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import asyncio
import contextvars
import datetime
import inspect
import json
import os
import random
import sys
import time
import traceback
import uuid
from concurrent import futures
from concurrent.futures import FIRST_COMPLETED, ThreadPoolExecutor, wait
from copy import deepcopy
from functools import partial
from typing import (
Any,
Callable,
Coroutine,
Dict,
List,
Literal,
Mapping,
Optional,
Type,
Union,
cast,
)
import dotenv
import httpx
import openai
import tiktoken
from pydantic import BaseModel
from typing_extensions import overload
import litellm
from litellm import ( # type: ignore
Logging,
client,
exception_type,
get_litellm_params,
get_optional_params,
)
from litellm.exceptions import LiteLLMUnknownProvider
from litellm.integrations.custom_logger import CustomLogger
from litellm.litellm_core_utils.audio_utils.utils import get_audio_file_for_health_check
from litellm.litellm_core_utils.health_check_utils import (
_create_health_check_response,
_filter_model_params,
)
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.litellm_core_utils.llm_request_utils import (
pick_cheapest_chat_models_from_llm_provider,
)
from litellm.litellm_core_utils.mock_functions import (
mock_embedding,
mock_image_generation,
)
from litellm.litellm_core_utils.prompt_templates.common_utils import (
get_content_from_model_response,
)
from litellm.llms.base_llm.chat.transformation import BaseConfig
from litellm.llms.bedrock.common_utils import BedrockModelInfo
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
from litellm.realtime_api.main import _realtime_health_check
from litellm.secret_managers.main import get_secret_str
from litellm.types.router import GenericLiteLLMParams
from litellm.types.utils import RawRequestTypedDict
from litellm.utils import (
CustomStreamWrapper,
ProviderConfigManager,
Usage,
add_openai_metadata,
async_mock_completion_streaming_obj,
convert_to_model_response_object,
create_pretrained_tokenizer,
create_tokenizer,
get_api_key,
get_llm_provider,
get_non_default_completion_params,
get_optional_params_embeddings,
get_optional_params_image_gen,
get_optional_params_transcription,
get_secret,
mock_completion_streaming_obj,
read_config_args,
supports_httpx_timeout,
token_counter,
validate_and_fix_openai_messages,
validate_chat_completion_tool_choice,
)
from ._logging import verbose_logger
from .caching.caching import disable_cache, enable_cache, update_cache
from .litellm_core_utils.fallback_utils import (
async_completion_with_fallbacks,
completion_with_fallbacks,
)
from .litellm_core_utils.prompt_templates.common_utils import get_completion_messages
from .litellm_core_utils.prompt_templates.factory import (
custom_prompt,
function_call_prompt,
map_system_message_pt,
ollama_pt,
prompt_factory,
stringify_json_tool_call_content,
)
from .litellm_core_utils.streaming_chunk_builder_utils import ChunkProcessor
from .llms import baseten, maritalk, ollama_chat
from .llms.anthropic.chat import AnthropicChatCompletion
from .llms.azure.audio_transcriptions import AzureAudioTranscription
from .llms.azure.azure import AzureChatCompletion, _check_dynamic_azure_params
from .llms.azure.chat.o_series_handler import AzureOpenAIO1ChatCompletion
from .llms.azure.completion.handler import AzureTextCompletion
from .llms.azure_ai.embed import AzureAIEmbedding
from .llms.bedrock.chat import BedrockConverseLLM, BedrockLLM
from .llms.bedrock.embed.embedding import BedrockEmbedding
from .llms.bedrock.image.image_handler import BedrockImageGeneration
from .llms.codestral.completion.handler import CodestralTextCompletion
from .llms.cohere.embed import handler as cohere_embed
from .llms.custom_httpx.aiohttp_handler import BaseLLMAIOHTTPHandler
from .llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler
from .llms.custom_llm import CustomLLM, custom_chat_llm_router
from .llms.databricks.chat.handler import DatabricksChatCompletion
from .llms.databricks.embed.handler import DatabricksEmbeddingHandler
from .llms.deprecated_providers import aleph_alpha, palm
from .llms.groq.chat.handler import GroqChatCompletion
from .llms.huggingface.chat.handler import Huggingface
from .llms.nlp_cloud.chat.handler import completion as nlp_cloud_chat_completion
from .llms.ollama.completion import handler as ollama
from .llms.oobabooga.chat import oobabooga
from .llms.openai.completion.handler import OpenAITextCompletion
from .llms.openai.image_variations.handler import OpenAIImageVariationsHandler
from .llms.openai.openai import OpenAIChatCompletion
from .llms.openai.transcriptions.handler import OpenAIAudioTranscription
from .llms.openai_like.chat.handler import OpenAILikeChatHandler
from .llms.openai_like.embedding.handler import OpenAILikeEmbeddingHandler
from .llms.petals.completion import handler as petals_handler
from .llms.predibase.chat.handler import PredibaseChatCompletion
from .llms.replicate.chat.handler import completion as replicate_chat_completion
from .llms.sagemaker.chat.handler import SagemakerChatHandler
from .llms.sagemaker.completion.handler import SagemakerLLM
from .llms.vertex_ai import vertex_ai_non_gemini
from .llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexLLM
from .llms.vertex_ai.gemini_embeddings.batch_embed_content_handler import (
GoogleBatchEmbeddings,
)
from .llms.vertex_ai.image_generation.image_generation_handler import (
VertexImageGeneration,
)
from .llms.vertex_ai.multimodal_embeddings.embedding_handler import (
VertexMultimodalEmbedding,
)
from .llms.vertex_ai.text_to_speech.text_to_speech_handler import VertexTextToSpeechAPI
from .llms.vertex_ai.vertex_ai_partner_models.main import VertexAIPartnerModels
from .llms.vertex_ai.vertex_embeddings.embedding_handler import VertexEmbedding
from .llms.vertex_ai.vertex_model_garden.main import VertexAIModelGardenModels
from .llms.vllm.completion import handler as vllm_handler
from .llms.watsonx.chat.handler import WatsonXChatHandler
from .llms.watsonx.common_utils import IBMWatsonXMixin
from .types.llms.anthropic import AnthropicThinkingParam
from .types.llms.openai import (
ChatCompletionAssistantMessage,
ChatCompletionAudioParam,
ChatCompletionModality,
ChatCompletionPredictionContentParam,
ChatCompletionUserMessage,
HttpxBinaryResponseContent,
)
from .types.utils import (
LITELLM_IMAGE_VARIATION_PROVIDERS,
AdapterCompletionStreamWrapper,
ChatCompletionMessageToolCall,
CompletionTokensDetails,
FileTypes,
HiddenParams,
LlmProviders,
PromptTokensDetails,
ProviderSpecificHeader,
all_litellm_params,
)
encoding = tiktoken.get_encoding("cl100k_base")
from litellm.utils import (
Choices,
EmbeddingResponse,
ImageResponse,
Message,
ModelResponse,
TextChoices,
TextCompletionResponse,
TextCompletionStreamWrapper,
TranscriptionResponse,
)
####### ENVIRONMENT VARIABLES ###################
openai_chat_completions = OpenAIChatCompletion()
openai_text_completions = OpenAITextCompletion()
openai_audio_transcriptions = OpenAIAudioTranscription()
openai_image_variations = OpenAIImageVariationsHandler()
databricks_chat_completions = DatabricksChatCompletion()
groq_chat_completions = GroqChatCompletion()
azure_ai_embedding = AzureAIEmbedding()
anthropic_chat_completions = AnthropicChatCompletion()
azure_chat_completions = AzureChatCompletion()
azure_o1_chat_completions = AzureOpenAIO1ChatCompletion()
azure_text_completions = AzureTextCompletion()
azure_audio_transcriptions = AzureAudioTranscription()
huggingface = Huggingface()
predibase_chat_completions = PredibaseChatCompletion()
codestral_text_completions = CodestralTextCompletion()
bedrock_converse_chat_completion = BedrockConverseLLM()
bedrock_embedding = BedrockEmbedding()
bedrock_image_generation = BedrockImageGeneration()
vertex_chat_completion = VertexLLM()
vertex_embedding = VertexEmbedding()
vertex_multimodal_embedding = VertexMultimodalEmbedding()
vertex_image_generation = VertexImageGeneration()
google_batch_embeddings = GoogleBatchEmbeddings()
vertex_partner_models_chat_completion = VertexAIPartnerModels()
vertex_model_garden_chat_completion = VertexAIModelGardenModels()
vertex_text_to_speech = VertexTextToSpeechAPI()
sagemaker_llm = SagemakerLLM()
watsonx_chat_completion = WatsonXChatHandler()
openai_like_embedding = OpenAILikeEmbeddingHandler()
openai_like_chat_completion = OpenAILikeChatHandler()
databricks_embedding = DatabricksEmbeddingHandler()
base_llm_http_handler = BaseLLMHTTPHandler()
base_llm_aiohttp_handler = BaseLLMAIOHTTPHandler()
sagemaker_chat_completion = SagemakerChatHandler()
####### COMPLETION ENDPOINTS ################
class LiteLLM:
def __init__(
self,
*,
api_key=None,
organization: Optional[str] = None,
base_url: Optional[str] = None,
timeout: Optional[float] = 600,
max_retries: Optional[int] = litellm.num_retries,
default_headers: Optional[Mapping[str, str]] = None,
):
self.params = locals()
self.chat = Chat(self.params, router_obj=None)
class Chat:
def __init__(self, params, router_obj: Optional[Any]):
self.params = params
if self.params.get("acompletion", False) is True:
self.params.pop("acompletion")
self.completions: Union[AsyncCompletions, Completions] = AsyncCompletions(
self.params, router_obj=router_obj
)
else:
self.completions = Completions(self.params, router_obj=router_obj)
class Completions:
def __init__(self, params, router_obj: Optional[Any]):
self.params = params
self.router_obj = router_obj
def create(self, messages, model=None, **kwargs):
for k, v in kwargs.items():
self.params[k] = v
model = model or self.params.get("model")
if self.router_obj is not None:
response = self.router_obj.completion(
model=model, messages=messages, **self.params
)
else:
response = completion(model=model, messages=messages, **self.params)
return response
class AsyncCompletions:
def __init__(self, params, router_obj: Optional[Any]):
self.params = params
self.router_obj = router_obj
async def create(self, messages, model=None, **kwargs):
for k, v in kwargs.items():
self.params[k] = v
model = model or self.params.get("model")
if self.router_obj is not None:
response = await self.router_obj.acompletion(
model=model, messages=messages, **self.params
)
else:
response = await acompletion(model=model, messages=messages, **self.params)
return response
@client
async def acompletion(
model: str,
# Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create
messages: List = [],
functions: Optional[List] = None,
function_call: Optional[str] = None,
timeout: Optional[Union[float, int]] = None,
temperature: Optional[float] = None,
top_p: Optional[float] = None,
n: Optional[int] = None,
stream: Optional[bool] = None,
stream_options: Optional[dict] = None,
stop=None,
max_tokens: Optional[int] = None,
max_completion_tokens: Optional[int] = None,
modalities: Optional[List[ChatCompletionModality]] = None,
prediction: Optional[ChatCompletionPredictionContentParam] = None,
audio: Optional[ChatCompletionAudioParam] = None,
presence_penalty: Optional[float] = None,
frequency_penalty: Optional[float] = None,
logit_bias: Optional[dict] = None,
user: Optional[str] = None,
# openai v1.0+ new params
response_format: Optional[Union[dict, Type[BaseModel]]] = None,
seed: Optional[int] = None,
tools: Optional[List] = None,
tool_choice: Optional[str] = None,
parallel_tool_calls: Optional[bool] = None,
logprobs: Optional[bool] = None,
top_logprobs: Optional[int] = None,
deployment_id=None,
reasoning_effort: Optional[Literal["low", "medium", "high"]] = None,
# set api_base, api_version, api_key
base_url: Optional[str] = None,
api_version: Optional[str] = None,
api_key: Optional[str] = None,
model_list: Optional[list] = None, # pass in a list of api_base,keys, etc.
extra_headers: Optional[dict] = None,
# Optional liteLLM function params
thinking: Optional[AnthropicThinkingParam] = None,
**kwargs,
) -> Union[ModelResponse, CustomStreamWrapper]:
"""
Asynchronously executes a litellm.completion() call for any of litellm supported llms (example gpt-4, gpt-3.5-turbo, claude-2, command-nightly)
Parameters:
model (str): The name of the language model to use for text completion. see all supported LLMs: https://docs.litellm.ai/docs/providers/
messages (List): A list of message objects representing the conversation context (default is an empty list).
OPTIONAL PARAMS
functions (List, optional): A list of functions to apply to the conversation messages (default is an empty list).
function_call (str, optional): The name of the function to call within the conversation (default is an empty string).
temperature (float, optional): The temperature parameter for controlling the randomness of the output (default is 1.0).
top_p (float, optional): The top-p parameter for nucleus sampling (default is 1.0).
n (int, optional): The number of completions to generate (default is 1).
stream (bool, optional): If True, return a streaming response (default is False).
stream_options (dict, optional): A dictionary containing options for the streaming response. Only use this if stream is True.
stop(string/list, optional): - Up to 4 sequences where the LLM API will stop generating further tokens.
max_tokens (integer, optional): The maximum number of tokens in the generated completion (default is infinity).
max_completion_tokens (integer, optional): An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens.
modalities (List[ChatCompletionModality], optional): Output types that you would like the model to generate for this request. You can use `["text", "audio"]`
prediction (ChatCompletionPredictionContentParam, optional): Configuration for a Predicted Output, which can greatly improve response times when large parts of the model response are known ahead of time. This is most common when you are regenerating a file with only minor changes to most of the content.
audio (ChatCompletionAudioParam, optional): Parameters for audio output. Required when audio output is requested with modalities: ["audio"]
presence_penalty (float, optional): It is used to penalize new tokens based on their existence in the text so far.
frequency_penalty: It is used to penalize new tokens based on their frequency in the text so far.
logit_bias (dict, optional): Used to modify the probability of specific tokens appearing in the completion.
user (str, optional): A unique identifier representing your end-user. This can help the LLM provider to monitor and detect abuse.
metadata (dict, optional): Pass in additional metadata to tag your completion calls - eg. prompt version, details, etc.
api_base (str, optional): Base URL for the API (default is None).
api_version (str, optional): API version (default is None).
api_key (str, optional): API key (default is None).
model_list (list, optional): List of api base, version, keys
timeout (float, optional): The maximum execution time in seconds for the completion request.
LITELLM Specific Params
mock_response (str, optional): If provided, return a mock completion response for testing or debugging purposes (default is None).
custom_llm_provider (str, optional): Used for Non-OpenAI LLMs, Example usage for bedrock, set model="amazon.titan-tg1-large" and custom_llm_provider="bedrock"
Returns:
ModelResponse: A response object containing the generated completion and associated metadata.
Notes:
- This function is an asynchronous version of the `completion` function.
- The `completion` function is called using `run_in_executor` to execute synchronously in the event loop.
- If `stream` is True, the function returns an async generator that yields completion lines.
"""
fallbacks = kwargs.get("fallbacks", None)
mock_timeout = kwargs.get("mock_timeout", None)
if mock_timeout is True:
await _handle_mock_timeout_async(mock_timeout, timeout, model)
loop = asyncio.get_event_loop()
custom_llm_provider = kwargs.get("custom_llm_provider", None)
# Adjusted to use explicit arguments instead of *args and **kwargs
completion_kwargs = {
"model": model,
"messages": messages,
"functions": functions,
"function_call": function_call,
"timeout": timeout,
"temperature": temperature,
"top_p": top_p,
"n": n,
"stream": stream,
"stream_options": stream_options,
"stop": stop,
"max_tokens": max_tokens,
"max_completion_tokens": max_completion_tokens,
"modalities": modalities,
"prediction": prediction,
"audio": audio,
"presence_penalty": presence_penalty,
"frequency_penalty": frequency_penalty,
"logit_bias": logit_bias,
"user": user,
"response_format": response_format,
"seed": seed,
"tools": tools,
"tool_choice": tool_choice,
"parallel_tool_calls": parallel_tool_calls,
"logprobs": logprobs,
"top_logprobs": top_logprobs,
"deployment_id": deployment_id,
"base_url": base_url,
"api_version": api_version,
"api_key": api_key,
"model_list": model_list,
"reasoning_effort": reasoning_effort,
"extra_headers": extra_headers,
"acompletion": True, # assuming this is a required parameter
"thinking": thinking,
}
if custom_llm_provider is None:
_, custom_llm_provider, _, _ = get_llm_provider(
model=model, api_base=completion_kwargs.get("base_url", None)
)
fallbacks = fallbacks or litellm.model_fallbacks
if fallbacks is not None:
response = await async_completion_with_fallbacks(
**completion_kwargs, kwargs={"fallbacks": fallbacks}
)
if response is None:
raise Exception(
"No response from fallbacks. Got none. Turn on `litellm.set_verbose=True` to see more details."
