diff --git a/app/service/chat_robot/script/callbacks/openai_token_record_callback.py b/app/service/chat_robot/script/callbacks/openai_token_record_callback.py index 64ed7f4..83d22a1 100644 --- a/app/service/chat_robot/script/callbacks/openai_token_record_callback.py +++ b/app/service/chat_robot/script/callbacks/openai_token_record_callback.py @@ -1,9 +1,13 @@ """Callback Handler that add on_chain_end function to record Token usage.""" from typing import Any, Dict -from langchain.callbacks import OpenAICallbackHandler +from langchain_community.callbacks.openai_info import OpenAICallbackHandler from langchain.schema import LLMResult -from langchain.callbacks.openai_info import standardize_model_name, MODEL_COST_PER_1K_TOKENS, get_openai_token_cost_for_model +from langchain_community.callbacks.openai_info import standardize_model_name, MODEL_COST_PER_1K_TOKENS, \ + get_openai_token_cost_for_model + + +# from langchain.callbacks.openai_info import standardize_model_name, MODEL_COST_PER_1K_TOKENS, get_openai_token_cost_for_model class OpenAITokenRecordCallbackHandler(OpenAICallbackHandler): diff --git a/app/service/chat_robot/script/callbacks/qwen_callback_handler.py b/app/service/chat_robot/script/callbacks/qwen_callback_handler.py new file mode 100644 index 0000000..9e3d4af --- /dev/null +++ b/app/service/chat_robot/script/callbacks/qwen_callback_handler.py @@ -0,0 +1,28 @@ +from typing import Dict + +from dashscope.api_entities.dashscope_response import GenerationUsage + + +class QWenCallbackHandler: + + total_tokens: int = 0 + input_tokens: int = 0 + output_tokens: int = 0 + total_cost: float = 0.0 + + def on_llm_end(self, response: GenerationUsage) -> None: + """Collect token usage.""" + + + self.input_tokens += response.input_tokens + self.output_tokens += response.output_tokens + self.total_tokens = self.input_tokens + self.output_tokens + self.total_cost = 0.04 * self.input_tokens / 1000 + 0.12 * self.output_tokens / 1000 + + def on_chain_end(self, outputs: Dict ) -> None: + """Write token usage to redis.""" + outputs["total_tokens"] = self.total_tokens + outputs["total_cost"] = self.total_cost + outputs["prompt_tokens"] = self.input_tokens + outputs["completion_tokens"] = self.output_tokens + print("input_tokens : {} \noutput_tokens : {}".format(outputs["prompt_tokens"] , outputs["completion_tokens"])) \ No newline at end of file diff --git a/app/service/chat_robot/script/database.py b/app/service/chat_robot/script/database.py index 8a5dfdb..bcc0b2c 100644 --- a/app/service/chat_robot/script/database.py +++ b/app/service/chat_robot/script/database.py @@ -3,7 +3,7 @@ import json from sqlalchemy import text # from langchain import SQLDatabase -from langchain.utilities import SQLDatabase +from langchain_community.utilities import SQLDatabase class CustomDatabase(SQLDatabase): diff --git a/app/service/chat_robot/script/main.py b/app/service/chat_robot/script/main.py index 2a62664..8ee8223 100644 --- a/app/service/chat_robot/script/main.py +++ b/app/service/chat_robot/script/main.py @@ -1,16 +1,19 @@ import logging + +from langchain_community.chat_models import ChatTongyi from loguru import logger from langchain.agents import Tool -from langchain.utilities import SerpAPIWrapper +from langchain_community.utilities import SerpAPIWrapper from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder from langchain.schema import SystemMessage, AIMessage -from langchain.chat_models import ChatOpenAI -from langchain.llms.openai import OpenAI +# from langchain_community.chat_models import ChatOpenAI +# from langchain_community.llms import OpenAI from langchain.callbacks import FileCallbackHandler from app.service.chat_robot.script.agents import CustomAgentExecutor, ConversationalFunctionsAgent from app.service.chat_robot.script.callbacks