feat

fix redis host 更换

app/service/prompt_generation/chatgpt_for_translation.py

@@ -1,9 +1,8 @@
-import os
+import logging
 
 from langchain.chains import LLMChain
 from langchain.chat_models import ChatOpenAI
-from langchain_core.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate, \
+from langchain_core.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate
-    PromptTemplate
 
 from app.core.config import OPENAI_MODEL, OPENAI_API_KEY
 
@@ -21,7 +20,8 @@ def translate_to_en(text):
         """You are a translation expert, proficient in various languages.
         And can translate various languages into English.
         Please translate to grammatically correct English regardless of the input language.
-        If the input is in English or numbers, check for grammatical errors. If there are no errors, output the input directly.
+        If the input is already in English, or consists of letters or numbers such as "cat", "abc", or "1", 
+        output the input text exactly as it is without any modifications or additions.
         If there are grammatical errors, correct them and then output the sentence."""
     )
     system_message_prompt = SystemMessagePromptTemplate.from_template(template)
@@ -36,34 +36,41 @@ def translate_to_en(text):
     )
     translate_chain = LLMChain(llm=llm, prompt=chat_prompt_template)
 
-    template = (
+    result = translate_chain.invoke(text)
-        """
-        Input sentence:
-        {translate}
-        1. Based on the input,adjust the input sentence to make it more suitable for prompts for generating images,
-        ensuring all key nouns or adjectives related to the image are retained.
-        2. Simplify complex sentence structures and clarify ambiguous expressions.
-        3. Only Output the adjusted English sentence.
 
-        Output :
+    logging.info("translate result : " + result.get('text'))
-        """
+    # print("translate result : " + result.get('text'))
-    )
+    return result.get('text')
-    # "Based on the input sentence, extract key adjectives and nouns.Only Output extracted key words."
-    # 1. Check if the input sentence contains any grammatical errors. If there are errors, please correct them before proceeding.
 
-    prompt_template = PromptTemplate(input_variables=["translate"], template=template)
+    # template = (
-    prompt_chain = LLMChain(llm=llm, prompt=prompt_template)
+    #     """
-
+    #     Input sentence:
-    from langchain.chains import SimpleSequentialChain
+    #     {translate}
-    overall_chain = SimpleSequentialChain(chains=[translate_chain, prompt_chain], verbose=True)
+    #     1. Based on the input,adjust the input sentence to make it more suitable for prompts for generating images,
-
+    #     ensuring all key nouns or adjectives related to the image are retained.
-    response = overall_chain.run(text)
+    #     2. Simplify complex sentence structures and clarify ambiguous expressions.
-    return response
+    #     3. Only Output the adjusted English sentence.
+    #
+    #     Output :
+    #     """
+    # )
+    # # "Based on the input sentence, extract key adjectives and nouns.Only Output extracted key words."
+    # # 1. Check if the input sentence contains any grammatical errors. If there are errors, please correct them before proceeding.
+    #
+    # prompt_template = PromptTemplate(input_variables=["translate"], template=template)
+    # prompt_chain = LLMChain(llm=llm, prompt=prompt_template)
+    #
+    # from langchain.chains import SimpleSequentialChain
+    # overall_chain = SimpleSequentialChain(chains=[translate_chain, prompt_chain], verbose=True)
+    #
+    # response = overall_chain.run(text)
+    # return response
 
 
 def main():
     """Main function"""
-    translate_to_en("生成一件运动风格的夹克，带有拉链和口袋，适合休闲穿着")
+    text = translate_to_en("fire")
+    print(text)
 
 
 if __name__ == "__main__":