mirror of
https://github.com/BerriAI/litellm.git
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1102 lines
43 KiB
Python
1102 lines
43 KiB
Python
import os, openai, sys, json, inspect
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from typing import Any
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from functools import partial
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import dotenv, traceback, random, asyncio, time, contextvars
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from copy import deepcopy
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import litellm
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from litellm import ( # type: ignore
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client,
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exception_type,
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timeout,
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get_optional_params,
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get_litellm_params,
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Logging,
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)
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from litellm.utils import (
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get_secret,
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CustomStreamWrapper,
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read_config_args,
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completion_with_fallbacks,
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get_llm_provider,
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mock_completion_streaming_obj
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)
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from .llms import anthropic
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from .llms import together_ai
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from .llms import ai21
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from .llms import sagemaker
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from .llms import bedrock
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from .llms import huggingface_restapi
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from .llms import replicate
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from .llms import aleph_alpha
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from .llms import nlp_cloud
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from .llms import baseten
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from .llms import vllm
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from .llms import ollama
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from .llms import cohere
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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
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encoding = tiktoken.get_encoding("cl100k_base")
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from litellm.utils import (
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get_secret,
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CustomStreamWrapper,
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ModelResponse,
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read_config_args,
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)
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####### ENVIRONMENT VARIABLES ###################
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dotenv.load_dotenv() # Loading env variables using dotenv
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####### COMPLETION ENDPOINTS ################
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#############################################
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async def acompletion(*args, **kwargs):
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loop = asyncio.get_event_loop()
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# Use a partial function to pass your keyword arguments
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func = partial(completion, *args, **kwargs)
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# Add the context to the function
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ctx = contextvars.copy_context()
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func_with_context = partial(ctx.run, func)
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# Call the synchronous function using run_in_executor
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response = await loop.run_in_executor(None, func_with_context)
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if kwargs.get("stream", False): # return an async generator
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# do not change this
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# for stream = True, always return an async generator
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# See OpenAI acreate https://github.com/openai/openai-python/blob/5d50e9e3b39540af782ca24e65c290343d86e1a9/openai/api_resources/abstract/engine_api_resource.py#L193
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return(
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line
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async for line in response
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)
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else:
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return response
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def mock_completion(model: str, messages: List, stream: bool = False, mock_response: str = "This is a mock request", **kwargs):
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try:
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model_response = ModelResponse(stream=stream)
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if stream is True:
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# don't try to access stream object,
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response = mock_completion_streaming_obj(model_response, mock_response=mock_response, model=model)
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return response
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completion_response = "This is a mock request"
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model_response["choices"][0]["message"]["content"] = completion_response
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model_response["created"] = time.time()
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model_response["model"] = model
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return model_response
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except:
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traceback.print_exc()
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raise Exception("Mock completion response failed")
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@client
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@timeout( # type: ignore
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600
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) ## set timeouts, in case calls hang (e.g. Azure) - default is 600s, override with `force_timeout`
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def completion(
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model: str,
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# Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create
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messages: List = [],
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functions: List = [],
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function_call: str = "", # optional params
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temperature: float = 1,
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top_p: float = 1,
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n: int = 1,
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stream: bool = False,
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stop=None,
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max_tokens: float = float("inf"),
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presence_penalty: float = 0,
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frequency_penalty=0,
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logit_bias: dict = {},
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user: str = "",
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deployment_id = None,
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# Optional liteLLM function params
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*,
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return_async=False,
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mock_response: Optional[str] = None,
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api_key: Optional[str] = None,
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api_version: Optional[str] = None,
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api_base: Optional[str] = None,
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force_timeout=600,
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num_beams=1,
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logger_fn=None,
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verbose=False,
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azure=False,
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custom_llm_provider=None,
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litellm_call_id=None,
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litellm_logging_obj=None,
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use_client=False,
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id=None, # this is an optional param to tag individual completion calls
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# model specific optional params
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top_k=40,# used by text-bison only
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task: Optional[str]="text-generation-inference", # used by huggingface inference endpoints
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remove_input: bool = True, # used by nlp cloud models - prevents input text from being returned as part of output
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request_timeout=0, # unused var for old version of OpenAI API
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fallbacks=[],
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caching = False,
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cache_params = {}, # optional to specify metadata for caching
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) -> ModelResponse:
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# If `mock_response` is set, execute the `mock_completion` method instead.
