forked from phoenix/litellm-mirror
fix(tests): fixing response objects for testing
This commit is contained in:
Krrish Dholakia
parent
9776126c8d
commit
8a3b771e50
6 changed files with 188 additions and 104 deletions
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@ -125,11 +125,18 @@ def completion(
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data=json.dumps(data),
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stream=optional_params["stream"],
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)
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if response.status_code != 200:
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raise AnthropicError(status_code=response.status_code, message=response.text)
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return response.iter_lines()
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else:
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response = requests.post(
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api_base, headers=headers, data=json.dumps(data)
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)
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if response.status_code != 200:
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raise AnthropicError(status_code=response.status_code, message=response.text)
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## LOGGING
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logging_obj.post_call(
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input=prompt,
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@ -56,7 +56,7 @@ from .llms.azure import AzureChatCompletion
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from .llms.prompt_templates.factory import prompt_factory, custom_prompt, function_call_prompt
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import tiktoken
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from concurrent.futures import ThreadPoolExecutor
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from typing import Callable, List, Optional, Dict, Union
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from typing import Callable, List, Optional, Dict, Union, Mapping
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encoding = tiktoken.get_encoding("cl100k_base")
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from litellm.utils import (
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@ -79,6 +79,35 @@ openai_text_completions = OpenAITextCompletion()
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azure_chat_completions = AzureChatCompletion()
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####### COMPLETION ENDPOINTS ################
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class LiteLLM:
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def __init__(self, *,
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api_key=None,
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organization: str | None = None,
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base_url: str = None,
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timeout: Union[float, None] = 600,
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max_retries: int | None = litellm.num_retries,
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default_headers: Mapping[str, str] | None = None,):
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self.params = locals()
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self.chat = Chat(self.params)
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class Chat():
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def __init__(self, params):
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self.params = params
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self.completions = Completions(self.params)
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class Completions():
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def __init__(self, params):
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self.params = params
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def create(self, model, messages, **kwargs):
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for k, v in kwargs.items():
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self.params[k] = v
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response = completion(model=model, messages=messages, **self.params)
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return response
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async def acompletion(*args, **kwargs):
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"""
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Asynchronously executes a litellm.completion() call for any of litellm supported llms (example gpt-4, gpt-3.5-turbo, claude-2, command-nightly)
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@ -240,7 +269,7 @@ def completion(
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deployment_id = None,
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# set api_base, api_version, api_key
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api_base: Optional[str] = None,
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base_url: Optional[str] = None,
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api_version: Optional[str] = None,
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api_key: Optional[str] = None,
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model_list: Optional[list] = None, # pass in a list of api_base,keys, etc.
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@ -288,6 +317,7 @@ def completion(
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"""
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######### unpacking kwargs #####################
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args = locals()
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api_base = kwargs.get('api_base', None)
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return_async = kwargs.get('return_async', False)
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mock_response = kwargs.get('mock_response', None)
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force_timeout= kwargs.get('force_timeout', 600)
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@ -299,7 +329,8 @@ def completion(
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metadata = kwargs.get('metadata', None)
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fallbacks = kwargs.get('fallbacks', None)
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headers = kwargs.get("headers", None)
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num_retries = kwargs.get("num_retries", None)
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num_retries = kwargs.get("num_retries", None) ## deprecated
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max_retries = kwargs.get("max_retries", None)
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context_window_fallback_dict = kwargs.get("context_window_fallback_dict", None)
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### CUSTOM PROMPT TEMPLATE ###
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initial_prompt_value = kwargs.get("intial_prompt_value", None)
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@ -309,13 +340,17 @@ def completion(
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eos_token = kwargs.get("eos_token", None)
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acompletion = kwargs.get("acompletion", False)
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######## end of unpacking kwargs ###########
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openai_params = ["functions", "function_call", "temperature", "temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "request_timeout", "api_base", "api_version", "api_key", "deployment_id"]
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litellm_params = ["metadata", "acompletion", "caching", "return_async", "mock_response", "api_key", "api_version", "api_base", "force_timeout", "logger_fn", "verbose", "custom_llm_provider", "litellm_logging_obj", "litellm_call_id", "use_client", "id", "fallbacks", "azure", "headers", "model_list", "num_retries", "context_window_fallback_dict", "roles", "final_prompt_value", "bos_token", "eos_token", "request_timeout", "complete_response"]
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openai_params = ["functions", "function_call", "temperature", "temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "request_timeout", "api_base", "api_version", "api_key", "deployment_id", "organization", "base_url", "default_headers", "timeout"]
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litellm_params = ["metadata", "acompletion", "caching", "return_async", "mock_response", "api_key", "api_version", "api_base", "force_timeout", "logger_fn", "verbose", "custom_llm_provider", "litellm_logging_obj", "litellm_call_id", "use_client", "id", "fallbacks", "azure", "headers", "model_list", "num_retries", "context_window_fallback_dict", "roles", "final_prompt_value", "bos_token", "eos_token", "request_timeout", "complete_response", "self", "max_retries"]
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default_params = openai_params + litellm_params
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non_default_params = {k: v for k,v in kwargs.items() if k not in default_params} # model-specific params - pass them straight to the model/provider
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if mock_response:
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return mock_completion(model, messages, stream=stream, mock_response=mock_response)
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try:
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if base_url:
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api_base = base_url
