personal-consumption-service/app/routes/base/routes.py

# @description: 
# @author: licanglong
# @date: 2025/11/20 14:22
import dirtyjson

from app.prompt import industry_planner_prompt, source_researcher_prompt, industry_data_extractor_prompt
from app.routes.base import base_router
from app.service.llm_client import llm_call


@base_router.post("/industry/data")
async def get_industry_data():
    industry_input_data = ["011", "谷物种植", "1010101000000000000", "谷物"]
    tools = [
        {
            "type": "function",
            "function": {
                "name": "get_current_time",
                "description": "用于获取当前时间，当需求中涉及到了时间概念时可以使用",
            }
        }
        ,
        {
            "type": "function",
            "function": {
                "name": "ali_search_tool",
                "description": "当需要从互联网获取额外信息支撑时使用",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "keyword": {
                            "type": "string",
                            "description": "用于搜索的完整关键词，尽量将问题描述完整。如需限定来源，可在结尾使用“+ 来源机构名称”。"
                        }
                    },
                    "required": ["keyword"]
                }
            }
        }

    ]

    industry_planner_messages = [
        {
            "role": "system",
            "content": industry_planner_prompt.system_prompt
        },
        {
            "role": "user",
            "content": industry_planner_prompt.user_prompt(industry_input_data)
        }
    ]

    industry_planner_data = await llm_call(tools=tools, messages=industry_planner_messages)

    source_researcher_messages = [
        {
            "role": "system",
            "content": source_researcher_prompt.system_prompt
        },
        {
            "role": "user",
            "content": source_researcher_prompt.user_prompt(industry_planner_data)
        }
    ]
    source_researcher_data = await llm_call(tools=tools, messages=source_researcher_messages)

    industry_data_extractor_messages = [
        {
            "role": "system",
            "content": industry_data_extractor_prompt.system_prompt
        },
        {
            "role": "user",
            "content": industry_data_extractor_prompt.user_prompt(
                [*industry_input_data, industry_planner_data, source_researcher_data])
        }
    ]

    industry_data_extractor = await llm_call(tools=tools, messages=industry_data_extractor_messages)
    return dirtyjson.loads(industry_data_extractor)