import OpenAITokenRecordCallbackHandler from app.service.chat_robot.script.database import CustomDatabase from app.service.chat_robot.script.prompt import FASHION_CHAT_BOT_PREFIX, TOOLS_FUNCTIONS_SUFFIX +from app.service.chat_robot.script.service import CallQWen from app.service.chat_robot.script.tools import (QuerySQLDataBaseTool, InfoSQLDatabaseTool, QuerySQLCheckerTool, ListSQLDatabaseTool) from app.service.chat_robot.script.memory import UserConversationBufferWindowMemory from app.service.chat_robot.script.tools.tutorial_tool import CustomTutorialTool @@ -26,10 +29,12 @@ logger.add(logfile, colorize=True, enqueue=True) log_handler = FileCallbackHandler(logfile) # Initiate our LLM 'gpt-3.5-turbo' -llm = ChatOpenAI(temperature=0.1, - openai_api_key=OPENAI_API_KEY, - # callbacks=[OpenAICallbackHandler()] - ) +# llm = ChatOpenAI(temperature=0.1, +# openai_api_key=OPENAI_API_KEY, +# # callbacks=[OpenAICallbackHandler()] +# ) + +llm = ChatTongyi(api_key="sk-7658298c6b99443c98184a5e634fe6ab") search = SerpAPIWrapper() db = CustomDatabase.from_uri(f'mysql+pymysql://{DB_USERNAME}:{DB_PASSWORD}@{DB_HOST}:{DB_PORT}/attribute_retrieval_V3', @@ -45,14 +50,15 @@ tools = [ QuerySQLDataBaseTool(db=db, return_direct=False), InfoSQLDatabaseTool(db=db), ListSQLDatabaseTool(db=db), - QuerySQLCheckerTool(db=db, llm=OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)), - Tool( - name="tutorial_tool", - description="Utilize this tool to retrieve specific statements related to user guidance tutorials." - "Input is an empty string", - func=CustomTutorialTool(), - return_direct=True - ) + # QuerySQLCheckerTool(db=db, llm=OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)), + QuerySQLCheckerTool(db=db, llm = ChatTongyi(temperature=0, api_key="sk-7658298c6b99443c98184a5e634fe6ab")), + # Tool( + # name="tutorial_tool", + # description="Utilize this tool to retrieve specific statements related to user guidance tutorials." + # "Input is an empty string", + # func=CustomTutorialTool(), + # return_direct=True + # ) ] messages = [ @@ -90,25 +96,47 @@ def chat(post_data): input_message = post_data.message gender = post_data.gender - final_outputs = agent_executor( - {"input": input_message, "gender": gender}, - callbacks=[OpenAITokenRecordCallbackHandler(), log_handler], - session_key=f"buffer:{user_id}:{session_id}", - ) + # final_outputs = agent_executor( + # {"input": input_message, "gender": gender}, + # callbacks=[OpenAITokenRecordCallbackHandler(), log_handler], + # session_key=f"buffer:{user_id}:{session_id}", + # ) + + final_outputs = CallQWen.call_with_messages(input_message) + # api_response = { + # 'user_id': user_id, + # 'session_id': session_id, + # # 'message_id': message_id, + # # 'create_time': created_time, + # 'input': final_outputs['input'], + # # 'conversion': messages, + # 'output': final_outputs['output'], + # # 'gpt_response_time': gpt_response_time, + # 'total_tokens': final_outputs['total_tokens'], + # 'total_cost': final_outputs['total_cost'], + # 'prompt_tokens': final_outputs['prompt_tokens'], + # 'completion_tokens': final_outputs['completion_tokens'], + # 'response_type': final_outputs['response_type'] + # } + # if final_outputs["output"].startswith("["): + # final_str = final_outputs["output"].replace("\\", "") + # else: + # final_str = final_outputs["output"] + api_response = { 'user_id': user_id, 'session_id': session_id, # 'message_id': message_id, # 'create_time': created_time, - 'input': final_outputs['input'], + 'input': input_message, # 'conversion': messages, - 'output': final_outputs['output'], + 'output': final_outputs["output"], # 'gpt_response_time': gpt_response_time, 'total_tokens': final_outputs['total_tokens'], 'total_cost': final_outputs['total_cost'], 'prompt_tokens': final_outputs['prompt_tokens'], 