forked from phoenix/litellm-mirror
124 lines
3.1 KiB
Python
124 lines
3.1 KiB
Python
# # #### What this tests ####
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# # # This tests the LiteLLM Class
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# import sys, os
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# import traceback
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# import pytest
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# sys.path.insert(
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# 0, os.path.abspath("../..")
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# ) # Adds the parent directory to the system path
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# import litellm
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# import asyncio
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# # litellm.set_verbose = True
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# # from litellm import Router
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# import instructor
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# from litellm import completion
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# from pydantic import BaseModel
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# class User(BaseModel):
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# name: str
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# age: int
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# client = instructor.from_litellm(completion)
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# litellm.set_verbose = True
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# resp = client.chat.completions.create(
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# model="gpt-3.5-turbo",
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# max_tokens=1024,
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# messages=[
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# {
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# "role": "user",
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# "content": "Extract Jason is 25 years old.",
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# }
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# ],
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# response_model=User,
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# num_retries=10,
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# )
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# assert isinstance(resp, User)
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# assert resp.name == "Jason"
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# assert resp.age == 25
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# # from pydantic import BaseModel
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# # # This enables response_model keyword
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# # # from client.chat.completions.create
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# # client = instructor.patch(
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# # Router(
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# # model_list=[
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# # {
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# # "model_name": "gpt-3.5-turbo", # openai model name
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# # "litellm_params": { # params for litellm completion/embedding call
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# # "model": "azure/chatgpt-v-2",
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# # "api_key": os.getenv("AZURE_API_KEY"),
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# # "api_version": os.getenv("AZURE_API_VERSION"),
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# # "api_base": os.getenv("AZURE_API_BASE"),
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# # },
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# # }
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# # ]
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# # )
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# # )
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# # class UserDetail(BaseModel):
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# # name: str
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# # age: int
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# # user = client.chat.completions.create(
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# # model="gpt-3.5-turbo",
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# # response_model=UserDetail,
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# # messages=[
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# # {"role": "user", "content": "Extract Jason is 25 years old"},
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# # ],
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# # )
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# # assert isinstance(user, UserDetail)
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# # assert user.name == "Jason"
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# # assert user.age == 25
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# # print(f"user: {user}")
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# # # import instructor
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# # # from openai import AsyncOpenAI
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# # aclient = instructor.apatch(
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# # Router(
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# # model_list=[
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# # {
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# # "model_name": "gpt-3.5-turbo", # openai model name
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# # "litellm_params": { # params for litellm completion/embedding call
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# # "model": "azure/chatgpt-v-2",
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# # "api_key": os.getenv("AZURE_API_KEY"),
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# # "api_version": os.getenv("AZURE_API_VERSION"),
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# # "api_base": os.getenv("AZURE_API_BASE"),
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# # },
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# # }
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# # ],
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# # default_litellm_params={"acompletion": True},
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# # )
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# # )
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# # class UserExtract(BaseModel):
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# # name: str
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# # age: int
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# # async def main():
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# # model = await aclient.chat.completions.create(
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# # model="gpt-3.5-turbo",
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# # response_model=UserExtract,
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# # messages=[
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# # {"role": "user", "content": "Extract jason is 25 years old"},
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# # ],
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# # )
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# # print(f"model: {model}")
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# # asyncio.run(main())
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