
+            ) from exc
+
+        client = TavilyClient(api_key=self.api_key)
+        response = client.search(
+            query=query,
+            search_depth="basic",
+            topic="general",
+            max_results=max_results,
+            include_answer=False,
+            include_raw_content=False,
+        )
+
+        # 只保留可引用片段和来源，不把搜索服务生成的 answer 当作事实答案。
+        evidence: list[Evidence] = []
+        for item in response.get("results", []):
+            url = str(item.get("url", "")).strip()
+            content = str(item.get("content", "")).strip()
+            if not url or not content:
+                continue
+            title = str(item.get("title", "网页结果")).strip()
+            evidence.append(
+                Evidence(
+                    source_type="web",
+                    source=url,
+                    content=f"{title}\n{content}",
+                    score=float(item.get("score", 0.0)),
+                    metadata={
+                        "title": title,
+                        "url": url,
+                        "published_date": item.get("published_date"),
+                    },
+                )
+            )
+        return evidence
+
+
+class VectorSearchTool:
+    name = "milvus_search"
+
+    def __init__(self, store: VectorStore) -> None:
+        self.store = store
+
+    def invoke(self, query: str, policy_type: str = "", top_k: int = 5) -> ToolResult:
+        started = perf_counter()
+        try:
+            # Adapter 负责把数据源异常统一转换为 ToolResult，Graph 不捕获 SDK 异常。
+            evidence = self.store.search(query, top_k=top_k, policy_type=policy_type)
+            return ToolResult(
+                status="success" if evidence else "empty",
+                tool=self.name,
+                evidence=evidence,
+                latency_ms=int((perf_counter() - started) * 1000),
+            )
+        except Exception as exc:
+            return ToolResult(
+                status="error",
+                tool=self.name,
+                latency_ms=int((perf_counter() - started) * 1000),
+                retryable=True,
+                error_code="MILVUS_SEARCH_FAILED",
+                error_message=str(exc),
+            )
+
+
+class SQLQueryTool:
+    name = "sql_query"
+
+    def __init__(self, repository: OrderRepository) -> None:
+        self.repository = repository
+
+    def invoke(
+        self,
+        user_id: str,
+        days: int = 30,
+        product_keyword: str = "",
+    ) -> ToolResult:
+        started = perf_counter()
+        try:
+            data, evidence = self.repository.summarize_recent(
+                user_id=user_id,
+                days=days,
+                product_keyword=product_keyword,
+            )
+            return ToolResult(
+                status="success",
+                tool=self.name,
+                data=[data],
+                evidence=[evidence],
+                latency_ms=int((perf_counter() - started) * 1000),
+            )
+        except (ValueError, RuntimeError) as exc:
+            return ToolResult(
+                status="error",
+                tool=self.name,
+                latency_ms=int((perf_counter() - started) * 1000),
+                retryable=False,
+                error_code="SQL_QUERY_REJECTED",
+                error_message=str(exc),
+            )
+
+
+class WebSearchTool:
+    name = "web_search"
+
+    def __init__(self, provider: WebSearchProvider) -> None:
+        self.provider = provider
+
+    def invoke(self, query: str, max_results: int = 5) -> ToolResult:
+        started = perf_counter()
+        try:
+            evidence = self.provider.search(query, max_results=max_results)
+            return ToolResult(
+                status="success" if evidence else "empty",
+                tool=self.name,
+                data=[item.metadata for item in evidence],
+                evidence=evidence,
+                latency_ms=int((perf_counter() - started) * 1000),
+            )
+        except RuntimeError as exc:
+            return ToolResult(
+                status="error",
+                tool=self.name,
+                latency_ms=int((perf_counter() - started) * 1000),
+                retryable=False,
+                error_code="WEB_SEARCH_NOT_CONFIGURED",
+                error_message=str(exc),
+            )
+        except Exception as exc:
+            return ToolResult(
+                status="error",
+                tool=self.name,
+                latency_ms=int((perf_counter() - started) * 1000),
+                retryable=True,
+                error_code="WEB_SEARCH_FAILED",
+                error_message=str(exc),
+            )

data/documents/price_protection_policy.md

@@ -0,0 +1,20 @@
+# 商品价保政策
+
+政策类型：price_protection
+适用类目：electronics
+
+## 申请期限
+
+带有价保标识的自营电子商品可以在支付后七日内申请价保。申请时间以提交价保申请的时间为准。
+
+## 差价计算
+
+差价按照同一商品、同一规格和相同购买数量的当前可购买价格计算。优惠券、赠品、会员专享价和限时秒杀不计入普通价保。
+
+## 不支持情形
+
+订单已取消、已全额退款、商品已下架或当前活动需要额外资格时，不支持价保。第三方店铺商品以店铺规则为准。
+
+## 处理结果
+
+审核通过后，差价退回原支付账户。价保只处理价格变化，不改变原订单的退换货期限。

