初始化

This commit is contained in:
2026-09-06 16:54:18 +08:00
parent 64a51f2567
commit 8efcd72eb2
8 changed files with 2709 additions and 55 deletions

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@@ -32,3 +32,6 @@ UPLOAD_DIR=./uploads
# 缓存配置
CACHE_TTL_SECONDS=300
# AI配置
DEEPSEEK_API_KEY=api_key

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@@ -143,6 +143,17 @@ class Settings(BaseSettings):
description="缓存过期时间(秒)"
)
# ---------- AI配置 ----------
DEEPSEEK_API_KEY: str = Field(
default="sk-d3e11a4229744fd29a8468e7df072a4b",
description="deepseek模型api_key"
)
TAVILY_API_KEY: str = Field(
default="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",
description="tavily搜索接口api_key"
)
# ---------- 计算属性 ----------
@property
def mysql_url(self) -> str:

0
lc/__init__.py Normal file
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71
lc/chef_agent.py Normal file
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@@ -0,0 +1,71 @@
import os
import sqlite3
from typing import cast, Optional
from langchain.agents import create_agent, AgentState
from langchain.agents.middleware import SummarizationMiddleware
from langchain.agents.middleware.summarization import ContextMessages
from langchain.chat_models import init_chat_model
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import HumanMessage
from langchain_core.runnables import RunnableConfig
from langgraph.checkpoint.sqlite import SqliteSaver # 需要安装langgraph-checkpoint-sqlite
from langchain_tavily import TavilySearch
from core.config.settings import settings
# 初始化模型 需要多模态大模型 deepseek已支持
llm_model = init_chat_model(
model="deepseek-v4-flash",
model_provider="deepseek",
api_key=settings.DEEPSEEK_API_KEY
)
# 定义工具 使用tavily搜索工具 langchain-tavily
search_tool = TavilySearch(
tavily_api_key=settings.TAVILY_API_KEY,
max_results=5, # 最大搜索结果条数
topic="general"
)
# 定义记忆策略 使用SummarizationMiddleware摘要策略中间件
summary = SummarizationMiddleware(
model=cast(BaseChatModel, llm_model), # 消息摘要的记忆管理策略的模型
trigger=cast(ContextMessages, ("messages", 10)), # 触发策略的条件
keep=cast(ContextMessages, ("messages", 5)) # 触发策略后保留的消息条数
)
# 创建数据库目录(如果不存在)
os.makedirs("sqlite", exist_ok=True)
# 创建 SQLite 数据库连接 check_same_thread=False 是为了确保在多线程环境下的安全性
conn = sqlite3.connect("sqlite/checkpoints.db", check_same_thread=False)
# 初始化 checkpointer
checkpointer = SqliteSaver(conn)
# 自动建表
checkpointer.setup()
# agent提示词
system_prompt = """
你是一名有名的国宴厨师。收到用户提供的食材照片或清单后,按照以下流程步骤操作:
1.识别和评估食材:若用户提供照片,首先辨别所有可见食材,基于食材的外观状态,评估其新鲜度和可用量,整理出一份“可用食材清单”。
2.智能食谱检索优先调用search_tool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与烹饪难度这2个维度对检索到的候选食谱进行量化打分并根据得分进行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成一份结构清晰的建议报告,要包括食谱信息、得分、推荐理由、食谱的参考图片,帮助用户快速做出决策。
"""
# 定义thread_config 用于记忆存储分组
thread_config: RunnableConfig = {"configurable": {"thread_id": "2"}}
# 创建agent
agent = create_agent(
model=cast(BaseChatModel, llm_model), # 指定类型 避免idea报错
tools=[search_tool],
checkpointer=checkpointer,
middleware=[summary],
system_prompt=system_prompt
)
if __name__ == "__main__":
question = input("> ")
res = agent.invoke({"messages": [HumanMessage(content=question)]}, thread_config)
print(res)

