error

  File "/home/thoraxe/Red_Hat/openshift/llamaindex-experiments/fastapi-lightspeed-service/ols.py", line 4, in <module>
    from model_context import get_watsonx_predictor
  File "/home/thoraxe/Red_Hat/openshift/llamaindex-experiments/fastapi-lightspeed-service/model_context.py", line 3, in <module>
    from watsonx_langchain_wrapper import WatsonxLLM
  File "/home/thoraxe/Red_Hat/openshift/llamaindex-experiments/fastapi-lightspeed-service/watsonx_langchain_wrapper.py", line 17, in <module>
    class WatsonxLLM(LLM, BaseModel):
TypeError: metaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its bases

watsonx_langchain_wrapper.py

from typing import Union
from fastapi import FastAPI
from pydantic import BaseModel

## watsonx stuff
from langchain.llms.base import LLM
from typing import Any, List, Mapping, Optional, Dict
from ibm_watson_machine_learning.foundation_models import Model
from pydantic import BaseModel, Extra
from langchain.llms.utils import enforce_stop_tokens

## internal stuff
#from model_context import get_watsonx_predictor

class LLMRequest(BaseModel):
    query: str

class WatsonxLLM(LLM, BaseModel):
    credentials: Optional[Dict] = None
    model: Optional[str] = None
    params: Optional[Dict] = None
    project_id : Optional[str]=None

    class Config:
        extra = Extra.forbid

    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        _params = self.params or {}
        return {
            **{"model": self.model},
            **{"params": _params},
        }
    
    @property
    def _llm_type(self) -> str:
        return "IBM WATSONX"

    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
        params = self.params or {}
        model = Model(model_id=self.model, params=params, credentials=self.credentials, project_id=self.project_id)
        text = model.generate_text(prompt)
        if stop is not None:
            text = enforce_stop_tokens(text, stop)
        return text


app = FastAPI()

@app.get("/healthz")
def read_root():
    return {"status": "1"}

@app.post("/base_llm_completion")
def base_llm_completion(llm_request: LLMRequest):

    return 'x:y'