)
return response
try:
# Use a partial function to pass your keyword arguments
func = partial(completion, **completion_kwargs, **kwargs)
# Add the context to the function
ctx = contextvars.copy_context()
func_with_context = partial(ctx.run, func)
init_response = await loop.run_in_executor(None, func_with_context)
if isinstance(init_response, dict) or isinstance(
init_response, ModelResponse
): ## CACHING SCENARIO
if isinstance(init_response, dict):
response = ModelResponse(**init_response)
response = init_response
elif asyncio.iscoroutine(init_response):
response = await init_response
else:
response = init_response # type: ignore
if (
custom_llm_provider == "text-completion-openai"
or custom_llm_provider == "text-completion-codestral"
) and isinstance(response, TextCompletionResponse):
response = litellm.OpenAITextCompletionConfig().convert_to_chat_model_response_object(
response_object=response,
model_response_object=litellm.ModelResponse(),
)
if isinstance(response, CustomStreamWrapper):
response.set_logging_event_loop(
loop=loop
) # sets the logging event loop if the user does sync streaming (e.g. on proxy for sagemaker calls)
return response
except Exception as e:
custom_llm_provider = custom_llm_provider or "openai"
raise exception_type(
model=model,
custom_llm_provider=custom_llm_provider,
original_exception=e,
completion_kwargs=completion_kwargs,
extra_kwargs=kwargs,
)
async def _async_streaming(response, model, custom_llm_provider, args):
try:
print_verbose(f"received response in _async_streaming: {response}")
if asyncio.iscoroutine(response):
response = await response
async for line in response:
print_verbose(f"line in async streaming: {line}")
yield line
except Exception as e:
custom_llm_provider = custom_llm_provider or "openai"
raise exception_type(
model=model,
custom_llm_provider=custom_llm_provider,
original_exception=e,
)
def _handle_mock_potential_exceptions(
mock_response: Union[str, Exception, dict],
model: str,
custom_llm_provider: Optional[str] = None,
):
if isinstance(mock_response, Exception):
if isinstance(mock_response, openai.APIError):
raise mock_response
raise litellm.MockException(
status_code=getattr(mock_response, "status_code", 500), # type: ignore
message=getattr(mock_response, "text", str(mock_response)),
llm_provider=getattr(
mock_response, "llm_provider", custom_llm_provider or "openai"
), # type: ignore
model=model, # type: ignore
request=httpx.Request(method="POST", url="https://api.openai.com/v1/"),
)
elif isinstance(mock_response, str) and mock_response == "litellm.RateLimitError":
raise litellm.RateLimitError(
message="this is a mock rate limit error",
llm_provider=getattr(
mock_response, "llm_provider", custom_llm_provider or "openai"
), # type: ignore
model=model,
)
elif (
isinstance(mock_response, str)
and mock_response == "litellm.ContextWindowExceededError"
):
raise litellm.ContextWindowExceededError(
message="this is a mock context window exceeded error",
llm_provider=getattr(
mock_response, "llm_provider", custom_llm_provider or "openai"
), # type: ignore
model=model,
)
elif (
isinstance(mock_response, str)
and mock_response == "litellm.InternalServerError"
):
raise litellm.InternalServerError(
message="this is a mock internal server error",
llm_provider=getattr(
mock_response, "llm_provider", custom_llm_provider or "openai"
), # type: ignore
model=model,
)
elif isinstance(mock_response, str) and mock_response.startswith(
"Exception: content_filter_policy"
):
raise litellm.MockException(
status_code=400,
message=mock_response,
llm_provider="azure",
model=model, # type: ignore
request=httpx.Request(method="POST", url="https://api.openai.com/v1/"),
)
def _handle_mock_timeout(
mock_timeout: Optional[bool],
timeout: Optional[Union[float, str, httpx.Timeout]],
model: str,
):
if mock_timeout is True and timeout is not None:
_sleep_for_timeout(timeout)
raise litellm.Timeout(
message="This is a mock timeout error",
llm_provider="openai",
model=model,
)
async def _handle_mock_timeout_async(
mock_timeout: Optional[bool],
timeout: Optional[Union[float, str, httpx.Timeout]],
model: str,
):
if mock_timeout is True and timeout is not None:
await _sleep_for_timeout_async(timeout)
raise litellm.Timeout(
message="This is a mock timeout error",
llm_provider="openai",
model=model,
)
def _sleep_for_timeout(timeout: Union[float, str, httpx.Timeout]):
if isinstance(timeout, float):
time.sleep(timeout)
elif isinstance(timeout, str):
time.sleep(float(timeout))
elif isinstance(timeout, httpx.Timeout) and timeout.connect is not None:
time.sleep(timeout.connect)
async def _sleep_for_timeout_async(timeout: Union[float, str, httpx.Timeout]):
if isinstance(timeout, float):
await asyncio.sleep(timeout)
elif isinstance(timeout, str):
await asyncio.sleep(float(timeout))
elif isinstance(timeout, httpx.Timeout) and timeout.connect is not None:
await asyncio.sleep(timeout.connect)
def mock_completion(
model: str,
messages: List,
stream: Optional[bool] = False,
n: Optional[int] = None,
mock_response: Union[str, Exception, dict] = "This is a mock request",
mock_tool_calls: Optional[List] = None,
mock_timeout: Optional[bool] = False,
logging=None,
custom_llm_provider=None,
timeout: Optional[Union[float, str, httpx.Timeout]] = None,
**kwargs,
):
"""
Generate a mock completion response for testing or debugging purposes.
This is a helper function that simulates the response structure of the OpenAI completion API.
Parameters:
model (str): The name of the language model for which the mock response is generated.
messages (List): A list of message objects representing the conversation context.
stream (bool, optional): If True, returns a mock streaming response (default is False).
mock_response (str, optional): The content of the mock response (default is "This is a mock request").
mock_timeout (bool, optional): If True, the mock response will be a timeout error (default is False).
timeout (float, optional): The timeout value to use for the mock response (default is None).
**kwargs: Additional keyword arguments that can be used but are not required.
Returns:
litellm.ModelResponse: A ModelResponse simulating a completion response with the specified model, messages, and mock response.
Raises:
Exception: If an error occurs during the generation of the mock completion response.
Note:
- This function is intended for testing or debugging purposes to generate mock completion responses.
- If 'stream' is True, it returns a response that mimics the behavior of a streaming completion.
"""
try:
if mock_response is None:
mock_response = "This is a mock request"
_handle_mock_timeout(mock_timeout=mock_timeout, timeout=timeout, model=model)
## LOGGING
if logging is not None:
logging.pre_call(
input=messages,
api_key="mock-key",
)
_handle_mock_potential_exceptions(
mock_response=mock_response,
model=model,
custom_llm_provider=custom_llm_provider,
)
mock_response = cast(
Union[str, dict], mock_response
) # after this point, mock_response is a string or dict
if isinstance(mock_response, str) and mock_response.startswith(
"Exception: mock_streaming_error"
):
mock_response = litellm.MockException(
message="This is a mock error raised mid-stream",
llm_provider="anthropic",
model=model,
status_code=529,
)
time_delay = kwargs.get("mock_delay", None)
if time_delay is not None:
time.sleep(time_delay)
if isinstance(mock_response, dict):
return ModelResponse(**mock_response)
model_response = ModelResponse(stream=stream)
if stream is True:
# don't try to access stream object,
if kwargs.get("acompletion", False) is True:
return CustomStreamWrapper(
completion_stream=async_mock_completion_streaming_obj(
model_response, mock_response=mock_response, model=model, n=n
),
model=model,
custom_llm_provider="openai",
logging_obj=logging,
)
return CustomStreamWrapper(
completion_stream=mock_completion_streaming_obj(
model_response, mock_response=mock_response, model=model, n=n
),
model=model,
custom_llm_provider="openai",
logging_obj=logging,
)
if isinstance(mock_response, litellm.MockException):
raise mock_response
if n is None:
model_response.choices[0].message.content = mock_response # type: ignore
else:
_all_choices = []
for i in range(n):
_choice = litellm.utils.Choices(
index=i,
message=litellm.utils.Message(
content=mock_response, role="assistant"
),
)
_all_choices.append(_choice)
model_response.choices = _all_choices # type: ignore
model_response.created = int(time.time())
model_response.model = model
if mock_tool_calls:
model_response.choices[0].message.tool_calls = [ # type: ignore
ChatCompletionMessageToolCall(**tool_call)
for tool_call in mock_tool_calls
]
setattr(
model_response,
"usage",
Usage(prompt_tokens=10, completion_tokens=20, total_tokens=30),
)
try:
_, custom_llm_provider, _, _ = litellm.utils.get_llm_provider(model=model)
model_response._hidden_params["custom_llm_provider"] = custom_llm_provider
except Exception:
# dont let setting a hidden param block a mock_respose
pass
if logging is not None:
logging.post_call(
input=messages,
api_key="my-secret-key",
original_response="my-original-response",
)
return model_response
except Exception as e:
if isinstance(e, openai.APIError):
raise e
raise Exception("Mock completion response failed - {}".format(e))
@client
def completion( # type: ignore # noqa: PLR0915
model: str,
# Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create
messages: List = [],
timeout: Optional[Union[float, str, httpx.Timeout]] = None,
temperature: Optional[float] = None,
top_p: Optional[float] = None,
n: Optional[int] = None,
stream: Optional[bool] = None,
stream_options: Optional[dict] = None,
stop=None,
max_completion_tokens: Optional[int] = None,
max_tokens: Optional[int] = None,
modalities: Optional[List[ChatCompletionModality]] = None,
prediction: Optional[ChatCompletionPredictionContentParam] = None,
audio: Optional[ChatCompletionAudioParam] = None,
presence_penalty: Optional[float] = None,
frequency_penalty: Optional[float] = None,
logit_bias: Optional[dict] = None,
user: Optional[str] = None,
# openai v1.0+ new params
reasoning_effort: Optional[Literal["low", "medium", "high"]] = None,
response_format: Optional[Union[dict, Type[BaseModel]]] = None,
seed: Optional[int] = None,
tools: Optional[List] = None,
tool_choice: Optional[Union[str, dict]] = None,
logprobs: Optional[bool] = None,
top_logprobs: Optional[int] = None,
parallel_tool_calls: Optional[bool] = None,
deployment_id=None,
extra_headers: Optional[dict] = None,
# soon to be deprecated params by OpenAI
functions: Optional[List] = None,
function_call: Optional[str] = None,
# set api_base, api_version, api_key
base_url: Optional[str] = None,
api_version: Optional[str] = None,
api_key: Optional[str] = None,
model_list: Optional[list] = None, # pass in a list of api_base,keys, etc.
# Optional liteLLM function params
thinking: Optional[AnthropicThinkingParam] = None,
**kwargs,
) -> Union[ModelResponse, CustomStreamWrapper]:
"""
Perform a completion() using any of litellm supported llms (example gpt-4, gpt-3.5-turbo, claude-2, command-nightly)
Parameters:
model (str): The name of the language model to use for text completion. see all supported LLMs: https://docs.litellm.ai/docs/providers/
messages (List): A list of message objects representing the conversation context (default is an empty list).
OPTIONAL PARAMS
functions (List, optional): A list of functions to apply to the conversation messages (default is an empty list).
function_call (str, optional): The name of the function to call within the conversation (default is an empty string).
temperature (float, optional): The temperature parameter for controlling the randomness of the output (default is 1.0).
top_p (float, optional): The top-p parameter for nucleus sampling (default is 1.0).
n (int, optional): The number of completions to generate (default is 1).
stream (bool, optional): If True, return a streaming response (default is False).
stream_options (dict, optional): A dictionary containing options for the streaming response. Only set this when you set stream: true.
stop(string/list, optional): - Up to 4 sequences where the LLM API will stop generating further tokens.
max_tokens (integer, optional): The maximum number of tokens in the generated completion (default is infinity).
max_completion_tokens (integer, optional): An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens.
modalities (List[ChatCompletionModality], optional): Output types that you would like the model to generate for this request.. You can use `["text", "audio"]`
prediction (ChatCompletionPredictionContentParam, optional): Configuration for a Predicted Output, which can greatly improve response times when large parts of the model response are known ahead of time. This is most common when you are regenerating a file with only minor changes to most of the content.
audio (ChatCompletionAudioParam, optional): Parameters for audio output. Required when audio output is requested with modalities: ["audio"]
presence_penalty (float, optional): It is used to penalize new tokens based on their existence in the text so far.
frequency_penalty: It is used to penalize new tokens based on their frequency in the text so far.
logit_bias (dict, optional): Used to modify the probability of specific tokens appearing in the completion.
user (str, optional): A unique identifier representing your end-user. This can help the LLM provider to monitor and detect abuse.
logprobs (bool, optional): Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message
top_logprobs (int, optional): An integer between 0 and 5 specifying the number of most likely tokens to return at each token position, each with an associated log probability. logprobs must be set to true if this parameter is used.
metadata (dict, optional): Pass in additional metadata to tag your completion calls - eg. prompt version, details, etc.
api_base (str, optional): Base URL for the API (default is None).
api_version (str, optional): API version (default is None).
api_key (str, optional): API key (default is None).
model_list (list, optional): List of api base, version, keys
extra_headers (dict, optional): Additional headers to include in the request.
LITELLM Specific Params
mock_response (str, optional): If provided, return a mock completion response for testing or debugging purposes (default is None).
custom_llm_provider (str, optional): Used for Non-OpenAI LLMs, Example usage for bedrock, set model="amazon.titan-tg1-large" and custom_llm_provider="bedrock"
max_retries (int, optional): The number of retries to attempt (default is 0).
Returns:
ModelResponse: A response object containing the generated completion and associated metadata.
Note:
- This function is used to perform completions() using the specified language model.
- It supports various optional parameters for customizing the completion behavior.
- If 'mock_response' is provided, a mock completion response is returned for testing or debugging.
"""
### VALIDATE Request ###
if model is None:
raise ValueError("model param not passed in.")
# validate messages
messages = validate_and_fix_openai_messages(messages=messages)
# validate tool_choice
tool_choice = validate_chat_completion_tool_choice(tool_choice=tool_choice)
######### unpacking kwargs #####################
args = locals()
api_base = kwargs.get("api_base", None)
mock_response = kwargs.get("mock_response", None)
mock_tool_calls = kwargs.get("mock_tool_calls", None)
mock_timeout = cast(Optional[bool], kwargs.get("mock_timeout", None))
force_timeout = kwargs.get("force_timeout", 600) ## deprecated
logger_fn = kwargs.get("logger_fn", None)
verbose = kwargs.get("verbose", False)
custom_llm_provider = kwargs.get("custom_llm_provider", None)
litellm_logging_obj = kwargs.get("litellm_logging_obj", None)
id = kwargs.get("id", None)
metadata = kwargs.get("metadata", None)
model_info = kwargs.get("model_info", None)
proxy_server_request = kwargs.get("proxy_server_request", None)
fallbacks = kwargs.get("fallbacks", None)
provider_specific_header = cast(
Optional[ProviderSpecificHeader], kwargs.get("provider_specific_header", None)
)
headers = kwargs.get("headers", None) or extra_headers
ensure_alternating_roles: Optional[bool] = kwargs.get(
"ensure_alternating_roles", None
)
user_continue_message: Optional[ChatCompletionUserMessage] = kwargs.get(
"user_continue_message", None
)
assistant_continue_message: Optional[ChatCompletionAssistantMessage] = kwargs.get(
"assistant_continue_message", None
)
if headers is None:
headers = {}
if extra_headers is not None:
headers.update(extra_headers)
num_retries = kwargs.get(
"num_retries", None
) ## alt. param for 'max_retries'. Use this to pass retries w/ instructor.