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lc/deepseek.ipynb Normal file
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@@ -0,0 +1,194 @@
{
"cells": [
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-09-03T09:14:58.535236Z",
"start_time": "2026-09-03T09:14:57.335343800Z"
}
},
"cell_type": "code",
"source": [
"from openai import OpenAI\n",
"from openai.types.chat import (\n",
" ChatCompletionSystemMessageParam,\n",
" ChatCompletionUserMessageParam,\n",
" ChatCompletionAssistantMessageParam\n",
")"
],
"id": "255e9b078be17a5f",
"outputs": [],
"execution_count": 1
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-09-03T09:19:09.642201600Z",
"start_time": "2026-09-03T09:19:09.628764300Z"
}
},
"cell_type": "code",
"source": [
"client = OpenAI(\n",
" api_key='sk-4bdcdad5d4cd4856bc0c308c3f74eb22',\n",
" base_url='https://api.deepseek.com'\n",
")"
],
"id": "6f8aeb64ad6aca1f",
"outputs": [],
"execution_count": 6
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-09-03T09:17:52.034706300Z",
"start_time": "2026-09-03T09:17:52.023338600Z"
}
},
"cell_type": "code",
"source": [
"messages: list[ChatCompletionSystemMessageParam | ChatCompletionUserMessageParam | ChatCompletionAssistantMessageParam] = [\n",
" ChatCompletionSystemMessageParam(role=\"system\", content=\"你是一个ai助手所有回答使用中文\")\n",
"]"
],
"id": "4530b047d03a8ab3",
"outputs": [],
"execution_count": 3
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": [
"# 第一轮对话\n",
"print(\"===========\")\n",
"messages.append(ChatCompletionUserMessageParam(role=\"user\", content=\"你好,我是大哥\"))\n",
"\n",
"response = client.chat.completions.create(\n",
" model='deepseek-v4-flash',\n",
" messages=messages,\n",
" stream=False\n",
")"
],
"id": "a42f7a8dbdfa3b8c"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-09-03T08:51:19.785432800Z",
"start_time": "2026-09-03T08:51:19.759474700Z"
}
},
"cell_type": "code",
"source": [
"# print(response.model_dump_json())\n",
"print(response.choices[0].message.content)"
],
"id": "fac34b2742f048e0",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"大哥你好我是你的AI助手随时听候差遣。今天有什么需要帮忙的吗\n"
]
}
],
"execution_count": 12
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-09-03T08:51:41.715208300Z",
"start_time": "2026-09-03T08:51:36.483133100Z"
}
},
"cell_type": "code",
"source": [
"# 第二轮对话\n",
"messages.append(ChatCompletionAssistantMessageParam(role=\"assistant\", content=response.choices[0].message.content))\n",
"messages.append(ChatCompletionUserMessageParam(role=\"user\", content=\"你还记得我吗\"))\n",
"response = client.chat.completions.create(\n",
" model='deepseek-v4-flash',\n",
" messages=messages,\n",
")\n",
"print(response.choices[0].message.content)"
],
"id": "3c9fa1f10b75c256",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"大哥,这个问题您刚刚才问过呢,我当然记得您!您就是“大哥”嘛,我怎么会忘?\n",
"\n",
"虽然我的记忆不能跨对话永久保存,但只要咱们在这个对话里,您说的每句话、问的每个问题,我都记得一清二楚——包括您刚才已经问过一次“还记得我吗”,我当时也解释过啦。\n",
"\n",
"您现在又问一遍,是在考验我,还是想看看我是不是“脸盲”呀?哈哈,放心吧!在这段对话期间,您永远是我的大哥,随叫随到,有事您吩咐!😄\n"
]
}
],
"execution_count": 14
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-09-03T09:19:38.275536400Z",
"start_time": "2026-09-03T09:19:31.090446900Z"
}
},
"cell_type": "code",
"source": [
"messages.append(ChatCompletionUserMessageParam(role=\"user\", content=\"你好,明天合肥天气怎么样\"))\n",
"\n",
"response = client.chat.completions.create(\n",
" model='deepseek-v4-flash',\n",
" messages=messages,\n",
" stream=False\n",
")\n",
"print(response.choices[0].message.content)"
],
"id": "ea34cc88d92b2832",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"你好!很抱歉,由于我无法实时联网获取最新的气象数据,所以不能直接为你提供明天合肥的准确天气预报。\n",
"\n",
"不过,你可以通过以下几种方式快速查到最准确的信息:\n",
"\n",
"1. 打开手机自带的“天气”应用,添加并定位到“合肥”。\n",
"2. 在搜索引擎(如百度)中直接搜索“**合肥明天天气**”,或其他(例如“墨迹天气”)\n",
"3. 访问“中国天气网”www.weather.com.cn输入“合肥”即可查询。\n",
"\n",
"另外,由于我无法预知你目前的实际日期,建议你在查询时留意一下,如果是明天出发,记得顺带看一眼**后天**的预报,以便更好地安排行程。祝生活愉快!\n"
]
}
],
"execution_count": 7
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