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if max_retries:
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num_retries = max_retries
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logging = litellm_logging_obj
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fallbacks = (
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fallbacks
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@ -648,8 +683,11 @@ def completion(
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response = model_response
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elif custom_llm_provider=="anthropic":
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anthropic_key = (
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api_key or litellm.anthropic_key or os.environ.get("ANTHROPIC_API_KEY") or litellm.api_key
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api_key = (
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api_key
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or litellm.anthropic_key
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or litellm.api_key
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or os.environ.get("ANTHROPIC_API_KEY")
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)
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api_base = (
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api_base
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@ -672,7 +710,7 @@ def completion(
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litellm_params=litellm_params,
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logger_fn=logger_fn,
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encoding=encoding, # for calculating input/output tokens
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api_key=anthropic_key,
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api_key=api_key,
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logging_obj=logging,
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)
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if "stream" in optional_params and optional_params["stream"] == True:
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34
litellm/tests/test_class.py
Normal file
34
litellm/tests/test_class.py
Normal file
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@ -0,0 +1,34 @@
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# #### 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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# litellm.set_verbose = True
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# from litellm import LiteLLM
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# import instructor
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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(LiteLLM())
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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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@ -1,107 +1,107 @@
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import os
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import sys, os
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import traceback
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from dotenv import load_dotenv
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# import os
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# import sys, os
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# import traceback
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# from dotenv import load_dotenv
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load_dotenv()
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import os, io
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# load_dotenv()
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# import os, io
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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 pytest
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import litellm
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from litellm import embedding, completion, text_completion, completion_cost
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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 pytest
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# import litellm
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# from litellm import embedding, completion, text_completion, completion_cost
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from langchain.chat_models import ChatLiteLLM
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from langchain.prompts.chat import (
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ChatPromptTemplate,
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SystemMessagePromptTemplate,
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AIMessagePromptTemplate,
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HumanMessagePromptTemplate,
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)
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from langchain.schema import AIMessage, HumanMessage, SystemMessage
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# from langchain.chat_models import ChatLiteLLM
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# from langchain.prompts.chat import (
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# ChatPromptTemplate,
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# SystemMessagePromptTemplate,
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# AIMessagePromptTemplate,
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# HumanMessagePromptTemplate,
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# )
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# from langchain.schema import AIMessage, HumanMessage, SystemMessage
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def test_chat_gpt():
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try:
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chat = ChatLiteLLM(model="gpt-3.5-turbo", max_tokens=10)
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messages = [
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HumanMessage(
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content="what model are you"
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)
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]
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resp = chat(messages)
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print(resp)
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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# test_chat_gpt()
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def test_claude():
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try:
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chat = ChatLiteLLM(model="claude-2", max_tokens=10)
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messages = [
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HumanMessage(
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content="what model are you"
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)
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]
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resp = chat(messages)
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print(resp)
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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# test_claude()
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def test_palm():
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try:
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chat = ChatLiteLLM(model="palm/chat-bison", max_tokens=10)
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messages = [
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HumanMessage(
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content="what model are you"
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)
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]
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resp = chat(messages)
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print(resp)
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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# test_palm()
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# def test_openai_with_params():
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# def test_chat_gpt():
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# try:
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# api_key = os.environ["OPENAI_API_KEY"]
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# os.environ.pop("OPENAI_API_KEY")
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# print("testing openai with params")
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# llm = ChatLiteLLM(
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# model="gpt-3.5-turbo",
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# openai_api_key=api_key,
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# # Prefer using None which is the default value, endpoint could be empty string
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# openai_api_base= None,
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# max_tokens=20,
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# temperature=0.5,
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# request_timeout=10,
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# model_kwargs={
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# "frequency_penalty": 0,
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# "presence_penalty": 0,
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# },
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# verbose=True,
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# max_retries=0,