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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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args = locals()
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try:
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logging = litellm_logging_obj
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if fallbacks != []:
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return completion_with_fallbacks(**args)
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if litellm.model_alias_map and model in litellm.model_alias_map:
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args["model_alias_map"] = litellm.model_alias_map
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model = litellm.model_alias_map[
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model
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] # update the model to the actual value if an alias has been passed in
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model_response = ModelResponse()
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if deployment_id != None: # azure llms
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model=deployment_id
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custom_llm_provider="azure"
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elif (
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model.split("/", 1)[0] in litellm.provider_list
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): # allow custom provider to be passed in via the model name "azure/chatgpt-test"
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custom_llm_provider = model.split("/", 1)[0]
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model = model.split("/", 1)[1]
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# check if user passed in any of the OpenAI optional params
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optional_params = get_optional_params(
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functions=functions,
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function_call=function_call,
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temperature=temperature,
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top_p=top_p,
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n=n,
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stream=stream,
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stop=stop,
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max_tokens=max_tokens,
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presence_penalty=presence_penalty,
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frequency_penalty=frequency_penalty,
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logit_bias=logit_bias,
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user=user,
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deployment_id=deployment_id,
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# params to identify the model
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model=model,
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custom_llm_provider=custom_llm_provider,
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top_k=top_k,
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task=task,
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remove_input=remove_input
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)
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# For logging - save the values of the litellm-specific params passed in
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litellm_params = get_litellm_params(
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return_async=return_async,
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api_key=api_key,
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force_timeout=force_timeout,
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logger_fn=logger_fn,
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verbose=verbose,
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custom_llm_provider=custom_llm_provider,
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api_base=api_base,
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litellm_call_id=litellm_call_id,
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model_alias_map=litellm.model_alias_map,
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completion_call_id=id
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)
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logging.update_environment_variables(model=model, user=user, optional_params=optional_params, litellm_params=litellm_params)
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get_llm_provider(model=model, custom_llm_provider=custom_llm_provider)
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if custom_llm_provider == "azure":
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# azure configs
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api_type = get_secret("AZURE_API_TYPE") or "azure"
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api_base = (
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api_base
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or litellm.api_base
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or get_secret("AZURE_API_BASE")
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)
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api_version = (
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api_version or
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litellm.api_version or
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get_secret("AZURE_API_VERSION")
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)
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api_key = (
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api_key or
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litellm.api_key or
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litellm.azure_key or
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get_secret("AZURE_API_KEY")
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)
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## LOGGING
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logging.pre_call(
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input=messages,
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api_key=api_key,
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additional_args={
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"headers": litellm.headers,
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"api_version": api_version,
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"api_base": api_base,
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},
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)
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## COMPLETION CALL
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response = openai.ChatCompletion.create(
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engine=model,
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messages=messages,
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headers=litellm.headers,
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api_key=api_key,
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api_base=api_base,
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api_version=api_version,
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api_type=api_type,
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**optional_params,
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)
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if "stream" in optional_params and optional_params["stream"] == True:
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response = CustomStreamWrapper(response, model, logging_obj=logging)
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return response
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## LOGGING
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logging.post_call(
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input=messages,
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api_key=api_key,
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original_response=response,
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additional_args={
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"headers": litellm.headers,
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"api_version": api_version,
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"api_base": api_base,
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},
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)
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elif (
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model in litellm.open_ai_chat_completion_models
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or custom_llm_provider == "custom_openai"
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or custom_llm_provider == "openai"
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or "ft:gpt-3.5-turbo" in model # finetuned gpt-3.5-turbo
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): # allow user to make an openai call with a custom base
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# note: if a user sets a custom base - we should ensure this works
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# allow for the setting of dynamic and stateful api-bases
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api_base = (
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api_base
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or litellm.api_base
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or get_secret("OPENAI_API_BASE")
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or "https://api.openai.com/v1"
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)
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if litellm.organization:
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openai.organization = litellm.organization
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# set API KEY
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api_key = (
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api_key or
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litellm.api_key or
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litellm.openai_key or
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get_secret("OPENAI_API_KEY")
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)
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## LOGGING
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logging.pre_call(
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input=messages,
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api_key=api_key,
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additional_args={"headers": litellm.headers, "api_base": api_base},
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)
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## COMPLETION CALL
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try:
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response = openai.ChatCompletion.create(
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model=model,
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messages=messages,
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headers=litellm.headers, # None by default
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api_base=api_base, # thread safe setting base, key, api_version
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api_key=api_key,
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api_type="openai",
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api_version=api_version, # default None
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**optional_params,
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)
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except Exception as e:
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## LOGGING - log the original exception returned
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logging.post_call(
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input=messages,
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api_key=api_key,
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original_response=str(e),
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additional_args={"headers": litellm.headers},
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)
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raise e
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if "stream" in optional_params and optional_params["stream"] == True:
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response = CustomStreamWrapper(response, model, logging_obj=logging)
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return response
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## LOGGING
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logging.post_call(
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input=messages,
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api_key=api_key,
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original_response=response,
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additional_args={"headers": litellm.headers},
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)
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elif (
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model in litellm.open_ai_text_completion_models
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or "ft:babbage-002" in model
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or "ft:davinci-002" in model # support for finetuned completion models
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# NOTE: Do NOT add custom_llm_provider == "openai".