data/documents/return_policy.md

@@ -0,0 +1,20 @@
+# 商品退货政策
+
+政策类型：return_policy
+适用类目：all
+
+## 七日无理由退货
+
+消费者在签收后七日内可以申请无理由退货。商品必须保持完好，配件、赠品、包装和发票应一并退回。
+
+## 不适用情形
+
+已激活的手机、已拆封且影响卫生安全的入耳式耳机、定制商品和数字化商品不适用无理由退货。商品存在质量问题时不受该限制。
+
+## 质量问题处理
+
+商品存在质量问题时，用户可以提交照片、视频或检测报告。审核通过后可选择退货、换货或维修，退回运费由平台承担。
+
+## 退款时效
+
+仓库验收通过后，退款通常在一至三个工作日原路发起。银行或支付渠道的实际到账时间不计入平台处理时间。

data/documents/shipping_policy.md

@@ -0,0 +1,20 @@
+# 配送与运费政策
+
+政策类型：shipping_policy
+适用类目：all
+
+## 基础运费规则
+
+单笔有效订单实付金额满 99 元免基础运费；不足 99 元收取 8 元基础运费。取消订单不参与金额计算。
+
+## 偏远地区与特殊商品
+
+偏远地区、超大件商品和冷链商品可能产生附加运费。附加运费不因订单满 99 元而自动免除，以结算页显示为准。
+
+## 拆单与合单
+
+同一订单因仓库不同被拆分发货时，不重复收取基础运费。多个独立订单不能合并计算免运费门槛。
+
+## 延迟配送
+
+超过承诺送达时间仍未签收时，用户可以申请物流催办。是否补偿由订单承诺时效和延迟原因决定。

data/source/orders.csv

@@ -0,0 +1,12 @@
+days_ago,order_id,user_id,product_name,category,amount,status
+1,O2026001,U1001,XPhone 15 Pro,phone,6999.00,paid
+5,O2026002,U1001,AirBuds Pro,headphone,1299.00,shipped
+12,O2026003,U1001,USB-C Charger,accessory,199.00,completed
+20,O2026004,U1001,SmartWatch S,wearable,1899.00,completed
+8,O2026005,U1001,Phone Case,accessory,59.00,cancelled
+35,O2026006,U1001,Tablet Mini,tablet,3299.00,completed
+2,O2026007,U1002,AirBuds Pro,headphone,1299.00,paid
+7,O2026008,U1002,Phone Case,accessory,59.00,completed
+16,O2026009,U1002,XPhone 15 Pro,phone,6999.00,completed
+3,O2026010,U1003,SmartWatch S,wearable,1899.00,shipped
+28,O2026011,U1003,USB-C Charger,accessory,199.00,completed

infra/milvus/docker-compose.yml

@@ -0,0 +1,67 @@
+services:
+  etcd:
+    container_name: milvus-etcd
+    image: quay.io/coreos/etcd:v3.5.25
+    environment:
+      - ETCD_AUTO_COMPACTION_MODE=revision
+      - ETCD_AUTO_COMPACTION_RETENTION=1000
+      - ETCD_QUOTA_BACKEND_BYTES=4294967296
+      - ETCD_SNAPSHOT_COUNT=50000
+    volumes:
+      - ./volumes/etcd:/etcd
+    command: >-
+      etcd -advertise-client-urls=http://etcd:2379
+      -listen-client-urls http://0.0.0.0:2379
+      --data-dir /etcd
+    healthcheck:
+      test: ["CMD", "etcdctl", "endpoint", "health"]
+      interval: 30s
+      timeout: 20s
+      retries: 3
+
+  minio:
+    container_name: milvus-minio
+    image: minio/minio:RELEASE.2024-05-28T17-19-04Z
+    environment:
+      MINIO_ACCESS_KEY: minioadmin
+      MINIO_SECRET_KEY: minioadmin
+    ports:
+      - "9000:9000"
+      - "9001:9001"
+    volumes:
+      - ./volumes/minio:/minio_data
+    command: minio server /minio_data --console-address ":9001"
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:9000/minio/health/live"]
+      interval: 30s
+      timeout: 20s
+      retries: 3
+
+  standalone:
+    container_name: milvus-standalone
+    image: milvusdb/milvus:v2.6.18
+    command: ["milvus", "run", "standalone"]
+    security_opt:
+      - seccomp:unconfined
+    environment:
+      MINIO_REGION: us-east-1
+      ETCD_ENDPOINTS: etcd:2379
+      MINIO_ADDRESS: minio:9000
+    volumes:
+      - ./volumes/milvus:/var/lib/milvus
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:9091/healthz"]
+      interval: 30s
+      start_period: 90s
+      timeout: 20s
+      retries: 3
+    ports:
+      - "19530:19530"
+      - "9091:9091"
+    depends_on:
+      - etcd
+      - minio
+
+networks:
+  default:
+    name: milvus

