langgraph-learning-examples/langgraph_chatopenai.py

"""
LangGraph Example with ChatOpenAI.

A multi-turn conversational agent with memory and tool routing.
"""

from typing import TypedDict, List, Literal
from langgraph.graph import StateGraph, START, END
from langchain_openai import ChatOpenAI
from pydantic import BaseModel, Field
import os
from dotenv import load_dotenv

load_dotenv()


# ============ Model Helper ============

def create_chat_model(model: str = "openai/gpt-5.4", temperature: float = 0.7):
    """Create a ChatOpenAI model instance."""
    return ChatOpenAI(
        model=model,
        temperature=temperature,
        max_retries=2,
        api_key=os.getenv("OPENAI_API_KEY"),
        base_url=os.getenv("OPENAI_BASE_URL", "https://api.qnaigc.com/v1"),
    )


# ============ State Schema ============

class Message(BaseModel):
    role: str
    content: str


class State(TypedDict):
    messages: List[Message]
    category: str


# ============ Router Function ============

def categorize_message(state: State) -> Literal["greeting", "question", "fallback"]:
    """Use LLM to categorize the user's message."""

    model = create_chat_model(temperature=0.1)

    last_message = state["messages"][-1].content if state["messages"] else ""

    prompt = f"""
    Categorize this message into ONE category: greeting, question, or fallback.
    Return ONLY the category name.

    Message: {last_message}
    """

    response = model.invoke(prompt).content.strip().lower()

    if "greeting" in response:
        return "greeting"
    elif "question" in response:
        return "question"
    else:
        return "fallback"


# ============ Node Functions ============

def handle_greeting(state: State) -> dict:
    """Handle greeting messages."""
    print("  [Node: Greeting]")

    model = create_chat_model()

    last_message = state["messages"][-1].content

    prompt = f"""
    Respond to this greeting in a friendly way. Keep it brief.

    User: {last_message}
    Assistant:
    """

    response = model.invoke(prompt).content

    return {
        "messages": state["messages"] + [Message(role="assistant", content=response)]
    }


def handle_question(state: State) -> dict:
    """Handle question messages."""
    print("  [Node: Question]")

    model = create_chat_model()

    last_message = state["messages"][-1].content

    prompt = f"""
    Answer this question helpfully and concisely.

    User: {last_message}
    Assistant:
    """

    response = model.invoke(prompt).content

    return {
        "messages": state["messages"] + [Message(role="assistant", content=response)]
    }


def handle_fallback(state: State) -> dict:
    """Handle unrecognized messages."""
    print("  [Node: Fallback]")

    model = create_chat_model()

    last_message = state["messages"][-1].content

    prompt = f"""
    Respond to this message in a helpful way.

    User: {last_message}
    Assistant:
    """

    response = model.invoke(prompt).content

    return {
        "messages": state["messages"] + [Message(role="assistant", content=response)]
    }


# ============ Build Graph ============

def create_chat_graph():
    """Create and compile the chat graph."""

    builder = StateGraph(State)

    # Add nodes
    builder.add_node("greeting", handle_greeting)
    builder.add_node("question", handle_question)
    builder.add_node("fallback", handle_fallback)

    # Add conditional routing
    builder.add_conditional_edges(
        START,
        categorize_message,
        {
            "greeting": "greeting",
            "question": "question",
            "fallback": "fallback"
        }
    )

    # All paths lead to END
    builder.add_edge("greeting", END)
    builder.add_edge("question", END)
    builder.add_edge("fallback", END)

    return builder.compile()


# ============ Main ============

def main():
    """Run the chat agent."""
    graph = create_chat_graph()

    print("=== LangGraph Chat Agent with ChatOpenAI ===\n")

    # Test messages
    test_inputs = [
        "你好，很高兴见到你！",
        "Python 中如何读取 JSON 文件？",
        "随便聊聊吧"
    ]

    for user_input in test_inputs:
        print(f"User: {user_input}")

        initial_state = {
            "messages": [Message(role="user", content=user_input)],
            "category": ""
        }

        result = graph.invoke(initial_state)

        assistant_message = result["messages"][-1].content
        print(f"Assistant: {assistant_message}\n")
        print("-" * 50 + "\n")


if __name__ == "__main__":
    main()