翻译前置语言判断
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@@ -26,7 +26,7 @@ def prompt_generation(request_data: PromptGenerationImageModel):
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"""
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try:
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logger.info(f"prompt_generation request item is : @@@@@@:{request_data}")
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data = get_translation_from_llama3("[" + request_data.text + "]")
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data = get_translation_from_llama3(request_data.text)
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logger.info(f"prompt_generation response @@@@@@:{data}")
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except Exception as e:
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logger.warning(f"prompt_generation Run Exception @@@@@@:{e}")
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@@ -69,3 +69,5 @@ TOOLS_FUNCTIONS_SUFFIX = (
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)
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TUTORIAL_TOOL_RETURN = "Commencing the systematic tutorial guide now."
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GET_LANGUAGE_PREFIX = "Please identify the language. Only output the language name"
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@@ -8,7 +8,8 @@ from urllib3.exceptions import NewConnectionError
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from app.core.config import *
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from app.service.chat_robot.script.callbacks.qwen_callback_handler import QWenCallbackHandler
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from app.service.chat_robot.script.database import CustomDatabase
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from app.service.chat_robot.script.prompt import FASHION_CHAT_BOT_PREFIX, TOOLS_FUNCTIONS_SUFFIX, TUTORIAL_TOOL_RETURN
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from app.service.chat_robot.script.prompt import FASHION_CHAT_BOT_PREFIX, TOOLS_FUNCTIONS_SUFFIX, TUTORIAL_TOOL_RETURN, \
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GET_LANGUAGE_PREFIX
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from app.service.search_image_with_text.service import query
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get_database_table_description = "Input is an empty string, output is a comma separated list of tables in the database."
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@@ -274,7 +275,7 @@ def call_with_messages(message, gender):
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flag = False
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result_content = tool_info['content']
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response_type = "image"
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else :
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else:
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tool_info = {"name": assistant_output.tool_calls[0]['function']['name'], 'content': 'null'}
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logging.info(assistant_output.tool_calls[0]['function']['name'] + "(unknown tools)")
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flag = False
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@@ -300,5 +301,23 @@ def tutorial_tool():
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return TUTORIAL_TOOL_RETURN
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def get_language(message: str) -> str:
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messages = [
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{
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"content": message, # 用户message
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"role": "user"
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},
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{
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"content": GET_LANGUAGE_PREFIX, # ai message
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"role": "assistant"
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}
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]
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first_response = get_response(messages)
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assistant_output = first_response.output.choices[0].message.content
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logging.info(f"大模型输出信息:{first_response}\n判断用户输入的语言为:{assistant_output}")
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return assistant_output
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if __name__ == '__main__':
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call_with_messages()
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get_language("")
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@@ -8,6 +8,7 @@ from requests import RequestException
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from retry import retry
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from app.core.config import QWEN_API_KEY
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from app.service.chat_robot.script.service.CallQWen import get_language
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logger = logging.getLogger(__name__)
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@@ -93,7 +94,13 @@ def get_translation_from_llama3(text):
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# prompt = f"System: {prefix_for_llama}\nUser:[{text}]"
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# 创建请求的负载
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# 先获取用户输入文本的语言
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language = get_language(text)
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if 'English' in language:
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return text
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# 创建请求的负载 translator是自定义的翻译模型
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payload = {
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"model": "translator",
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"prompt": f"[{text}]",
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@@ -117,6 +124,26 @@ def get_translation_from_llama3(text):
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print(response.text)
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# 在llama3中创建一个翻译模型
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# def create_model_with_llama(text):
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# url = "http://localhost:11434/api/create"
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# # url = "http://10.1.1.240:1143/api/generate"
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#
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# # prompt = f"System: {prefix_for_llama}\nUser:[{text}]"
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#
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# # 创建翻译器的配置文件
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# payload = {
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# "model": "translator",
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# "modelfile": "FROM llama3\nSYSTEM Translate everything within the brackets [] into English."
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# "Never translate or modify any English input."
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# "The input must be fully translated into coherent English sentences."
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# }
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#
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# # 将负载转换为 JSON 格式
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# headers = {'Content-Type': 'application/json'}
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# response = requests.post(url, data=json.dumps(payload), headers=headers)
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def main():
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"""Main function"""
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text = get_translation_from_llama3("[火焰]")
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