max_retries = kwargs.get("max_retries", None)
cooldown_time = kwargs.get("cooldown_time", None)
context_window_fallback_dict = kwargs.get("context_window_fallback_dict", None)
organization = kwargs.get("organization", None)
### VERIFY SSL ###
ssl_verify = kwargs.get("ssl_verify", None)
### CUSTOM MODEL COST ###
input_cost_per_token = kwargs.get("input_cost_per_token", None)
output_cost_per_token = kwargs.get("output_cost_per_token", None)
input_cost_per_second = kwargs.get("input_cost_per_second", None)
output_cost_per_second = kwargs.get("output_cost_per_second", None)
### CUSTOM PROMPT TEMPLATE ###
initial_prompt_value = kwargs.get("initial_prompt_value", None)
roles = kwargs.get("roles", None)
final_prompt_value = kwargs.get("final_prompt_value", None)
bos_token = kwargs.get("bos_token", None)
eos_token = kwargs.get("eos_token", None)
preset_cache_key = kwargs.get("preset_cache_key", None)
hf_model_name = kwargs.get("hf_model_name", None)
supports_system_message = kwargs.get("supports_system_message", None)
base_model = kwargs.get("base_model", None)
### DISABLE FLAGS ###
disable_add_transform_inline_image_block = kwargs.get(
"disable_add_transform_inline_image_block", None
)
### TEXT COMPLETION CALLS ###
text_completion = kwargs.get("text_completion", False)
atext_completion = kwargs.get("atext_completion", False)
### ASYNC CALLS ###
acompletion = kwargs.get("acompletion", False)
client = kwargs.get("client", None)
### Admin Controls ###
no_log = kwargs.get("no-log", False)
### PROMPT MANAGEMENT ###
prompt_id = cast(Optional[str], kwargs.get("prompt_id", None))
prompt_variables = cast(Optional[dict], kwargs.get("prompt_variables", None))
### COPY MESSAGES ### - related issue https://github.com/BerriAI/litellm/discussions/4489
messages = get_completion_messages(
messages=messages,
ensure_alternating_roles=ensure_alternating_roles or False,
user_continue_message=user_continue_message,
assistant_continue_message=assistant_continue_message,
)
######## end of unpacking kwargs ###########
non_default_params = get_non_default_completion_params(kwargs=kwargs)
litellm_params = {} # used to prevent unbound var errors
## PROMPT MANAGEMENT HOOKS ##
if isinstance(litellm_logging_obj, LiteLLMLoggingObj) and prompt_id is not None:
model, messages, optional_params = (
litellm_logging_obj.get_chat_completion_prompt(
model=model,
messages=messages,
non_default_params=non_default_params,
prompt_id=prompt_id,
prompt_variables=prompt_variables,
)
)
try:
if base_url is not None:
api_base = base_url
if num_retries is not None:
max_retries = num_retries
logging = litellm_logging_obj
fallbacks = fallbacks or litellm.model_fallbacks
if fallbacks is not None:
return completion_with_fallbacks(**args)
if model_list is not None:
deployments = [
m["litellm_params"] for m in model_list if m["model_name"] == model
]
return litellm.batch_completion_models(deployments=deployments, **args)
if litellm.model_alias_map and model in litellm.model_alias_map:
model = litellm.model_alias_map[
model
] # update the model to the actual value if an alias has been passed in
model_response = ModelResponse()
setattr(model_response, "usage", litellm.Usage())
if (
kwargs.get("azure", False) is True
): # don't remove flag check, to remain backwards compatible for repos like Codium
custom_llm_provider = "azure"
if deployment_id is not None: # azure llms
model = deployment_id
custom_llm_provider = "azure"
model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(
model=model,
custom_llm_provider=custom_llm_provider,
api_base=api_base,
api_key=api_key,
)
if (
provider_specific_header is not None
and provider_specific_header["custom_llm_provider"] == custom_llm_provider
):
headers.update(provider_specific_header["extra_headers"])
if model_response is not None and hasattr(model_response, "_hidden_params"):
model_response._hidden_params["custom_llm_provider"] = custom_llm_provider
model_response._hidden_params["region_name"] = kwargs.get(
"aws_region_name", None
) # support region-based pricing for bedrock
### TIMEOUT LOGIC ###
timeout = timeout or kwargs.get("request_timeout", 600) or 600
# set timeout for 10 minutes by default
if isinstance(timeout, httpx.Timeout) and not supports_httpx_timeout(
custom_llm_provider
):
timeout = timeout.read or 600 # default 10 min timeout
elif not isinstance(timeout, httpx.Timeout):
timeout = float(timeout) # type: ignore
### REGISTER CUSTOM MODEL PRICING -- IF GIVEN ###
if input_cost_per_token is not None and output_cost_per_token is not None:
litellm.register_model(
{
f"{custom_llm_provider}/{model}": {
"input_cost_per_token": input_cost_per_token,
"output_cost_per_token": output_cost_per_token,
"litellm_provider": custom_llm_provider,
}
}
)
elif (
input_cost_per_second is not None
): # time based pricing just needs cost in place
output_cost_per_second = output_cost_per_second
litellm.register_model(
{
f"{custom_llm_provider}/{model}": {
"input_cost_per_second": input_cost_per_second,
"output_cost_per_second": output_cost_per_second,
"litellm_provider": custom_llm_provider,
}
}
)
### BUILD CUSTOM PROMPT TEMPLATE -- IF GIVEN ###
custom_prompt_dict = {} # type: ignore
if (
initial_prompt_value
or roles
or final_prompt_value
or bos_token
or eos_token
):
custom_prompt_dict = {model: {}}
if initial_prompt_value:
custom_prompt_dict[model]["initial_prompt_value"] = initial_prompt_value
if roles:
custom_prompt_dict[model]["roles"] = roles
if final_prompt_value:
custom_prompt_dict[model]["final_prompt_value"] = final_prompt_value
if bos_token:
custom_prompt_dict[model]["bos_token"] = bos_token
if eos_token:
custom_prompt_dict[model]["eos_token"] = eos_token
provider_config: Optional[BaseConfig] = None
if custom_llm_provider is not None and custom_llm_provider in [
provider.value for provider in LlmProviders
]:
provider_config = ProviderConfigManager.get_provider_chat_config(
model=model, provider=LlmProviders(custom_llm_provider)
)
if provider_config is not None:
messages = provider_config.translate_developer_role_to_system_role(
messages=messages
)
if (
supports_system_message is not None
and isinstance(supports_system_message, bool)
and supports_system_message is False
):
messages = map_system_message_pt(messages=messages)
if dynamic_api_key is not None:
api_key = dynamic_api_key
# check if user passed in any of the OpenAI optional params
optional_params = get_optional_params(
functions=functions,
function_call=function_call,
temperature=temperature,
top_p=top_p,
n=n,
stream=stream,
stream_options=stream_options,
stop=stop,
max_tokens=max_tokens,
max_completion_tokens=max_completion_tokens,
modalities=modalities,
prediction=prediction,
audio=audio,
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,
response_format=response_format,
seed=seed,
tools=tools,
tool_choice=tool_choice,
max_retries=max_retries,
logprobs=logprobs,
top_logprobs=top_logprobs,
api_version=api_version,
parallel_tool_calls=parallel_tool_calls,
messages=messages,
reasoning_effort=reasoning_effort,
thinking=thinking,
**non_default_params,
)
if litellm.add_function_to_prompt and optional_params.get(
"functions_unsupported_model", None
): # if user opts to add it to prompt, when API doesn't support function calling
functions_unsupported_model = optional_params.pop(
"functions_unsupported_model"
)
messages = function_call_prompt(
messages=messages, functions=functions_unsupported_model
)
# For logging - save the values of the litellm-specific params passed in
litellm_params = get_litellm_params(
acompletion=acompletion,
api_key=api_key,
force_timeout=force_timeout,
logger_fn=logger_fn,
verbose=verbose,
custom_llm_provider=custom_llm_provider,
api_base=api_base,
litellm_call_id=kwargs.get("litellm_call_id", None),
model_alias_map=litellm.model_alias_map,
completion_call_id=id,
metadata=metadata,
model_info=model_info,
proxy_server_request=proxy_server_request,
preset_cache_key=preset_cache_key,
no_log=no_log,
input_cost_per_second=input_cost_per_second,
input_cost_per_token=input_cost_per_token,
output_cost_per_second=output_cost_per_second,
output_cost_per_token=output_cost_per_token,
cooldown_time=cooldown_time,
text_completion=kwargs.get("text_completion"),
azure_ad_token_provider=kwargs.get("azure_ad_token_provider"),
user_continue_message=kwargs.get("user_continue_message"),
base_model=base_model,
litellm_trace_id=kwargs.get("litellm_trace_id"),
hf_model_name=hf_model_name,
custom_prompt_dict=custom_prompt_dict,
litellm_metadata=kwargs.get("litellm_metadata"),
disable_add_transform_inline_image_block=disable_add_transform_inline_image_block,
drop_params=kwargs.get("drop_params"),
prompt_id=prompt_id,
prompt_variables=prompt_variables,
ssl_verify=ssl_verify,
merge_reasoning_content_in_choices=kwargs.get(
"merge_reasoning_content_in_choices", None
),
api_version=api_version,
azure_ad_token=kwargs.get("azure_ad_token"),
tenant_id=kwargs.get("tenant_id"),
client_id=kwargs.get("client_id"),
client_secret=kwargs.get("client_secret"),
azure_username=kwargs.get("azure_username"),
azure_password=kwargs.get("azure_password"),
max_retries=max_retries,
timeout=timeout,
)
logging.update_environment_variables(
model=model,
user=user,
optional_params=optional_params,
litellm_params=litellm_params,
custom_llm_provider=custom_llm_provider,
)
if mock_response or mock_tool_calls or mock_timeout:
kwargs.pop("mock_timeout", None) # remove for any fallbacks triggered
return mock_completion(
model,
messages,
stream=stream,
n=n,
mock_response=mock_response,
mock_tool_calls=mock_tool_calls,
logging=logging,
acompletion=acompletion,
mock_delay=kwargs.get("mock_delay", None),
custom_llm_provider=custom_llm_provider,
mock_timeout=mock_timeout,
timeout=timeout,
)
if custom_llm_provider == "azure":
# azure configs
## check dynamic params ##
dynamic_params = False
if client is not None and (
isinstance(client, openai.AzureOpenAI)
or isinstance(client, openai.AsyncAzureOpenAI)
):
dynamic_params = _check_dynamic_azure_params(
azure_client_params={"api_version": api_version},
azure_client=client,
)
api_type = get_secret("AZURE_API_TYPE") or "azure"
api_base = api_base or litellm.api_base or get_secret("AZURE_API_BASE")
api_version = (
api_version
or litellm.api_version
or get_secret("AZURE_API_VERSION")
or litellm.AZURE_DEFAULT_API_VERSION
)
api_key = (
api_key
or litellm.api_key
or litellm.azure_key
or get_secret("AZURE_OPENAI_API_KEY")
or get_secret("AZURE_API_KEY")
)
azure_ad_token = optional_params.get("extra_body", {}).pop(
"azure_ad_token", None
) or get_secret("AZURE_AD_TOKEN")
azure_ad_token_provider = litellm_params.get(
"azure_ad_token_provider", None
)
headers = headers or litellm.headers
if extra_headers is not None:
optional_params["extra_headers"] = extra_headers
if max_retries is not None:
optional_params["max_retries"] = max_retries
if litellm.AzureOpenAIO1Config().is_o_series_model(model=model):
## LOAD CONFIG - if set
config = litellm.AzureOpenAIO1Config.get_config()
for k, v in config.items():
if (
k not in optional_params
): # completion(top_k=3) > azure_config(top_k=3) <- allows for dynamic variables to be passed in
optional_params[k] = v
response = azure_o1_chat_completions.completion(
model=model,
messages=messages,
headers=headers,
api_key=api_key,
api_base=api_base,
api_version=api_version,
dynamic_params=dynamic_params,
azure_ad_token=azure_ad_token,
model_response=model_response,
print_verbose=print_verbose,
optional_params=optional_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
logging_obj=logging,
acompletion=acompletion,
timeout=timeout, # type: ignore
client=client, # pass AsyncAzureOpenAI, AzureOpenAI client
custom_llm_provider=custom_llm_provider,
)
else:
## LOAD CONFIG - if set
config = litellm.AzureOpenAIConfig.get_config()
for k, v in config.items():
if (
k not in optional_params
): # completion(top_k=3) > azure_config(top_k=3) <- allows for dynamic variables to be passed in
optional_params[k] = v
## COMPLETION CALL
response = azure_chat_completions.completion(
model=model,
messages=messages,
headers=headers,
api_key=api_key,
api_base=api_base,
api_version=api_version,
api_type=api_type,
dynamic_params=dynamic_params,
azure_ad_token=azure_ad_token,
azure_ad_token_provider=azure_ad_token_provider,
model_response=model_response,
print_verbose=print_verbose,
optional_params=optional_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
logging_obj=logging,
acompletion=acompletion,
timeout=timeout, # type: ignore
client=client, # pass AsyncAzureOpenAI, AzureOpenAI client
)
if optional_params.get("stream", False):
## LOGGING
logging.post_call(
input=messages,
api_key=api_key,
original_response=response,
additional_args={
"headers": headers,
"api_version": api_version,
"api_base": api_base,
},
)
elif custom_llm_provider == "azure_text":
# azure configs
api_type = get_secret("AZURE_API_TYPE") or "azure"
api_base = api_base or litellm.api_base or get_secret("AZURE_API_BASE")
api_version = (
api_version or litellm.api_version or get_secret("AZURE_API_VERSION")
)
api_key = (
api_key
or litellm.api_key
or litellm.azure_key
or get_secret("AZURE_OPENAI_API_KEY")
or get_secret("AZURE_API_KEY")
)
azure_ad_token = optional_params.get("extra_body", {}).pop(
"azure_ad_token", None
) or get_secret("AZURE_AD_TOKEN")
azure_ad_token_provider = litellm_params.get(
"azure_ad_token_provider", None
)
headers = headers or litellm.headers
if extra_headers is not None:
optional_params["extra_headers"] = extra_headers
## LOAD CONFIG - if set
config = litellm.AzureOpenAIConfig.get_config()
for k, v in config.items():
if (
k not in optional_params
): # completion(top_k=3) > azure_config(top_k=3) <- allows for dynamic variables to be passed in
optional_params[k] = v
## COMPLETION CALL
response = azure_text_completions.completion(
model=model,
messages=messages,
headers=headers,
api_key=api_key,
api_base=api_base,
api_version=api_version,
api_type=api_type,
azure_ad_token=azure_ad_token,
azure_ad_token_provider=azure_ad_token_provider,
model_response=model_response,
print_verbose=print_verbose,
optional_params=optional_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
logging_obj=logging,
acompletion=acompletion,
timeout=timeout,
client=client, # pass AsyncAzureOpenAI, AzureOpenAI client
)
if optional_params.get("stream", False) or acompletion is True:
## LOGGING
logging.post_call(
input=messages,
api_key=api_key,
original_response=response,
additional_args={
"headers": headers,
"api_version": api_version,
"api_base": api_base,
},
)
elif custom_llm_provider == "deepseek":
## COMPLETION CALL
try:
response = base_llm_http_handler.completion(
model=model,
messages=messages,
headers=headers,
model_response=model_response,
api_key=api_key,
api_base=api_base,
acompletion=acompletion,
logging_obj=logging,
optional_params=optional_params,
litellm_params=litellm_params,
timeout=timeout, # type: ignore
client=client,
custom_llm_provider=custom_llm_provider,
encoding=encoding,
stream=stream,
)
except Exception as e:
## LOGGING - log the original exception returned
logging.post_call(
input=messages,
api_key=api_key,
original_response=str(e),
additional_args={"headers": headers},
)
raise e
elif custom_llm_provider == "azure_ai":
api_base = (
api_base # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there
or litellm.api_base
or get_secret("AZURE_AI_API_BASE")
)
# set API KEY
api_key = (
api_key
or litellm.api_key # for deepinfra/perplexity/anyscale/friendliai we check in get_llm_provider and pass in the api key from there
or litellm.openai_key
or get_secret("AZURE_AI_API_KEY")
)
headers = headers or litellm.headers
if extra_headers is not None:
optional_params["extra_headers"] = extra_headers
## FOR COHERE
if "command-r" in model: # make sure tool call in messages are str
messages = stringify_json_tool_call_content(messages=messages)
## COMPLETION CALL
try:
response = base_llm_http_handler.completion(
model=model,
messages=messages,
headers=headers,
model_response=model_response,
api_key=api_key,
api_base=api_base,
acompletion=acompletion,
logging_obj=logging,
optional_params=optional_params,
litellm_params=litellm_params,