167
lc/lc.ipynb Normal file
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@@ -0,0 +1,167 @@
{
"cells": [
{
"cell_type": "code",
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2026-09-05T01:34:44.550869800Z",
"start_time": "2026-09-05T01:34:44.535351500Z"
}
},
"source": [
"from typing import cast, Literal\n",
"\n",
"from langchain.agents.middleware.summarization import ContextMessages\n",
"from langchain.chat_models import init_chat_model\n",
"from langchain.agents import create_agent\n",
"from langchain_core.runnables import RunnableConfig\n",
"from langgraph.checkpoint.memory import InMemorySaver\n",
"from langchain.agents.middleware import SummarizationMiddleware\n",
"\n",
"from core.config.settings import settings\n",
"from langchain.tools import tool\n",
"from langchain.messages import HumanMessage\n",
"from langchain_core.language_models import BaseChatModel"
],
"outputs": [],
"execution_count": 15
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-09-05T01:37:24.572533800Z",
"start_time": "2026-09-05T01:37:24.555523100Z"
}
},
"cell_type": "code",
"source": [
"# 初始化模型\n",
"my_model = init_chat_model(\n",
" model=\"deepseek-v4-flash\",\n",
" api_key=settings.DEEPSEEK_API_KEY,\n",
")\n",
"\n",
"# 初始化checkpointer 记忆管理的存储方式\n",
"checkpointer = InMemorySaver()\n",
"\n",
"# 初始化记忆策略中间件\n",
"middleware = SummarizationMiddleware(\n",
" model=cast(BaseChatModel, my_model), # 消息摘要的记忆管理策略的模型\n",
" trigger=cast(ContextMessages, (\"messages\", 3)), # 触发策略的条件\n",
" keep=cast(ContextMessages, (\"messages\", 1)) # 触发策略后保留的消息条数\n",
")"
],
"id": "b905be968f5c7761",
"outputs": [],
"execution_count": 22
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-09-05T01:37:26.422888800Z",
"start_time": "2026-09-05T01:37:26.408643600Z"
}
},
"cell_type": "code",
"source": [
"# 初始化agent\n",
"agent = create_agent(\n",
" model=cast(BaseChatModel, my_model),\n",
" checkpointer=InMemorySaver(), # 短期记忆 通过thread_id进行记忆分组\n",
" middleware=[middleware]\n",
")"
],
"id": "cb5ae2fa4aeaf130",
"outputs": [],
"execution_count": 23
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-09-05T01:37:59.930524700Z",
"start_time": "2026-09-05T01:37:27.968100300Z"
}
},
"cell_type": "code",
"source": [
"config: RunnableConfig = {\"configurable\": {\"thread_id\": \"thread_1\"}}\n",
"agent.invoke({\"messages\": [HumanMessage(\"你好,我是胖哥\")]}, config)\n",
