openai.ChatOpenAI.completion_with_retry.<locals>._completion_with_retry in 1.0 seconds as it raised RateLimitError: That model is currently overloaded with other requests. You can retry your request, or contact us through our help center at help.openai.com if the error persists. (Please includ...
Thought:Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-gpt-3.5-turbo in organization org-FDYSniIsv0FIQBi9p4P9Dinn on requests per min. Limit: 3 / min. Please tr...
extend(response["choices"]) else: response = completion_with_retry(self, prompt=_prompts, **params) choices.extend(response["choices"]) if not self.streaming: # Can't update token usage if streaming update_token_usage(_keys, response, token_usage) return self.create_llm_result(choices, ...
response = self.completion_with_retry(messages=message_dicts, **params) C:\Users\Tdf\anaconda3\envs\aillmflow\lib\site-packages\bisheng_langchain\chat_models\host_llm.py:236: in completion_with_retry returncompletion_with_retry(**kwargs) C:\Users\Tdf\anaconda3\envs\aillmflow\lib\site-pac...
File "/public/home/robertchen/anaconda3/envs/lmfchatchat/lib/python3.10/site-packages/langchain_community/chat_models/openai.py", line 105, in acompletion_with_retry return await llm.async_client.create(**kwargs) File "/public/home/robertchen/anaconda3/envs/lmfchatchat/lib/python3.10/site-...
格式错误的原因是因为json文件需要双引号进行标记,但是这里用了单引号,此时利用该解析器进行解析就会出现报错,但是此时可以利用RetryWithErrorOutputParser进行修复错误,则会正常输出不报错。 from langchain.output_parsers import RetryWithErrorOutputParser from langchain.llms import OpenAI retry_parser = RetryWithErro...
如今各类AI模型层出不穷,百花齐放,大佬们开发的速度永远遥遥领先于学习者的学习速度。。为了解放生产力,不让应用层开发人员受限于各语言模型的生产部署中..LangChain横空出世界。
从上述例子,可以直观的看到ChatPromptTemplate可以将prompt中声明的输入变量style和text准确提取出来,使prompt更清晰。当然,Langchain对于prompt的优化不止这一种方式,它还提供了各类其他接口将prompt进一步优化,这里只是举例一个较为基础且直观的方法,让大家感受一下。
"""defparse_with_prompt(self,completion:str,prompt:PromptValue)->Any:"""Optional method to parse the output of an LLM call with a prompt. The prompt is largely provided in the event the OutputParser wants to retry or fix the output in some way, and needs information from ...
def parse_with_prompt(self, completion: str, prompt: PromptValue) -> Any: """Optional method to parse the output of an LLM call with a prompt. The prompt is largely provided in the event the OutputParser wants to retry or fix the output in some way, and needs information from ...