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# this will break hosted vllm/proxy calls.
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# see: https://docs.litellm.ai/docs/providers/vllm#calling-hosted-vllm-server.
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# VLLM expects requests to call openai.ChatCompletion we need those requests to always
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# call openai.ChatCompletion
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):
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# print("calling custom openai provider")
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openai.api_type = "openai"
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api_base = (
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api_base
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or litellm.api_base
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or get_secret("OPENAI_API_BASE")
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or "https://api.openai.com/v1"
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)
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openai.api_version = None
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# set API KEY
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api_key = (
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api_key or
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litellm.api_key or
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litellm.openai_key or
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get_secret("OPENAI_API_KEY")
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)
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if litellm.organization:
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openai.organization = litellm.organization
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prompt = " ".join([message["content"] for message in messages])
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## LOGGING
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logging.pre_call(
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input=prompt,
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api_key=api_key,
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additional_args={
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"openai_organization": litellm.organization,
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"headers": litellm.headers,
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"api_base": api_base,
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"api_type": openai.api_type,
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},
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)
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## COMPLETION CALL
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response = openai.Completion.create(
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model=model,
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prompt=prompt,
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headers=litellm.headers,
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api_key = api_key,
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api_base=api_base,
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**optional_params
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)
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if "stream" in optional_params and optional_params["stream"] == True:
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response = CustomStreamWrapper(response, model, logging_obj=logging)
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return response
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## LOGGING
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logging.post_call(
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input=prompt,
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api_key=api_key,
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original_response=response,
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additional_args={
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"openai_organization": litellm.organization,
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"headers": litellm.headers,
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"api_base": openai.api_base,
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"api_type": openai.api_type,
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},
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)
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## RESPONSE OBJECT
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completion_response = response["choices"][0]["text"]
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model_response["choices"][0]["message"]["content"] = completion_response
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model_response["created"] = response.get("created", time.time())
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model_response["model"] = model
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model_response["usage"] = response.get("usage", 0)
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response = model_response
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elif (
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"replicate" in model or
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custom_llm_provider == "replicate" or
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model in litellm.replicate_models
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):
