add langgraph use

examples/stream_modes/debug_demo.py

@@ -0,0 +1,53 @@
+from typing import TypedDict, Annotated
+import operator
+import time
+
+from langgraph.graph import StateGraph, END
+
+
+class GraphState(TypedDict):
+    messages: Annotated[list, operator.add]
+    step_count: int
+    result: str
+
+
+def step_1(state: GraphState) -> GraphState:
+    time.sleep(0.2)
+    return {"messages": ["S1"], "step_count": state.get("step_count", 0) + 1, "result": "r1"}
+
+
+def step_2(state: GraphState) -> GraphState:
+    time.sleep(0.2)
+    return {"messages": ["S2"], "step_count": state.get("step_count", 0) + 1, "result": "r2"}
+
+
+def step_3(state: GraphState) -> GraphState:
+    time.sleep(0.2)
+    return {"messages": ["S3"], "step_count": state.get("step_count", 0) + 1, "result": "done"}
+
+
+def create_workflow():
+    wf = StateGraph(GraphState)
+    wf.add_node("step_1", step_1)
+    wf.add_node("step_2", step_2)
+    wf.add_node("step_3", step_3)
+    wf.set_entry_point("step_1")
+    wf.add_edge("step_1", "step_2")
+    wf.add_edge("step_2", "step_3")
+    wf.add_edge("step_3", END)
+    return wf.compile()
+
+
+def main():
+    print("=" * 60)
+    print("🛠️  LangGraph stream_mode='debug' 示例（开发定位用）")
+    print("=" * 60)
+    app = create_workflow()
+    initial = {"messages": [], "step_count": 0, "result": ""}
+    for info in app.stream(initial, stream_mode="debug"):
+        print(info)
+
+
+if __name__ == "__main__":
+    main()
+

examples/stream_modes/graph_visualize.py

@@ -0,0 +1,67 @@
+"""
+Demonstrate compiling a LangGraph and visualizing its structure.
+Requires no external viz packages for ASCII. To export PNG, install:
+  uv pip install -e '.[viz]'
+and ensure Graphviz is present on your system.
+"""
+
+from typing import TypedDict, Annotated
+import operator
+
+from langgraph.graph import StateGraph, START, END
+
+
+class S(TypedDict):
+    a: int
+    b: int
+    c: int
+
+
+def n1(s: S) -> S:
+    return {"a": s.get("a", 0) + 1, "b": s.get("b", 0), "c": s.get("c", 0)}
+
+
+def n2(s: S) -> S:
+    return {"a": s.get("a", 0), "b": s.get("b", 0) + 2, "c": s.get("c", 0)}
+
+
+def n3(s: S) -> S:
+    return {"a": s.get("a", 0), "b": s.get("b", 0), "c": s.get("c", 0) + 3}
+
+
+def build_app():
+    g = StateGraph(S)
+    g.add_node("n1", n1)
+    g.add_node("n2", n2)
+    g.add_node("n3", n3)
+    g.add_edge(START, "n1")
+    g.add_edge("n1", "n2")
+    g.add_edge("n2", "n3")
+    g.add_edge("n3", END)
+    return g.compile()
+
+
+def main():
+    app = build_app()
+    graph = app.get_graph()
+
+    # Try ASCII render if available
+    draw_ascii = getattr(graph, "draw_ascii", None)
+    if callable(draw_ascii):
+        print("=== Graph ASCII ===")
+        print(draw_ascii())
+
+    # Try PNG export if available and pydot/graphviz installed
+    draw_png = getattr(graph, "draw_png", None)
+    if callable(draw_png):
+        try:
+            out = "graph_demo.png"
+            draw_png(out)
+            print(f"Saved PNG: {out}")
+        except Exception as e:
+            print(f"PNG export unavailable: {e}")
+
+
+if __name__ == "__main__":
+    main()
+

examples/stream_modes/messages_demo.py

@@ -0,0 +1,43 @@
+from typing import TypedDict
+
+from langgraph.graph import StateGraph, START
+from langchain_core.messages import HumanMessage
+
+from langgraph_qwen import ChatQwenOpenAICompat
+
+
+class SimpleState(TypedDict):
+    topic: str
+    joke: str
+
+
+model = ChatQwenOpenAICompat(temperature=0)
+
+
+def call_model(state: SimpleState):
+    llm_response = model.invoke([HumanMessage(content=f"Generate a joke about {state['topic']}")])
+    return {"joke": llm_response.content}
+
+
+graph = (
+    StateGraph(SimpleState)
+    .add_node("call_model", call_model)
+    .add_edge(START, "call_model")
+    .compile()
+)
+
+
+def main():
+    print("=" * 60)
+    print("💬 LangGraph stream_mode='messages' 示例（捕获 LLM tokens）")
+    print("=" * 60)
+    for msg, meta in graph.stream({"topic": "cats"}, stream_mode="messages"):
+        if hasattr(msg, "content") and msg.content:
+            node = meta.get("langgraph_node", "unknown")
+            print(f"[{node}] {msg.content}", end="", flush=True)
+    print("\n\n✅ 完成！")
+
+
+if __name__ == "__main__":
+    main()
+

