打造高考志愿规划AIAgent(附代码)

### 高考专业申报建议在当今社会，随着科技与文化的深度融合，艺术设计领域展现出前所未有的活力与潜力，尤其在数字媒体、交互设计、视觉传达等方向，为有志于艺术创新与技术应用结合的青年学子提供了广阔的发展舞台。根据当前就业市场趋势及地域特色，以下是一份精心准备的专业申报建议，旨在帮助您做出更符合未来职业规划的选择。
#### 专业方向推荐1. **数字媒体艺术**：结合艺术创意与数字技术，探索多媒体内容创作的新边界。2. **交互设计**：聚焦用户体验，设计人与产品间的高效互动方式。3. **视觉传达设计**：通过图形、文字等视觉元素，传达信息与理念，广泛应用于广告、包装等领域。4. **游戏设计**：融合创意故事讲述、美术设计与软件开发，创造引人入胜的游戏体验。
#### 推荐申请院校（按城市）：- **北京**：北京师范大学、北京工业大学，这两所高校在艺术与科技领域有着深厚的教学资源和研究基础。- **广州**：暨南大学，其艺术设计学院在数字媒体与视觉传达方面享有良好声誉。- **杭州**：虽然浙江中医药大学主攻医学领域，但在杭州这座城市，其周边的艺术氛围和技术环境对设计类学生亦有积极影响。- **成都**：成都虽以成都中医药大学著称，但该市作为西南文化中心，艺术设计类实习与就业机会不容忽视。- **南京**：南京理工大学，工程技术与艺术设计的交叉融合为学生提供独特视角。- **武汉**：武汉大学，综合型大学背景下的艺术设计教育，有利于学生跨学科学习。- **青岛**：青岛大学，地处海滨，艺术设计专业结合地域特色，发展潜力大。- **苏州**：苏州大学，其艺术学院在传统与现代设计的结合上独具特色。
#### 就业展望一二线城市，尤其是北京、深圳、上海等地，对数字媒体艺术、交互设计、视觉传达设计及游戏设计等专业的毕业生需求旺盛。这些领域的应届生起薪可观，如北京平均月薪可达13283元，深圳和上海分别为12783元和12317元，反映出市场对高质量艺术设计人才的高度认可。
总之，在选择专业与目标院校时，建议结合个人兴趣、职业愿景及地域发展优势综合考虑，充分利用各城市的教育资源与产业环境，为未来职业生涯奠定坚实基础。

sample_standard_app\app\core\agent\peer_agent_case\demo_executing_agent.yaml

info:name: 'demo_executing_agent'description: 'demo executing agent'profile:prompt_version: demo_executing_agent.cnllm_model:name: 'qwen_llm'model_name: 'qwen-max'temperature: 0.4plan:planner:name: 'executing_planner'memory:name: ''action:knowledge:- 'demo_knowledge'tool:- 'kfind_search_tool'#mock_search_toolmetadata:type: 'AGENT'module: 'agentuniverse.agent.default.executing_agent.executing_agent'class: 'ExecutingAgent'

sample_standard_app\app\core\tool\kfind_search_tool.py

import requestsimport json
from agentuniverse.agent.action.tool.tool import Tool, ToolInputfrom agentuniverse.base.util.env_util import get_from_env
url = 'https://hikos.cn/kmind/api/act'
headers = {'Content-Type': 'application/json'}
ACCESS_KEY = get_from_env("ACCESS_KEY")ACCESS_SECRET_KEY = get_from_env("ACCESS_SECRET_KEY")
BASE_DATA = {"actCode": "searchAndAnswer","accessKey": ACCESS_KEY,"accessSecretKey": ACCESS_SECRET_KEY,"actExecType": "sync","isStreaming": False,"apiRequest": {"input": ""}}

class KfindSearchTool(Tool):"""The mock search tool.
In this tool, we mocked the search engine's answers to search for information about BYD and Warren Buffett.
Note:The tool is only suitable for users searching for Buffett or BYD related queries.We recommend that you configure your `SERPER_API_KEY` and use google_search_tool to get information."""
def execute(self, tool_input: ToolInput):"""Demonstrates the execute method of the Tool class."""new_data = BASE_DATAquery_str = "{\"query\":\"{query_key}\",\"searchSource\":\"INTERNET\",\"searchMode\":\"SIMPLE\"}".replace('{query_key}', tool_input.input)new_data["apiRequest"]["input"] = query_str
response = requests.post(url, headers=headers, data=json.dumps(new_data))res_json = response.json()
act_api_result_string = res_json['data']['actApiResultString']
# 解析包含answer的字符串，获取answer字段answer_dict = json.loads(act_api_result_string)
# 将answer字段转换为字符串answer_str = str(answer_dict['answer'])
#print('###############', answer_str)return answer_str

