Embedding API could return empty embedding while using completion API from LiteLLM

To reproduce: Launch a LiteLLM service: ```bash litellm --model ollama/openhermes2.5-mistral --drop_params ``` Call the service `/completion` API continuously first, meanwhile you call embedding API via Langchain, and hopefully during the very gap (very short) between each `/completion` call you get empty embedding from Langchain. To call the `/completion` API: ```python import os os.environ['OPENAI_API_KEY'] = 'any' os.environ['OPENAI_API_BASE'] = 'http://0.0.0.0:8000' from contextlib import contextmanager from langchain.llms import OpenAI import tiktoken def print_center(banner: str): print(banner.center(50, "=")) class LLM: """ A class for running a Language Model Chain. """ def __init__(self, prompt: str, temperature=0, gpt_4=False): """ Initializes the LLM class. Args: prompt (PromptTemplate): The prompt template to use. temperature (int): The temperature to use for the model. gpt_4 (bool): Whether to use GPT-4 or Text-Davinci-003. Side Effects: Sets the class attributes. """ self.prompt = prompt self.prompt_size = self.number_of_tokens(prompt) self.temperature = temperature self.gpt_4 = gpt_4 self.model_name = "gpt-4" if self.gpt_4 else "text-davinci-003" self.max_tokens = 4097 * 2 if self.gpt_4 else 4097 self.show_init_config() def show_init_config(self): print_center("init params") print(f"Model: {self.model_name}") print(f"Max Tokens: {self.max_tokens}") print(f"Prompt Size: {self.prompt_size}") print(f"Temperature: {self.temperature}") print_center("init config") print(self.prompt) def run(self, query): """ Runs the Language Model Chain. Args: code (str): The code to use for the chain. **kwargs (dict): Additional keyword arguments. Returns: str: The generated text. …