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# Setting the relevant API KEY for replicate, replicate defaults to using os.environ.get("REPLICATE_API_TOKEN")
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replicate_key = None
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replicate_key = (
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get_secret("REPLICATE_API_KEY")
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or get_secret("REPLICATE_API_TOKEN")
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or api_key
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or litellm.replicate_key
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or litellm.api_key
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)
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model_response = replicate.completion(
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model=model,
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messages=messages,
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model_response=model_response,
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print_verbose=print_verbose,
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optional_params=optional_params,
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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=replicate_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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# don't try to access stream object,
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response = CustomStreamWrapper(model_response, model, logging_obj=logging, custom_llm_provider="replicate")
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return response
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response = model_response
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elif model in litellm.anthropic_models:
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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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)
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model_response = anthropic.completion(
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model=model,
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messages=messages,
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model_response=model_response,
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print_verbose=print_verbose,
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optional_params=optional_params,
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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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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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# don't try to access stream object,
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response = CustomStreamWrapper(model_response, model, logging_obj=logging)
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return response
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response = model_response
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elif model in litellm.nlp_cloud_models or custom_llm_provider == "nlp_cloud":
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nlp_cloud_key = (
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api_key or litellm.nlp_cloud_key or get_secret("NLP_CLOUD_API_KEY") or litellm.api_key
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)
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model_response = nlp_cloud.completion(
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model=model,
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messages=messages,
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model_response=model_response,
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print_verbose=print_verbose,
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optional_params=optional_params,
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litellm_params=litellm_params,
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logger_fn=logger_fn,
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encoding=encoding,
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api_key=nlp_cloud_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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# don't try to access stream object,
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response = CustomStreamWrapper(model_response, model, custom_llm_provider="nlp_cloud", logging_obj=logging)
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return response
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response = model_response
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elif model in litellm.aleph_alpha_models:
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aleph_alpha_key = (
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api_key or litellm.aleph_alpha_key or get_secret("ALEPH_ALPHA_API_KEY") or get_secret("ALEPHALPHA_API_KEY") or litellm.api_key
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)
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model_response = aleph_alpha.completion(
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model=model,
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messages=messages,
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model_response=model_response,
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print_verbose=print_verbose,
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optional_params=optional_params,