pyproject.toml

@@ -0,0 +1,30 @@
+[build-system]
+requires = ["setuptools>=75"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "agentic-rag-course-project"
+version = "1.0.0"
+description = "电商售后 Agentic RAG 实战：LangGraph + Milvus + SQLite + Web Search"
+requires-python = ">=3.11,<3.13"
+dependencies = [
+  "fastapi==0.139.0",
+  "langgraph==1.2.9",
+  "pydantic-settings==2.14.2",
+  "pymilvus==2.6.16",
+  "uvicorn[standard]==0.51.0",
+]
+
+[project.optional-dependencies]
+deepseek = ["langchain-openai>=1.0,<2"]
+web = ["tavily-python>=0.7,<1"]
+embeddings = ["sentence-transformers>=5.0,<6"]
+test = ["httpx==0.28.1", "pytest==9.1.1"]
+
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["app*"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+addopts = "-q"

requirements.txt

@@ -0,0 +1,2 @@
+-e .[test]
+

scripts/prepare_data.py

@@ -0,0 +1,98 @@
+from __future__ import annotations
+
+import re
+from pathlib import Path
+
+from app.config import Settings, get_settings
+from app.embeddings import create_embedding_provider
+from app.milvus_store import IndexedDocument, MilvusVectorStore
+from app.sql_store import initialize_database
+
+
+def split_document(path: Path) -> list[IndexedDocument]:
+    text = path.read_text(encoding="utf-8").strip()
+    if not text:
+        raise ValueError(f"Milvus 原始文档为空：{path}")
+
+    metadata_lines = {
+        line.split("：", 1)[0]: line.split("：", 1)[1].strip()
+        for line in text.splitlines()
+        if "：" in line
+    }
+    policy_type = metadata_lines.get("政策类型", "")
+    category = metadata_lines.get("适用类目", "")
+    if not policy_type or not category:
+        raise ValueError(f"文档缺少政策类型或适用类目：{path.name}")
+
+    # 课程文档以二级标题作为稳定 Chunk 边界，保留章节语义和来源定位。
+    sections = [
+        section.strip()
+        for section in re.split(r"(?=^## )", text, flags=re.MULTILINE)
+        if section.strip()
+    ]
+    title = text.splitlines()[0].lstrip("# ")
+    return [
+        IndexedDocument(
+            content=section,
+            source=path.name,
+            doc_type="commerce_policy",
+            chunk_index=index,
+            policy_type=policy_type,
+            category=category,
+            metadata={"title": title},
+        )
+        for index, section in enumerate(sections)
+    ]
+
+
+def prepare_sqlite(settings: Settings) -> int:
+    return initialize_database(
+        path=settings.sqlite_path,
+        source_path=settings.orders_source_path,
+        reset=True,
+    )
+
+
+def prepare_milvus(settings: Settings) -> tuple[int, int, int]:
+    document_paths = sorted(settings.documents_path.glob("*.md"))
+    if not document_paths:
+        raise FileNotFoundError(f"Milvus 原始文档不存在：{settings.documents_path}")
+
+    # 先完成所有文档校验和切分，再连接 Milvus，避免半批脏数据。
+    documents = [
+        document
+        for path in document_paths
+        for document in split_document(path)
+    ]
+    embeddings = create_embedding_provider(settings)
+    store = MilvusVectorStore(
+        uri=settings.milvus_uri,
+        token=settings.milvus_token.get_secret_value(),
+        collection_name=settings.milvus_collection,
+        embeddings=embeddings,
+    )
+    # 演示数据采用全量重建，确保 Schema、索引和向量维度保持一致。
+    store.create_collection(recreate=True)
+    inserted = store.insert_documents(documents)
+    return len(document_paths), inserted, embeddings.dimension
+
+
+def main() -> None:
+    settings = get_settings()
+    print("数据准备开始")
+    print(f"- SQLite 原始订单：{settings.orders_source_path}")
+    print(f"- Milvus 原始文档：{settings.documents_path}")
+
+    sqlite_rows = prepare_sqlite(settings)
+    document_count, chunk_count, dimension = prepare_milvus(settings)
+
+    print(f"SQLite 完成：database={settings.sqlite_path}，rows={sqlite_rows}")
+    print(
+        "Milvus 完成："
+        f"collection={settings.milvus_collection}，"
+        f"documents={document_count}，chunks={chunk_count}，dimension={dimension}"
+    )
+
+
+if __name__ == "__main__":
+    main()