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# )
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# chat = ChatLiteLLM(model="gpt-3.5-turbo", max_tokens=10)
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# messages = [
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# HumanMessage(
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# content="what model are you"
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# )
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# ]
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# resp = llm(messages)
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# resp = chat(messages)
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# print(resp)
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# except Exception as e:
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# pytest.fail(f"Error occurred: {e}")
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# test_openai_with_params()
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# # test_chat_gpt()
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# def test_claude():
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# try:
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# chat = ChatLiteLLM(model="claude-2", max_tokens=10)
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# messages = [
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# HumanMessage(
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# content="what model are you"
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# )
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# ]
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# resp = chat(messages)
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# print(resp)
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# except Exception as e:
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# pytest.fail(f"Error occurred: {e}")
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# # test_claude()
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# def test_palm():
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# try:
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# chat = ChatLiteLLM(model="palm/chat-bison", max_tokens=10)
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# messages = [
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# HumanMessage(
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# content="what model are you"
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# )
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# ]
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# resp = chat(messages)
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# print(resp)
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# except Exception as e:
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# pytest.fail(f"Error occurred: {e}")
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# # test_palm()
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# # def test_openai_with_params():
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# # try:
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# # api_key = os.environ["OPENAI_API_KEY"]
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# # os.environ.pop("OPENAI_API_KEY")
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# # print("testing openai with params")
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# # llm = ChatLiteLLM(
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# # model="gpt-3.5-turbo",
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# # openai_api_key=api_key,
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# # # Prefer using None which is the default value, endpoint could be empty string
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# # openai_api_base= None,
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# # max_tokens=20,
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# # temperature=0.5,
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# # request_timeout=10,
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# # model_kwargs={
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# # "frequency_penalty": 0,
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# # "presence_penalty": 0,
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# # },
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# # verbose=True,
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# # max_retries=0,
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# # )
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# # messages = [
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# # HumanMessage(
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# # content="what model are you"
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# # )
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# # ]
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# # resp = llm(messages)
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# # print(resp)
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# # except Exception as e:
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# # pytest.fail(f"Error occurred: {e}")
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# # test_openai_with_params()
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@ -166,7 +166,7 @@ def test_completion_cohere_stream():
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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test_completion_cohere_stream()
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# test_completion_cohere_stream()
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def test_completion_cohere_stream_bad_key():
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try:
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@ -464,6 +464,7 @@ def test_completion_palm_stream():
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def test_completion_claude_stream_bad_key():
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try:
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litellm.cache = None
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litellm.set_verbose = True
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api_key = "bad-key"
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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@ -492,7 +493,7 @@ def test_completion_claude_stream_bad_key():
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pytest.fail(f"Error occurred: {e}")
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# test_completion_claude_stream_bad_key()
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test_completion_claude_stream_bad_key()
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# test_completion_replicate_stream()
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# def test_completion_vertexai_stream():
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@ -53,7 +53,7 @@ from .exceptions import (
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APIError,
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BudgetExceededError
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)
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from typing import cast, List, Dict, Union, Optional, Literal
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from typing import cast, List, Dict, Union, Optional, Literal, TypedDict, Required
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from .caching import Cache
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####### ENVIRONMENT VARIABLES ####################
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@ -118,6 +118,10 @@ def map_finish_reason(finish_reason: str): # openai supports 5 stop sequences -
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return "stop"
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return finish_reason
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class FunctionCall(OpenAIObject):
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arguments: str
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name: str
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class Message(OpenAIObject):
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def __init__(self, content="default", role="assistant", logprobs=None, function_call=None, **params):
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super(Message, self).__init__(**params)
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@ -125,7 +129,7 @@ class Message(OpenAIObject):
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self.role = role
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self._logprobs = logprobs
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if function_call:
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self.function_call = function_call
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self.function_call = FunctionCall(**function_call)
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def get(self, key, default=None):
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# Custom .get() method to access attributes with a default value if the attribute doesn't exist
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@ -922,7 +926,7 @@ class Logging:
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callback.log_failure_event(
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start_time=start_time,
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end_time=end_time,
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messages=self.messages,
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response_obj=result,
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kwargs=self.model_call_details,
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)
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except Exception as e:
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