refactor(config):manage API_KEY vai .env in project root

08_langgraph_basics/02_langsmith_trace.py

@@ -1,19 +1,11 @@
-# pip install --pre -U langchain langchain-openai
-from dotenv import load_dotenv
-import os
-
-
-load_dotenv()
-langchain_api_key = os.getenv("LANGCHAIN_API_KEY")
-
-
+from config import OPENAI_API_KEY,LANGCHAIN_API_KEY
 from typing import TypedDict
 from langgraph.graph import StateGraph,END,START
-
+import os
 
 os.environ["LANGCHAIN_TRACING_V2"] = "true" # 总开关，决定启用追踪功能
 os.environ["LANGCHAIN_PROJECT"] = "my_demo" # 自定义项目名
-os.environ["LANGCHAIN_API_KEY"] = langchain_api_key
+os.environ["LANGCHAIN_API_KEY"] = LANGCHAIN_API_KEY
 
 
 # 1. 定义State(状态) -- 白板上只有一个字段"count"

08_langgraph_basics/03_conditional_router.py

@@ -1,24 +1,19 @@
-from dotenv import load_dotenv
-import os
-
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-langchain_api_key = os.getenv("LANGCHAIN_API_KEY")
-
-from langchain.schema import HumanMessage
+from config import OPENAI_API_KEY,LANGCHAIN_API_KEY
+from langchain_core.messages import HumanMessage
 from langchain.tools import tool
 from langchain_openai import ChatOpenAI
 from langgraph.graph import StateGraph, MessagesState, END,START
 from langgraph.prebuilt import ToolNode
+import os
 
 os.environ["LANGCHAIN_TRACING_V2"] = "true" # 总开关，决定启用追踪功能
 os.environ["LANGCHAIN_PROJECT"] = "demo01" # 自定义项目名
-os.environ["LANGCHAIN_API_KEY"] = langchain_api_key
+os.environ["LANGCHAIN_API_KEY"] = LANGCHAIN_API_KEY
 
 # LLM配置
 llm = ChatOpenAI(
     model="deepseek-chat",
-    api_key=api_key,
+    api_key=OPENAI_API_KEY,
     base_url="https://api.deepseek.com"
 )
 

08_langgraph_basics/04_agent_with_memory.py

@@ -1,11 +1,6 @@
-from dotenv import load_dotenv
 import os
-
-load_dotenv()
-api_key = os.getenv("OPENAI_API_KEY")
-langchain_api_key = os.getenv("LANGCHAIN_API_KEY")
-
-from langchain.schema import HumanMessage
+from config import OPENAI_API_KEY,LANGCHAIN_API_KEY
+from langchain_core.messages import HumanMessage
 from langchain.tools import tool
 from langchain_openai import ChatOpenAI
 from langgraph.graph import StateGraph, MessagesState, END,START
@@ -15,12 +10,12 @@ from langgraph.checkpoint.memory import MemorySaver
 
 os.environ["LANGCHAIN_TRACING_V2"] = "true" # 总开关，决定启用追踪功能
 os.environ["LANGCHAIN_PROJECT"] = "demo02" # 自定义项目名
-os.environ["LANGCHAIN_API_KEY"] = langchain_api_key
+os.environ["LANGCHAIN_API_KEY"] = LANGCHAIN_API_KEY
 
 # LLM配置
 llm = ChatOpenAI(
     model="deepseek-chat",
-    api_key=api_key,
+    api_key=OPENAI_API_KEY,
     base_url="https://api.deepseek.com"
 )
 

08_langgraph_basics/README.md

@@ -97,5 +97,7 @@
 ---
 
 ### 💡 **建议**
-
-先运行 `01` 和 `02` 理解 LangGraph 底层机制，再重点掌握 `03` 和 `04` —— 它们构成了后续所有进阶 Agent（多工具、人工干预、自定义状态等）的**标准范式**。
+- 尝试扩展 `04_agent_with_memory.py`，添加更多自定义工具（如RAG检索工具）
+- 实验不同的条件路由逻辑，实现更复杂的Agent决策路径
+- 使用LangSmith深入分析和优化Agent的推理过程
+- 尝试实现多Agent协作系统，通过LangGraph连接多个专业化Agent