scripts/smoke_test.py

@@ -0,0 +1,30 @@
+from app.config import get_settings
+from app.schemas import QueryRequest
+from app.service import AgenticRAGService
+
+
+def main() -> None:
+    service = AgenticRAGService(get_settings())
+    response = service.invoke(
+        QueryRequest(
+            query=(
+                "统计 U1001 最近 30 天有效订单数、实付总额和低于 99 元的订单数，"
+                "并结合内部运费规则说明是否存在需支付基础运费的订单。"
+            ),
+            debug=True,
+        )
+    )
+    print(response.model_dump_json(indent=2))
+    if response.termination_reason != "evidence_accepted":
+        raise SystemExit("Smoke Test 未通过：证据未被接受。")
+    if {item.source_type for item in response.citations} != {"milvus", "sql"}:
+        raise SystemExit("Smoke Test 未通过：未同时获得 Milvus 与 SQL 证据。")
+    if {item["tool"] for item in response.executed_queries} != {
+        "milvus_search",
+        "sql_query",
+    }:
+        raise SystemExit("Smoke Test 未通过：实际工具 Query 记录不完整。")
+
+
+if __name__ == "__main__":
+    main()

scripts/wait_for_milvus.py

@@ -0,0 +1,32 @@
+from __future__ import annotations
+
+import time
+
+from pymilvus import MilvusClient
+
+from app.config import get_settings
+
+
+def main() -> None:
+    settings = get_settings()
+    kwargs = {"uri": settings.milvus_uri}
+    token = settings.milvus_token.get_secret_value()
+    if token:
+        kwargs["token"] = token
+
+    deadline = time.monotonic() + 120
+    last_error: Exception | None = None
+    while time.monotonic() < deadline:
+        try:
+            client = MilvusClient(**kwargs)
+            client.list_collections()
+            print(f"Milvus 已就绪：{settings.milvus_uri}")
+            return
+        except Exception as exc:
+            last_error = exc
+            time.sleep(2)
+    raise SystemExit(f"Milvus 在 120 秒内未就绪：{last_error}")
+
+
+if __name__ == "__main__":
+    main()

tests/test_api.py

@@ -0,0 +1,10 @@
+from fastapi.testclient import TestClient
+
+from app.main import app
+
+
+def test_health_endpoint() -> None:
+    response = TestClient(app).get("/health")
+    assert response.status_code == 200
+    assert response.json() == {"status": "ok"}
+