examples/stream_modes/updates_demo.py

@@ -0,0 +1,75 @@
+from typing import TypedDict, Annotated
+import operator
+import time
+
+from langgraph.graph import StateGraph, END
+
+
+class GraphState(TypedDict):
+    messages: Annotated[list, operator.add]
+    step_count: int
+    result: str
+
+
+def step_1(state: GraphState) -> GraphState:
+    time.sleep(0.3)
+    return {
+        "messages": ["步骤1: 开始数据准备"],
+        "step_count": state.get("step_count", 0) + 1,
+        "result": "数据准备完成",
+    }
+
+
+def step_2(state: GraphState) -> GraphState:
+    time.sleep(0.4)
+    return {
+        "messages": ["步骤2: 正在处理数据"],
+        "step_count": state.get("step_count", 0) + 1,
+        "result": "数据处理完成，准备分析",
+    }
+
+
+def step_3(state: GraphState) -> GraphState:
+    time.sleep(0.5)
+    total_messages = len(state.get("messages", []))
+    return {
+        "messages": ["步骤3: 分析完成，生成最终结果"],
+        "step_count": state.get("step_count", 0) + 1,
+        "result": f"分析完成！共 {total_messages + 1} 条消息，步骤 {state.get('step_count', 0) + 1}",
+    }
+
+
+def create_workflow():
+    wf = StateGraph(GraphState)
+    wf.add_node("step_1", step_1)
+    wf.add_node("step_2", step_2)
+    wf.add_node("step_3", step_3)
+    wf.set_entry_point("step_1")
+    wf.add_edge("step_1", "step_2")
+    wf.add_edge("step_2", "step_3")
+    wf.add_edge("step_3", END)
+    return wf.compile()
+
+
+def main():
+    print("=" * 60)
+    print("🔄 LangGraph stream_mode='updates' 示例（仅返回增量）")
+    print("=" * 60)
+
+    app = create_workflow()
+    initial = {"messages": [], "step_count": 0, "result": ""}
+
+    # 自行维护完整状态
+    current_state = dict(initial)
+    for chunk in app.stream(initial, stream_mode="updates"):
+        for node_name, updates in chunk.items():
+            print(f"📦 {node_name} 更新: {updates}")
+            current_state.update(updates)
+
+    print("\n✅ 最终合并状态:")
+    print(current_state)
+
+
+if __name__ == "__main__":
+    main()
+

examples/stream_modes/values_demo.py

@@ -0,0 +1,74 @@
+from typing import TypedDict, Annotated
+import operator
+import time
+
+from langgraph.graph import StateGraph, END
+
+
+class GraphState(TypedDict):
+    messages: Annotated[list, operator.add]
+    step_count: int
+    result: str
+
+
+def step_1(state: GraphState) -> GraphState:
+    print("🔄 执行步骤1: 数据准备阶段")
+    time.sleep(0.5)
+    return {
+        "messages": ["步骤1: 开始数据准备"],
+        "step_count": state.get("step_count", 0) + 1,
+        "result": "数据准备完成",
+    }
+
+
+def step_2(state: GraphState) -> GraphState:
+    print("🔄 执行步骤2: 数据处理阶段")
+    time.sleep(0.7)
+    return {
+        "messages": ["步骤2: 正在处理数据"],
+        "step_count": state.get("step_count", 0) + 1,
+        "result": "数据处理完成，准备分析",
+    }
+
+
+def step_3(state: GraphState) -> GraphState:
+    print("🔄 执行步骤3: 数据分析阶段")
+    time.sleep(0.9)
+    total_messages = len(state.get("messages", []))
+    return {
+        "messages": ["步骤3: 分析完成，生成最终结果"],
+        "step_count": state.get("step_count", 0) + 1,
+        "result": f"分析完成！总共处理了 {total_messages + 1} 条消息，执行了 {state.get('step_count', 0) + 1} 个步骤",
+    }
+
+
+def create_workflow():
+    wf = StateGraph(GraphState)
+    wf.add_node("step_1", step_1)
+    wf.add_node("step_2", step_2)
+    wf.add_node("step_3", step_3)
+    wf.set_entry_point("step_1")
+    wf.add_edge("step_1", "step_2")
+    wf.add_edge("step_2", "step_3")
+    wf.add_edge("step_3", END)
+    return wf.compile()
+
+
+def main():
+    print("=" * 60)
+    print("🚀 LangGraph stream_mode='values' 示例（返回完整状态）")
+    print("=" * 60)
+    app = create_workflow()
+    initial = {"messages": [], "step_count": 0, "result": ""}
+    for i, state in enumerate(app.stream(initial, stream_mode="values")):
+        print(f"\n🔍 步骤 {i} 完成后的状态:")
+        print(f"  📝 messages: {state.get('messages', [])}")
+        print(f"  🔢 step_count: {state.get('step_count', 0)}")
+        print(f"  ✨ result: {state.get('result', '')}")
+
+    print("\n✅ 完成！")
+
+
+if __name__ == "__main__":
+    main()
+