add chatbot skeleton code

Author: ks6088ts

langgraph.json

@@ -5,7 +5,8 @@
   "graphs": {
     "chat_with_tools_agent": "template_langgraph.agents.chat_with_tools_agent.agent:graph",
     "kabuto_helpdesk_agent": "template_langgraph.agents.kabuto_helpdesk_agent:graph",
-    "issue_formatter_agent": "template_langgraph.agents.issue_formatter_agent.agent:graph"
+    "issue_formatter_agent": "template_langgraph.agents.issue_formatter_agent.agent:graph",
+    "task_decomposer_agent": "template_langgraph.agents.task_decomposer_agent.agent:graph"
   },
   "env": ".env"
 }

template_langgraph/agents/task_decomposer_agent/__init__.py

@@ -0,0 +1,43 @@
+from langgraph.graph import END, StateGraph
+
+from template_langgraph.agents.chat_with_tools_agent.models import AgentState
+from template_langgraph.llms.azure_openais import AzureOpenAiWrapper
+from template_langgraph.loggers import get_logger
+
+logger = get_logger(__name__)
+
+
+class TaskDecomposerAgent:
+    def __init__(self):
+        self.llm = AzureOpenAiWrapper().chat_model
+
+    def create_graph(self):
+        """Create the main graph for the agent."""
+        # Create the workflow state graph
+        workflow = StateGraph(AgentState)
+
+        # Create nodes
+        workflow.add_node("chat", self.chat)
+
+        # Create edges
+        workflow.set_entry_point("chat")
+        workflow.add_edge("chat", END)
+
+        # Compile the graph
+        return workflow.compile()
+
+    def chat(self, state: AgentState) -> AgentState:
+        """Chat with tools using the state."""
+        logger.info(f"Chatting with tools using state: {state}")
+        return {
+            "messages": [
+                self.llm.invoke(state["messages"]),
+            ]
+        }
+
+    def draw_mermaid_png(self) -> bytes:
+        """Draw the graph in Mermaid format."""
+        return self.create_graph().get_graph().draw_mermaid_png()
+
+
+graph = TaskDecomposerAgent().create_graph()

template_langgraph/agents/task_decomposer_agent/agent.py

@@ -0,0 +1,32 @@
+from collections.abc import Sequence
+from typing import (
+    Annotated,
+    TypedDict,
+)
+
+from langchain_core.messages import (
+    BaseMessage,
+)
+from langgraph.graph.message import add_messages
+from pydantic import BaseModel, Field
+
+
+class Task(BaseModel):
+    title: str = Field(..., description="Title of the task")
+    description: str = Field(..., description="Description of the task")
+    priority: int = Field(..., description="Priority of the task (1-5)")
+    due_date: str | None = Field(None, description="Due date of the task (YYYY-MM-DD format)")
+    assigned_to: str | None = Field(None, description="Name of the agent assigned to the task")
+
+
+class AgentInput(BaseModel):
+    request: str = Field(..., description="Request from the user")
+
+
+class AgentOutput(BaseModel):
+    response: str = Field(..., description="Response from the agent")
+
+
+class AgentState(TypedDict):
+    messages: Annotated[Sequence[BaseMessage], add_messages]
+    decomposed_tasks: Sequence[Task]