tests/test_graph.py

@@ -0,0 +1,196 @@
+from pathlib import Path
+
+from app.config import Settings
+from app.decision_engine import DemoDecisionEngine
+from app.schemas import Evidence, QualityGrade, QueryRequest, RouteName
+from app.service import AgenticRAGService
+from app.sql_store import initialize_database
+
+
+class FakeVectorStore:
+    def __init__(self, return_evidence: bool = True) -> None:
+        self.return_evidence = return_evidence
+        self.calls = 0
+
+    def search(
+        self,
+        query: str,
+        top_k: int = 5,
+        policy_type: str = "",
+    ) -> list[Evidence]:
+        self.calls += 1
+        if not self.return_evidence:
+            return []
+        return [
+            Evidence(
+                source_type="milvus",
+                source="shipping_policy.md",
+                content="单笔有效订单实付满 99 元免基础运费。",
+                score=0.91,
+                metadata={"policy_type": policy_type},
+            )
+        ]
+
+
+class FakeWebSearchProvider:
+    def __init__(self) -> None:
+        self.calls = 0
+
+    def search(self, query: str, max_results: int = 5) -> list[Evidence]:
+        self.calls += 1
+        return [
+            Evidence(
+                source_type="web",
+                source="https://brand.example/xphone-15-pro-notice",
+                content="品牌官网发布了 XPhone 15 Pro 最新服务公告。",
+                score=0.88,
+                metadata={"title": "XPhone 15 Pro 最新公告"},
+            )
+        ]
+
+
+class AlwaysRewriteDecisionEngine(DemoDecisionEngine):
+    """模拟模型忽略检索预算、每轮都要求继续改写。"""
+
+    def grade(self, *args, **kwargs) -> QualityGrade:
+        return QualityGrade(
+            relevant=False,
+            sufficient=False,
+            missing_aspects=["网页证据"],
+            recommended_action="rewrite_query",
+            reason="模拟模型持续要求 Rewrite",
+        )
+
+
+def create_settings(sqlite_path: Path, max_rounds: int = 2) -> Settings:
+    return Settings(
+        llm_provider="demo",
+        sqlite_path=sqlite_path,
+        max_retrieval_rounds=max_rounds,
+        min_evidence_score=0.35,
+    )
+
+
+def test_multi_source_graph(tmp_path) -> None:
+    database = tmp_path / "shop.db"
+    initialize_database(database, reset=True)
+    service = AgenticRAGService(create_settings(database), FakeVectorStore())
+
+    response = service.invoke(
+        QueryRequest(
+            query="统计 U1001 最近 30 天订单，并结合内部运费规则说明是否包邮",
+            debug=True,
+        )
+    )
+
+    assert response.route.routes == [RouteName.MILVUS_SEARCH, RouteName.SQL_QUERY]
+    assert response.termination_reason == "evidence_accepted"
+    assert {item.source_type for item in response.citations} == {"milvus", "sql"}
+    assert {item["tool"] for item in response.executed_queries} == {
+        "milvus_search",
+        "sql_query",
+    }
+    assert all(item["query"] for item in response.executed_queries)
+    assert [event["node"] for event in response.trace] == [
+        "normalize_query",
+        "route_query",
+        "plan_query",
+        "execute_plan",
+        "grade_evidence",
+        "generate_answer",
+    ]
+
+
+def test_empty_retrieval_stops_after_budget(tmp_path) -> None:
+    database = tmp_path / "shop.db"
+    initialize_database(database, reset=True)
+    vector_store = FakeVectorStore(return_evidence=False)
+    service = AgenticRAGService(
+        create_settings(database, max_rounds=1), vector_store
+    )
+
+    response = service.invoke(
+        QueryRequest(query="耳机拆封后还能七日无理由退货吗？", debug=True)
+    )
+
+    assert vector_store.calls == 2
+    assert response.termination_reason == "retrieval_budget_exhausted"
+    assert "未获得" in response.answer
+    assert [event["node"] for event in response.trace].count("rewrite_query") == 1
+
+
+def test_graph_forces_stop_when_model_ignores_rewrite_budget(
+    tmp_path, monkeypatch
+) -> None:
+    database = tmp_path / "shop.db"
+    initialize_database(database, reset=True)
+    vector_store = FakeVectorStore(return_evidence=False)
+    monkeypatch.setattr(
+        "app.service.create_decision_engine",
+        lambda settings: AlwaysRewriteDecisionEngine(),
+    )
+    service = AgenticRAGService(
+        create_settings(database, max_rounds=1),
+        vector_store,
+    )
+
+    response = service.invoke(
+        QueryRequest(query="耳机退货政策", debug=True)
+    )
+
+    assert vector_store.calls == 2
+    assert response.termination_reason == "retrieval_budget_exhausted"
+    assert response.trace[-2]["node"] == "grade_evidence"
+    assert response.trace[-2]["detail"]["recommended_action"] == "stop"
+
+
+def test_three_source_graph(tmp_path) -> None:
+    database = tmp_path / "shop.db"
+    initialize_database(database, reset=True)
+    web_provider = FakeWebSearchProvider()
+    service = AgenticRAGService(
+        create_settings(database),
+        FakeVectorStore(),
+        web_provider,
+    )
+
+    response = service.invoke(
+        QueryRequest(
+            query=(
+                "统计 U1001 最近 30 天购买 XPhone 15 Pro 的订单金额，"
+                "结合内部退货政策和品牌官网最新公告给出售后建议"
+            ),
+            debug=True,
+        )
+    )
+
+    assert response.route.routes == [
+        RouteName.MILVUS_SEARCH,
+        RouteName.SQL_QUERY,
+        RouteName.WEB_SEARCH,
+    ]
+    assert web_provider.calls == 1
+    assert {item.source_type for item in response.citations} == {
+        "milvus",
+        "sql",
+        "web",
+    }
+    assert response.termination_reason == "evidence_accepted"
+    assert len(response.executed_queries) == 3
+    sql_call = next(
+        item for item in response.executed_queries if item["tool"] == "sql_query"
+    )
+    assert sql_call["arguments"]["user_id"] == "U1001"
+    assert sql_call["arguments"]["product_keyword"] == "XPhone 15 Pro"
+
+
+def test_clarification_does_not_call_vector_store(tmp_path) -> None:
+    database = tmp_path / "shop.db"
+    initialize_database(database, reset=True)
+    vector_store = FakeVectorStore()
+    service = AgenticRAGService(create_settings(database), vector_store)
+
+    response = service.invoke(QueryRequest(query="我最近 30 天买了几单？"))
+
+    assert response.termination_reason == "clarification_required"
+    assert vector_store.calls == 0