agentuniverse\agent\action\knowledge\embeddingqwen_embeding.py

from typing import List, Optional
import dashscopefrom langchain_community.embeddings import DashScopeEmbeddingsfrom pydantic import Field
from agentuniverse.agent.action.knowledge.embedding.embedding import Embeddingfrom agentuniverse.base.util.env_util import get_from_env

class QwenEmbedding(Embedding):dashscope_api_key: Optional[str] = Field(default_factory=lambda: get_from_env("DASHSCOPE_API_KEY"))"""The DashScope client."""model: Optional[str] = "text-embedding-v1"
def get_embeddings(self, texts: List[str]) -> List[List[float]]:"""Get the embeddings.
Note:The `embedding_model_name` parameter of the openai embedding class must be provided.The `dimensions` parameter of the openai embedding class is optional.
 Args: texts (List[str]): A list of texts that need to be embedded.
 Returns: List[List[float]]: Each text gets a float list, and the result is a list of the results for each text.
 Raises: ValueError: If texts exceed the embedding model token limit or missing some required parameters. """result = []resp = dashscope.TextEmbedding.call(model=self.model,input=texts,api_key=self.dashscope_api_key)if resp.status_code == 200:result = [item['embedding'] for item in resp.output.get('embeddings')]elif resp.status_code in [400, 401]:raise ValueError(f"status_code: {resp.status_code} \n "f"code: {resp.code} \n message: {resp.message}")else:raise Exception(f"status_code: {resp.status_code} \n code: {resp.code} \n message: {resp.message}")return result
async def async_get_embeddings(self, texts: List[str]) -> List[List[float]]:"""Asynchronously get the embeddings.
Note:The `embedding_model_name` parameter of the openai embedding class must be provided.The `dimensions` parameter of the openai embedding class is optional.
 Args: texts (List[str]): A list of texts that need to be embedded.
 Returns: List[List[float]]: Each text gets a float list, and the result is a list of the results for each text. Raises: ValueError: If texts exceed the embedding model token limit or missing some required parameters. """raise NotImplementedError
def as_langchain(self) -> DashScopeEmbeddings:"""Convert the agentUniverse(aU) openai embedding class to the langchain embedding class."""return DashScopeEmbeddings(model=self.model,dashscope_api_key=self.dashscope_api_key,)

agentuniverse\agent\action\knowledge.reader.file.txt_reader

from pathlib import Pathfrom typing import List, Optional, Dict
from agentuniverse.agent.action.knowledge.reader.reader import Readerfrom agentuniverse.agent.action.knowledge.store.document import Document

class LineTxtReader(Reader):

def load_data(self, fpath: Path, ext_info: Optional[Dict] = None) -> List[Document]:dlist = []
with open(fpath, 'r', encoding='utf-8') as file:
metadata = {"file_name": file.name}if ext_info is not None:metadata.update(ext_info)
for line in file:dlist.append(Document(text=line, metadata=metadata or {}))
return dlist

class TxtReader(Reader):"""Txt reader."""
def load_data(self, fpath: Path, ext_info: Optional[Dict] = None) -> List[Document]:
with open(fpath, 'r', encoding='utf-8') as file:
metadata = {"file_name": file.name}if ext_info is not None:metadata.update(ext_info)
txt = file.read()
return [Document(text=txt, metadata=metadata or {})]

zero_shot_template_decomposition = """introduction: 你是高考志愿填报专家。instruction:需要遵守下面的规则：1.去除重复的信息。2.去除对回答原始问题没有帮助的信息。        3.简要拆解问题，重点突出。        4.拆解问题中不要重复，一个问题只出现一次。5.尽量多的使用数值类信息。6.提问需要高度注意信息的时效性。
Task: 将给定的问题分解成更易于回答的简单子问题。注意：只能返回分解后的问题。不要回答它们中的任何一个，包括原始问题。.
    原始问题: {question}    分解后的问题:     """