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from langchain_core.messages import AIMessage
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from data import debug_print,llm1
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from nodes.intent import get_pretty_state_string,CreditCardState
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from langchain_core.messages import AIMessage, SystemMessage, HumanMessage
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async def agent_node(state: CreditCardState):
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debug_print("NODE", f"Entered agent_node with state:\n{get_pretty_state_string(state)}\n")
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full_query = state["raw_query"]
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selected_preferences = f"\nUser Preferences: {state['preferences']}\n" if state["preferences"] else ""
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ranked_cards_raw = state.get("ranked_cards", [])
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formatted_cards = ""
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if isinstance(ranked_cards_raw, list):
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for idx, card in enumerate(ranked_cards_raw, 1):
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name = card.get("name", "Unnamed Card")
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desc = card.get("description", "")
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formatted_cards += f"Card {idx}: {name}; {desc}\n"
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else:
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formatted_cards = str(ranked_cards_raw)
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ranked_cards = formatted_cards.strip()
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input_message = f"""## User Query: {full_query}
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{selected_preferences}
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Ranked Cards: {ranked_cards}
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### Instructions:
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You are given a user query and a list of ranked cards.
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1. First, assess whether the provided card list is sufficient and relevant to confidently answer the user's query.
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- If the list is not relevant or lacks information, state that more information is needed.
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2. If the list is relevant, follow these strict rules to select and explain the best one:
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1. Analyze the user's need from the given query.
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2. Select the best card based only on the details in the descriptions.
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3. Explain why it's the best choice (mention only what's explicitly written).
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4. Do not assume any benefit that is not stated.
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5. Use simple, structured output with no symbols like * or #.
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6. If the user asks for FD-based cards or is a beginner, assume all given cards are FD-based and choose the best.
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"""
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messages_for_llm_input = []
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messages_for_llm_input.append(SystemMessage(content="You are a credit card recommendation agent."))
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messages_for_llm_input.append(HumanMessage(content=input_message))
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try:
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debug_print("AGENT", "Starting vLLM agent generation via ChatOpenAI...")
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response_obj = await llm1.ainvoke(
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messages_for_llm_input,
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config={
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"max_tokens": 512,
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"temperature": 0.7,
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"top_p": 0.9,
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}
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)
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response_text = response_obj.content
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debug_print("AGENT", f"Decoded Response (truncated):\n{response_text[:500]}...")
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except Exception as e:
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error_str = str(e)
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debug_print("ERROR", f"Error during generation in agent_node: {error_str}")
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return {
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"messages": [
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AIMessage(
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content=f"Oops! An error occurred during AI generation: {error_str}",
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additional_kwargs={"error": True}
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)
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]
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}
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return {"messages": [AIMessage(content=response_text)]} |