'completion_tokens': final_outputs['completion_tokens'], - 'response_type': final_outputs['response_type'] + 'response_type': final_outputs["response_type"] } logging.info(api_response) return api_response diff --git a/app/service/chat_robot/script/memory/user_buffer_window.py b/app/service/chat_robot/script/memory/user_buffer_window.py index 9fbc2d6..1c70f02 100644 --- a/app/service/chat_robot/script/memory/user_buffer_window.py +++ b/app/service/chat_robot/script/memory/user_buffer_window.py @@ -4,7 +4,6 @@ import json import redis from redis import Redis -from langchain.memory import RedisChatMessageHistory from langchain.memory.chat_memory import BaseChatMemory from langchain.schema.messages import BaseMessage, get_buffer_string, HumanMessage, AIMessage from langchain.schema.messages import _message_to_dict, messages_from_dict diff --git a/app/service/chat_robot/script/service/CallQWen.py b/app/service/chat_robot/script/service/CallQWen.py new file mode 100644 index 0000000..6067804 --- /dev/null +++ b/app/service/chat_robot/script/service/CallQWen.py @@ -0,0 +1,235 @@ +import json +from typing import Dict, Any + +from dashscope import Generation + +from app.core.config import * +from app.service.chat_robot.script.callbacks.qwen_callback_handler import QWenCallbackHandler +from app.service.chat_robot.script.database import CustomDatabase +from app.service.chat_robot.script.prompt import FASHION_CHAT_BOT_PREFIX, TOOLS_FUNCTIONS_SUFFIX + + +get_database_table_description = "Input is an empty string, output is a comma separated list of tables in the database." + +get_table_info_description = ( + "Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables." + "There are eight tables covering eight fashion categories: female_top, female_pants, female_dress," + "female_skirt, female_outwear, male_bottom, male_top, and male_outwear." + + "Example Input: 'female_outwear, male_top'" +) + +query_database_description = ( + "The input of this tool is a detailed and correct SQL select query statement, " + "and the output is the result of the database, and it can only return up to 4 results." + "If the query is not correct, an error message will be returned." + "If an error is returned, rewrite the query, check the query, and try again." + "If you encounter an issue with Unknown column 'xxxx' in 'field list' or Table 'attribute_retrieval.xxxx' doesn't exist," + "use get_table_info to query the correct table fields." + + "Example Input: 'SELECT img_name FROM female_skirt WHERE opening_type = 'Button' ORDER BY RAND() LIMIT 1'" + "Example Input 2: 'SELECT img_name FROM female_top WHERE sleeve_length = 'Long' AND type = 'Blouse' " + "order by rand() LIMIT 2'" + ) + +tools = [ + # 工具一 + # { + # "type": "function", + # "function": { + # "name": "search_from_internet", + # "description": "从网络搜索结果。", + # "parameters": { + # "type" : "object", + # "properties" : { + # "user_input" : { + # "type" : "string", + # "description" : "用户输入。比如 ： 2025年的时尚潮流趋势是什么？" + # } + # } + # } + # } + # }, + # 工具二 + { + "type": "function", + "function": { + "name": "get_database_table", + "description": get_database_table_description, + "parameters": { + } + } + }, + # 工具三 + { + "type": "function", + "function": { + "name": "get_table_info", + "description": get_table_info_description, + "parameters": { + "type": "object", + "properties": { + "table_names": { + "type": "list", + "description": "需要查询表结构的表名" + } + } + }, + "required": ["table_names"] + } + }, + # 工具四 + { + "type": "function", + "function": { + "name": "query_database", + "description": query_database_description, + "parameters": { + "type": "object", + "properties": { + "sql_string": { + "type": "string", + "description": "由模型生成的sql语句" + } + } + }, + "required": ["sql_string"] + } + } +] + +db = CustomDatabase.from_uri(f'mysql+pymysql://{DB_USERNAME}:{DB_PASSWORD}@{DB_HOST}:{DB_PORT}/attribute_retrieval_V3', + include_tables=['female_top', 'female_skirt', 'female_pants', 'female_dress', + 'female_outwear', 'male_bottom', 'male_top', 'male_outwear'], + engine_args={"pool_recycle": 7200}) +qwen = QWenCallbackHandler() + +def search_from_internet(message): + response = Generation.call( + model='qwen-turbo', + api_key='sk-7658298c6b99443c98184a5e634fe6ab', + messages=message, + tools=tools, + # seed=random.randint(1, 10000), # 设置随机数种子seed，如果没有设置，则随机数种子默认为1234 + result_format='message', # 将输出设置为message形式 + enable_search='True' + ) + return response + +def get_database_table(): + return 'female_top, female_skirt, female_pants, female_dress, female_outwear, male_bottom, male_top, male_outwear' + +def get_table_info(table_names): + return CustomDatabase.get_table_info(db, table_names) + +def query_database(sql_string): + return CustomDatabase.run(db, sql_string) + + +def get_response(messages): + response = Generation.call( + model='qwen-max', + api_key='sk-7658298c6b99443c98184a5e634fe6ab', + messages=messages, + tools=tools, + # seed=random.randint(1, 10000), # 设置随机数种子seed，如果没有设置，则随机数种子默认为1234 + result_format='message', # 将输出设置为message形式 + enable_search='True' + ) + return response + + +def call_with_messages(message): + print('\n') + # messages = [ + # { + # "content": input('请输入：'), # 提问示例："现在几点了？" "一个小时后几点" "北京天气如何？" + # "role": "user" + # } + # ] + + messages = [ + { + "content": FASHION_CHAT_BOT_PREFIX, # 系统message + "role": "system" + }, + { + # "content": input('请输入：'), # 用户message + "content": message, # 用户message + "role": "user" + }, + { + "content": TOOLS_FUNCTIONS_SUFFIX, # ai message + "role": "assistant" + } + ] + + # 模型的第一轮调用 + # first_response = get_response(messages) + # assistant_output = first_response.output.choices[0].message + # print(f"\n大模型第一轮输出信息：{first_response}\n") + # messages.append(assistant_output) + flag = True + count = 1 + result_content = "我是一个时尚AI助手，请问有什么可以帮您" + response_type = "chat" + + while flag and count <= 3: + first_response = get_response(messages) + assistant_output = first_response.output.choices[0].message + QWenCallbackHandler.on_llm_end(qwen, first_response.usage) + print(f"\n大模型第 {count} 轮输出信息：{first_response}\n") + messages.append(assistant_output) + + if 'tool_calls' not in assistant_output: # 如果模型判断无需调用工具，则将assistant的回复直接打印出来，无需进行模型的第二轮调用 + print(f"最终答案：{assistant_output.content}") # 此处直接返回模型的回复，您可以根据您的业务，选择当无需调用工具时最终回复的内容 + result_content = assistant_output.content + break + # 如果模型选择的工具是search_from_internet + # elif assistant_output.tool_calls[0]['function']['name'] == 'search_from_internet': + # tool_info = {"name": "search_from_internet", "role": "tool"} + # user_input = json.loads(assistant_output.tool_calls[0]['function']['arguments'])['user_input'] + # tool_info['content'] = search_from_internet(user_input) + # 如果模型选择的工具是get_database_table + elif assistant_output.tool_calls[0]['function']['name'] == 'get_database_table': + tool_info = {"name": "get_database_table", "role": "tool", 'content': get_database_table()} + # 如果模型选择的工具是get_table_info + elif assistant_output.tool_calls[0]['function']['name'] == 'get_table_info': + tool_info = {"name": "get_table_info", "role": "tool"} + table_names = json.loads(assistant_output.tool_calls[0]['function']['arguments'])['table_names'] + tool_info['content'] = get_table_info(table_names) + # 如果模型选择的工具是query_database + elif assistant_output.tool_calls[0]['function']['name'] == 'query_database': + tool_info = {"name": "query_database", "role": "tool"} + sql_string = json.loads(assistant_output.tool_calls[0]['function']['arguments'])['sql_string'] + tool_info['content'] = query_database(sql_string) + flag = False + result_content = tool_info['content'] + response_type = "image" + + print(f"工具输出信息：{tool_info['content']}\n") + messages.append(tool_info) + count += 1 + + final_output = {"output": result_content} + final_output["response_type"] = response_type + QWenCallbackHandler.on_chain_end(qwen, final_output) + + + + # 模型的第二轮调用，对工具的输出进行总结 + # if flag : + # second_response = get_response(messages) + # print(f"大模型第二轮输出信息：{second_response}\n") + # print(f"最终答案：{second_response.output.choices[0].message['content']}") + # result_content = second_response.output.choices[0].message['content'] + + return final_output + + +if __name__ == '__main__': + call_with_messages() + + + + diff --git a/app/service/chat_robot/script/tools/sql_tools.py b/app/service/chat_robot/script/tools/sql_tools.py index 92b8003..89576d3 100644 --- a/app/service/chat_robot/script/tools/sql_tools.py +++ b/app/service/chat_robot/script/tools/sql_tools.py @@ -1,6 +1,6 @@ # flake8: noqa """Tools for interacting with a SQL database.""" -from typing import Any, Dict, Optional +from typing import Any, Dict, Optional, Type from pydantic import BaseModel, Extra, Field, root_validator @@ -12,9 +12,11 @@ from langchain.callbacks.manager import ( from langchain.chains.llm import LLMChain from langchain.prompts import PromptTemplate # from langchain.sql_database import SQLDatabase -from langchain.utilities import SQLDatabase +from langchain_community.utilities import SQLDatabase from langchain.tools.base import BaseTool -from langchain.tools.sql_database.prompt import QUERY_CHECKER +from langchain_community.tools.sql_database.prompt import QUERY_CHECKER + +from langchain_community.tools.sql_database.tool import QuerySQLCheckerTool, _QuerySQLCheckerToolInput class BaseSQLDatabaseTool(BaseModel): @@ -135,32 +137,70 @@ class ListSQLDatabaseTool(BaseSQLDatabaseTool, BaseTool): raise NotImplementedError("ListTablesSqlDbTool does not support async") +# class QuerySQLCheckerTool(BaseSQLDatabaseTool, BaseTool): +# """Use an LLM to check if a query is correct. +# Adapted from https://www.patterns.app/blog/2023/01/18/crunchbot-sql-analyst-gpt/""" +# +# template: str = QUERY_CHECKER +# llm: BaseLanguageModel +# llm_chain: LLMChain = Field(init=False) +# name = "sql_db_query_checker" +# description = ( +# "Use this tools to double check if your query is correct before executing it." +# "Always use this tools before executing a query with sql_db_query!" +# ) +# +# @root_validator(pre=True) +# def initialize_llm_chain(cls, values: Dict[str, Any]) -> Dict[str, Any]: +# if "llm_chain" not in values: +# values["llm_chain"] = LLMChain( +# llm=values.get("llm"), +# prompt=PromptTemplate( +# template=QUERY_CHECKER, +# input_variables=["query", "dialect"] +# ), +# ) +# +# if values["llm_chain"].prompt.input_variables != ["dialect", "query"]: +# # if values["llm_chain"].prompt.input_variables != ["query", "dialect"]: +# raise ValueError( +# "LLM chain for QueryCheckerTool must have input variables ['query', 'dialect']" +# ) +# +# return values class QuerySQLCheckerTool(BaseSQLDatabaseTool, BaseTool): """Use an LLM to check if a query is correct. Adapted from https://www.patterns.app/blog/2023/01/18/crunchbot-sql-analyst-gpt/""" template: str = QUERY_CHECKER llm: BaseLanguageModel - llm_chain: LLMChain = Field(init=False) - name = "sql_db_query_checker" - description = ( - "Use this tools to double check if your query is correct before executing it." - "Always use this tools before executing a query with sql_db_query!" - ) + llm_chain: Any = Field(init=False) + name: str = "sql_db_query_checker" + description: str = """ + Use this tool to double check if your query is correct before executing it. + Always use this tool before executing a query with sql_db_query! + """ + args_schema: Type[BaseModel] = _QuerySQLCheckerToolInput @root_validator(pre=True) def initialize_llm_chain(cls, values: Dict[str, Any]) -> Dict[str, Any]: if "llm_chain" not in values: - values["llm_chain"] = LLMChain( - llm=values.get("llm"), - prompt=PromptTemplate( - template=QUERY_CHECKER, - input_variables=["query", "dialect"] - ), - ) + # from langchain.chains.llm import LLMChain - if values["llm_chain"].prompt.input_variables != ["dialect", "query"]: - # if values["llm_chain"].prompt.input_variables != ["query", "dialect"]: + llm = values.get("llm") # type: ignore[arg-type] + prompt = PromptTemplate( + template=QUERY_CHECKER, input_variables=["dialect", "query"] + ) + values["llm_chain"] = prompt | llm + # values["llm_chain"] = LLMChain( + # llm=values.get("llm"), # type: ignore[arg-type] + # prompt=PromptTemplate( + # template=QUERY_CHECKER, input_variables=["dialect", "query"] + # ), + # ) + + # if values["llm_chain"].prompt.input_variables != ["dialect", "query"]: + if values["llm_chain"].first.input_variables != ["dialect", "query"]: raise ValueError( "LLM chain for QueryCheckerTool must have input variables ['query', 'dialect']" ) diff --git a/app/service/prompt_generation/chatgpt_for_translation.py b/app/service/prompt_generation/chatgpt_for_translation.py index 4ade635..8ef0dbd 100644 --- a/app/service/prompt_generation/chatgpt_for_translation.py +++ b/app/service/prompt_generation/chatgpt_for_translation.py @@ -1,8 +1,11 @@ import logging -from langchain.chains import LLMChain -from langchain.chat_models import ChatOpenAI +from dashscope import Generation +# from langchain.chains import LLMChain +from langchain_community.chat_models import QianfanChatEndpoint, ChatTongyi +# from langchain.chat_models import ChatOpenAI from langchain_core.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate +from langchain_core.runnables import RunnableSequence from app.core.config import OPENAI_MODEL, OPENAI_API_KEY @@ -10,9 +13,9 @@ from app.core.config import OPENAI_MODEL, OPENAI_API_KEY # os.environ["https_proxy"] = "http://127.0.0.1:7890" -llm = ChatOpenAI(model_name=OPENAI_MODEL, - openai_api_key=OPENAI_API_KEY, - temperature=0) +# llm = ChatOpenAI(model_name=OPENAI_MODEL, +# openai_api_key=OPENAI_API_KEY, +# temperature=0) def translate_to_en(text): @@ -24,48 +27,34 @@ def translate_to_en(text): 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) + messages = [ + { + "content": template, # 系统message + "role": "system" + }, + { + # "content": input('请输入：'), # 用户message + "content": text, # 用户message + "role": "user" + } + ] + first_response = get_response(messages) + assistant_output = first_response.output.choices[0].message + print("translate result : {}".format(assistant_output)) + return assistant_output.content - # 待翻译文本由 Human 角色输入 - human_template = "User input : {text}" - human_message_prompt = HumanMessagePromptTemplate.from_template(input_variables=["text"], template=human_template) - # 使用 System 和 Human 角色的提示模板构造 ChatPromptTemplate - chat_prompt_template = ChatPromptTemplate.from_messages( - [system_message_prompt, human_message_prompt] + +def get_response(messages): + response = Generation.call( + model='qwen-max', + api_key='sk-7658298c6b99443c98184a5e634fe6ab', + messages=messages, + # seed=random.randint(1, 10000), # 设置随机数种子seed，如果没有设置，则随机数种子默认为1234 + result_format='message', # 将输出设置为message形式 + enable_search='True' ) - translate_chain = LLMChain(llm=llm, prompt=chat_prompt_template) - - result = translate_chain.invoke(text) - - logging.info("translate result : " + result.get('text')) - # print("translate result : " + result.get('text')) - return result.get('text') - - # template = ( - # """ - # 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 : - # """ - # ) - # # "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 - + return response def main(): """Main function"""