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litellm_params=litellm_params,
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logger_fn=logger_fn,
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encoding=encoding,
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default_max_tokens_to_sample=litellm.max_tokens,
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api_key=aleph_alpha_key,
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logging_obj=logging # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements
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)
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|
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if "stream" in optional_params and optional_params["stream"] == True:
|
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# don't try to access stream object,
|
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response = CustomStreamWrapper(model_response, model, logging_obj=logging)
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return response
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response = model_response
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elif model in litellm.openrouter_models or custom_llm_provider == "openrouter":
|
|
openai.api_type = "openai"
|
|
# not sure if this will work after someone first uses another API
|
|
openai.api_base = (
|
|
litellm.api_base
|
|
if litellm.api_base is not None
|
|
else "https://openrouter.ai/api/v1"
|
|
)
|
|
openai.api_version = None
|
|
if litellm.organization:
|
|
openai.organization = litellm.organization
|
|
if api_key:
|
|
openai.api_key = api_key
|
|
elif litellm.openrouter_key:
|
|
openai.api_key = litellm.openrouter_key
|
|
else:
|
|
openai.api_key = get_secret("OPENROUTER_API_KEY") or get_secret(
|
|
"OR_API_KEY"
|
|
) or litellm.api_key
|
|
## LOGGING
|
|
logging.pre_call(input=messages, api_key=openai.api_key)
|
|
## COMPLETION CALL
|
|
if litellm.headers:
|
|
response = openai.ChatCompletion.create(
|
|
model=model,
|
|
messages=messages,
|
|
headers=litellm.headers,
|
|
**optional_params,
|
|
)
|
|
else:
|
|
openrouter_site_url = get_secret("OR_SITE_URL")
|
|
openrouter_app_name = get_secret("OR_APP_NAME")
|
|
# if openrouter_site_url is None, set it to https://litellm.ai
|
|
if openrouter_site_url is None:
|
|
openrouter_site_url = "https://litellm.ai"
|
|
# if openrouter_app_name is None, set it to liteLLM
|
|
if openrouter_app_name is None:
|
|
openrouter_app_name = "liteLLM"
|
|
response = openai.ChatCompletion.create(
|
|
model=model,
|
|
messages=messages,
|
|
headers={
|
|
"HTTP-Referer": openrouter_site_url, # To identify your site
|
|
"X-Title": openrouter_app_name, # To identify your app
|
|
},
|
|
**optional_params,
|
|
)
|
|
## LOGGING
|
|
logging.post_call(
|
|
input=messages, api_key=openai.api_key, original_response=response
|
|
)
|
|
elif model in litellm.cohere_models:
|
|
cohere_key = (
|
|
api_key
|
|
or litellm.cohere_key
|
|
or get_secret("COHERE_API_KEY")
|
|
or get_secret("CO_API_KEY")
|
|
or litellm.api_key
|
|
)
|
|
model_response = cohere.completion(
|
|
model=model,
|
|
messages=messages,
|
|
model_response=model_response,
|
|
print_verbose=print_verbose,
|
|
optional_params=optional_params,
|
|
litellm_params=litellm_params,
|
|
logger_fn=logger_fn,
|
|
encoding=encoding,
|
|
api_key=cohere_key,
|
|
logging_obj=logging # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements
|
|
)
|
|
|
|
if "stream" in optional_params and optional_params["stream"] == True:
|
|
# don't try to access stream object,
|
|
response = CustomStreamWrapper(model_response, model, logging_obj=logging)
|
|
return response
|
|
response = model_response
|
|
elif (
|
|
(
|
|
model in litellm.huggingface_models and
|
|
custom_llm_provider!="custom" # if users use a hf model, with a custom/provider. See implementation of custom_llm_provider == custom
|
|
) or
|
|
custom_llm_provider == "huggingface"
|
|
):
|
|
custom_llm_provider = "huggingface"
|
|
huggingface_key = (
|
|
api_key
|
|
or litellm.huggingface_key
|
|
or os.environ.get("HF_TOKEN")
|
|
or os.environ.get("HUGGINGFACE_API_KEY")
|
|
or litellm.api_key
|
|
)
|
|
model_response = huggingface_restapi.completion(
|
|
model=model,
|
|
messages=messages,
|
|
api_base=api_base, # type: ignore
|
|
model_response=model_response,
|
|
print_verbose=print_verbose,
|
|
optional_params=optional_params,
|
|
litellm_params=litellm_params,
|
|
logger_fn=logger_fn,
|
|
encoding=encoding,
|
|
api_key=huggingface_key,
|
|
logging_obj=logging,
|
|
custom_prompt_dict=litellm.custom_prompt_dict
|
|
)
|
|
if "stream" in optional_params and optional_params["stream"] == True:
|
|
# don't try to access stream object,
|
|
response = CustomStreamWrapper(
|
|
model_response, model, custom_llm_provider="huggingface", logging_obj=logging
|
|
)
|
|
return response
|
|
response = model_response
|
|
elif custom_llm_provider == "together_ai" or ("togethercomputer" in model) or (model in litellm.together_ai_models):
|
|
custom_llm_provider = "together_ai"
|
|
together_ai_key = (
|
|
api_key
|
|
or litellm.togetherai_api_key
|
|
or get_secret("TOGETHER_AI_TOKEN")
|
|
or get_secret("TOGETHERAI_API_KEY")
|
|
or litellm.api_key
|
|
)
|
|
|
|
model_response = together_ai.completion(
|
|
model=model,
|
|
messages=messages,
|
|
model_response=model_response,
|
|
print_verbose=print_verbose,
|
|
optional_params=optional_params,
|
|
litellm_params=litellm_params,
|
|
logger_fn=logger_fn,
|
|
encoding=encoding,
|
|
api_key=together_ai_key,
|
|
logging_obj=logging
|
|
)
|
|
