AgentCoreService/pqai_agent_server/utils/prompt_util.py

import json

from datetime import datetime
from typing import List, Dict

from openai import OpenAI

from pqai_agent import logging_service, chat_service
from pqai_agent.response_type_detector import ResponseTypeDetector
from pqai_agent.user_profile_extractor import UserProfileExtractor
from pqai_agent.dialogue_manager import DialogueManager
from pqai_agent.mq_message import MessageType
from pqai_agent.utils.prompt_utils import format_agent_profile

logger = logging_service.logger


def compose_openai_chat_messages_no_time(dialogue_history, multimodal=False):
    messages = []
    for entry in dialogue_history:
        role = entry["role"]
        msg_type = entry.get("type", MessageType.TEXT)
        fmt_time = DialogueManager.format_timestamp(entry["timestamp"])
        if msg_type in (MessageType.IMAGE_GW, MessageType.IMAGE_QW, MessageType.GIF):
            if multimodal:
                messages.append(
                    {
                        "role": role,
                        "content": [
                            {
                                "type": "image_url",
                                "image_url": {"url": entry["content"]},
                            }
                        ],
                    }
                )
            else:
                logger.warning("Image in non-multimodal mode")
                messages.append({"role": role, "content": "[图片]"})
        else:
            messages.append({"role": role, "content": f'{entry["content"]}'})
    return messages


def run_openai_chat(messages, model_name, **kwargs):
    volcengine_models = [
        chat_service.VOLCENGINE_MODEL_DOUBAO_PRO_32K,
        chat_service.VOLCENGINE_MODEL_DOUBAO_PRO_1_5,
        chat_service.VOLCENGINE_MODEL_DOUBAO_1_5_VISION_PRO,
        chat_service.VOLCENGINE_MODEL_DEEPSEEK_V3,
    ]
    deepseek_models = [
        chat_service.DEEPSEEK_CHAT_MODEL,
    ]
    volcengine_bots = [
        chat_service.VOLCENGINE_BOT_DEEPSEEK_V3_SEARCH,
    ]
    if model_name in volcengine_models:
        llm_client = OpenAI(
            api_key=chat_service.VOLCENGINE_API_TOKEN,
            base_url=chat_service.VOLCENGINE_BASE_URL,
        )
    elif model_name in volcengine_bots:
        llm_client = OpenAI(
            api_key=chat_service.VOLCENGINE_API_TOKEN,
            base_url=chat_service.VOLCENGINE_BOT_BASE_URL,
        )
    elif model_name in deepseek_models:
        llm_client = OpenAI(
            api_key=chat_service.DEEPSEEK_API_TOKEN,
            base_url=chat_service.DEEPSEEK_BASE_URL,
        )
    else:
        raise Exception("model not supported")
    response = llm_client.chat.completions.create(
        messages=messages, model=model_name, **kwargs
    )
    logger.debug(response)
    return response


def run_extractor_prompt(req_data):
    prompt = req_data["prompt"]
    user_profile = req_data["user_profile"]
    staff_profile = req_data["staff_profile"]
    dialogue_history = req_data["dialogue_history"]
    model_name = req_data["model_name"]
    prompt_context = {
        "formatted_staff_profile": format_agent_profile(staff_profile),
        **user_profile,
        "dialogue_history": UserProfileExtractor.compose_dialogue(dialogue_history),
    }
    prompt = prompt.format(**prompt_context)
    messages = [
        {"role": "system", "content": "你是一个专业的用户画像分析助手。"},
        {"role": "user", "content": prompt},
    ]
    tools = [UserProfileExtractor.get_extraction_function()]
    response = run_openai_chat(messages, model_name, tools=tools, temperature=0)
    tool_calls = response.choices[0].message.tool_calls
    if tool_calls:
        function_call = tool_calls[0]
        if function_call.function.name == "update_user_profile":
            profile_info = json.loads(function_call.function.arguments)
            return {k: v for k, v in profile_info.items() if v}
        else:
            logger.error("llm does not return update_user_profile")
            return {}
    else:
        return {}


def run_chat_prompt(req_data):
    prompt = req_data["prompt"]
    staff_profile = req_data.get("staff_profile", {})
    user_profile = req_data.get("user_profile", {})
    dialogue_history = req_data.get("dialogue_history", [])
    model_name = req_data["model_name"]
    current_timestamp = req_data["current_timestamp"] / 1000
    prompt_context = {
        'formatted_staff_profile': format_agent_profile(staff_profile),
        **user_profile
    }
    current_hour = datetime.fromtimestamp(current_timestamp).hour
    prompt_context["last_interaction_interval"] = 0
    prompt_context["current_time_period"] = DialogueManager.get_time_context(
        current_hour
    )
    prompt_context["current_hour"] = current_hour
    prompt_context["if_first_interaction"] = False if dialogue_history else True
    last_message = dialogue_history[-1] if dialogue_history else {"role": "assistant"}
    prompt_context["if_active_greeting"] = (
        False if last_message["role"] == "user" else True
    )

    current_time_str = datetime.fromtimestamp(current_timestamp).strftime(
        "%Y-%m-%d %H:%M:%S"
    )
    system_prompt = {"role": "system", "content": prompt.format(**prompt_context)}
    messages = [system_prompt]
    if req_data["scene"] == "custom_debugging":
        messages.extend(compose_openai_chat_messages_no_time(dialogue_history))
        if "头像" in system_prompt["content"]:
            messages.append(
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "image_url",
                            "image_url": {"url": user_profile["avatar"]},
                        }
                    ],
                }
            )
    else:
        messages.extend(
            DialogueManager.compose_chat_messages_openai_compatible(
                dialogue_history, current_time_str
            )
        )
    return run_openai_chat(
        messages, model_name, temperature=1, top_p=0.7, max_tokens=1024
    )


def run_response_type_prompt(req_data):
    prompt = req_data["prompt"]
    dialogue_history = req_data["dialogue_history"]
    model_name = req_data["model_name"]

    composed_dialogue = ResponseTypeDetector.compose_dialogue(dialogue_history[:-1])
    next_message = DialogueManager.format_dialogue_content(dialogue_history[-1])
    prompt = prompt.format(dialogue_history=composed_dialogue, message=next_message)
    messages = [
        {"role": "system", "content": "你是一个专业的智能助手"},
        {"role": "user", "content": prompt},
    ]
    return run_openai_chat(messages, model_name, temperature=0.2, max_tokens=128)


def format_dialogue_history(dialogue: List[Dict]) -> str:
    role_map = {'user': '用户', 'assistant': '客服'}
    messages = []
    for msg in dialogue:
        if not msg['content']:
            continue
        if msg['role'] not in role_map:
            continue
        format_dt = datetime.fromtimestamp(msg['timestamp'] / 1000).strftime('%Y-%m-%d %H:%M:%S')
        msg_type = MessageType(msg.get('type', MessageType.TEXT.value)).description
        messages.append('[{}][{}][{}]{}'.format(role_map[msg['role']], format_dt, msg_type, msg['content']))
    return '\n'.join(messages)