add structured output node demo

docs/index.md

@@ -28,10 +28,15 @@ uv run python -m template_langgraph.tasks.run_kabuto_helpdesk_agent "KABUTOの
 # BasicWorkflowAgent
 uv run python -m template_langgraph.tasks.draw_basic_workflow_agent_mermaid_png "data/basic_workflow_agent.png"
 uv run python -m template_langgraph.tasks.run_basic_workflow_agent "KABUTOの起動時に、画面全体が紫色に点滅し、システムがフリーズします。"
+uv run python -m template_langgraph.tasks.run_basic_workflow_agent "私の名前はフグ田 サザエ。東京都世田谷区桜新町あさひが丘3丁目に住んでいる 24 歳の主婦です。夫のノリスケと子供のタラちゃんがいます。"
 ```
 
 ## References
 
+### Sample Codes
+
+- [「現場で活用するためのAIエージェント実践入門」リポジトリ](https://github.com/masamasa59/genai-agent-advanced-book)
+
 ### Models
 
 - [AzureOpenAIEmbeddings](https://python.langchain.com/docs/integrations/text_embedding/azureopenai/)

template_langgraph/agents/basic_workflow_agent/agent.py

@@ -1,7 +1,7 @@
 from langchain_core.messages import AIMessage
 from langgraph.graph import END, START, StateGraph
 
-from template_langgraph.agents.basic_workflow_agent.models import AgentInput, AgentOutput, AgentState
+from template_langgraph.agents.basic_workflow_agent.models import AgentInput, AgentOutput, AgentState, Profile
 from template_langgraph.llms.azure_openais import AzureOpenAiWrapper
 from template_langgraph.loggers import get_logger
 
@@ -20,12 +20,14 @@ def create_graph(self):
         # Create nodes
         workflow.add_node("initialize", self.initialize)
         workflow.add_node("do_something", self.do_something)
+        workflow.add_node("extract_profile", self.extract_profile)
         workflow.add_node("finalize", self.finalize)
 
         # Create edges
         workflow.add_edge(START, "initialize")
         workflow.add_edge("initialize", "do_something")
-        workflow.add_edge("do_something", "finalize")
+        workflow.add_edge("do_something", "extract_profile")
+        workflow.add_edge("extract_profile", "finalize")
         workflow.add_edge("finalize", END)
 
         # Compile the graph
@@ -55,6 +57,15 @@ def do_something(self, state: AgentState) -> AgentState:
 
         return state
 
+    def extract_profile(self, state: AgentState) -> AgentState:
+        """Extract profile information from the state."""
+        logger.info(f"Extracting profile from state: {state}")
+        profile = self.llm.with_structured_output(Profile).invoke(
+            input=state["messages"],
+        )
+        state["profile"] = profile
+        return state
+
     def finalize(self, state: AgentState) -> AgentState:
         """Finalize the agent's work and prepare the output."""
         logger.info(f"Finalizing BasicWorkflowAgent with state: {state}")
@@ -75,7 +86,10 @@ def run_agent(self, input: AgentInput) -> AgentOutput:
         }
         final_state = app.invoke(initial_state)
         logger.info(f"Final state after running agent: {final_state}")
-        return AgentOutput(response=final_state["messages"][-1].content)
+        return AgentOutput(
+            response=final_state["messages"][-1].content,
+            profile=final_state["profile"],
+        )
 
     def draw_mermaid_png(self) -> bytes:
         """Draw the graph in Mermaid format."""

template_langgraph/agents/basic_workflow_agent/models.py

@@ -4,13 +4,22 @@
 from pydantic import BaseModel, Field
 
 
+class Profile(BaseModel):
+    first_name: str = Field(..., description="名")
+    last_name: str = Field(..., description="姓")
+    age: int | None = Field(None, description="年齢")
+    address: str | None = Field(None, description="住所")
+
+
 class AgentInput(BaseModel):
     request: str = Field(..., description="ユーザーからのリクエスト")
 
 
 class AgentOutput(BaseModel):
     response: str = Field(..., description="エージェントの応答")
+    profile: Profile | None = Field(None, description="抽出されたプロファイル情報")
 
 
 class AgentState(TypedDict):
     messages: Annotated[list, add_messages]
+    profile: Profile | None = Field(None, description="抽出されたプロファイル情報")