if "stream_tokens" in optional_params and optional_params["stream_tokens"] == True:
|
|
# don't try to access stream object,
|
|
response = CustomStreamWrapper(
|
|
model_response, model, custom_llm_provider="together_ai", logging_obj=logging
|
|
)
|
|
return response
|
|
response = model_response
|
|
elif model in litellm.vertex_chat_models:
|
|
try:
|
|
import vertexai
|
|
except:
|
|
raise Exception("vertexai import failed please run `pip install google-cloud-aiplatform`")
|
|
from vertexai.preview.language_models import ChatModel, InputOutputTextPair
|
|
|
|
vertexai.init(
|
|
project=litellm.vertex_project, location=litellm.vertex_location
|
|
)
|
|
# vertexai does not use an API key, it looks for credentials.json in the environment
|
|
|
|
prompt = " ".join([message["content"] for message in messages])
|
|
## LOGGING
|
|
logging.pre_call(input=prompt, api_key=None)
|
|
|
|
chat_model = ChatModel.from_pretrained(model)
|
|
|
|
chat = chat_model.start_chat()
|
|
completion_response = chat.send_message(prompt, **optional_params)
|
|
|
|
## LOGGING
|
|
logging.post_call(
|
|
input=prompt, api_key=None, original_response=completion_response
|
|
)
|
|
|
|
## RESPONSE OBJECT
|
|
model_response["choices"][0]["message"]["content"] = str(completion_response)
|
|
model_response["created"] = time.time()
|
|
model_response["model"] = model
|
|
response = model_response
|
|
elif model in litellm.vertex_text_models:
|
|
try:
|
|
import vertexai
|
|
except:
|
|
raise Exception("vertexai import failed please run `pip install google-cloud-aiplatform`")
|
|
from vertexai.language_models import TextGenerationModel
|
|
|
|
vertexai.init(
|
|
project=litellm.vertex_project, location=litellm.vertex_location
|
|
)
|
|
# vertexai does not use an API key, it looks for credentials.json in the environment
|
|
|
|
prompt = " ".join([message["content"] for message in messages])
|
|
## LOGGING
|
|
logging.pre_call(input=prompt, api_key=None)
|
|
|
|
vertex_model = TextGenerationModel.from_pretrained(model)
|
|
completion_response = vertex_model.predict(prompt, **optional_params)
|
|
|
|
## LOGGING
|
|
logging.post_call(
|
|
input=prompt, api_key=None, original_response=completion_response
|
|
)
|
|
## RESPONSE OBJECT
|
|
model_response["choices"][0]["message"]["content"] = str(completion_response)
|
|
model_response["created"] = time.time()
|
|
model_response["model"] = model
|
|
response = model_response
|
|
elif model in litellm.ai21_models:
|
|
custom_llm_provider = "ai21"
|
|
ai21_key = (
|
|
api_key
|
|
or litellm.ai21_key
|
|
or os.environ.get("AI21_API_KEY")
|
|
or litellm.api_key
|
|
)
|
|
model_response = ai21.completion(
|
|
model=model,
|
|
messages=messages,
|
|
model_response=model_response,
|
|
print_verbose=print_verbose,
|
|
optional_params=optional_params,
|
|
litellm_params=litellm_params,
|
|
logger_fn=logger_fn,
|
|
encoding=encoding,
|
|
api_key=ai21_key,
|
|
logging_obj=logging
|
|
)
|
|
|
|
if "stream" in optional_params and optional_params["stream"] == True:
|
|
# don't try to access stream object,
|
|
response = CustomStreamWrapper(
|
|
model_response, model, custom_llm_provider="ai21", logging_obj=logging
|
|
)
|
|
return response
|
|
|
|
## RESPONSE OBJECT
|
|
response = model_response
|
|
elif custom_llm_provider == "sagemaker":
|
|
# boto3 reads keys from .env
|
|
model_response = sagemaker.completion(
|
|
model=model,
|
|
messages=messages,
|
|
model_response=model_response,
|
|
print_verbose=print_verbose,
|
|
optional_params=optional_params,
|
|
litellm_params=litellm_params,
|
|
logger_fn=logger_fn,
|
|
encoding=encoding,
|
|
logging_obj=logging
|
|
)
|
|
|
|
if "stream" in optional_params and optional_params["stream"] == True: ## [BETA]
|
|
# don't try to access stream object,
|
|
response = CustomStreamWrapper(
|
|
iter(model_response), model, custom_llm_provider="sagemaker", logging_obj=logging
|
|
)
|
|
return response
|
|
|
|
## RESPONSE OBJECT
|
|
response = model_response
|
|
elif custom_llm_provider == "bedrock":
|
|
# boto3 reads keys from .env
|
|
model_response = bedrock.completion(
|
|
model=model,
|
|
messages=messages,
|
|
model_response=model_response,
|
|
print_verbose=print_verbose,
|
|
optional_params=optional_params,
|
|
litellm_params=litellm_params,
|
|
logger_fn=logger_fn,
|
|
encoding=encoding,
|
|
logging_obj=logging
|
|
)
|
|
|
|
if "stream" in optional_params and optional_params["stream"] == True: ## [BETA]
|
|
# don't try to access stream object,
|
|
response = CustomStreamWrapper(
|
|
iter(model_response), model, custom_llm_provider="bedrock", logging_obj=logging
|
|
)
|
|
return response
|
|
|
|
## RESPONSE OBJECT
|
|
response = model_response
|
|
elif custom_llm_provider == "vllm":
|
|
model_response = vllm.completion(
|
|
model=model,
|
|
messages=messages,
|
|
model_response=model_response,
|
|
print_verbose=print_verbose,
|
|
optional_params=optional_params,
|
|
litellm_params=litellm_params,
|
|
logger_fn=logger_fn,
|
|
encoding=encoding,
|
|
logging_obj=logging
|
|
)
|
|
|
|
if "stream" in optional_params and optional_params["stream"] == True: ## [BETA]
|
|
# don't try to access stream object,
|
|
response = CustomStreamWrapper(
|
|
model_response, model, custom_llm_provider="vllm", logging_obj=logging
|
|
)
|
|
return response
|
|
|
|
## RESPONSE OBJECT
|
|
response = model_response
|
|
elif custom_llm_provider == "ollama":
|
|
endpoint = (
|
|
litellm.api_base if litellm.api_base is not None else api_base
|
|
)
|
|
prompt = " ".join([message["content"] for message in messages])
|
|
|
|
## LOGGING
|
|
logging.pre_call(
|
|
input=prompt, api_key=None, additional_args={"endpoint": endpoint}
|
|
)
|
|