timeout=timeout, # type: ignore
client=client, # pass AsyncOpenAI, OpenAI client
custom_llm_provider=custom_llm_provider,
encoding=encoding,
stream=stream,
)
except Exception as e:
## LOGGING - log the original exception returned
logging.post_call(
input=messages,
api_key=api_key,
original_response=str(e),
additional_args={"headers": headers},
)
raise e
if optional_params.get("stream", False):
## LOGGING
logging.post_call(
input=messages,
api_key=api_key,
original_response=response,
additional_args={"headers": headers},
)
elif (
custom_llm_provider == "text-completion-openai"
or "ft:babbage-002" in model
or "ft:davinci-002" in model # support for finetuned completion models
or custom_llm_provider
in litellm.openai_text_completion_compatible_providers
and kwargs.get("text_completion") is True
):
openai.api_type = "openai"
api_base = (
api_base
or litellm.api_base
or get_secret("OPENAI_API_BASE")
or "https://api.openai.com/v1"
)
openai.api_version = None
# set API KEY
api_key = (
api_key
or litellm.api_key
or litellm.openai_key
or get_secret("OPENAI_API_KEY")
)
headers = headers or litellm.headers
if extra_headers is not None:
optional_params["extra_headers"] = extra_headers
## LOAD CONFIG - if set
config = litellm.OpenAITextCompletionConfig.get_config()
for k, v in config.items():
if (
k not in optional_params
): # completion(top_k=3) > openai_text_config(top_k=3) <- allows for dynamic variables to be passed in
optional_params[k] = v
if litellm.organization:
openai.organization = litellm.organization
if (
len(messages) > 0
and "content" in messages[0]
and isinstance(messages[0]["content"], list)
):
# text-davinci-003 can accept a string or array, if it's an array, assume the array is set in messages[0]['content']
# https://platform.openai.com/docs/api-reference/completions/create
prompt = messages[0]["content"]
else:
prompt = " ".join([message["content"] for message in messages]) # type: ignore
## COMPLETION CALL
_response = openai_text_completions.completion(
model=model,
messages=messages,
model_response=model_response,
print_verbose=print_verbose,
api_key=api_key,
custom_llm_provider=custom_llm_provider,
api_base=api_base,
acompletion=acompletion,
client=client, # pass AsyncOpenAI, OpenAI client
logging_obj=logging,
optional_params=optional_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
timeout=timeout, # type: ignore
)
if (
optional_params.get("stream", False) is False
and acompletion is False
and text_completion is False
):
# convert to chat completion response
_response = litellm.OpenAITextCompletionConfig().convert_to_chat_model_response_object(
response_object=_response, model_response_object=model_response
)
if optional_params.get("stream", False) or acompletion is True:
## LOGGING
logging.post_call(
input=messages,
api_key=api_key,
original_response=_response,
additional_args={"headers": headers},
)
response = _response
elif custom_llm_provider == "groq":
api_base = (
api_base # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there
or litellm.api_base
or get_secret("GROQ_API_BASE")
or "https://api.groq.com/openai/v1"
)
# set API KEY
api_key = (
api_key
or litellm.api_key # for deepinfra/perplexity/anyscale/friendliai we check in get_llm_provider and pass in the api key from there
or litellm.groq_key
or get_secret("GROQ_API_KEY")
)
headers = headers or litellm.headers
## LOAD CONFIG - if set
config = litellm.GroqChatConfig.get_config()
for k, v in config.items():
if (
k not in optional_params
): # completion(top_k=3) > openai_config(top_k=3) <- allows for dynamic variables to be passed in
optional_params[k] = v
response = groq_chat_completions.completion(
model=model,
messages=messages,
headers=headers,
model_response=model_response,
print_verbose=print_verbose,
api_key=api_key,
api_base=api_base,
acompletion=acompletion,
logging_obj=logging,
optional_params=optional_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
timeout=timeout, # type: ignore
custom_prompt_dict=custom_prompt_dict,
client=client, # pass AsyncOpenAI, OpenAI client
custom_llm_provider=custom_llm_provider,
encoding=encoding,
)
elif custom_llm_provider == "aiohttp_openai":
# NEW aiohttp provider for 10-100x higher RPS
api_base = (
api_base # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there
or litellm.api_base
or get_secret("OPENAI_API_BASE")
or "https://api.openai.com/v1"
)
# set API KEY
api_key = (
api_key
or litellm.api_key # for deepinfra/perplexity/anyscale/friendliai we check in get_llm_provider and pass in the api key from there
or litellm.openai_key
or get_secret("OPENAI_API_KEY")
)
headers = headers or litellm.headers
if extra_headers is not None:
optional_params["extra_headers"] = extra_headers
response = base_llm_aiohttp_handler.completion(
model=model,
messages=messages,
headers=headers,
model_response=model_response,
api_key=api_key,
api_base=api_base,
acompletion=acompletion,
logging_obj=logging,
optional_params=optional_params,
litellm_params=litellm_params,
timeout=timeout,
client=client,
custom_llm_provider=custom_llm_provider,
encoding=encoding,
stream=stream,
)
elif (
model in litellm.open_ai_chat_completion_models
or custom_llm_provider == "custom_openai"
or custom_llm_provider == "deepinfra"
or custom_llm_provider == "perplexity"
or custom_llm_provider == "nvidia_nim"
or custom_llm_provider == "cerebras"
or custom_llm_provider == "sambanova"
or custom_llm_provider == "volcengine"
or custom_llm_provider == "anyscale"
or custom_llm_provider == "mistral"
or custom_llm_provider == "openai"
or custom_llm_provider == "together_ai"
or custom_llm_provider in litellm.openai_compatible_providers
or "ft:gpt-3.5-turbo" in model # finetune gpt-3.5-turbo
): # allow user to make an openai call with a custom base
# note: if a user sets a custom base - we should ensure this works
# allow for the setting of dynamic and stateful api-bases
api_base = (
api_base # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there
or litellm.api_base
or get_secret("OPENAI_API_BASE")
or "https://api.openai.com/v1"
)
organization = (
organization
or litellm.organization
or get_secret("OPENAI_ORGANIZATION")
or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
)
openai.organization = organization
# set API KEY
api_key = (
api_key
or litellm.api_key # for deepinfra/perplexity/anyscale/friendliai we check in get_llm_provider and pass in the api key from there
or litellm.openai_key
or get_secret("OPENAI_API_KEY")
)
headers = headers or litellm.headers
if extra_headers is not None:
optional_params["extra_headers"] = extra_headers
if (
litellm.enable_preview_features and metadata is not None
): # [PREVIEW] allow metadata to be passed to OPENAI
optional_params["metadata"] = add_openai_metadata(metadata)
## LOAD CONFIG - if set
config = litellm.OpenAIConfig.get_config()
for k, v in config.items():
if (
k not in optional_params
): # completion(top_k=3) > openai_config(top_k=3) <- allows for dynamic variables to be passed in
optional_params[k] = v
## COMPLETION CALL
try:
response = openai_chat_completions.completion(
model=model,
messages=messages,
headers=headers,
model_response=model_response,
print_verbose=print_verbose,
api_key=api_key,
api_base=api_base,
acompletion=acompletion,
logging_obj=logging,
optional_params=optional_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
timeout=timeout, # type: ignore
custom_prompt_dict=custom_prompt_dict,
client=client, # pass AsyncOpenAI, OpenAI client
organization=organization,
custom_llm_provider=custom_llm_provider,
)
except Exception as e:
## LOGGING - log the original exception returned
logging.post_call(
input=messages,
api_key=api_key,
original_response=str(e),
additional_args={"headers": headers},
)
raise e
if optional_params.get("stream", False):
## LOGGING
logging.post_call(
input=messages,
api_key=api_key,
original_response=response,
additional_args={"headers": headers},
)
elif (
"replicate" in model
or custom_llm_provider == "replicate"
or model in litellm.replicate_models
):
# Setting the relevant API KEY for replicate, replicate defaults to using os.environ.get("REPLICATE_API_TOKEN")
replicate_key = (
api_key
or litellm.replicate_key
or litellm.api_key
or get_secret("REPLICATE_API_KEY")
or get_secret("REPLICATE_API_TOKEN")
)
api_base = (
api_base
or litellm.api_base
or get_secret("REPLICATE_API_BASE")
or "https://api.replicate.com/v1"
)
custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
model_response = replicate_chat_completion( # type: ignore
model=model,
messages=messages,
api_base=api_base,
model_response=model_response,
print_verbose=print_verbose,
optional_params=optional_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
encoding=encoding, # for calculating input/output tokens
api_key=replicate_key,
logging_obj=logging,
custom_prompt_dict=custom_prompt_dict,
acompletion=acompletion,
headers=headers,
)
if optional_params.get("stream", False) is True:
## LOGGING
logging.post_call(
input=messages,
api_key=replicate_key,
original_response=model_response,
)
response = model_response
elif (
"clarifai" in model
or custom_llm_provider == "clarifai"
or model in litellm.clarifai_models
):
clarifai_key = None
clarifai_key = (
api_key
or litellm.clarifai_key
or litellm.api_key
or get_secret("CLARIFAI_API_KEY")
or get_secret("CLARIFAI_API_TOKEN")
)
api_base = (
api_base
or litellm.api_base
or get_secret("CLARIFAI_API_BASE")
or "https://api.clarifai.com/v2"
)
api_base = litellm.ClarifaiConfig()._convert_model_to_url(model, api_base)
response = base_llm_http_handler.completion(
model=model,
stream=stream,
fake_stream=True, # clarifai does not support streaming, we fake it
messages=messages,
acompletion=acompletion,
api_base=api_base,
model_response=model_response,
optional_params=optional_params,
litellm_params=litellm_params,
custom_llm_provider="clarifai",
timeout=timeout,
headers=headers,
encoding=encoding,
api_key=clarifai_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
)
elif custom_llm_provider == "anthropic_text":
api_key = (
api_key
or litellm.anthropic_key
or litellm.api_key
or os.environ.get("ANTHROPIC_API_KEY")
)
custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
api_base = (
api_base
or litellm.api_base
or get_secret("ANTHROPIC_API_BASE")
or get_secret("ANTHROPIC_BASE_URL")
or "https://api.anthropic.com/v1/complete"
)
if api_base is not None and not api_base.endswith("/v1/complete"):
api_base += "/v1/complete"
response = base_llm_http_handler.completion(
model=model,
stream=stream,
messages=messages,
acompletion=acompletion,
api_base=api_base,
model_response=model_response,
optional_params=optional_params,
litellm_params=litellm_params,
custom_llm_provider="anthropic_text",
timeout=timeout,
headers=headers,
encoding=encoding,
api_key=api_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
)
elif custom_llm_provider == "anthropic":
api_key = (
api_key
or litellm.anthropic_key
or litellm.api_key
or os.environ.get("ANTHROPIC_API_KEY")
)
custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
# call /messages
# default route for all anthropic models
api_base = (
api_base
or litellm.api_base
or get_secret("ANTHROPIC_API_BASE")
or get_secret("ANTHROPIC_BASE_URL")
or "https://api.anthropic.com/v1/messages"
)
if api_base is not None and not api_base.endswith("/v1/messages"):
api_base += "/v1/messages"
response = anthropic_chat_completions.completion(
model=model,
messages=messages,
api_base=api_base,
acompletion=acompletion,
custom_prompt_dict=litellm.custom_prompt_dict,
model_response=model_response,
print_verbose=print_verbose,
optional_params=optional_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
encoding=encoding, # for calculating input/output tokens
api_key=api_key,
logging_obj=logging,
headers=headers,
timeout=timeout,
client=client,
custom_llm_provider=custom_llm_provider,
)
if optional_params.get("stream", False) or acompletion is True:
## LOGGING
logging.post_call(
input=messages,
api_key=api_key,
original_response=response,
)
response = response
elif custom_llm_provider == "nlp_cloud":
nlp_cloud_key = (
api_key
or litellm.nlp_cloud_key
or get_secret("NLP_CLOUD_API_KEY")
or litellm.api_key
)
api_base = (
api_base
or litellm.api_base
or get_secret("NLP_CLOUD_API_BASE")
or "https://api.nlpcloud.io/v1/gpu/"
)
response = nlp_cloud_chat_completion(
model=model,
messages=messages,
api_base=api_base,
model_response=model_response,
print_verbose=print_verbose,
optional_params=optional_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
encoding=encoding,
api_key=nlp_cloud_key,
logging_obj=logging,
)
if "stream" in optional_params and optional_params["stream"] is True:
# don't try to access stream object,
response = CustomStreamWrapper(
response,
model,
custom_llm_provider="nlp_cloud",
logging_obj=logging,
)
if optional_params.get("stream", False) or acompletion is True:
## LOGGING
logging.post_call(
input=messages,
api_key=api_key,
original_response=response,
)
response = response
elif custom_llm_provider == "aleph_alpha":
aleph_alpha_key = (
api_key
or litellm.aleph_alpha_key
or get_secret("ALEPH_ALPHA_API_KEY")
or get_secret("ALEPHALPHA_API_KEY")
or litellm.api_key
)
api_base = (
api_base
or litellm.api_base
or get_secret("ALEPH_ALPHA_API_BASE")
or "https://api.aleph-alpha.com/complete"
)
model_response = aleph_alpha.completion(
model=model,
messages=messages,
api_base=api_base,
model_response=model_response,
print_verbose=print_verbose,
optional_params=optional_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
encoding=encoding,
default_max_tokens_to_sample=litellm.max_tokens,
api_key=aleph_alpha_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"] is True:
# don't try to access stream object,
response = CustomStreamWrapper(
model_response,
model,
custom_llm_provider="aleph_alpha",
logging_obj=logging,
)
return response
response = model_response
elif custom_llm_provider == "cohere":
cohere_key = (
api_key
or litellm.cohere_key
or get_secret("COHERE_API_KEY")
or get_secret("CO_API_KEY")
or litellm.api_key
)
api_base = (
api_base
or litellm.api_base
or get_secret("COHERE_API_BASE")
or "https://api.cohere.ai/v1/generate"
)
headers = headers or litellm.headers or {}
if headers is None:
headers = {}
if extra_headers is not None:
headers.update(extra_headers)
response = base_llm_http_handler.completion(
model=model,
stream=stream,
messages=messages,
acompletion=acompletion,
api_base=api_base,
model_response=model_response,
optional_params=optional_params,
litellm_params=litellm_params,
custom_llm_provider="cohere",
timeout=timeout,
headers=headers,
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
client=client,
)
elif custom_llm_provider == "cohere_chat":
cohere_key = (
api_key
or litellm.cohere_key
or get_secret_str("COHERE_API_KEY")
or get_secret_str("CO_API_KEY")
or litellm.api_key
)
api_base = (
api_base
or litellm.api_base
or get_secret_str("COHERE_API_BASE")
or "https://api.cohere.ai/v1/chat"
)
headers = headers or litellm.headers or {}
if headers is None:
headers = {}
if extra_headers is not None:
headers.update(extra_headers)
response = base_llm_http_handler.completion(
model=model,
stream=stream,
messages=messages,
acompletion=acompletion,
api_base=api_base,
model_response=model_response,
optional_params=optional_params,
litellm_params=litellm_params,
custom_llm_provider="cohere_chat",
timeout=timeout,
headers=headers,
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
)
elif custom_llm_provider == "maritalk":
maritalk_key = (
api_key
or litellm.maritalk_key
or get_secret("MARITALK_API_KEY")
or litellm.api_key
)
api_base = (
api_base
or litellm.api_base
or get_secret("MARITALK_API_BASE")
or "https://chat.maritaca.ai/api"
)
model_response = openai_like_chat_completion.completion(
model=model,
messages=messages,
api_base=api_base,
model_response=model_response,
print_verbose=print_verbose,
optional_params=optional_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
encoding=encoding,
api_key=maritalk_key,
logging_obj=logging,
custom_llm_provider="maritalk",
custom_prompt_dict=custom_prompt_dict,
)
response = model_response