tests/test_milvus_store.py

@@ -0,0 +1,116 @@
+from app.embeddings import HashEmbeddingProvider
+from app.milvus_store import IndexedDocument, MilvusVectorStore
+
+
+class FakeSchema:
+    def __init__(self) -> None:
+        self.fields = []
+
+    def add_field(self, *args, **kwargs) -> None:
+        self.fields.append((args, kwargs))
+
+
+class FakeIndexParams:
+    def __init__(self) -> None:
+        self.indexes = []
+
+    def add_index(self, **kwargs) -> None:
+        self.indexes.append(kwargs)
+
+
+class FakeMilvusClient:
+    last_instance = None
+
+    def __init__(self, **kwargs) -> None:
+        self.kwargs = kwargs
+        self.created = None
+        self.inserted = []
+        self.search_args = None
+        self.index_params = FakeIndexParams()
+        FakeMilvusClient.last_instance = self
+
+    @classmethod
+    def create_schema(cls, **kwargs):
+        return FakeSchema()
+
+    def list_collections(self):
+        return []
+
+    def has_collection(self, **kwargs):
+        return False
+
+    def prepare_index_params(self):
+        return self.index_params
+
+    def create_collection(self, **kwargs):
+        self.created = kwargs
+
+    def load_collection(self, **kwargs):
+        return None
+
+    def insert(self, **kwargs):
+        self.inserted = kwargs["data"]
+
+    def flush(self, **kwargs):
+        return None
+
+    def search(self, **kwargs):
+        self.search_args = kwargs
+        return [
+            [
+                {
+                    "id": 1,
+                    "distance": 0.88,
+                    "entity": {
+                        "content": "耳机拆封且影响卫生安全时不适用无理由退货。",
+                        "source": "return_policy.md",
+                        "doc_type": "commerce_policy",
+                        "chunk_index": 1,
+                        "policy_type": "return_policy",
+                        "category": "all",
+                        "metadata": {"title": "商品退货政策"},
+                    },
+                }
+            ]
+        ]
+
+
+def test_milvus_schema_insert_and_filtered_search(monkeypatch) -> None:
+    monkeypatch.setattr("app.milvus_store.MilvusClient", FakeMilvusClient)
+    embeddings = HashEmbeddingProvider(128)
+    store = MilvusVectorStore(
+        uri="http://localhost:19530",
+        token="",
+        collection_name="test_docs",
+        embeddings=embeddings,
+    )
+    store.create_collection(recreate=True)
+
+    client = FakeMilvusClient.last_instance
+    assert client.created["collection_name"] == "test_docs"
+    assert {item["index_type"] for item in client.index_params.indexes} == {
+        "HNSW",
+        "INVERTED",
+    }
+
+    inserted = store.insert_documents(
+        [
+            IndexedDocument(
+                content="耳机拆封后不适用无理由退货",
+                source="return_policy.md",
+                doc_type="commerce_policy",
+                chunk_index=1,
+                policy_type="return_policy",
+                category="all",
+                metadata={},
+            )
+        ]
+    )
+    assert inserted == 1
+    assert len(client.inserted[0]["embedding"]) == 128
+
+    evidence = store.search("耳机拆封后能退货吗", top_k=4, policy_type="return_policy")
+    assert client.search_args["filter"] == 'policy_type == "return_policy"'
+    assert client.search_args["limit"] == 4
+    assert evidence[0].source_type == "milvus"
+    assert evidence[0].score == 0.88

