AI创想

标题: LangGraph使用 [打印本页]

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作者: 米落枫    时间: 2 小时前
标题: LangGraph使用
作者：大得369

1. pip install langgraph --upgrade

复制代码

1. from langchain_core.prompts import ChatPromptTemplate
2. from langchain_core.output_parsers import StrOutputParser
3. from langchain_openai import ChatOpenAI
4. from langchain_core.tools import tool
5. from langgraph.graph import StateGraph, END
6. from typing import TypedDict
7. import os
9. # ===================== 配置 =====================
10. os.environ["DEEPSEEK_API_KEY"]="sk-"
12. llm = ChatOpenAI(model="deepseek-v4-pro",
13.     api_key=os.environ["DEEPSEEK_API_KEY"],
14.     base_url="https://api.deepseek.com",
15.     temperature=0.2,
16.     max_tokens=4096,
17.     streaming=True
18. )
19. parser = StrOutputParser()# ===================== MCP 工具 =====================
20. @tool
21. def mcp_create_file(file_path: str, content: str) -> str:
22.     """MCP 文件创建工具"""
23.     try:
24.         with open(file_path, "w", encoding="utf-8") as f:
25.             f.write(content)return f"[MCP] 文件已保存：{file_path}"
26.     except Exception as e:
27.         return f"[MCP] 错误：{str(e)}"# ===================== 定义状态（LangGraph 核心） =====================
28. class WorkState(TypedDict):
29.     user_task: str
30.     analyse_result: str
31.     code_result: str
32.     optimize_result: str
33.     summary_result: str
36. # ===================== 提示词 =====================
37. prompt_analyse = ChatPromptTemplate.from_messages([("system", "资深后端工程师，精准拆解开发需求，梳理实现思路"),
38.     ("user", "需求内容：{user_task}")])
39. chain_analyse = prompt_analyse | llm | parser
41. prompt_code = ChatPromptTemplate.from_messages([("system", "根据需求思路编写完整可运行代码，规范整洁"),
42.     ("user", "需求思路：{analyse_result}")])
43. chain_code = prompt_code | llm | parser
45. prompt_optimize = ChatPromptTemplate.from_messages([("system", "优化代码性能、可读性、异常处理"),
46.     ("user", "原始代码：{code_result}")])
47. chain_optimize = prompt_optimize | llm | parser
49. prompt_summary = ChatPromptTemplate.from_messages([("system", "总结功能，并自动保存代码"),
50.     ("user", "优化后代码：{optimize_result}")])
51. chain_summary = prompt_summary | llm | parser
54. # ===================== 流式输出 =====================
55. def stream_output(chain, input_data):
56.     result =""forchunkin chain.stream(input_data):
57.         print(chunk, end="", flush=True)
58.         result += chunk
59.     print("\n")return result
62. # ===================== LangGraph 节点函数 =====================
63. def node_analyse(state: WorkState):
64.     print("===== 1.需求解析中 =====\n")
65.     res = stream_output(chain_analyse, {"user_task": state["user_task"]})return{"analyse_result": res}
68. def node_code(state: WorkState):
69.     print("===== 2.生成代码中 =====\n")
70.     res = stream_output(chain_code, {"analyse_result": state["analyse_result"]})return{"code_result": res}
73. def node_optimize(state: WorkState):
74.     print("===== 3.代码优化中 =====\n")
75.     res = stream_output(chain_optimize, {"code_result": state["code_result"]})return{"optimize_result": res}
78. def node_summary(state: WorkState):
79.     print("===== 4.最终总结 + 自动保存文件 =====\n")
80.     res = stream_output(chain_summary, {"optimize_result": state["optimize_result"]})# MCP 保存
81.     print(mcp_create_file.invoke({"file_path":"output_code2.html", "content": state["optimize_result"]}))return{"summary_result": res}# ===================== 构建 LangGraph 流程图 =====================
82. builder = StateGraph(WorkState)# 添加节点
83. builder.add_node("analyse", node_analyse)
84. builder.add_node("code", node_code)
85. builder.add_node("optimize", node_optimize)
86. builder.add_node("summary", node_summary)# 流程连线
87. builder.set_entry_point("analyse")
88. builder.add_edge("analyse", "code")
89. builder.add_edge("code", "optimize")
90. builder.add_edge("optimize", "summary")
91. builder.add_edge("summary", END)# 编译图
92. graph = builder.compile()# ===================== 运行 =====================if __name__ =="__main__":
93.     task ="写个漂亮的登录页面，html"
94.     result = graph.invoke({"user_task": task})
95.     print("\n✅ LangGraph 工作流执行完成！")

复制代码

有记忆，简单智能体
[code]from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from langgraph.graph import StateGraph, END
from typing import TypedDict, List, Dict
import os
import json
import datetime

# ===================== 配置 =====================
os.environ["DEEPSEEK_API_KEY"]="sk-"

llm = ChatOpenAI(model="deepseek-v4-pro",
    api_key=os.environ["DEEPSEEK_API_KEY"],
    base_url="https://api.deepseek.com",
    temperature=0.2,
    max_tokens=2048,
    streaming=True
)
parser = StrOutputParser()# 闲聊专用轻量对话链
chat_prompt = ChatPromptTemplate.from_messages([("system", "你是友好AI助手，简洁自然聊天，记住上下文"),
    ("user", "历史上下文：{memory}\n用户：{query}")])
chat_chain = chat_prompt | llm | parser