elif 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
)
hf_headers = headers or litellm.headers
custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
model_response = huggingface.completion(
model=model,
messages=messages,
api_base=api_base, # type: ignore
headers=hf_headers or {},
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,
acompletion=acompletion,
logging_obj=logging,
custom_prompt_dict=custom_prompt_dict,
timeout=timeout, # type: ignore
client=client,
)
if (
"stream" in optional_params
and optional_params["stream"] is True
and acompletion is False
):
# 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 == "oobabooga":
custom_llm_provider = "oobabooga"
model_response = oobabooga.completion(
model=model,
messages=messages,
model_response=model_response,
api_base=api_base, # type: ignore
print_verbose=print_verbose,
optional_params=optional_params,
litellm_params=litellm_params,
api_key=None,
logger_fn=logger_fn,
encoding=encoding,
logging_obj=logging,
)
if "stream" in optional_params and optional_params["stream"] is True:
# don't try to access stream object,
response = CustomStreamWrapper(
model_response,
model,
custom_llm_provider="oobabooga",
logging_obj=logging,
)
return response
response = model_response
elif custom_llm_provider == "databricks":
api_base = (
api_base # for databricks we check in get_llm_provider and pass in the api base from there
or litellm.api_base
or os.getenv("DATABRICKS_API_BASE")
)
# set API KEY
api_key = (
api_key
or litellm.api_key # for databricks we check in get_llm_provider and pass in the api key from there
or litellm.databricks_key
or get_secret("DATABRICKS_API_KEY")
)
headers = headers or litellm.headers
## COMPLETION CALL
try:
response = databricks_chat_completions.completion(
model=model,
messages=messages,
headers=headers,
model_response=model_response,
print_verbose=print_verbose,
api_key=api_key,
api_base=api_base,
acompletion=acompletion,
logging_obj=logging,
optional_params=optional_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
timeout=timeout, # type: ignore
custom_prompt_dict=custom_prompt_dict,
client=client, # pass AsyncOpenAI, OpenAI client
encoding=encoding,
custom_llm_provider="databricks",
)
except Exception as e:
## LOGGING - log the original exception returned
logging.post_call(
input=messages,
api_key=api_key,
original_response=str(e),
additional_args={"headers": headers},
)
raise e
if optional_params.get("stream", False):
## LOGGING
logging.post_call(
input=messages,
api_key=api_key,
original_response=response,
additional_args={"headers": headers},
)
elif custom_llm_provider == "openrouter":
api_base = (
api_base
or litellm.api_base
or get_secret_str("OPENROUTER_API_BASE")
or "https://openrouter.ai/api/v1"
)
api_key = (
api_key
or litellm.api_key
or litellm.openrouter_key
or get_secret("OPENROUTER_API_KEY")
or get_secret("OR_API_KEY")
)
openrouter_site_url = get_secret("OR_SITE_URL") or "https://litellm.ai"
openrouter_app_name = get_secret("OR_APP_NAME") or "liteLLM"
openrouter_headers = {
"HTTP-Referer": openrouter_site_url,
"X-Title": openrouter_app_name,
}
_headers = headers or litellm.headers
if _headers:
openrouter_headers.update(_headers)
headers = openrouter_headers
## Load Config
config = litellm.OpenrouterConfig.get_config()
for k, v in config.items():
if k == "extra_body":
# we use openai 'extra_body' to pass openrouter specific params - transforms, route, models
if "extra_body" in optional_params:
optional_params[k].update(v)
else:
optional_params[k] = v
elif k not in optional_params:
optional_params[k] = v
data = {"model": model, "messages": messages, **optional_params}
## COMPLETION CALL
response = base_llm_http_handler.completion(
model=model,
stream=stream,
messages=messages,
acompletion=acompletion,
api_base=api_base,
model_response=model_response,
optional_params=optional_params,
litellm_params=litellm_params,
custom_llm_provider="openrouter",
timeout=timeout,
headers=headers,
encoding=encoding,
api_key=api_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
client=client,
)
## LOGGING
logging.post_call(
input=messages, api_key=openai.api_key, original_response=response
)
elif (
custom_llm_provider == "together_ai"
or ("togethercomputer" in model)
or (model in litellm.together_ai_models)
):
"""
Deprecated. We now do together ai calls via the openai client - https://docs.together.ai/docs/openai-api-compatibility
"""
pass
elif custom_llm_provider == "palm":
raise ValueError(
"Palm was decommisioned on October 2024. Please use the `gemini/` route for Gemini Google AI Studio Models. Announcement: https://ai.google.dev/palm_docs/palm?hl=en"
)
elif custom_llm_provider == "vertex_ai_beta" or custom_llm_provider == "gemini":
vertex_ai_project = (
optional_params.pop("vertex_project", None)
or optional_params.pop("vertex_ai_project", None)
or litellm.vertex_project
or get_secret("VERTEXAI_PROJECT")
)
vertex_ai_location = (
optional_params.pop("vertex_location", None)
or optional_params.pop("vertex_ai_location", None)
or litellm.vertex_location
or get_secret("VERTEXAI_LOCATION")
)
vertex_credentials = (
optional_params.pop("vertex_credentials", None)
or optional_params.pop("vertex_ai_credentials", None)
or get_secret("VERTEXAI_CREDENTIALS")
)
gemini_api_key = (
api_key
or get_secret("GEMINI_API_KEY")
or get_secret("PALM_API_KEY") # older palm api key should also work
or litellm.api_key
)
new_params = deepcopy(optional_params)
response = vertex_chat_completion.completion( # type: ignore
model=model,
messages=messages,
model_response=model_response,
print_verbose=print_verbose,
optional_params=new_params,
litellm_params=litellm_params, # type: ignore
logger_fn=logger_fn,
encoding=encoding,
vertex_location=vertex_ai_location,
vertex_project=vertex_ai_project,
vertex_credentials=vertex_credentials,
gemini_api_key=gemini_api_key,
logging_obj=logging,
acompletion=acompletion,
timeout=timeout,
custom_llm_provider=custom_llm_provider,
client=client,
api_base=api_base,
extra_headers=extra_headers,
)
elif custom_llm_provider == "vertex_ai":
vertex_ai_project = (
optional_params.pop("vertex_project", None)
or optional_params.pop("vertex_ai_project", None)
or litellm.vertex_project
or get_secret("VERTEXAI_PROJECT")
)
vertex_ai_location = (
optional_params.pop("vertex_location", None)
or optional_params.pop("vertex_ai_location", None)
or litellm.vertex_location
or get_secret("VERTEXAI_LOCATION")
)
vertex_credentials = (
optional_params.pop("vertex_credentials", None)
or optional_params.pop("vertex_ai_credentials", None)
or get_secret("VERTEXAI_CREDENTIALS")
)
new_params = deepcopy(optional_params)
if (
model.startswith("meta/")
or model.startswith("mistral")
or model.startswith("codestral")
or model.startswith("jamba")
or model.startswith("claude")
):
model_response = vertex_partner_models_chat_completion.completion(
model=model,
messages=messages,
model_response=model_response,
print_verbose=print_verbose,
optional_params=new_params,
litellm_params=litellm_params, # type: ignore
logger_fn=logger_fn,
encoding=encoding,
api_base=api_base,
vertex_location=vertex_ai_location,
vertex_project=vertex_ai_project,
vertex_credentials=vertex_credentials,
logging_obj=logging,
acompletion=acompletion,
headers=headers,
custom_prompt_dict=custom_prompt_dict,
timeout=timeout,
client=client,
)
elif "gemini" in model or (
litellm_params.get("base_model") is not None
and "gemini" in litellm_params["base_model"]
):
model_response = vertex_chat_completion.completion( # type: ignore
model=model,
messages=messages,
model_response=model_response,
print_verbose=print_verbose,
optional_params=new_params,
litellm_params=litellm_params, # type: ignore
logger_fn=logger_fn,
encoding=encoding,
vertex_location=vertex_ai_location,
vertex_project=vertex_ai_project,
vertex_credentials=vertex_credentials,
gemini_api_key=None,
logging_obj=logging,
acompletion=acompletion,
timeout=timeout,
custom_llm_provider=custom_llm_provider,
client=client,
api_base=api_base,
extra_headers=extra_headers,
)
elif "openai" in model:
# Vertex Model Garden - OpenAI compatible models
model_response = vertex_model_garden_chat_completion.completion(
model=model,
messages=messages,
model_response=model_response,
print_verbose=print_verbose,
optional_params=new_params,
litellm_params=litellm_params, # type: ignore
logger_fn=logger_fn,
encoding=encoding,
api_base=api_base,
vertex_location=vertex_ai_location,
vertex_project=vertex_ai_project,
vertex_credentials=vertex_credentials,
logging_obj=logging,
acompletion=acompletion,
headers=headers,
custom_prompt_dict=custom_prompt_dict,
timeout=timeout,
client=client,
)
else:
model_response = vertex_ai_non_gemini.completion(
model=model,
messages=messages,
model_response=model_response,
print_verbose=print_verbose,
optional_params=new_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
encoding=encoding,
vertex_location=vertex_ai_location,
vertex_project=vertex_ai_project,
vertex_credentials=vertex_credentials,
logging_obj=logging,
acompletion=acompletion,
)
if (
"stream" in optional_params
and optional_params["stream"] is True
and acompletion is False
):
response = CustomStreamWrapper(
model_response,
model,
custom_llm_provider="vertex_ai",
logging_obj=logging,
)
return response
response = model_response
elif custom_llm_provider == "predibase":
tenant_id = (
optional_params.pop("tenant_id", None)
or optional_params.pop("predibase_tenant_id", None)
or litellm.predibase_tenant_id
or get_secret("PREDIBASE_TENANT_ID")
)
if tenant_id is None:
raise ValueError(
"Missing Predibase Tenant ID - Required for making the request. Set dynamically (e.g. `completion(..tenant_id=<MY-ID>)`) or in env - `PREDIBASE_TENANT_ID`."
)
api_base = (
api_base
or optional_params.pop("api_base", None)
or optional_params.pop("base_url", None)
or litellm.api_base
or get_secret("PREDIBASE_API_BASE")
)
api_key = (
api_key
or litellm.api_key
or litellm.predibase_key
or get_secret("PREDIBASE_API_KEY")
)
_model_response = predibase_chat_completions.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,
acompletion=acompletion,
api_base=api_base,
custom_prompt_dict=custom_prompt_dict,
api_key=api_key,
tenant_id=tenant_id,
timeout=timeout,
)
if (
"stream" in optional_params
and optional_params["stream"] is True
and acompletion is False
):
return _model_response
response = _model_response
elif custom_llm_provider == "text-completion-codestral":
api_base = (
api_base
or optional_params.pop("api_base", None)
or optional_params.pop("base_url", None)
or litellm.api_base
or "https://codestral.mistral.ai/v1/fim/completions"
)
api_key = api_key or litellm.api_key or get_secret("CODESTRAL_API_KEY")
text_completion_model_response = litellm.TextCompletionResponse(
stream=stream
)
_model_response = codestral_text_completions.completion( # type: ignore
model=model,
messages=messages,
model_response=text_completion_model_response,
print_verbose=print_verbose,
optional_params=optional_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
encoding=encoding,
logging_obj=logging,
acompletion=acompletion,
api_base=api_base,
custom_prompt_dict=custom_prompt_dict,
api_key=api_key,
timeout=timeout,
)
if (
"stream" in optional_params
and optional_params["stream"] is True
and acompletion is False
):
return _model_response
response = _model_response
elif custom_llm_provider == "sagemaker_chat":
# boto3 reads keys from .env
model_response = sagemaker_chat_completion.completion(
model=model,
messages=messages,
model_response=model_response,
print_verbose=print_verbose,
optional_params=optional_params,
litellm_params=litellm_params,
timeout=timeout,
custom_prompt_dict=custom_prompt_dict,
logger_fn=logger_fn,
encoding=encoding,
logging_obj=logging,
acompletion=acompletion,
client=client,
)
## RESPONSE OBJECT
response = model_response
elif custom_llm_provider == "sagemaker":
# boto3 reads keys from .env
model_response = sagemaker_llm.completion(
model=model,
messages=messages,
model_response=model_response,
print_verbose=print_verbose,
optional_params=optional_params,
litellm_params=litellm_params,
custom_prompt_dict=custom_prompt_dict,
hf_model_name=hf_model_name,
logger_fn=logger_fn,
encoding=encoding,
logging_obj=logging,
acompletion=acompletion,
)
## RESPONSE OBJECT
response = model_response
elif custom_llm_provider == "bedrock":
# boto3 reads keys from .env
custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
if "aws_bedrock_client" in optional_params:
verbose_logger.warning(
"'aws_bedrock_client' is a deprecated param. Please move to another auth method - https://docs.litellm.ai/docs/providers/bedrock#boto3---authentication."
)
# Extract credentials for legacy boto3 client and pass thru to httpx
aws_bedrock_client = optional_params.pop("aws_bedrock_client")
creds = aws_bedrock_client._get_credentials().get_frozen_credentials()
if creds.access_key:
optional_params["aws_access_key_id"] = creds.access_key
if creds.secret_key:
optional_params["aws_secret_access_key"] = creds.secret_key
if creds.token:
optional_params["aws_session_token"] = creds.token
if (
"aws_region_name" not in optional_params
or optional_params["aws_region_name"] is None
):
optional_params["aws_region_name"] = (
aws_bedrock_client.meta.region_name
)
bedrock_route = BedrockModelInfo.get_bedrock_route(model)
if bedrock_route == "converse":
model = model.replace("converse/", "")
response = bedrock_converse_chat_completion.completion(
model=model,
messages=messages,
custom_prompt_dict=custom_prompt_dict,
model_response=model_response,
optional_params=optional_params,
litellm_params=litellm_params, # type: ignore
logger_fn=logger_fn,
encoding=encoding,
logging_obj=logging,
extra_headers=extra_headers,
timeout=timeout,
acompletion=acompletion,
client=client,
api_base=api_base,
)
elif bedrock_route == "converse_like":
model = model.replace("converse_like/", "")
response = base_llm_http_handler.completion(
model=model,
stream=stream,
messages=messages,
acompletion=acompletion,
api_base=api_base,
model_response=model_response,
optional_params=optional_params,
litellm_params=litellm_params,
custom_llm_provider="bedrock",
timeout=timeout,
headers=headers,
encoding=encoding,
api_key=api_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
client=client,
)
else:
response = base_llm_http_handler.completion(
model=model,
stream=stream,
messages=messages,
acompletion=acompletion,
api_base=api_base,
model_response=model_response,
optional_params=optional_params,
litellm_params=litellm_params,
custom_llm_provider="bedrock",
timeout=timeout,
headers=headers,
encoding=encoding,
api_key=api_key,
logging_obj=logging,
client=client,
)
elif custom_llm_provider == "watsonx":
response = watsonx_chat_completion.completion(
model=model,
messages=messages,
headers=headers,
model_response=model_response,
print_verbose=print_verbose,
api_key=api_key,
api_base=api_base,
acompletion=acompletion,
logging_obj=logging,
optional_params=optional_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
timeout=timeout, # type: ignore
custom_prompt_dict=custom_prompt_dict,
client=client, # pass AsyncOpenAI, OpenAI client
encoding=encoding,
custom_llm_provider="watsonx",
)
elif custom_llm_provider == "watsonx_text":
api_key = (
api_key
or optional_params.pop("apikey", None)
or get_secret_str("WATSONX_APIKEY")
or get_secret_str("WATSONX_API_KEY")
or get_secret_str("WX_API_KEY")
)
api_base = (
api_base
or optional_params.pop(
"url",
optional_params.pop(
"api_base", optional_params.pop("base_url", None)
),
)
or get_secret_str("WATSONX_API_BASE")
or get_secret_str("WATSONX_URL")
or get_secret_str("WX_URL")
or get_secret_str("WML_URL")
)
wx_credentials = optional_params.pop(
"wx_credentials",
optional_params.pop(
"watsonx_credentials", None
), # follow {provider}_credentials, same as vertex ai
)
token: Optional[str] = None
if wx_credentials is not None:
api_base = wx_credentials.get("url", api_base)
api_key = wx_credentials.get(
"apikey", wx_credentials.get("api_key", api_key)
)
token = wx_credentials.get(
"token",
wx_credentials.get(
"watsonx_token", None
), # follow format of {provider}_token, same as azure - e.g. 'azure_ad_token=..'
