If your LLM response result is json-format data, you should provide the method to extract the real result, for example:

- 可移植代码模块点[这里](../../operategpt/llms)

如果你的LLM推理服务响应是一个json格式数据，你应该提供提取真正结果的方法，例如：

VECTOR_STORE_TYPE=os.getenv("VECTOR_STORE_TYPE","Chroma")

EMBEDDING_MODEL=os.getenv("EMBEDDING_MODEL","all-MiniLM-L6-v2")

DOWNLOAD_FOLDER=os.getenv("DOWNLOAD_FOLDER",GENERATED_OPERATE_DATA)

GENERATED_OPERATE_DATA_DEFAULT_DIR=os.getenv("GENERATED_OPERATE_DATA_DEFAULT_DIR","/var/www/html/experience")

GENERATED_OPERATE_DATA_DEFAULT_DIR=os.getenv(

"GENERATED_OPERATE_DATA_DEFAULT_DIR","/var/www/html/experience"

)

LANGUAGE=os.getenv("LANGUAGE","en")

SERVER_MODE=os.getenv("SERVER_MODE","local")

defparse_image_info(summary_data:str,lang:str)->dict:

images_prompt_info=OperatePromptManager.get_instance().get_prompt(lang).image_desc_prompt.format(summary_data)

images_prompt_info= (

OperatePromptManager.get_instance()

.get_prompt(lang)

.image_desc_prompt.format(summary_data)

)

logger.info(

)

defdownload_file(download_url:str)->str:

try:

parts=download_url.rsplit('/',2)

parts=download_url.rsplit("/",2)

os.makedirs(f"{GENERATED_OPERATE_DATA_DEFAULT_DIR}/{parts[-2]}",exist_ok=True)

response=requests.get(download_url)

defdownload_video_file(download_url:str)->str:

try:

parts=download_url.rsplit('/',1)

parts=download_url.rsplit("/",1)

origin_file=parts[-1]

response=requests.get(download_url)

forimage_name,image_promptinconverted_dict.items():

download_url=sd_proxy.sd_request(prompt=image_prompt,image_name=image_name)

download_url=sd_proxy.sd_request(

prompt=image_prompt,image_name=image_name

)

ifSERVER_MODE=='online':

ifSERVER_MODE=="online":

download_url=download_file(download_url)

image_dict.append({"image_name":image_name,"url":download_url})

defparse_video_info(summary_data:str,lang:str)->dict:

videos_prompt_info=OperatePromptManager.get_instance().get_prompt(lang).video_desc_prompt.format(summary_data)

videos_prompt_info= (

OperatePromptManager.get_instance()

.get_prompt(lang)

.video_desc_prompt.format(summary_data)

)

logger.info(

)

t2v_prompt=T2VPrompt()

t2v_prompt.prompt=video_prompt

download_url=t2v_request(t2v_prompt)

ifSERVER_MODE=='online':

ifSERVER_MODE=="online":

download_url=download_video_file(download_url)

ifdownload_urlisNone:

withopen(full_path,"w")asfile:

file.write(content)

defstartup(idea:str,lang:str=Language.ENGLISH.value):

video_data=generate_videos(video_prompt_dict)

logger.info(f"\ncompleted generate_videos=\n{video_data}")

prompt_req=OperatePromptManager.get_instance().get_prompt(lang).operate_prompt.format(summary_data,image_data,

video_data)

prompt_req= (

OperatePromptManager.get_instance()

.get_prompt(lang)

.operate_prompt.format(summary_data,image_data,video_data)