tests/test_router.py

@@ -0,0 +1,176 @@
+import sys
+from pathlib import Path
+from types import SimpleNamespace
+
+import pytest
+
+from app.config import PROJECT_ROOT, Settings
+from app.decision_engine import (
+    DeepSeekDecisionEngine,
+    DemoDecisionEngine,
+    create_decision_engine,
+)
+from app.schemas import RouteName
+from app.tools import TavilyWebSearchProvider, WebSearchTool
+
+
+def test_multi_source_route() -> None:
+    decision = DemoDecisionEngine().route(
+        "统计 U1001 最近 30 天订单，并结合内部运费规则说明是否包邮",
+        max_rounds=2,
+    )
+    assert decision.requires_decomposition is True
+    assert decision.routes == [RouteName.MILVUS_SEARCH, RouteName.SQL_QUERY]
+    assert decision.filters["user_id"] == "U1001"
+    assert decision.filters["policy_type"] == "shipping_policy"
+
+
+def test_vector_only_route() -> None:
+    decision = DemoDecisionEngine().route(
+        "耳机拆封后还能七日无理由退货吗？",
+        max_rounds=2,
+    )
+    assert decision.routes == [RouteName.MILVUS_SEARCH]
+    assert decision.filters["policy_type"] == "return_policy"
+
+
+def test_sql_only_route() -> None:
+    decision = DemoDecisionEngine().route(
+        "U1001 最近 30 天有几笔有效订单，实付总额是多少？",
+        max_rounds=2,
+    )
+    assert decision.routes == [RouteName.SQL_QUERY]
+    assert decision.filters["user_id"] == "U1001"
+
+
+def test_clarify_route() -> None:
+    decision = DemoDecisionEngine().route("我最近 30 天买了几单？", max_rounds=2)
+    assert decision.routes == [RouteName.CLARIFY]
+    assert decision.needs_retrieval is False
+
+
+def test_restricted_route() -> None:
+    decision = DemoDecisionEngine().route("导出所有买家手机号", max_rounds=2)
+    assert decision.routes == [RouteName.REFUSE]
+
+
+def test_deepseek_defaults_and_api_key_guard() -> None:
+    settings = Settings(
+        _env_file=None,
+        llm_provider="deepseek",
+        deepseek_api_key="",
+    )
+    assert settings.deepseek_base_url == "https://api.deepseek.com"
+    assert settings.deepseek_model_name == "deepseek-v4-flash"
+
+    with pytest.raises(RuntimeError, match="DEEPSEEK_API_KEY"):
+        create_decision_engine(settings)
+
+
+def test_web_search_route() -> None:
+    decision = DemoDecisionEngine().route(
+        "查询品牌官网关于 XPhone 15 Pro 的最新公告",
+        max_rounds=2,
+    )
+    assert decision.routes == [RouteName.WEB_SEARCH]
+    assert decision.reason_code == "CURRENT_PUBLIC_INFORMATION_REQUIRED"
+
+
+def test_three_source_route() -> None:
+    decision = DemoDecisionEngine().route(
+        "统计 U1001 最近 30 天购买 XPhone 15 Pro 的订单金额，"
+        "结合内部退货政策和品牌官网最新公告给出售后建议",
+        max_rounds=2,
+    )
+    assert decision.routes == [
+        RouteName.MILVUS_SEARCH,
+        RouteName.SQL_QUERY,
+        RouteName.WEB_SEARCH,
+    ]
+    assert decision.requires_decomposition is True
+
+
+def test_web_search_without_key_returns_isolated_error() -> None:
+    tool = WebSearchTool(TavilyWebSearchProvider(api_key=""))
+    result = tool.invoke("查询官网最新公告")
+
+    assert result.status == "error"
+    assert result.error_code == "WEB_SEARCH_NOT_CONFIGURED"
+    assert result.retryable is False
+
+
+def test_secret_values_are_masked_in_settings_repr() -> None:
+    settings = Settings(
+        _env_file=None,
+        deepseek_api_key="deepseek-test-secret",
+        tavily_api_key="tavily-test-secret",
+    )
+
+    rendered = repr(settings)
+    assert "deepseek-test-secret" not in rendered
+    assert "tavily-test-secret" not in rendered
+    assert settings.deepseek_api_key.get_secret_value() == "deepseek-test-secret"
+    assert settings.tavily_api_key.get_secret_value() == "tavily-test-secret"
+
+
+def test_deepseek_structured_output_disables_thinking(monkeypatch) -> None:
+    created_clients: list[dict] = []
+
+    class FakeChatOpenAI:
+        def __init__(self, **kwargs) -> None:
+            created_clients.append(kwargs)
+
+        def with_structured_output(self, schema, method: str):
+            return SimpleNamespace(schema=schema, method=method)
+
+    monkeypatch.setitem(
+        sys.modules,
+        "langchain_openai",
+        SimpleNamespace(ChatOpenAI=FakeChatOpenAI),
+    )
+    settings = Settings(
+        _env_file=None,
+        llm_provider="deepseek",
+        deepseek_api_key="test-key",
+        deepseek_answer_thinking=True,
+    )
+
+    DeepSeekDecisionEngine(settings)
+
+    assert created_clients[0]["extra_body"] == {
+        "thinking": {"type": "enabled"}
+    }
+    assert created_clients[1]["extra_body"] == {
+        "thinking": {"type": "disabled"}
+    }
+
+
+def test_deepseek_grader_falls_back_when_structured_output_is_none() -> None:
+    engine = DeepSeekDecisionEngine.__new__(DeepSeekDecisionEngine)
+    engine.grader = SimpleNamespace(invoke=lambda prompt: None)
+    decision = DemoDecisionEngine().route("耳机退货政策", max_rounds=1)
+
+    grade = engine.grade(
+        query="耳机退货政策",
+        decision=decision,
+        evidence=[],
+        current_round=1,
+        max_rounds=1,
+        min_score=0.35,
+    )
+
+    assert grade.recommended_action == "stop"
+    assert grade.sufficient is False
+
+
+def test_relative_data_paths_are_anchored_to_project_root() -> None:
+    settings = Settings(
+        _env_file=None,
+        sqlite_path=Path("data/custom-shop.db"),
+        documents_path=Path("data/custom-documents"),
+    )
+
+    assert settings.sqlite_path == (PROJECT_ROOT / "data/custom-shop.db").resolve()
+    assert settings.documents_path == (
+        PROJECT_ROOT / "data/custom-documents"
+    ).resolve()