)
if token is not None:
optional_params["token"] = token
response = base_llm_http_handler.completion(
model=model,
stream=stream,
messages=messages,
acompletion=acompletion,
api_base=api_base,
model_response=model_response,
optional_params=optional_params,
litellm_params=litellm_params,
custom_llm_provider="watsonx_text",
timeout=timeout,
headers=headers,
encoding=encoding,
api_key=api_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
client=client,
)
elif custom_llm_provider == "vllm":
custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
model_response = vllm_handler.completion(
model=model,
messages=messages,
custom_prompt_dict=custom_prompt_dict,
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"] is 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":
api_base = (
litellm.api_base
or api_base
or get_secret("OLLAMA_API_BASE")
or "http://localhost:11434"
)
response = base_llm_http_handler.completion(
model=model,
stream=stream,
messages=messages,
acompletion=acompletion,
api_base=api_base,
model_response=model_response,
optional_params=optional_params,
litellm_params=litellm_params,
custom_llm_provider="ollama",
timeout=timeout,
headers=headers,
encoding=encoding,
api_key=api_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
client=client,
)
elif custom_llm_provider == "ollama_chat":
api_base = (
litellm.api_base
or api_base
or get_secret("OLLAMA_API_BASE")
or "http://localhost:11434"
)
api_key = (
api_key
or litellm.ollama_key
or os.environ.get("OLLAMA_API_KEY")
or litellm.api_key
)
## LOGGING
generator = ollama_chat.get_ollama_response(
api_base=api_base,
api_key=api_key,
model=model,
messages=messages,
optional_params=optional_params,
logging_obj=logging,
acompletion=acompletion,
model_response=model_response,
encoding=encoding,
client=client,
)
if acompletion is True or optional_params.get("stream", False) is True:
return generator
response = generator
elif custom_llm_provider == "triton":
api_base = litellm.api_base or api_base
response = base_llm_http_handler.completion(
model=model,
stream=stream,
messages=messages,
acompletion=acompletion,
api_base=api_base,
model_response=model_response,
optional_params=optional_params,
litellm_params=litellm_params,
custom_llm_provider=custom_llm_provider,
timeout=timeout,
headers=headers,
encoding=encoding,
api_key=api_key,
logging_obj=logging,
)
elif custom_llm_provider == "cloudflare":
api_key = (
api_key
or litellm.cloudflare_api_key
or litellm.api_key
or get_secret("CLOUDFLARE_API_KEY")
)
account_id = get_secret("CLOUDFLARE_ACCOUNT_ID")
api_base = (
api_base
or litellm.api_base
or get_secret("CLOUDFLARE_API_BASE")
or f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/"
)
custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
response = base_llm_http_handler.completion(
model=model,
stream=stream,
messages=messages,
acompletion=acompletion,
api_base=api_base,
model_response=model_response,
optional_params=optional_params,
litellm_params=litellm_params,
custom_llm_provider="cloudflare",
timeout=timeout,
headers=headers,
encoding=encoding,
api_key=api_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
)
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"] is 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 == "petals" or model in litellm.petals_models:
api_base = api_base or litellm.api_base
custom_llm_provider = "petals"
stream = optional_params.pop("stream", False)
model_response = petals_handler.completion(
model=model,
messages=messages,
api_base=api_base,
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,
client=client,
)
if stream is True: ## [BETA]
# Fake streaming for petals
resp_string = model_response["choices"][0]["message"]["content"]
response = CustomStreamWrapper(
resp_string,
model,
custom_llm_provider="petals",
logging_obj=logging,
)
return response
response = model_response
elif custom_llm_provider == "snowflake" or model in litellm.snowflake_models:
try:
client = (
HTTPHandler(timeout=timeout) if stream is False else None
) # Keep this here, otherwise, the httpx.client closes and streaming is impossible
response = base_llm_http_handler.completion(
model=model,
messages=messages,
headers=headers,
model_response=model_response,
api_key=api_key,
api_base=api_base,
acompletion=acompletion,
logging_obj=logging,
optional_params=optional_params,
litellm_params=litellm_params,
timeout=timeout, # type: ignore
client=client,
custom_llm_provider=custom_llm_provider,
encoding=encoding,
stream=stream,
)
except Exception as e:
## LOGGING - log the original exception returned
logging.post_call(
input=messages,
api_key=api_key,
original_response=str(e),
additional_args={"headers": headers},
)
raise e
elif custom_llm_provider == "custom":
url = litellm.api_base or api_base or ""
if url is 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 = litellm.module_level_client.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]) # type: ignore
resp = litellm.module_level_client.post(
url,
json={
"model": model,
"params": {
"prompt": [prompt],
"max_tokens": max_tokens,
"temperature": temperature,
"top_p": top_p,
"top_k": kwargs.get("top_k", 40),
},
},
)
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 # type: ignore
model_response.created = int(time.time())
model_response.model = model
response = model_response
elif (
custom_llm_provider in litellm._custom_providers
): # Assume custom LLM provider
# Get the Custom Handler
custom_handler: Optional[CustomLLM] = None
for item in litellm.custom_provider_map:
if item["provider"] == custom_llm_provider:
custom_handler = item["custom_handler"]
if custom_handler is None:
raise LiteLLMUnknownProvider(
model=model, custom_llm_provider=custom_llm_provider
)
## ROUTE LLM CALL ##
handler_fn = custom_chat_llm_router(
async_fn=acompletion, stream=stream, custom_llm=custom_handler
)
headers = headers or litellm.headers
## CALL FUNCTION
response = handler_fn(
model=model,
messages=messages,
headers=headers,
model_response=model_response,
print_verbose=print_verbose,
api_key=api_key,
api_base=api_base,
acompletion=acompletion,
logging_obj=logging,
optional_params=optional_params,
litellm_params=litellm_params,
logger_fn=logger_fn,
timeout=timeout, # type: ignore
custom_prompt_dict=custom_prompt_dict,
client=client, # pass AsyncOpenAI, OpenAI client
encoding=encoding,
)
if stream is True:
return CustomStreamWrapper(
completion_stream=response,
model=model,
custom_llm_provider=custom_llm_provider,
logging_obj=logging,
)
else:
raise LiteLLMUnknownProvider(
model=model, custom_llm_provider=custom_llm_provider
)
return response
except Exception as e:
## Map to OpenAI Exception
raise exception_type(
model=model,
custom_llm_provider=custom_llm_provider,
original_exception=e,
completion_kwargs=args,
extra_kwargs=kwargs,
)
def completion_with_retries(*args, **kwargs):
"""
Executes a litellm.completion() with 3 retries
"""
try:
import tenacity
except Exception as e:
raise Exception(
f"tenacity import failed please run `pip install tenacity`. Error{e}"
)
num_retries = kwargs.pop("num_retries", 3)
# reset retries in .completion()
kwargs["max_retries"] = 0
kwargs["num_retries"] = 0
retry_strategy: Literal["exponential_backoff_retry", "constant_retry"] = kwargs.pop(
"retry_strategy", "constant_retry"
) # type: ignore
original_function = kwargs.pop("original_function", completion)
if retry_strategy == "exponential_backoff_retry":
retryer = tenacity.Retrying(
wait=tenacity.wait_exponential(multiplier=1, max=10),
stop=tenacity.stop_after_attempt(num_retries),
reraise=True,
)
else:
retryer = tenacity.Retrying(
stop=tenacity.stop_after_attempt(num_retries), reraise=True
)
return retryer(original_function, *args, **kwargs)
async def acompletion_with_retries(*args, **kwargs):
"""
[DEPRECATED]. Use 'acompletion' or router.acompletion instead!
Executes a litellm.completion() with 3 retries
"""
try:
import tenacity
except Exception as e:
raise Exception(
f"tenacity import failed please run `pip install tenacity`. Error{e}"
)
num_retries = kwargs.pop("num_retries", 3)
kwargs["max_retries"] = 0
kwargs["num_retries"] = 0
retry_strategy = kwargs.pop("retry_strategy", "constant_retry")
original_function = kwargs.pop("original_function", completion)
if retry_strategy == "exponential_backoff_retry":
retryer = tenacity.Retrying(
wait=tenacity.wait_exponential(multiplier=1, max=10),
stop=tenacity.stop_after_attempt(num_retries),
reraise=True,
)
else:
retryer = tenacity.Retrying(
stop=tenacity.stop_after_attempt(num_retries), reraise=True
)
return await retryer(original_function, *args, **kwargs)
### EMBEDDING ENDPOINTS ####################
@client
async def aembedding(*args, **kwargs) -> EmbeddingResponse:
"""
Asynchronously calls the `embedding` function with the given arguments and keyword arguments.
Parameters:
- `args` (tuple): Positional arguments to be passed to the `embedding` function.
- `kwargs` (dict): Keyword arguments to be passed to the `embedding` function.
Returns:
- `response` (Any): The response returned by the `embedding` function.
"""
loop = asyncio.get_event_loop()
model = args[0] if len(args) > 0 else kwargs["model"]
### PASS ARGS TO Embedding ###
kwargs["aembedding"] = True
custom_llm_provider = None
try:
# Use a partial function to pass your keyword arguments
func = partial(embedding, *args, **kwargs)
# Add the context to the function
ctx = contextvars.copy_context()
func_with_context = partial(ctx.run, func)
_, custom_llm_provider, _, _ = get_llm_provider(
model=model, api_base=kwargs.get("api_base", None)
)
# Await normally
init_response = await loop.run_in_executor(None, func_with_context)
response: Optional[EmbeddingResponse] = None
if isinstance(init_response, dict):
response = EmbeddingResponse(**init_response)
elif isinstance(init_response, EmbeddingResponse): ## CACHING SCENARIO
response = init_response
elif asyncio.iscoroutine(init_response):
response = await init_response # type: ignore
if (
response is not None
and isinstance(response, EmbeddingResponse)
and hasattr(response, "_hidden_params")
):
response._hidden_params["custom_llm_provider"] = custom_llm_provider
if response is None:
raise ValueError(
"Unable to get Embedding Response. Please pass a valid llm_provider."
)
return response
except Exception as e:
custom_llm_provider = custom_llm_provider or "openai"
raise exception_type(
model=model,
custom_llm_provider=custom_llm_provider,
original_exception=e,
completion_kwargs=args,
extra_kwargs=kwargs,
)
@client
def embedding( # noqa: PLR0915
model,
input=[],
# Optional params
dimensions: Optional[int] = None,
encoding_format: Optional[str] = None,
timeout=600, # default to 10 minutes
# set api_base, api_version, api_key
api_base: Optional[str] = None,
api_version: Optional[str] = None,
api_key: Optional[str] = None,
api_type: Optional[str] = None,
caching: bool = False,
user: Optional[str] = None,
custom_llm_provider=None,
litellm_call_id=None,
logger_fn=None,
**kwargs,
) -> Union[EmbeddingResponse, Coroutine[Any, Any, EmbeddingResponse]]:
"""
Embedding function that calls an API to generate embeddings for the given input.
Parameters:
- model: The embedding model to use.
- input: The input for which embeddings are to be generated.
- encoding_format: Optional[str] The format to return the embeddings in. Can be either `float` or `base64`
- dimensions: The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
- timeout: The timeout value for the API call, default 10 mins
- litellm_call_id: The call ID for litellm logging.
- litellm_logging_obj: The litellm logging object.
- logger_fn: The logger function.
- api_base: Optional. The base URL for the API.
- api_version: Optional. The version of the API.
- api_key: Optional. The API key to use.
- api_type: Optional. The type of the API.
- caching: A boolean indicating whether to enable caching.
- custom_llm_provider: The custom llm provider.
Returns:
- response: The response received from the API call.
Raises:
- exception_type: If an exception occurs during the API call.
"""
azure = kwargs.get("azure", None)
client = kwargs.pop("client", None)
max_retries = kwargs.get("max_retries", None)
litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore
mock_response: Optional[List[float]] = kwargs.get("mock_response", None) # type: ignore
azure_ad_token_provider = kwargs.pop("azure_ad_token_provider", None)
aembedding = kwargs.get("aembedding", None)
extra_headers = kwargs.get("extra_headers", None)
headers = kwargs.get("headers", None)
### CUSTOM MODEL COST ###
input_cost_per_token = kwargs.get("input_cost_per_token", None)
output_cost_per_token = kwargs.get("output_cost_per_token", None)
input_cost_per_second = kwargs.get("input_cost_per_second", None)
output_cost_per_second = kwargs.get("output_cost_per_second", None)
openai_params = [
"user",
"dimensions",
"request_timeout",
"api_base",
"api_version",
"api_key",
"deployment_id",
"organization",
"base_url",
"default_headers",
"timeout",
"max_retries",
"encoding_format",
]
litellm_params = [
"aembedding",
"extra_headers",
] + all_litellm_params
default_params = openai_params + litellm_params
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
model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(
model=model,
custom_llm_provider=custom_llm_provider,
api_base=api_base,
api_key=api_key,
)
if dynamic_api_key is not None:
api_key = dynamic_api_key
optional_params = get_optional_params_embeddings(
model=model,
user=user,
dimensions=dimensions,
encoding_format=encoding_format,
custom_llm_provider=custom_llm_provider,
**non_default_params,
)
### REGISTER CUSTOM MODEL PRICING -- IF GIVEN ###
if input_cost_per_token is not None and output_cost_per_token is not None:
litellm.register_model(
{
f"{custom_llm_provider}/{model}": {
"input_cost_per_token": input_cost_per_token,
"output_cost_per_token": output_cost_per_token,
"litellm_provider": custom_llm_provider,
}
}
)
if input_cost_per_second is not None: # time based pricing just needs cost in place
output_cost_per_second = output_cost_per_second or 0.0
litellm.register_model(
{
f"{custom_llm_provider}/{model}": {
"input_cost_per_second": input_cost_per_second,
"output_cost_per_second": output_cost_per_second,
"litellm_provider": custom_llm_provider,
}
}
)
litellm_params_dict = get_litellm_params(**kwargs)
logging: Logging = litellm_logging_obj # type: ignore
logging.update_environment_variables(
model=model,
user=user,
optional_params=optional_params,
litellm_params=litellm_params_dict,
custom_llm_provider=custom_llm_provider,
)
if mock_response is not None:
return mock_embedding(model=model, mock_response=mock_response)
try:
response: Optional[
Union[EmbeddingResponse, Coroutine[Any, Any, EmbeddingResponse]]
] = None
if azure is True or custom_llm_provider == "azure":
# azure configs
api_base = api_base or litellm.api_base or get_secret_str("AZURE_API_BASE")
api_version = (
api_version
or litellm.api_version
or get_secret_str("AZURE_API_VERSION")
or litellm.AZURE_DEFAULT_API_VERSION
)
azure_ad_token = optional_params.pop(
"azure_ad_token", None
) or get_secret_str("AZURE_AD_TOKEN")
api_key = (
api_key
or litellm.api_key
or litellm.azure_key
or get_secret_str("AZURE_API_KEY")
)
if api_base is None:
raise ValueError(
"No API Base provided for Azure OpenAI LLM provider. Set 'AZURE_API_BASE' in .env"
)
## EMBEDDING CALL
response = azure_chat_completions.embedding(
model=model,
input=input,
api_base=api_base,
api_key=api_key,
api_version=api_version,
azure_ad_token=azure_ad_token,
azure_ad_token_provider=azure_ad_token_provider,
logging_obj=logging,
timeout=timeout,
model_response=EmbeddingResponse(),
optional_params=optional_params,
client=client,
aembedding=aembedding,
max_retries=max_retries,
headers=headers or extra_headers,
litellm_params=litellm_params_dict,
)
elif (
model in litellm.open_ai_embedding_models
or custom_llm_provider == "openai"
or custom_llm_provider == "together_ai"
or custom_llm_provider == "nvidia_nim"
or custom_llm_provider == "litellm_proxy"
):
api_base = (
api_base
or litellm.api_base
or get_secret_str("OPENAI_API_BASE")
or "https://api.openai.com/v1"
)
openai.organization = (
litellm.organization
or get_secret_str("OPENAI_ORGANIZATION")
or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
)
# set API KEY
api_key = (
api_key
or litellm.api_key
or litellm.openai_key
or get_secret_str("OPENAI_API_KEY")
)
if extra_headers is not None:
optional_params["extra_headers"] = extra_headers
api_version = None
## EMBEDDING CALL
response = openai_chat_completions.embedding(
model=model,
input=input,
api_base=api_base,
api_key=api_key,
logging_obj=logging,
timeout=timeout,
model_response=EmbeddingResponse(),
optional_params=optional_params,
client=client,
aembedding=aembedding,
max_retries=max_retries,
)
elif custom_llm_provider == "databricks":
api_base = api_base or litellm.api_base or get_secret("DATABRICKS_API_BASE") # type: ignore
# set API KEY
api_key = (
api_key
or litellm.api_key
or litellm.databricks_key
or get_secret("DATABRICKS_API_KEY")
) # type: ignore
## EMBEDDING CALL
response = databricks_embedding.embedding(
model=model,
input=input,
api_base=api_base,
api_key=api_key,
logging_obj=logging,
timeout=timeout,
model_response=EmbeddingResponse(),
optional_params=optional_params,
client=client,
aembedding=aembedding,
)
elif (
custom_llm_provider == "openai_like"
or custom_llm_provider == "jina_ai"
or custom_llm_provider == "hosted_vllm"
or custom_llm_provider == "lm_studio"
):
api_base = (
api_base or litellm.api_base or get_secret_str("OPENAI_LIKE_API_BASE")
)
# set API KEY
if api_key is None:
api_key = (
api_key
or litellm.api_key
or litellm.openai_like_key
or get_secret_str("OPENAI_LIKE_API_KEY")
)
## EMBEDDING CALL
response = openai_like_embedding.embedding(
model=model,
input=input,
api_base=api_base,
api_key=api_key,
logging_obj=logging,
timeout=timeout,
model_response=EmbeddingResponse(),
optional_params=optional_params,
client=client,
aembedding=aembedding,
)
elif custom_llm_provider == "cohere" or custom_llm_provider == "cohere_chat":
cohere_key = (
api_key
or litellm.cohere_key
or get_secret("COHERE_API_KEY")
or get_secret("CO_API_KEY")
or litellm.api_key
)
if extra_headers is not None and isinstance(extra_headers, dict):
headers = extra_headers
else:
headers = {}
response = cohere_embed.embedding(
model=model,
input=input,
optional_params=optional_params,
encoding=encoding,
api_key=cohere_key, # type: ignore
headers=headers,
logging_obj=logging,
model_response=EmbeddingResponse(),
aembedding=aembedding,
timeout=timeout,
client=client,
)
elif custom_llm_provider == "huggingface":
api_key = (
api_key
or litellm.huggingface_key
or get_secret("HUGGINGFACE_API_KEY")
