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| import os | |
| from langgraph.graph import StateGraph, END | |
| from langchain_core.runnables import Runnable | |
| from langchain_core.messages import HumanMessage | |
| from langchain_core.runnables import RunnableLambda | |
| from langchain_openai import ChatOpenAI | |
| class LangGraphAgent: | |
| def __init__(self): | |
| print("LangGraphAgent initialized.") | |
| open_key = os.environ.get("OPEN_AI_KEY") | |
| # Define a simple LangGraph that uses OpenAI to answer questions | |
| self.llm = ChatOpenAI(model="gpt-4.1", temperature=0, api_key=open_key)) | |
| def answer_fn(state): | |
| question = state["question"] | |
| print(f"[LangGraphAgent] Received: {question[:50]}...") | |
| response = self.llm.invoke([HumanMessage(content=question)]) | |
| return {"answer": response.content} | |
| builder = StateGraph(state={"question": str, "answer": str}) | |
| builder.add_node("answer_question", RunnableLambda(answer_fn)) | |
| builder.set_entry_point("answer_question") | |
| builder.set_finish_point("answer_question") | |
| self.graph = builder.compile() | |
| def __call__(self, question: str) -> str: | |
| output = self.graph.invoke({"question": question}) | |
| return output["answer"] | |