mirror of
https://github.com/BoardWare-Genius/jarvis-models.git
synced 2025-12-14 00:53:25 +00:00
145 lines
5.2 KiB
Python
Executable File
145 lines
5.2 KiB
Python
Executable File
from typing import Any, Coroutine
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from fastapi import Request, Response, status
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from fastapi.responses import JSONResponse
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from .blackbox import Blackbox
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import requests
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import json
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from modelscope_agent.agents import RolePlay
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from modelscope_agent.tools.base import BaseTool
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from modelscope_agent.tools import register_tool
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class Modelscope(Blackbox):
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def __call__(self, *args, **kwargs):
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return self.processing(*args, **kwargs)
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def valid(self, *args, **kwargs) -> bool:
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data = args[0]
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return isinstance(data, list)
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# model_name有 Qwen1.5-14B-Chat , internlm2-chat-20b
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def processing(self, model_name, prompt, template, context: list) -> str:
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if context == None:
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context = []
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@register_tool('ChromaQuery')
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class AliyunRenewInstanceTool(BaseTool):
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description = '查询chroma数据库中的数据'
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name = 'ChromaQuery'
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parameters: list = [{
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'name': 'id',
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'description': '用户的chroma id',
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'required': True,
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'type': 'string'
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}, {
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'name': 'query',
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'description': '用户需要在chroma中查询的问题',
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'required': True,
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'type': 'string'
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}]
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def call(self, params: str, **kwargs):
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params = self._verify_args(params)
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id = params['id']
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query = params['query']
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query_data = {
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"chroma_query_data": {
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"id": id,
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"question": query
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}
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}
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url = "http://10.6.80.75:7003"
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response = requests.post(f"{url}/api/chroma_query", json=query_data)
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result = response.json()['response']
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return str({'result': f'Chroma ID为{id}的用户，查询结果为{response}。'})
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@register_tool('WebSearch')
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class WebSearchTool(BaseTool):
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description = '查询网络中的内容'
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name = 'WebSearch'
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parameters: list = [ {
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'name': 'search_term',
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'description': '用户需要在Web中查询的问题',
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'required': True,
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'type': 'string'
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}]
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def call(self, params: str, **kwargs):
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params = self._verify_args(params)
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search_term = params['search_term']
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api_key='9e51be0aaecb5a56fe2faead6e2c702fde92e62a'
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headers = {
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'X-API-KEY': api_key,
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'Content-Type': 'application/json',
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}
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params = {
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'q': search_term
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}
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try:
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response = requests.post(
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f'https://google.serper.dev/search',
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headers=headers,
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params=params,
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timeout=5)
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except Exception as e:
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return -1, str(e)
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result = response.json()['answerBox']['snippet']
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return str({'result': f'WebSearch查询结果为{search_term}{result}。'})
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# define LLM
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api_base_url = "http://120.196.116.194:48892/v1"
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api_key= "EMPTY"
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LLM_MODEL = model_name
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llm_config = {
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'model': LLM_MODEL,
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'model_server': 'openai',
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'api_base':api_base_url,
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'api_key': api_key
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}
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function_list = ['WebSearch', 'ChromaQuery']
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bot = RolePlay(function_list=function_list,llm=llm_config, instruction=template)
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response = bot.run(prompt, history=context, lang='zh')
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text = ''
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for chunk in response:
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text += chunk
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return text
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async def fast_api_handler(self, request: Request) -> Response:
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try:
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data = await request.json()
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except:
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return JSONResponse(content={"error": "json parse error"}, status_code=status.HTTP_400_BAD_REQUEST)
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user_model_name = data.get("model_name")
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user_context = data.get("context")
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user_prompt = data.get("prompt")
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user_template = data.get("template")
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if user_prompt is None:
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return JSONResponse(content={"error": "question is required"}, status_code=status.HTTP_400_BAD_REQUEST)
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if user_model_name is None or user_model_name.isspace():
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user_model_name = "Qwen1.5-14B-Chat"
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if user_template is None or user_template.isspace():
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# user_template 是定义LLM的语气，例如template = "使用小丑的语气说话。"，user_template可以为空字串，或者是用户自定义的语气，或者是使用我们提供的语气
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user_template = ""
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else:
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user_template = f"使用{user_template}的语气说话。"
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return JSONResponse(content={"response": self.processing(user_model_name, user_prompt, user_template, user_context)}, status_code=status.HTTP_200_OK) |