feat:初始化工程

.gitignore

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+# Python 字节码文件
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C 扩展
+*.so
+
+# 分发/打包
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# 虚拟环境
+venv/
+env/
+ENV/
+.env
+.venv
+
+# 测试
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Django 相关
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+media/
+
+# PyCharm IDE
+.idea/
+*.iml
+*.iws
+*.ipr
+
+# VS Code
+.vscode/
+*.code-workspace
+.history/
+
+# 其他
+.DS_Store
+
+logs/
+packages/

Dockerfile

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+FROM python:3.10-slim
+WORKDIR /app
+RUN ln -sf /usr/share/zoneinfo/Asia/Shanghai /etc/localtime
+RUN echo 'Asia/Shanghai' > /etc/timezone
+COPY ./packages /app/packages
+COPY requirements.txt /app/
+RUN pip install --no-cache-dir --no-index --find-links=/app/packages -r requirements.txt
+COPY . /app/
+EXPOSE 8000
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8300"]
+# pip download -r requirements.txt -d ./packages --only-binary=:all: --platform manylinux2014_x86_64 -i https://pypi.tuna.tsinghua.edu.cn/simple

agent.py

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+from langchain.agents import create_agent
+from langchain.chat_models import init_chat_model
+import sqlite3
+
+from langchain_tavily import TavilySearch
+from langgraph.checkpoint.sqlite import SqliteSaver
+
+# 连接sqlite
+connection = sqlite3.connect("chief.db", check_same_thread=False)
+# 初始化checkpointer
+checkpointer = SqliteSaver(connection)
+# 自动建表
+checkpointer.setup()
+
+# web搜索工具，使用tavily作为web搜索工具
+web_search = TavilySearch(
+    tavily_api_key="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",
+    max_results=5,
+    topic="general"
+)
+
+system_prompt = """
+    你是一名私人厨师。收到用户提供的清单后，请按以下流程操作：
+    1.识别和评估食材：根据用户提供的信息，整理出一份“当前可用食材清单”。
+    2.智能食谱检索：优先调用 web_search 工具，以“可用食材清单”为核心关键词，查找可行菜谱。
+    3.多维度评估与排序：从营养价值和制作难度两个维度对检索到的候选食谱进行量化打分，并根据得分排序，制作简单且营养丰富的排名靠前。
+    4.结构化方案输出：把排序后的食谱整理为一份结构清晰的建议报告，要包含食谱信息、得分、推荐理由、食谱的参考图片，帮助用户快速做出决策。
+
+    请严格按照流程，优先调用 web_search 工具搜索食谱，搜索不到的情况下才能自己发挥。
+    """
+
+model = init_chat_model(
+    model="qwen3-max-2026-01-23",
+    model_provider="openai",
+    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+    api_key="sk-52bcd98e9c1d45908437c4e8706eefff",
+    temperature=1.5,
+)
+
+agent = create_agent(
+    model=model,  # 模型
+    tools=[web_search],  # 工具
+    checkpointer=checkpointer,  # 记忆
+    system_prompt=system_prompt  # 系统提示词
+)

main.py

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+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from router import router
+
+# 初始化FastAPI
+app = FastAPI(
+    title="Personal Chief API",
+    description="私厨",
+    version="0.1.0"
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+app.include_router(router)

model.py

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+from pydantic import BaseModel
+
+class ChatRequest(BaseModel):
+    message: str

requirements.txt

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+fastapi~=0.136.3
+uvicorn~=0.48.0
+langchain~=1.3.2
+langchain-openai~=1.2.2
+langchain-tavily~=0.2.18
+langgraph~=1.2.2
+langgraph-checkpoint-sqlite~=3.1.0
+pydantic~=2.12.5
+langchain-core~=1.4.0

router.py

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+import uuid
+
+from fastapi import APIRouter
+from fastapi.responses import StreamingResponse
+
+from model import ChatRequest
+from service import get_messages, clear_messages, search_recipes
+
+router = APIRouter()
+
+
+@router.post("/chat/stream")
+async def chat_endpoint(request: ChatRequest):
+    """流式对话"""
+    return StreamingResponse(
+        search_recipes(request.message, str(uuid.uuid4())),
+        media_type="text/event-stream"
+    )
+
+
+@router.get("/chat/messages")
+async def get_chat_messages(thread_id: str):
+    """获取历史消息"""
+    messages = get_messages(thread_id)
+    return {"messages": messages}
+
+
+@router.delete("/chat/messages")
+async def clear_chat_messages(thread_id: str):
+    """清空历史消息"""
+    clear_messages(thread_id)
+    return {"success": True}

service.py

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+from langchain_core.messages import HumanMessage, AIMessageChunk, AIMessage
+
+from agent import agent, checkpointer
+
+
+async def search_recipes(prompt: str, thread_id: str):
+    """调用agent搜索食谱"""
+    print(f"[用户]: {prompt}, thread_id: {thread_id}")
+    try:
+        message = HumanMessage(content=prompt)
+
+        # 流式调用Agent
+        for chunk, metadata in agent.stream(
+                {"messages": [message]},
+                {"configurable": {"thread_id": thread_id}},
+                stream_mode="messages"
+        ):
+            if isinstance(chunk, AIMessageChunk) and chunk.content:
+                yield chunk.content
+
+    except Exception as e:
+        print(f"\n[错误]: {str(e)}")
+        yield "信息检索失败，试试看手动输入食物列表？"
+
+
+# 清空会话
+def clear_messages(thread_id: str):
+    """清空会话"""
+    print(f"清空历史消息，thread_id: {thread_id}")
+    checkpointer.delete_thread(thread_id)
+
+
+# 查询会话历史
+def get_messages(thread_id: str) -> list[dict[str, str]]:
+    """获取会话历史"""
+    print(f"获取历史消息，thread_id: {thread_id}")
+
+    # 根据 thread_id 查询 checkpoint
+    checkpoint = checkpointer.get({"configurable": {"thread_id": thread_id}})
+
+    # 如果不存在，返回空列表
+    if not checkpoint:
+        return []
+
+    # 安全获取 messages
+    channel_values = checkpoint.get("channel_values")
+    if not channel_values:
+        return []
+
+    messages = channel_values.get("messages", [])
+    if not messages:
+        return []
+
+    # 转换消息格式
+    result = []
+    for msg in messages:
+        if not msg.content:
+            continue
+
+        if isinstance(msg, HumanMessage):
+            result.append({"role": "user", "content": msg.content})
+        elif isinstance(msg, AIMessage):
+            result.append({"role": "assistant", "content": msg.content})
+
+    return result