or litellm.api_key
) # type: ignore
response = huggingface.embedding(
model=model,
input=input,
encoding=encoding, # type: ignore
api_key=api_key,
api_base=api_base,
logging_obj=logging,
model_response=EmbeddingResponse(),
optional_params=optional_params,
client=client,
aembedding=aembedding,
)
elif custom_llm_provider == "bedrock":
if isinstance(input, str):
transformed_input = [input]
else:
transformed_input = input
response = bedrock_embedding.embeddings(
model=model,
input=transformed_input,
encoding=encoding,
logging_obj=logging,
optional_params=optional_params,
model_response=EmbeddingResponse(),
client=client,
timeout=timeout,
aembedding=aembedding,
litellm_params={},
api_base=api_base,
print_verbose=print_verbose,
extra_headers=extra_headers,
)
elif custom_llm_provider == "triton":
if api_base is None:
raise ValueError(
"api_base is required for triton. Please pass `api_base`"
)
response = base_llm_http_handler.embedding(
model=model,
input=input,
custom_llm_provider=custom_llm_provider,
api_base=api_base,
api_key=api_key,
logging_obj=logging,
timeout=timeout,
model_response=EmbeddingResponse(),
optional_params=optional_params,
client=client,
aembedding=aembedding,
litellm_params={},
)
elif custom_llm_provider == "gemini":
gemini_api_key = (
api_key or get_secret_str("GEMINI_API_KEY") or litellm.api_key
)
response = google_batch_embeddings.batch_embeddings( # type: ignore
model=model,
input=input,
encoding=encoding,
logging_obj=logging,
optional_params=optional_params,
model_response=EmbeddingResponse(),
vertex_project=None,
vertex_location=None,
vertex_credentials=None,
aembedding=aembedding,
print_verbose=print_verbose,
custom_llm_provider="gemini",
api_key=gemini_api_key,
)
elif custom_llm_provider == "vertex_ai":
vertex_ai_project = (
optional_params.pop("vertex_project", None)
or optional_params.pop("vertex_ai_project", None)
or litellm.vertex_project
or get_secret_str("VERTEXAI_PROJECT")
or get_secret_str("VERTEX_PROJECT")
)
vertex_ai_location = (
optional_params.pop("vertex_location", None)
or optional_params.pop("vertex_ai_location", None)
or litellm.vertex_location
or get_secret_str("VERTEXAI_LOCATION")
or get_secret_str("VERTEX_LOCATION")
)
vertex_credentials = (
optional_params.pop("vertex_credentials", None)
or optional_params.pop("vertex_ai_credentials", None)
or get_secret_str("VERTEXAI_CREDENTIALS")
or get_secret_str("VERTEX_CREDENTIALS")
)
if (
"image" in optional_params
or "video" in optional_params
or model
in vertex_multimodal_embedding.SUPPORTED_MULTIMODAL_EMBEDDING_MODELS
):
# multimodal embedding is supported on vertex httpx
response = vertex_multimodal_embedding.multimodal_embedding(
model=model,
input=input,
encoding=encoding,
logging_obj=logging,
optional_params=optional_params,
model_response=EmbeddingResponse(),
vertex_project=vertex_ai_project,
vertex_location=vertex_ai_location,
vertex_credentials=vertex_credentials,
aembedding=aembedding,
print_verbose=print_verbose,
custom_llm_provider="vertex_ai",
)
else:
response = vertex_embedding.embedding(
model=model,
input=input,
encoding=encoding,
logging_obj=logging,
optional_params=optional_params,
model_response=EmbeddingResponse(),
vertex_project=vertex_ai_project,
vertex_location=vertex_ai_location,
vertex_credentials=vertex_credentials,
custom_llm_provider="vertex_ai",
timeout=timeout,
aembedding=aembedding,
print_verbose=print_verbose,
api_key=api_key,
)
elif custom_llm_provider == "oobabooga":
response = oobabooga.embedding(
model=model,
input=input,
encoding=encoding,
api_base=api_base,
logging_obj=logging,
optional_params=optional_params,
model_response=EmbeddingResponse(),
api_key=api_key,
)
elif custom_llm_provider == "ollama":
api_base = (
litellm.api_base
or api_base
or get_secret_str("OLLAMA_API_BASE")
or "http://localhost:11434"
) # type: ignore
if isinstance(input, str):
input = [input]
if not all(isinstance(item, str) for item in input):
raise litellm.BadRequestError(
message=f"Invalid input for ollama embeddings. input={input}",
model=model, # type: ignore
llm_provider="ollama", # type: ignore
)
ollama_embeddings_fn = (
ollama.ollama_aembeddings
if aembedding is True
else ollama.ollama_embeddings
)
response = ollama_embeddings_fn( # type: ignore
api_base=api_base,
model=model,
prompts=input,
encoding=encoding,
logging_obj=logging,
optional_params=optional_params,
model_response=EmbeddingResponse(),
)
elif custom_llm_provider == "sagemaker":
response = sagemaker_llm.embedding(
model=model,
input=input,
encoding=encoding,
logging_obj=logging,
optional_params=optional_params,
model_response=EmbeddingResponse(),
print_verbose=print_verbose,
)
elif custom_llm_provider == "mistral":
api_key = api_key or litellm.api_key or get_secret_str("MISTRAL_API_KEY")
response = openai_chat_completions.embedding(
model=model,
input=input,
api_base=api_base,
api_key=api_key,
logging_obj=logging,
timeout=timeout,
model_response=EmbeddingResponse(),
optional_params=optional_params,
client=client,
aembedding=aembedding,
)
elif custom_llm_provider == "fireworks_ai":
api_key = (
api_key or litellm.api_key or get_secret_str("FIREWORKS_AI_API_KEY")
)
response = openai_chat_completions.embedding(
model=model,
input=input,
api_base=api_base,
api_key=api_key,
logging_obj=logging,
timeout=timeout,
model_response=EmbeddingResponse(),
optional_params=optional_params,
client=client,
aembedding=aembedding,
)
elif custom_llm_provider == "voyage":
response = base_llm_http_handler.embedding(
model=model,
input=input,
custom_llm_provider=custom_llm_provider,
api_base=api_base,
api_key=api_key,
logging_obj=logging,
timeout=timeout,
model_response=EmbeddingResponse(),
optional_params=optional_params,
client=client,
aembedding=aembedding,
litellm_params={},
)
elif custom_llm_provider == "watsonx":
credentials = IBMWatsonXMixin.get_watsonx_credentials(
optional_params=optional_params, api_key=api_key, api_base=api_base
)
api_key = credentials["api_key"]
api_base = credentials["api_base"]
if "token" in credentials:
optional_params["token"] = credentials["token"]
response = base_llm_http_handler.embedding(
model=model,
input=input,
custom_llm_provider=custom_llm_provider,
api_base=api_base,
api_key=api_key,
logging_obj=logging,
timeout=timeout,
model_response=EmbeddingResponse(),
optional_params=optional_params,
litellm_params={},
client=client,
aembedding=aembedding,
)
elif custom_llm_provider == "xinference":
api_key = (
api_key
or litellm.api_key
or get_secret_str("XINFERENCE_API_KEY")
or "stub-xinference-key"
) # xinference does not need an api key, pass a stub key if user did not set one
api_base = (
api_base
or litellm.api_base
or get_secret_str("XINFERENCE_API_BASE")
or "http://127.0.0.1:9997/v1"
)
response = openai_chat_completions.embedding(
model=model,
input=input,
api_base=api_base,
api_key=api_key,
logging_obj=logging,
timeout=timeout,
model_response=EmbeddingResponse(),
optional_params=optional_params,
client=client,
aembedding=aembedding,
)
elif custom_llm_provider == "azure_ai":
api_base = (
api_base # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there
or litellm.api_base
or get_secret_str("AZURE_AI_API_BASE")
)
# set API KEY
api_key = (
api_key
or litellm.api_key # for deepinfra/perplexity/anyscale/friendliai we check in get_llm_provider and pass in the api key from there
or litellm.openai_key
or get_secret_str("AZURE_AI_API_KEY")
)
## EMBEDDING CALL
response = azure_ai_embedding.embedding(
model=model,
input=input,
api_base=api_base,
api_key=api_key,
logging_obj=logging,
timeout=timeout,
model_response=EmbeddingResponse(),
optional_params=optional_params,
client=client,
aembedding=aembedding,
)
else:
raise LiteLLMUnknownProvider(
model=model, custom_llm_provider=custom_llm_provider
)
if (
response is not None
and hasattr(response, "_hidden_params")
and isinstance(response, EmbeddingResponse)
):
response._hidden_params["custom_llm_provider"] = custom_llm_provider
if response is None:
raise LiteLLMUnknownProvider(
model=model, custom_llm_provider=custom_llm_provider
)
return response
except Exception as e:
## LOGGING
litellm_logging_obj.post_call(
input=input,
api_key=api_key,
original_response=str(e),
)
## Map to OpenAI Exception
raise exception_type(
model=model,
original_exception=e,
custom_llm_provider=custom_llm_provider,
extra_kwargs=kwargs,
)
###### Text Completion ################
@client
async def atext_completion(
*args, **kwargs
) -> Union[TextCompletionResponse, TextCompletionStreamWrapper]:
"""
Implemented to handle async streaming for the text completion endpoint
"""
loop = asyncio.get_event_loop()
model = args[0] if len(args) > 0 else kwargs["model"]
### PASS ARGS TO COMPLETION ###
kwargs["acompletion"] = True
custom_llm_provider = None
try:
# Use a partial function to pass your keyword arguments
func = partial(text_completion, *args, **kwargs)
# Add the context to the function
ctx = contextvars.copy_context()
func_with_context = partial(ctx.run, func)
init_response = await loop.run_in_executor(None, func_with_context)
if isinstance(init_response, dict) or isinstance(
init_response, TextCompletionResponse
): ## CACHING SCENARIO
if isinstance(init_response, dict):
response = TextCompletionResponse(**init_response)
else:
response = init_response
elif asyncio.iscoroutine(init_response):
response = await init_response
else:
response = init_response # type: ignore
if (
kwargs.get("stream", False) is True
or isinstance(response, TextCompletionStreamWrapper)
or isinstance(response, CustomStreamWrapper)
): # return an async generator
return TextCompletionStreamWrapper(
completion_stream=_async_streaming(
response=response,
model=model,
custom_llm_provider=custom_llm_provider,
args=args,
),
model=model,
custom_llm_provider=custom_llm_provider,
stream_options=kwargs.get("stream_options"),
)
else:
## OpenAI / Azure Text Completion Returns here
if isinstance(response, TextCompletionResponse):
return response
elif asyncio.iscoroutine(response):
response = await response
text_completion_response = TextCompletionResponse()
text_completion_response = litellm.utils.LiteLLMResponseObjectHandler.convert_chat_to_text_completion(
text_completion_response=text_completion_response,
response=response,
custom_llm_provider=custom_llm_provider,
)
return text_completion_response
except Exception as e:
custom_llm_provider = custom_llm_provider or "openai"
raise exception_type(
model=model,
custom_llm_provider=custom_llm_provider,
original_exception=e,
completion_kwargs=args,
extra_kwargs=kwargs,
)
@client
def text_completion( # noqa: PLR0915
prompt: Union[
str, List[Union[str, List[Union[str, List[int]]]]]
], # Required: The prompt(s) to generate completions for.
model: Optional[str] = None, # Optional: either `model` or `engine` can be set
best_of: Optional[
int
] = None, # Optional: Generates best_of completions server-side.
echo: Optional[
bool
] = None, # Optional: Echo back the prompt in addition to the completion.
frequency_penalty: Optional[
float
] = None, # Optional: Penalize new tokens based on their existing frequency.
logit_bias: Optional[
Dict[int, int]
] = None, # Optional: Modify the likelihood of specified tokens.
logprobs: Optional[
int
] = None, # Optional: Include the log probabilities on the most likely tokens.
max_tokens: Optional[
int
] = None, # Optional: The maximum number of tokens to generate in the completion.
n: Optional[
int
] = None, # Optional: How many completions to generate for each prompt.
presence_penalty: Optional[
float
] = None, # Optional: Penalize new tokens based on whether they appear in the text so far.
stop: Optional[
Union[str, List[str]]
] = None, # Optional: Sequences where the API will stop generating further tokens.
stream: Optional[bool] = None, # Optional: Whether to stream back partial progress.
stream_options: Optional[dict] = None,
suffix: Optional[
str
] = None, # Optional: The suffix that comes after a completion of inserted text.
temperature: Optional[float] = None, # Optional: Sampling temperature to use.
top_p: Optional[float] = None, # Optional: Nucleus sampling parameter.
user: Optional[
str
] = None, # Optional: A unique identifier representing your end-user.
# set api_base, api_version, api_key
api_base: Optional[str] = None,
api_version: Optional[str] = None,
api_key: Optional[str] = None,
model_list: Optional[list] = None, # pass in a list of api_base,keys, etc.
# Optional liteLLM function params
custom_llm_provider: Optional[str] = None,
*args,
**kwargs,
):
import copy
"""
Generate text completions using the OpenAI API.
Args:
model (str): ID of the model to use.
prompt (Union[str, List[Union[str, List[Union[str, List[int]]]]]): The prompt(s) to generate completions for.
best_of (Optional[int], optional): Generates best_of completions server-side. Defaults to 1.
echo (Optional[bool], optional): Echo back the prompt in addition to the completion. Defaults to False.
frequency_penalty (Optional[float], optional): Penalize new tokens based on their existing frequency. Defaults to 0.
logit_bias (Optional[Dict[int, int]], optional): Modify the likelihood of specified tokens. Defaults to None.
logprobs (Optional[int], optional): Include the log probabilities on the most likely tokens. Defaults to None.
max_tokens (Optional[int], optional): The maximum number of tokens to generate in the completion. Defaults to 16.
n (Optional[int], optional): How many completions to generate for each prompt. Defaults to 1.
presence_penalty (Optional[float], optional): Penalize new tokens based on whether they appear in the text so far. Defaults to 0.
stop (Optional[Union[str, List[str]]], optional): Sequences where the API will stop generating further tokens. Defaults to None.
stream (Optional[bool], optional): Whether to stream back partial progress. Defaults to False.
suffix (Optional[str], optional): The suffix that comes after a completion of inserted text. Defaults to None.
temperature (Optional[float], optional): Sampling temperature to use. Defaults to 1.
top_p (Optional[float], optional): Nucleus sampling parameter. Defaults to 1.
user (Optional[str], optional): A unique identifier representing your end-user.
Returns:
TextCompletionResponse: A response object containing the generated completion and associated metadata.
Example:
Your example of how to use this function goes here.
"""
if "engine" in kwargs:
_engine = kwargs["engine"]
if model is None and isinstance(_engine, str):
# only use engine when model not passed
model = _engine
kwargs.pop("engine")
text_completion_response = TextCompletionResponse()
optional_params: Dict[str, Any] = {}
# default values for all optional params are none, litellm only passes them to the llm when they are set to non None values
if best_of is not None:
optional_params["best_of"] = best_of
if echo is not None:
optional_params["echo"] = echo
if frequency_penalty is not None:
optional_params["frequency_penalty"] = frequency_penalty
if logit_bias is not None:
optional_params["logit_bias"] = logit_bias
if logprobs is not None:
optional_params["logprobs"] = logprobs
if max_tokens is not None:
optional_params["max_tokens"] = max_tokens
if n is not None:
optional_params["n"] = n
if presence_penalty is not None:
optional_params["presence_penalty"] = presence_penalty
if stop is not None:
optional_params["stop"] = stop
if stream is not None:
optional_params["stream"] = stream
if stream_options is not None:
optional_params["stream_options"] = stream_options
if suffix is not None:
optional_params["suffix"] = suffix
if temperature is not None:
optional_params["temperature"] = temperature
if top_p is not None:
optional_params["top_p"] = top_p
if user is not None:
optional_params["user"] = user
if api_base is not None:
optional_params["api_base"] = api_base
if api_version is not None:
optional_params["api_version"] = api_version
if api_key is not None:
optional_params["api_key"] = api_key
if custom_llm_provider is not None:
optional_params["custom_llm_provider"] = custom_llm_provider
# get custom_llm_provider
_model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(
model=model, # type: ignore
custom_llm_provider=custom_llm_provider,
api_base=api_base,
)
if custom_llm_provider == "huggingface":
# if echo == True, for TGI llms we need to set top_n_tokens to 3
if echo is True:
# for tgi llms
if "top_n_tokens" not in kwargs:
kwargs["top_n_tokens"] = 3
# processing prompt - users can pass raw tokens to OpenAI Completion()
if isinstance(prompt, list):
import concurrent.futures
tokenizer = tiktoken.encoding_for_model("text-davinci-003")
## if it's a 2d list - each element in the list is a text_completion() request
if len(prompt) > 0 and isinstance(prompt[0], list):
responses = [None for x in prompt] # init responses
def process_prompt(i, individual_prompt):
decoded_prompt = tokenizer.decode(individual_prompt)
all_params = {**kwargs, **optional_params}
response: TextCompletionResponse = text_completion( # type: ignore
model=model,
prompt=decoded_prompt,
num_retries=3, # ensure this does not fail for the batch
*args,
**all_params,
)
text_completion_response["id"] = response.get("id", None)
text_completion_response["object"] = "text_completion"
text_completion_response["created"] = response.get("created", None)
text_completion_response["model"] = response.get("model", None)
return response["choices"][0]
with concurrent.futures.ThreadPoolExecutor() as executor:
completed_futures = [
executor.submit(process_prompt, i, individual_prompt)
for i, individual_prompt in enumerate(prompt)
]
for i, future in enumerate(
concurrent.futures.as_completed(completed_futures)
):
responses[i] = future.result()
text_completion_response.choices = responses # type: ignore
return text_completion_response
# else:
# check if non default values passed in for best_of, echo, logprobs, suffix
# these are the params supported by Completion() but not ChatCompletion
# default case, non OpenAI requests go through here
# handle prompt formatting if prompt is a string vs. list of strings
messages = []
if isinstance(prompt, list) and len(prompt) > 0 and isinstance(prompt[0], str):
for p in prompt:
message = {"role": "user", "content": p}
messages.append(message)
elif isinstance(prompt, str):
messages = [{"role": "user", "content": prompt}]
elif (
(
custom_llm_provider == "openai"
or custom_llm_provider == "azure"
or custom_llm_provider == "azure_text"
or custom_llm_provider == "text-completion-codestral"
or custom_llm_provider == "text-completion-openai"
)
and isinstance(prompt, list)
and len(prompt) > 0
and isinstance(prompt[0], list)
):
verbose_logger.warning(
msg="List of lists being passed. If this is for tokens, then it might not work across all models."
