能调通千问大模型了

langgraph.json

@@ -1,7 +1,8 @@
 {
   "dependencies": ["."],
   "graphs": {
-    "agent": "./src/agent/graph.py:graph"
+    "agent": "./src/agent/graph.py:graph",
+    "my_agent": "./src/agent/my_graph.py:graph"
   },
   "env": ".env",
   "image_distro": "wolfi"

requirements.txt

@@ -0,0 +1 @@
+langchain

src/agent/my_graph.py

@@ -0,0 +1,53 @@
+from typing import Annotated
+
+from langchain.chat_models import init_chat_model
+from langchain_core.tools import tool
+from langchain_tavily import TavilySearch
+from langchain_core.messages import BaseMessage
+from typing_extensions import TypedDict
+
+from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.graph import StateGraph
+from langgraph.graph.message import add_messages
+from langgraph.prebuilt import ToolNode, tools_condition
+from langchain_community.chat_models.tongyi import ChatTongyi
+
+llm = ChatTongyi(
+    model="qwen-max",   # 此处以qwen-max为例，您可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
+    streaming=True,
+    # other params...
+)
+
+
+class State(TypedDict):
+    messages: Annotated[list, add_messages]
+
+graph_builder = StateGraph(State)
+
+# tool = TavilySearch(max_results=2)
+
+@tool
+def get_wheather(location: str) -> str:
+    """输入城市查询天气"""
+    return f"The weather in {location} is 20 degrees Celsius."
+
+tools = [get_wheather]
+llm_with_tools = llm.bind_tools(tools)
+
+def chatbot(state: State):
+    return {"messages": [llm_with_tools.invoke(state["messages"])]}
+
+graph_builder.add_node("chatbot", chatbot)
+
+tool_node = ToolNode(tools=[tool])
+graph_builder.add_node("tools", tool_node)
+
+graph_builder.add_conditional_edges(
+    "chatbot",
+    tools_condition,
+)
+graph_builder.add_edge("tools", "chatbot")
+graph_builder.set_entry_point("chatbot")
+# memory = InMemorySaver()
+# graph = graph_builder.compile(checkpointer=memory)
+graph = graph_builder.compile()