# ===================== 本地记忆管理（永久保存） =====================
MEMORY_PATH ="agent_memory.json"

def load_memory() -> List[Dict]:
    if os.path.exists(MEMORY_PATH):
        with open(MEMORY_PATH, "r", encoding="utf-8") as f:
            return json.load(f)return[]

def save_memory(task: str, result: str):
    memory = load_memory()
    memory.append({"time": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
        "task": task,
        "result": result
    })
    with open(MEMORY_PATH, "w", encoding="utf-8") as f:
        json.dump(memory, f, ensure_ascii=False, indent=2)

def get_memory_prompt() -> str:
    memory = load_memory()if not memory:
        return"无历史记录"
    text ="\n".join([f"用户：{i['task']} 助手：{i['result']}"foriin memory[-5:]])return text

# 智能判断：是否为编程开发类需求
def is_dev_task(text: str) -> bool:
    dev_key ={"写代码", "编写", "html", "css", "js", "python", "java", "页面", "接口", "脚本", "功能", "程序", "搭建", "开发", "前端", "后端"}return any(k in text.lower()forkin dev_key)# ===================== MCP 文件工具 =====================
@tool
def mcp_create_file(file_path: str, content: str) -> str:
    """创建并保存代码文件到本地"""
    try:
        with open(file_path, "w", encoding="utf-8") as f:
            f.write(content)return f"[MCP] 已保存：{file_path}"
    except Exception as e:
        return f"[MCP] 错误：{str(e)}"# ===================== LangGraph 状态 =====================
class WorkState(TypedDict):
    user_task: str
    memory: str
    analyse_result: str
    code_result: str
    optimize_result: str
    summary_result: str

# ===================== 工作流提示词 =====================
prompt_analyse = ChatPromptTemplate.from_messages([("system", "你有记忆，能记住历史对话。\n{memory}\n资深工程师，拆解需求"),
    ("user", "需求：{user_task}")])
chain_analyse = prompt_analyse | llm | parser

prompt_code = ChatPromptTemplate.from_messages([("system", "直接输出完整干净代码，无多余解释"),
    ("user", "{analyse_result}")])
chain_code = prompt_code | llm | parser

prompt_optimize = ChatPromptTemplate.from_messages([("system", "优化代码，保持可运行，直接输出"),
    ("user", "{code_result}")])
chain_optimize = prompt_optimize | llm | parser

prompt_summary = ChatPromptTemplate.from_messages([("system", "总结结果，并保存文件"),
    ("user", "{optimize_result}")])
chain_summary = prompt_summary | llm | parser

# ===================== 流式输出 =====================
def stream_output(chain, input_data):
    result =""forchunkin chain.stream(input_data):
        print(chunk, end="", flush=True)
        result += chunk
    print("\n")return result

# ===================== LangGraph 节点 =====================
def node_analyse(state: WorkState):
    print("===== 1. 分析需求 =====")
    res = stream_output(chain_analyse, {"user_task": state["user_task"],
        "memory": state["memory"]})return{"analyse_result": res}

def node_code(state: WorkState):
    print("===== 2. 生成代码 =====")
    res = stream_output(chain_code, {"analyse_result": state["analyse_result"]})return{"code_result": res}

def node_optimize(state: WorkState):
    print("===== 3. 优化代码 =====")
    res = stream_output(chain_optimize, {"code_result": state["code_result"]})return{"optimize_result": res}

def node_summary(state: WorkState):
    print("===== 4. 总结 + 保存 =====")
    res = stream_output(chain_summary, {"optimize_result": state["optimize_result"]})
    print(mcp_create_file.invoke({"file_path":"output.html",
        "content": state["optimize_result"]}))return{"summary_result": res}# ===================== 构建流程图 =====================
builder = StateGraph(WorkState)
builder.add_node("analyse", node_analyse)
builder.add_node("code", node_code)
builder.add_node("optimize", node_optimize)
builder.add_node("summary", node_summary)

builder.set_entry_point("analyse")
builder.add_edge("analyse", "code")
builder.add_edge("code", "optimize")
builder.add_edge("optimize", "summary")
builder.add_edge("summary", END)

graph = builder.compile()# ===================== 交互主逻辑（智能分流） =====================if __name__ =="__main__":
    print("✅ AI 编程助手已启动（输入 exit 退出）")
    print("✅ 闲聊自动聊天，开发需求自动走工作流\n")while True:
        user_input = input("你：")if user_input.lower()in["exit", "quit", "q"]:
            print("再见！")break

        mem_text = get_memory_prompt()if is_dev_task(user_input):
            res_data = graph.invoke({"user_task": user_input,
                "memory": mem_text
            })
            final_reply = res_data["summary_result"]
        else:
            print("助手：", end="", flush=True)
            final_reply = stream_output(chat_chain, {"memory": mem_text, "query": user_input})

        save_memory(user_input, final_reply)
        print("\n

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