tests/test_sql_store.py

@@ -0,0 +1,51 @@
+from datetime import date
+
+from app.sql_store import OrderRepository, initialize_database, load_orders
+
+
+def test_parameterized_order_summary(tmp_path) -> None:
+    path = tmp_path / "shop.db"
+    total = initialize_database(path, reset=True)
+    assert total == 11
+
+    data, evidence = OrderRepository(path).summarize_recent("U1001", days=30)
+    assert data["order_count"] == 4
+    assert data["total_paid"] == 10396.0
+    assert data["below_99_count"] == 0
+    assert evidence.source_type == "sql"
+    assert evidence.metadata["days"] == 30
+    assert date.today().isoformat() not in evidence.content
+
+
+def test_invalid_days_are_rejected(tmp_path) -> None:
+    path = tmp_path / "shop.db"
+    initialize_database(path, reset=True)
+    repository = OrderRepository(path)
+
+    try:
+        repository.summarize_recent("U1001", days=1000)
+    except ValueError as exc:
+        assert "1 到 365" in str(exc)
+    else:
+        raise AssertionError("非法 days 未被拒绝")
+
+
+def test_sqlite_rows_are_loaded_from_csv(tmp_path) -> None:
+    source = tmp_path / "orders.csv"
+    source.write_text(
+        "days_ago,order_id,user_id,product_name,category,amount,status\n"
+        "1,O9000001,U9999,Demo Phone,phone,999.00,paid\n"
+        "2,O9000002,U9999,Demo Case,accessory,49.00,completed\n",
+        encoding="utf-8",
+    )
+
+    rows = load_orders(source)
+    assert len(rows) == 2
+
+    database = tmp_path / "shop.db"
+    total = initialize_database(database, source_path=source, reset=True)
+    data, _ = OrderRepository(database).summarize_recent("U9999", days=30)
+    assert total == 2
+    assert data["order_count"] == 2
+    assert data["total_paid"] == 1048.0
+    assert data["below_99_count"] == 1

uv.lock

@@ -0,0 +1,1996 @@
+version = 1
+revision = 3
+requires-python = ">=3.11, <3.13"
+resolution-markers = [
+    "python_full_version >= '3.12' and sys_platform == 'win32'",
+    "python_full_version < '3.12' and sys_platform == 'win32'",
+    "python_full_version >= '3.12' and sys_platform == 'emscripten'",
+    "python_full_version < '3.12' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+
+[[package]]
+name = "agentic-rag-course-project"
+version = "1.0.0"
+source = { editable = "." }
+dependencies = [
+    { name = "fastapi" },
+    { name = "langgraph" },
+    { name = "pydantic-settings" },
+    { name = "pymilvus" },
+    { name = "uvicorn", extra = ["standard"] },
+]
+
+[package.optional-dependencies]
+deepseek = [
+    { name = "langchain-openai" },
+]
+embeddings = [
+    { name = "sentence-transformers" },
+]
+test = [
+    { name = "httpx" },
+    { name = "pytest" },
+]
+web = [
+    { name = "tavily-python" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "fastapi", specifier = "==0.139.0" },
+    { name = "httpx", marker = "extra == 'test'", specifier = "==0.28.1" },
+    { name = "langchain-openai", marker = "extra == 'deepseek'", specifier = ">=1.0,<2" },
+    { name = "langgraph", specifier = "==1.2.9" },
+    { name = "pydantic-settings", specifier = "==2.14.2" },
+    { name = "pymilvus", specifier = "==2.6.16" },
+    { name = "pytest", marker = "extra == 'test'", specifier = "==9.1.1" },
+    { name = "sentence-transformers", marker = "extra == 'embeddings'", specifier = ">=5.0,<6" },
+    { name = "tavily-python", marker = "extra == 'web'", specifier = ">=0.7,<1" },
+    { name = "uvicorn", extras = ["standard"], specifier = "==0.51.0" },
+]
+provides-extras = ["deepseek", "web", "embeddings", "test"]
+
+[[package]]
+name = "annotated-doc"
+version = "0.0.4"
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