)

logger.info(f"\ngenerated markdown content prompt request=\n{prompt_req}")

result=query_from_openai_proxy(prompt_req)

logger.info(f"\ngenerated markdown content:\n{result}")

download_url=write_markdown_content(

result,operation_name,GENERATED_OPERATE_DATAifSERVER_MODE=='local'elseGENERATED_OPERATE_DATA_DEFAULT_DIR

result,

operation_name,

elseGENERATED_OPERATE_DATA_DEFAULT_DIR,

)

logger.info(f"\nwrite file completed, download_url={download_url}")

worker:Worker=Worker(LLM_NAME)

defgen(req:LLMReq):

print(f"input={req.input}")

msg:ModelMessage=ModelMessage(role="human",content=req.input)

self,

model_name:str,

req_type:str="POST",

req_url_template:str="http://localhost:8008/generate",

req_url_template:str="http://localhost:8008/api/generate",

req_param:dict=None,

is_stream:bool=False,

header:dict=None,

returnWorkerManager.get_instance().remove_worker(conn)

defworkers():

wk_map:Dict[str,List["Worker"]]=WorkerManager.get_instance().get_all_workers()

classLanguage(Enum):

defget_all_langs(cls):

image_desc_prompt:str

video_desc_prompt:str

def__init__(self,operate_prompt:str,image_decs_prompt:str,video_desc_prompt:str):

def__init__(

self,operate_prompt:str,image_decs_prompt:str,video_desc_prompt:str

):

self.operate_prompt=operate_prompt

self.image_desc_prompt=image_decs_prompt

self.video_desc_prompt=video_desc_prompt

cls._instance=super().__new__(cls)

cls._instance.prompts= {}

print("INIT operate manager running!")

prompt_en:OperatePrompt=OperatePrompt(OPERATE_PROMPT,IMAGE_DESC_PROMPT,VIDEO_DESC_PROMPT)

prompt_zh:OperatePrompt=OperatePrompt(OPERATE_PROMPT_ZH,IMAGE_DESC_PROMPT_ZH,VIDEO_DESC_PROMPT_ZH)

prompt_en:OperatePrompt=OperatePrompt(

OPERATE_PROMPT,IMAGE_DESC_PROMPT,VIDEO_DESC_PROMPT

)

prompt_zh:OperatePrompt=OperatePrompt(

OPERATE_PROMPT_ZH,IMAGE_DESC_PROMPT_ZH,VIDEO_DESC_PROMPT_ZH

)

cls._instance.prompts[Language.ENGLISH.value]=prompt_en

cls._instance.prompts[Language.CHINESE.value]=prompt_zh

print("INIT operate manager succeed!")

defcreate_task(user_id:int,prompt:str,lang:str="en"):

try:

iflangnotinLanguage.get_all_langs():

return {"success":False,"msg":f"create task failed: lang{lang} is not supported!"}

return {

"success":False,

"msg":f"create task failed: lang{lang} is not supported!",

}

task_uuid=generate_uuid()

session=Session()

task=Task(user_id=user_id,prompt=prompt,status="init",uuid=task_uuid,gmt_create=datetime.now(),gmt_modified=datetime.now(),lang=lang)

task=Task(

user_id=user_id,

prompt=prompt,

status="init",

uuid=task_uuid,

gmt_create=datetime.now(),

gmt_modified=datetime.now(),

lang=lang,

)

session.add(task)

session.commit()

session.close()

return {"success":True,"msg":f"create task success, please remember taskid for query result! uuid={task_uuid}"}

return {

"success":True,

"msg":f"create task success, please remember taskid for query result! uuid={task_uuid}",

}

exceptExceptionase:

return {"success":False,"msg":f"create task failed:{str(e)}"}

deflist_tasks(task_uuid:str):

session=Session()

task=session.query(Task).filter(Task.uuid==task_uuid).first()

init_task_count=session.query(func.count(Task.id)).filter(Task.id<task.id,Task.status=='init').scalar()

init_task_count= (

session.query(func.count(Task.id))

.filter(Task.id<task.id,Task.status=="init")

.scalar()

)

session.close()

task_dict= {

"id":task.id,

"gmt_modified":str(task.gmt_modified),

"lang":str(task.lang),

"result":task.result,

"queue_count":init_task_count

"queue_count":init_task_count,

}

returnResult.success(task_dict)

defstart_task_processing():

thread=threading.Thread(target=process_tasks)

thread.start()