当前位置：首页 > news >正文

由浅入深使用LangGraph创建一个Agent工作流

news 2025/8/3 10:54:15

创建一个简单的工作流：Start ——> 节点1(固定输入输出) ——> End

from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict
from typing import Annotatedhistory_messages = [SystemMessage(content="你是一个聊天助理"),HumanMessage(content="你好,你能介绍下你自己吗？"),AIMessage(content="我是一个小模型聊天助理"),
]
messages = history_messages + [HumanMessage(content="今天天气怎么样？")]
class MyState(TypedDict):messages:Annotated[list,""]def node1(state:MyState):return {"messages": AIMessage(content="今天天气很好")}graph_builder = StateGraph(MyState)
graph_builder.add_node("node1", node1)
graph_builder.add_edge(START, "node1")
graph_builder.add_edge("node1", END)
graph = graph_builder.compile()
response = graph.stream({"messages": messages})
for event in response:print(event)

创建一个简单的工作流：Start ——> 节点1(固定输入输出) ——> 节点2(固定输入输出) ——> End

同时数据存到Mastate的数组里传递给下一个节点

from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict
from typing import Annotated
from langgraph.graph.message import add_messageshistory_messages = [SystemMessage(content="你是一个聊天助理"),HumanMessage(content="你好,你能介绍下你自己吗？"),AIMessage(content="我是一个小模型聊天助理"),
]
messages = history_messages + [HumanMessage(content="今天天气怎么样？")]
class MyState(TypedDict):messages:Annotated[list,add_messages]def node1(state:MyState):return {"messages": AIMessage(content="今天天气很好")}def node2(state:MyState):return {"messages": AIMessage(content="明天天气也很好")}graph_builder = StateGraph(MyState)
graph_builder.add_node("node1", node1)
graph_builder.add_node("node2", node2)
graph_builder.add_edge("node1", "node2")
graph_builder.add_edge(START, "node1")
graph_builder.add_edge("node2", END)
graph = graph_builder.compile()
response = graph.stream({"messages": messages})
for event in response:print(event)

创建一个简单的会话工作流：Start ——> 会话节点1 ——> End

from langchain.chat_models import init_chat_model
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict
from typing import Annotated
from langgraph.graph.message import add_messagesclass MyState(TypedDict):messages: Annotated[list, add_messages]llm = init_chat_model("deepseek-r1:7b",model_provider="ollama")def chatbot(state: MyState):return {"messages": llm.invoke(state["messages"])}graph_builder = StateGraph(MyState)
graph_builder.add_node("chatbot", chatbot)
graph_builder.add_edge(START, "chatbot")
graph_builder.add_edge("chatbot", END)
graph = graph_builder.compile()
response = graph.stream({"messages": "你好"})
for event in response:if "chatbot" in event:value = event["chatbot"]if "messages" in value and isinstance(value["messages"], AIMessage):print(event["chatbot"]["messages"].content)

用langChain创建一个简单的Rag :

需要再安装两个包：

pip install pypdf

pip install dashscope

from langchain_community.embeddings import DashScopeEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import PyPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter# 加载文档
loader = PyPDFLoader("./data/你的PDF文档.pdf")
pages = loader.load_and_split()# 文档切片
text_splitter = RecursiveCharacterTextSplitter(chunk_size=512,chunk_overlap=200,length_function=len,add_start_index=True,
)texts = text_splitter.create_documents([page.page_content for page in pages[:2]],
)# 灌库
embeddings = DashScopeEmbeddings(model="text-embedding-v1",dashscope_api_key="你的阿里云百炼平台APIkey")
db = FAISS.from_documents(texts, embeddings)# 检索 top-5 结果
retriever = db.as_retriever(search_kwargs={"k": 5})docs=retriever.invoke("文档相关的问题")
print(docs)

作为一个条件节点放到会话工作流会话节点前：Start——> 条件Rag节点 ——> 会话节点1 ——> End

from langchain.chat_models import init_chat_model
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
from langgraph.graph import StateGraph, MessagesState, START, END
from langchain_community.embeddings import DashScopeEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import PyPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitterllm = init_chat_model("deepseek-r1:7b",model_provider="ollama")def chatbot(state: MessagesState):return {"messages": llm.invoke(state["messages"])}def retriever(state: MessagesState):question = state["messages"][-1].contentmessages = "请参照一下上下："# 加载文档loader = PyPDFLoader("./data/你的PDF文档.pdf")pages = loader.load_and_split()# 文档切片text_splitter = RecursiveCharacterTextSplitter(chunk_size=512,chunk_overlap=200,length_function=len,add_start_index=True,)texts = text_splitter.create_documents([page.page_content for page in pages[:2]],)# 灌库embeddings = DashScopeEmbeddings(model="text-embedding-v1", dashscope_api_key="你的apikey")db = FAISS.from_documents(texts, embeddings)# 检索 top-5 结果dbRetriever = db.as_retriever(search_kwargs={"k": 5})docs = dbRetriever.invoke(question)for doc in docs:messages = messages + doc.page_contentmessages = messages + "回答问题：" + questionreturn {"messages": HumanMessage(content=messages)}graph_builder = StateGraph(MessagesState)
graph_builder.add_node("retriever", retriever)
graph_builder.add_node("chatbot", chatbot)
graph_builder.add_edge(START, "retriever")
graph_builder.add_edge("retriever", "chatbot")
graph_builder.add_edge("chatbot", END)
graph = graph_builder.compile()
response = graph.stream({"messages": "你的问题？"})
for event in response:if "chatbot" in event:value = event["chatbot"]if "messages" in value and isinstance(value["messages"], AIMessage):print(event["chatbot"]["messages"].content)

查看全文

http://www.dtcms.com/a/311908.html