generator = ollama.get_ollama_response_stream(endpoint, model, prompt)
|
|
if optional_params.get("stream", False) == True:
|
|
# assume all ollama responses are streamed
|
|
return generator
|
|
else:
|
|
response_string = ""
|
|
for chunk in generator:
|
|
response_string+=chunk['choices'][0]['delta']['content']
|
|
|
|
## RESPONSE OBJECT
|
|
model_response["choices"][0]["message"]["content"] = response_string
|
|
model_response["created"] = time.time()
|
|
model_response["model"] = "ollama/" + model
|
|
prompt_tokens = len(encoding.encode(prompt))
|
|
completion_tokens = len(encoding.encode(response_string))
|
|
model_response["usage"] = {
|
|
"prompt_tokens": prompt_tokens,
|
|
"completion_tokens": completion_tokens,
|
|
"total_tokens": prompt_tokens + completion_tokens,
|
|
}
|
|
response = model_response
|
|
elif (
|
|
custom_llm_provider == "baseten"
|
|
or litellm.api_base == "https://app.baseten.co"
|
|
):
|
|
custom_llm_provider = "baseten"
|
|
baseten_key = (
|
|
api_key or litellm.baseten_key or os.environ.get("BASETEN_API_KEY") or litellm.api_key
|
|
)
|
|
|
|
model_response = baseten.completion(
|
|
model=model,
|
|
messages=messages,
|
|
model_response=model_response,
|
|
print_verbose=print_verbose,
|
|
optional_params=optional_params,
|
|
litellm_params=litellm_params,
|
|
logger_fn=logger_fn,
|
|
encoding=encoding,
|
|
api_key=baseten_key,
|
|
logging_obj=logging
|
|
)
|
|
if inspect.isgenerator(model_response) or ("stream" in optional_params and optional_params["stream"] == True):
|
|
# don't try to access stream object,
|
|
response = CustomStreamWrapper(
|
|
model_response, model, custom_llm_provider="baseten", logging_obj=logging
|
|
)
|
|
return response
|
|
response = model_response
|
|
elif (
|
|
custom_llm_provider == "custom"
|
|
):
|
|
import requests
|
|
|
|
url = (
|
|
litellm.api_base or
|
|
api_base or
|
|
""
|
|
)
|
|
if url == None or url == "":
|
|
raise ValueError("api_base not set. Set api_base or litellm.api_base for custom endpoints")
|
|
|
|
"""
|
|
assume input to custom LLM api bases follow this format:
|
|
resp = requests.post(
|
|
api_base,
|
|
json={
|
|
'model': 'meta-llama/Llama-2-13b-hf', # model name
|
|
'params': {
|
|
'prompt': ["The capital of France is P"],
|
|
'max_tokens': 32,
|
|
'temperature': 0.7,
|
|
'top_p': 1.0,
|
|
'top_k': 40,
|
|
}
|
|
}
|
|
)
|
|
|
|
"""
|
|
prompt = " ".join([message["content"] for message in messages])
|
|
resp = requests.post(url, json={
|
|
'model': model,
|
|
'params': {
|
|
'prompt': [prompt],
|
|
'max_tokens': max_tokens,
|
|
'temperature': temperature,
|
|
'top_p': top_p,
|
|
'top_k': top_k,
|
|
}
|
|
})
|
|
response_json = resp.json()
|
|
"""
|
|
assume all responses from custom api_bases of this format:
|
|
{
|
|
'data': [
|
|
{
|
|
'prompt': 'The capital of France is P',
|
|
'output': ['The capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France is PARIS.\nThe capital of France'],
|
|
'params': {'temperature': 0.7, 'top_k': 40, 'top_p': 1}}],
|
|
'message': 'ok'
|
|
}
|
|
]
|
|
}
|
|
"""
|
|
string_response = response_json['data'][0]['output'][0]
|
|
## RESPONSE OBJECT
|
|
model_response["choices"][0]["message"]["content"] = string_response
|
|
model_response["created"] = time.time()
|
|
model_response["model"] = model
|
|
response = model_response
|
|
else:
|
|
raise ValueError(
|
|
f"Unable to map your input to a model. Check your input - {args}"
|
|
)
|
|
return response
|
|
except Exception as e:
|
|
## Map to OpenAI Exception
|
|
raise exception_type(
|
|
model=model, custom_llm_provider=custom_llm_provider, original_exception=e
|
|
)
|
|
|
|
|
|
def completion_with_retries(*args, **kwargs):
|
|
try:
|
|
import tenacity
|
|
except:
|
|
raise Exception("tenacity import failed please run `pip install tenacity`")
|
|
|
|
retryer = tenacity.Retrying(stop=tenacity.stop_after_attempt(3), reraise=True)
|
|
return retryer(completion, *args, **kwargs)
|
|
|
|
|
|
def batch_completion(
|
|
model: str,
|
|
# Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create
|
|
messages: List = [],
|
|
functions: List = [],
|
|
function_call: str = "", # optional params
|
|
temperature: float = 1,
|
|
top_p: float = 1,
|
|
n: int = 1,
|
|
stream: bool = False,
|
|
stop=None,
|
|
max_tokens: float = float("inf"),
|
|
presence_penalty: float = 0,
|
|
frequency_penalty=0,
|
|
logit_bias: dict = {},
|
|
user: str = "",
|
|
# Optional liteLLM function params
|
|
*,
|
|
return_async=False,
|
|
api_key: Optional[str] = None,
|
|
api_version: Optional[str] = None,
|
|
api_base: Optional[str] = None,
|
|
force_timeout=600,
|
|
# used by text-bison only
|
|
top_k=40,
|
|
custom_llm_provider=None,):
|
|
args = locals()
|
|
batch_messages = messages
|
|
completions = []
|
|
model = model
|
|
custom_llm_provider = None
|
|
if model.split("/", 1)[0] in litellm.provider_list:
|
|
custom_llm_provider = model.split("/", 1)[0]
|
|
model = model.split("/", 1)[1]
|
|
if custom_llm_provider == "vllm":
|
|
optional_params = get_optional_params(
|
|
functions=functions,
|
|
function_call=function_call,
|
|
temperature=temperature,
|
|
top_p=top_p,
|
|
n=n,
|
|
stream=stream,
|
|
stop=stop,
|
|
max_tokens=max_tokens,
|
|
presence_penalty=presence_penalty,
|
|
frequency_penalty=frequency_penalty,
|
|
logit_bias=logit_bias,
|
|
user=user,
|
|
# params to identify the model
|
|
model=model,
|
|
custom_llm_provider=custom_llm_provider,
|
|
top_k=top_k,
|
|
)
|
|
results = vllm.batch_completions(model=model, messages=batch_messages, custom_prompt_dict=litellm.custom_prompt_dict, optional_params=optional_params)
|
|
else:
|
|
def chunks(lst, n):
|
|
"""Yield successive n-sized chunks from lst."""
