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from typing import Annotated, TypedDict
from langchain_openai import ChatOpenAI
from tools import add, subtract, multiply, divide, exponentiate, web_search, paper_search, load_web_page, understand_image, transcribe_audio
from langchain_core.messages import AnyMessage
from langgraph.graph.message import add_messages
from langchain_core.messages import AnyMessage, HumanMessage
from langgraph.prebuilt import ToolNode, tools_condition
from langgraph.graph import START, StateGraph
class AgentState(TypedDict):
messages: Annotated[list[AnyMessage], add_messages]
class QAAgent:
def __init__(self):
print("QA Agent initialized.")
self.agent = self.build_agent()
def __call__(self, question: str) -> str:
print(f"Agent received question (first 50 chars): {question[:50]}...")
response = self.agent.invoke({"messages": [HumanMessage(content=question)]})
ret = response['messages'][-1].content
print(f"Agent returning fixed answer: {ret}")
return ret
def build_agent(self):
model = ChatOpenAI(model="gpt-4o-mini", temperature=0)
tools = [add, subtract, multiply, divide, exponentiate, web_search, paper_search, load_web_page, understand_image, transcribe_audio]
model_with_tools = model.bind_tools(tools)
def assistant(state: AgentState):
return {
"messages": [model_with_tools.invoke(state["messages"])],
}
builder = StateGraph(AgentState)
builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode(tools))
builder.add_edge(START, "assistant")
builder.add_conditional_edges("assistant", tools_condition)
builder.add_edge("tools", "assistant")
agent = builder.compile()
return agent
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