"agent.invoke({\"messages\": [HumanMessage(\"我喜欢吃美食\")]}, config)\n",
"agent.invoke({\"messages\": [HumanMessage(\"我喜欢运动\")]}, config)\n",
"\n",
"result = agent.invoke({\"messages\": [HumanMessage(\"你还记得我吗\")]}, config)\n",
"# print(result)\n",
"for message in result['messages']:\n",
" message.pretty_print()"
],
"id": "1ed1327c973d75cc",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"================================\u001B[1m Human Message \u001B[0m=================================\n",
"\n",
"Here is a summary of the conversation to date:\n",
"\n",
"## SESSION INTENT\n",
"\n",
"开放式、友好的中文闲聊会话,无具体交付物。用户自称“胖哥”,目前已透露两大爱好:美食与运动。整体目标是延续话题、维持轻松氛围,进一步了解他的偏好,从而围绕美食/运动展开聊天或提供推荐。\n",
"\n",
"## SUMMARY\n",
"\n",
"- 用户为“胖哥”,须以中文称呼;全会话均使用中文。\n",
"- 话题线索已在两条线上推进:\n",
" 1. **美食**:胖哥说“我喜欢吃美食”。助手此前已问他偏好哪些菜系(川菜/粤菜/湘菜)以及喜欢哪种类型(街边小吃、家常菜还是精致餐饮),并邀请他分享最近特别喜欢的一道菜。胖哥尚未回答此组问题。\n",
" 2. **运动(新增)**:胖哥随后说“我喜欢运动”。助手将美食与运动联系起来称赞(爱吃又会动、搭配健康),并追问:喜欢哪种运动——健身房撸铁、户外跑步骑行,还是打篮球羽毛球等对抗性项目;同时主动提出可以推荐运动后补充能量的美食搭配。\n",
"- 尚无任何结论、决定或策略形成;没有选项被否决。\n",
"- 需要注意的是,胖哥对“美食偏好”相关问题尚未作答,该线索仍处于待回应状态。\n",
"\n",
"## ARTIFACTS\n",
"\n",
"None.\n",
"\n",
"## NEXT STEPS\n",
"\n",
"- 等待胖哥回复:他喜欢哪种运动(健身房/户外/球类对抗等)。\n",
"- 得到答复后顺势深入聊天:讨论该运动,可结合他未答复的那组问题,推荐适合运动后的营养美食搭配,或顺带再把菜系/餐饮类型偏好问出来,延长话题。\n",
"- 不要重复寒暄,也不要重复之前已问过的两组问题(菜系类型与偏好风格、运动类型选择)。\n",
"- 保持中文、热情随意的口吻继续对话。\n",
"================================\u001B[1m Human Message \u001B[0m=================================\n",
"\n",
"你还记得我吗\n",
"==================================\u001B[1m Ai Message \u001B[0m==================================\n",
"\n",
"胖哥,这话说的——当然记得你呀!爱美食又爱运动,能吃会练,这反差感可太让人印象深刻了。咱俩这不正聊到运动嘛,我还等着听你细说呢。 \n",
"\n",
"不过不急,你先说说最近一次痛快出汗是啥时候?是去健身房撸铁,还是户外疯跑了一圈?反正不管哪种,你要是一会儿饿了,我脑子里的运动后美食搭配可已经在排队了😄\n"
]
}
],
"execution_count": 24
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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@@ -1,11 +1,19 @@
[project]
name = "lotteryserver"
version = "0.1.0"
requires-python = ">=3.14"
requires-python = ">=3.13"
dependencies = [
"apscheduler==3.11.3",
"fastapi>=0.141.1",
"ipykernel>=7.3.0",
"langchain>=1.4.0",
"langchain-core==1.6.2",
"langchain-deepseek==1.1.0",
"langchain-tavily==0.2.18",
"langgraph>=1.2.11",
"langgraph-checkpoint-sqlite==3.1.1",
"notebook>=7.6.2",
"openai==3.8.0",
"pydantic==2.13.5",
"pydantic-settings==2.15.0",
"pymysql==1.2.0",

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