"""
创建一个静态模型的智能体
"""
import json
from langchain.agents import create_agent
from langchain.chat_models import init_chat_model
from langchain.tools import tool
from langgraph.graph.state import CompiledStateGraph
from env_utils import DEEPSEEK_API_KEY, DEEPSEEK_BASE_URL
from langchain_core.messages import AnyMessage
"""
基本控制: 串行控制
基本控制: 分支控制
Successfully installed langchain-core-1.4.0
langchain-protocol-0.0.15 langgraph-1.2.0
langgraph-checkpoint-4.1.0 langgraph-prebuilt-1.1.0 ormsgpack-1.12.2
"""
from langchain_core.messages import AnyMessage
from typing_extensions import TypedDict
from langgraph.graph import START,StateGraph,END
from IPython.display import Image,display
from typing import Any, Annotated, Literal
import operator
class State(TypedDict):
aggregate: Annotatedlist,operator.add
def a(state:State):
print(f"Node A sees {state'aggregate'}")
return {"aggregate":"A"}
def b(state:State):
print(f"Node B sees {state'aggregate'}")
return {"aggregate": "B"}
builder=StateGraph(State)
builder.add_node(a)
builder.add_node(b)
def route(state: State) -> Literal"b",END:
if len(state"aggregate")<7:
return "b"
else:
return END
builder.add_edge(START,"a")
builder.add_conditional_edges("a",route)
builder.add_edge("b","a")
graph = builder.compile()
from IPython.display import Image, display
with open('graph.png', 'wb') as f:
f.write(graph.get_graph().draw_mermaid_png())
display(Image('graph.png'))
from PIL import Image
import io
假设这是你的 PNG 字节数据
png_data = graph.get_graph().draw_mermaid_png()
if 'png_data' in locals():
将字节数据解码为图片a
img = Image.open(io.BytesIO(png_data))
img.show()
res=graph.invoke({"value_1": "c"})
print(res)
def route(state: State) -> Literal"b",END:
if len(state"aggregate")<7:
return "b"
else:
return END
builder.add_edge(START,"a")
builder.add_conditional_edges("a",route)
builder.add_edge("b","a")
条件边, 如果返回b，就是a->b,b->a，循环， 直到b->END