|
|
for i in range(0, len(lst), n):
|
|
yield lst[i:i + n]
|
|
with ThreadPoolExecutor(max_workers=100) as executor:
|
|
for sub_batch in chunks(batch_messages, 100):
|
|
for message_list in sub_batch:
|
|
kwargs_modified = args
|
|
kwargs_modified["messages"] = message_list
|
|
future = executor.submit(completion, **kwargs_modified)
|
|
completions.append(future)
|
|
|
|
# Retrieve the results from the futures
|
|
results = [future.result() for future in completions]
|
|
return results
|
|
|
|
### EMBEDDING ENDPOINTS ####################
|
|
@client
|
|
@timeout( # type: ignore
|
|
60
|
|
) ## set timeouts, in case calls hang (e.g. Azure) - default is 60s, override with `force_timeout`
|
|
def embedding(
|
|
model, input=[], azure=False, force_timeout=60, litellm_call_id=None, litellm_logging_obj=None, logger_fn=None, caching=False,
|
|
):
|
|
try:
|
|
response = None
|
|
logging = litellm_logging_obj
|
|
logging.update_environment_variables(model=model, user="", optional_params={}, litellm_params={"force_timeout": force_timeout, "azure": azure, "litellm_call_id": litellm_call_id, "logger_fn": logger_fn})
|
|
if azure == True:
|
|
# azure configs
|
|
openai.api_type = get_secret("AZURE_API_TYPE") or "azure"
|
|
openai.api_base = get_secret("AZURE_API_BASE")
|
|
openai.api_version = get_secret("AZURE_API_VERSION")
|
|
openai.api_key = get_secret("AZURE_API_KEY")
|
|
## LOGGING
|
|
logging.pre_call(
|
|
input=input,
|
|
api_key=openai.api_key,
|
|
additional_args={
|
|
"api_type": openai.api_type,
|
|
"api_base": openai.api_base,
|
|
"api_version": openai.api_version,
|
|
},
|
|
)
|
|
## EMBEDDING CALL
|
|
response = openai.Embedding.create(input=input, engine=model)
|
|
elif model in litellm.open_ai_embedding_models:
|
|
openai.api_type = "openai"
|
|
openai.api_base = "https://api.openai.com/v1"
|
|
openai.api_version = None
|
|
openai.api_key = get_secret("OPENAI_API_KEY")
|
|
## LOGGING
|
|
logging.pre_call(
|
|
input=input,
|
|
api_key=openai.api_key,
|
|
additional_args={
|
|
"api_type": openai.api_type,
|
|
"api_base": openai.api_base,
|
|
"api_version": openai.api_version,
|
|
},
|
|
)
|
|
## EMBEDDING CALL
|
|
response = openai.Embedding.create(input=input, model=model)
|
|
else:
|
|
args = locals()
|
|
raise ValueError(f"No valid embedding model args passed in - {args}")
|
|
## LOGGING
|
|
logging.post_call(input=input, api_key=openai.api_key, original_response=response)
|
|
return response
|
|
except Exception as e:
|
|
## LOGGING
|
|
logging.post_call(
|
|
input=input,
|
|
api_key=openai.api_key,
|
|
original_response=str(e),
|
|
)
|
|
## Map to OpenAI Exception
|
|
raise exception_type(
|
|
model=model,
|
|
original_exception=e,
|
|
custom_llm_provider="azure" if azure == True else None,
|
|
)
|
|
|
|
|
|
###### Text Completion ################
|
|
def text_completion(*args, **kwargs):
|
|
if "prompt" in kwargs:
|
|
messages = [{"role": "system", "content": kwargs["prompt"]}]
|
|
kwargs["messages"] = messages
|
|
kwargs.pop("prompt")
|
|
return completion(*args, **kwargs)
|
|
|
|
|
|
####### HELPER FUNCTIONS ################
|
|
## Set verbose to true -> ```litellm.set_verbose = True```
|
|
def print_verbose(print_statement):
|
|
if litellm.set_verbose:
|
|
print(f"LiteLLM: {print_statement}")
|
|
|
|
def config_completion(**kwargs):
|
|
if litellm.config_path != None:
|
|
config_args = read_config_args(litellm.config_path)
|
|
# overwrite any args passed in with config args
|
|
return completion(**kwargs, **config_args)
|
|
else:
|
|
raise ValueError(
|
|
"No config path set, please set a config path using `litellm.config_path = 'path/to/config.json'`"
|
|
)
|