tests/local_testing/test_class.py

# # #### What this tests ####
# # #    This tests the LiteLLM Class

# import sys, os
# import traceback
# import pytest

# sys.path.insert(
#     0, os.path.abspath("../..")
# )  # Adds the parent directory to the system path
# import litellm
# import asyncio

# # litellm.set_verbose = True
# # from litellm import Router
# import instructor

# from litellm import completion
# from pydantic import BaseModel


# class User(BaseModel):
#     name: str
#     age: int


# client = instructor.from_litellm(completion)

# litellm.set_verbose = True

# resp = client.chat.completions.create(
#     model="gpt-3.5-turbo",
#     max_tokens=1024,
#     messages=[
#         {
#             "role": "user",
#             "content": "Extract Jason is 25 years old.",
#         }
#     ],
#     response_model=User,
#     num_retries=10,
# )

# assert isinstance(resp, User)
# assert resp.name == "Jason"
# assert resp.age == 25

# # from pydantic import BaseModel

# # # This enables response_model keyword
# # # from client.chat.completions.create
# # client = instructor.patch(
# #     Router(
# #         model_list=[
# #             {
# #                 "model_name": "gpt-3.5-turbo",  # openai model name
# #                 "litellm_params": {  # params for litellm completion/embedding call
# #                     "model": "azure/chatgpt-v-3",
# #                     "api_key": os.getenv("AZURE_API_KEY"),
# #                     "api_version": os.getenv("AZURE_API_VERSION"),
# #                     "api_base": os.getenv("AZURE_API_BASE"),
# #                 },
# #             }
# #         ]
# #     )
# # )


# # class UserDetail(BaseModel):
# #     name: str
# #     age: int


# # user = client.chat.completions.create(
# #     model="gpt-3.5-turbo",
# #     response_model=UserDetail,
# #     messages=[
# #         {"role": "user", "content": "Extract Jason is 25 years old"},
# #     ],
# # )

# # assert isinstance(user, UserDetail)
# # assert user.name == "Jason"
# # assert user.age == 25

# # print(f"user: {user}")
# # # import instructor
# # # from openai import AsyncOpenAI

# # aclient = instructor.apatch(
# #     Router(
# #         model_list=[
# #             {
# #                 "model_name": "gpt-3.5-turbo",  # openai model name
# #                 "litellm_params": {  # params for litellm completion/embedding call
# #                     "model": "azure/chatgpt-v-3",
# #                     "api_key": os.getenv("AZURE_API_KEY"),
# #                     "api_version": os.getenv("AZURE_API_VERSION"),
# #                     "api_base": os.getenv("AZURE_API_BASE"),
# #                 },
# #             }
# #         ],
# #         default_litellm_params={"acompletion": True},
# #     )
# # )


# # class UserExtract(BaseModel):
# #     name: str
# #     age: int


# # async def main():
# #     model = await aclient.chat.completions.create(
# #         model="gpt-3.5-turbo",
# #         response_model=UserExtract,
# #         messages=[
# #             {"role": "user", "content": "Extract jason is 25 years old"},
# #         ],
# #     )
# #     print(f"model: {model}")


# # asyncio.run(main())