)
messages = [{"role": "user", "content": prompt}] # type: ignore
else:
raise Exception(
f"Unmapped prompt format. Your prompt is neither a list of strings nor a string. prompt={prompt}. File an issue - https://github.com/BerriAI/litellm/issues"
)
kwargs.pop("prompt", None)
if _model is not None and (
custom_llm_provider == "openai"
): # for openai compatible endpoints - e.g. vllm, call the native /v1/completions endpoint for text completion calls
if _model not in litellm.open_ai_chat_completion_models:
model = "text-completion-openai/" + _model
optional_params.pop("custom_llm_provider", None)
if model is None:
raise ValueError("model is not set. Set either via 'model' or 'engine' param.")
kwargs["text_completion"] = True
response = completion(
model=model,
messages=messages,
*args,
**kwargs,
**optional_params,
)
if kwargs.get("acompletion", False) is True:
return response
if (
stream is True
or kwargs.get("stream", False) is True
or isinstance(response, CustomStreamWrapper)
):
response = TextCompletionStreamWrapper(
completion_stream=response,
model=model,
stream_options=stream_options,
custom_llm_provider=custom_llm_provider,
)
return response
elif isinstance(response, TextCompletionStreamWrapper):
return response
# OpenAI Text / Azure Text will return here
if isinstance(response, TextCompletionResponse):
return response
text_completion_response = (
litellm.utils.LiteLLMResponseObjectHandler.convert_chat_to_text_completion(
response=response,
text_completion_response=text_completion_response,
)
)
return text_completion_response
###### Adapter Completion ################
async def aadapter_completion(
*, adapter_id: str, **kwargs
) -> Optional[Union[BaseModel, AdapterCompletionStreamWrapper]]:
"""
Implemented to handle async calls for adapter_completion()
"""
try:
translation_obj: Optional[CustomLogger] = None
for item in litellm.adapters:
if item["id"] == adapter_id:
translation_obj = item["adapter"]
if translation_obj is None:
raise ValueError(
"No matching adapter given. Received 'adapter_id'={}, litellm.adapters={}".format(
adapter_id, litellm.adapters
)
)
new_kwargs = translation_obj.translate_completion_input_params(kwargs=kwargs)
response: Union[ModelResponse, CustomStreamWrapper] = await acompletion(**new_kwargs) # type: ignore
translated_response: Optional[
Union[BaseModel, AdapterCompletionStreamWrapper]
] = None
if isinstance(response, ModelResponse):
translated_response = translation_obj.translate_completion_output_params(
response=response
)
if isinstance(response, CustomStreamWrapper):
translated_response = (
translation_obj.translate_completion_output_params_streaming(
completion_stream=response
)
)
return translated_response
except Exception as e:
raise e
def adapter_completion(
*, adapter_id: str, **kwargs
) -> Optional[Union[BaseModel, AdapterCompletionStreamWrapper]]:
translation_obj: Optional[CustomLogger] = None
for item in litellm.adapters:
if item["id"] == adapter_id:
translation_obj = item["adapter"]
if translation_obj is None:
raise ValueError(
"No matching adapter given. Received 'adapter_id'={}, litellm.adapters={}".format(
adapter_id, litellm.adapters
)
)
new_kwargs = translation_obj.translate_completion_input_params(kwargs=kwargs)
response: Union[ModelResponse, CustomStreamWrapper] = completion(**new_kwargs) # type: ignore
translated_response: Optional[Union[BaseModel, AdapterCompletionStreamWrapper]] = (
None
)
if isinstance(response, ModelResponse):
translated_response = translation_obj.translate_completion_output_params(
response=response
)
elif isinstance(response, CustomStreamWrapper) or inspect.isgenerator(response):
translated_response = (
translation_obj.translate_completion_output_params_streaming(
completion_stream=response
)
)
return translated_response
##### Moderation #######################
def moderation(
input: str, model: Optional[str] = None, api_key: Optional[str] = None, **kwargs
):
# only supports open ai for now
api_key = (
api_key
or litellm.api_key
or litellm.openai_key
or get_secret_str("OPENAI_API_KEY")
)
openai_client = kwargs.get("client", None)
if openai_client is None:
openai_client = openai.OpenAI(
api_key=api_key,
)
if model is not None:
response = openai_client.moderations.create(input=input, model=model)
else:
response = openai_client.moderations.create(input=input)
return response
@client
async def amoderation(
input: str,
model: Optional[str] = None,
api_key: Optional[str] = None,
custom_llm_provider: Optional[str] = None,
**kwargs,
):
from openai import AsyncOpenAI
# only supports open ai for now
api_key = (
api_key
or litellm.api_key
or litellm.openai_key
or get_secret_str("OPENAI_API_KEY")
)
openai_client = kwargs.get("client", None)
if openai_client is None or not isinstance(openai_client, AsyncOpenAI):
# call helper to get OpenAI client
# _get_openai_client maintains in-memory caching logic for OpenAI clients
_openai_client: AsyncOpenAI = openai_chat_completions._get_openai_client( # type: ignore
is_async=True,
api_key=api_key,
)
else:
_openai_client = openai_client
optional_params = GenericLiteLLMParams(**kwargs)
try:
model, _custom_llm_provider, _dynamic_api_key, _dynamic_api_base = (
litellm.get_llm_provider(
model=model or "",
custom_llm_provider=custom_llm_provider,
api_base=optional_params.api_base,
api_key=optional_params.api_key,
)
)
except litellm.BadRequestError:
# `model` is optional field for moderation - get_llm_provider will throw BadRequestError if model is not set / not recognized
pass
if model is not None:
response = await _openai_client.moderations.create(input=input, model=model)
else:
response = await _openai_client.moderations.create(input=input)
return response
##### Image Generation #######################
@client
async def aimage_generation(*args, **kwargs) -> ImageResponse:
"""
Asynchronously calls the `image_generation` function with the given arguments and keyword arguments.
Parameters:
- `args` (tuple): Positional arguments to be passed to the `image_generation` function.
- `kwargs` (dict): Keyword arguments to be passed to the `image_generation` function.
Returns:
- `response` (Any): The response returned by the `image_generation` function.
"""
loop = asyncio.get_event_loop()
model = args[0] if len(args) > 0 else kwargs["model"]
### PASS ARGS TO Image Generation ###
kwargs["aimg_generation"] = True
custom_llm_provider = None
try:
# Use a partial function to pass your keyword arguments
func = partial(image_generation, *args, **kwargs)
# Add the context to the function
ctx = contextvars.copy_context()
func_with_context = partial(ctx.run, func)
_, custom_llm_provider, _, _ = get_llm_provider(
model=model, api_base=kwargs.get("api_base", None)
)
# Await normally
init_response = await loop.run_in_executor(None, func_with_context)
if isinstance(init_response, dict) or isinstance(
init_response, ImageResponse
): ## CACHING SCENARIO
if isinstance(init_response, dict):
init_response = ImageResponse(**init_response)
response = init_response
elif asyncio.iscoroutine(init_response):
response = await init_response # type: ignore
else:
# Call the synchronous function using run_in_executor
response = await loop.run_in_executor(None, func_with_context)
return response
except Exception as e:
custom_llm_provider = custom_llm_provider or "openai"
raise exception_type(
model=model,
custom_llm_provider=custom_llm_provider,
original_exception=e,
completion_kwargs=args,
extra_kwargs=kwargs,
)
@client
def image_generation( # noqa: PLR0915
prompt: str,
model: Optional[str] = None,
n: Optional[int] = None,
quality: Optional[str] = None,
response_format: Optional[str] = None,
size: Optional[str] = None,
style: Optional[str] = None,
user: Optional[str] = None,
timeout=600, # default to 10 minutes
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
custom_llm_provider=None,
**kwargs,
) -> ImageResponse:
"""
Maps the https://api.openai.com/v1/images/generations endpoint.
Currently supports just Azure + OpenAI.
"""
try:
args = locals()
aimg_generation = kwargs.get("aimg_generation", False)
litellm_call_id = kwargs.get("litellm_call_id", None)
logger_fn = kwargs.get("logger_fn", None)
mock_response: Optional[str] = kwargs.get("mock_response", None) # type: ignore
proxy_server_request = kwargs.get("proxy_server_request", None)
azure_ad_token_provider = kwargs.get("azure_ad_token_provider", None)
model_info = kwargs.get("model_info", None)
metadata = kwargs.get("metadata", {})
litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore
client = kwargs.get("client", None)
extra_headers = kwargs.get("extra_headers", None)
headers: dict = kwargs.get("headers", None) or {}
if extra_headers is not None:
headers.update(extra_headers)
model_response: ImageResponse = litellm.utils.ImageResponse()
if model is not None or custom_llm_provider is not None:
model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(
model=model, # type: ignore
custom_llm_provider=custom_llm_provider,
api_base=api_base,
)
else:
model = "dall-e-2"
custom_llm_provider = "openai" # default to dall-e-2 on openai
model_response._hidden_params["model"] = model
openai_params = [
"user",
"request_timeout",
"api_base",
"api_version",
"api_key",
"deployment_id",
"organization",
"base_url",
"default_headers",
"timeout",
"max_retries",
"n",
"quality",
"size",
"style",
]
litellm_params = all_litellm_params
default_params = openai_params + litellm_params
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
optional_params = get_optional_params_image_gen(
model=model,
n=n,
quality=quality,
response_format=response_format,
size=size,
style=style,
user=user,
custom_llm_provider=custom_llm_provider,
**non_default_params,
)
litellm_params_dict = get_litellm_params(**kwargs)
logging: Logging = litellm_logging_obj
logging.update_environment_variables(
model=model,
user=user,
optional_params=optional_params,
litellm_params={
"timeout": timeout,
"azure": False,
"litellm_call_id": litellm_call_id,
"logger_fn": logger_fn,
"proxy_server_request": proxy_server_request,
"model_info": model_info,
"metadata": metadata,
"preset_cache_key": None,
"stream_response": {},
},
custom_llm_provider=custom_llm_provider,
)
if "custom_llm_provider" not in logging.model_call_details:
logging.model_call_details["custom_llm_provider"] = custom_llm_provider
if mock_response is not None:
return mock_image_generation(model=model, mock_response=mock_response)
if custom_llm_provider == "azure":
# azure configs
api_type = get_secret_str("AZURE_API_TYPE") or "azure"
api_base = api_base or litellm.api_base or get_secret_str("AZURE_API_BASE")
api_version = (
api_version
or litellm.api_version
or get_secret_str("AZURE_API_VERSION")
)
api_key = (
api_key
or litellm.api_key
or litellm.azure_key
or get_secret_str("AZURE_OPENAI_API_KEY")
or get_secret_str("AZURE_API_KEY")
)
azure_ad_token = optional_params.pop(
"azure_ad_token", None
) or get_secret_str("AZURE_AD_TOKEN")
default_headers = {
"Content-Type": "application/json;",
"api-key": api_key,
}
for k, v in default_headers.items():
if k not in headers:
headers[k] = v
model_response = azure_chat_completions.image_generation(
model=model,
prompt=prompt,
timeout=timeout,
api_key=api_key,
api_base=api_base,
azure_ad_token=azure_ad_token,
azure_ad_token_provider=azure_ad_token_provider,
logging_obj=litellm_logging_obj,
optional_params=optional_params,
model_response=model_response,
api_version=api_version,
aimg_generation=aimg_generation,
client=client,
headers=headers,
litellm_params=litellm_params_dict,
)
elif (
custom_llm_provider == "openai"
or custom_llm_provider in litellm.openai_compatible_providers
):
model_response = openai_chat_completions.image_generation(
model=model,
prompt=prompt,
timeout=timeout,
api_key=api_key,
api_base=api_base,
logging_obj=litellm_logging_obj,
optional_params=optional_params,
model_response=model_response,
aimg_generation=aimg_generation,
client=client,
)
elif custom_llm_provider == "bedrock":
if model is None:
raise Exception("Model needs to be set for bedrock")
model_response = bedrock_image_generation.image_generation( # type: ignore
model=model,
prompt=prompt,
timeout=timeout,
logging_obj=litellm_logging_obj,
optional_params=optional_params,
model_response=model_response,
aimg_generation=aimg_generation,
client=client,
)
elif custom_llm_provider == "vertex_ai":
vertex_ai_project = (
optional_params.pop("vertex_project", None)
or optional_params.pop("vertex_ai_project", None)
or litellm.vertex_project
or get_secret_str("VERTEXAI_PROJECT")
)
vertex_ai_location = (
optional_params.pop("vertex_location", None)
or optional_params.pop("vertex_ai_location", None)
or litellm.vertex_location
or get_secret_str("VERTEXAI_LOCATION")
)
vertex_credentials = (
optional_params.pop("vertex_credentials", None)
or optional_params.pop("vertex_ai_credentials", None)
or get_secret_str("VERTEXAI_CREDENTIALS")
)
model_response = vertex_image_generation.image_generation(
model=model,
prompt=prompt,
timeout=timeout,
logging_obj=litellm_logging_obj,
optional_params=optional_params,
model_response=model_response,
vertex_project=vertex_ai_project,
vertex_location=vertex_ai_location,
vertex_credentials=vertex_credentials,
aimg_generation=aimg_generation,
)
elif (
custom_llm_provider in litellm._custom_providers
): # Assume custom LLM provider
# Get the Custom Handler
custom_handler: Optional[CustomLLM] = None
for item in litellm.custom_provider_map:
if item["provider"] == custom_llm_provider:
custom_handler = item["custom_handler"]
if custom_handler is None:
raise LiteLLMUnknownProvider(
model=model, custom_llm_provider=custom_llm_provider
)
## ROUTE LLM CALL ##
if aimg_generation is True:
async_custom_client: Optional[AsyncHTTPHandler] = None
if client is not None and isinstance(client, AsyncHTTPHandler):
async_custom_client = client
## CALL FUNCTION
model_response = custom_handler.aimage_generation( # type: ignore
model=model,
prompt=prompt,
api_key=api_key,
api_base=api_base,
model_response=model_response,
optional_params=optional_params,
logging_obj=litellm_logging_obj,
timeout=timeout,
client=async_custom_client,
)
else:
custom_client: Optional[HTTPHandler] = None
if client is not None and isinstance(client, HTTPHandler):
custom_client = client
## CALL FUNCTION
model_response = custom_handler.image_generation(
model=model,
prompt=prompt,
api_key=api_key,
api_base=api_base,
model_response=model_response,
optional_params=optional_params,
logging_obj=litellm_logging_obj,
timeout=timeout,
client=custom_client,
)
return model_response
except Exception as e:
## Map to OpenAI Exception
raise exception_type(
model=model,
custom_llm_provider=custom_llm_provider,
original_exception=e,
completion_kwargs=locals(),
extra_kwargs=kwargs,
)
@client
async def aimage_variation(*args, **kwargs) -> ImageResponse:
"""
Asynchronously calls the `image_variation` function with the given arguments and keyword arguments.
Parameters:
- `args` (tuple): Positional arguments to be passed to the `image_variation` function.
- `kwargs` (dict): Keyword arguments to be passed to the `image_variation` function.
Returns:
- `response` (Any): The response returned by the `image_variation` function.
"""
loop = asyncio.get_event_loop()
model = kwargs.get("model", None)
custom_llm_provider = kwargs.get("custom_llm_provider", None)
### PASS ARGS TO Image Generation ###
kwargs["async_call"] = True
try:
# Use a partial function to pass your keyword arguments
func = partial(image_variation, *args, **kwargs)
# Add the context to the function
ctx = contextvars.copy_context()
func_with_context = partial(ctx.run, func)
if custom_llm_provider is None and model is not None:
_, custom_llm_provider, _, _ = get_llm_provider(
model=model, api_base=kwargs.get("api_base", None)
)
# Await normally
init_response = await loop.run_in_executor(None, func_with_context)
if isinstance(init_response, dict) or isinstance(
init_response, ImageResponse
): ## CACHING SCENARIO
if isinstance(init_response, dict):
init_response = ImageResponse(**init_response)
response = init_response
elif asyncio.iscoroutine(init_response):
response = await init_response # type: ignore
else:
# Call the synchronous function using run_in_executor
response = await loop.run_in_executor(None, func_with_context)
return response
except Exception as e:
custom_llm_provider = custom_llm_provider or "openai"
raise exception_type(
model=model,
custom_llm_provider=custom_llm_provider,
original_exception=e,
completion_kwargs=args,
extra_kwargs=kwargs,
)
@client
def image_variation(
image: FileTypes,
model: str = "dall-e-2", # set to dall-e-2 by default - like OpenAI.
n: int = 1,
response_format: Literal["url", "b64_json"] = "url",
size: Optional[str] = None,
user: Optional[str] = None,
**kwargs,
) -> ImageResponse:
# get non-default params
client = kwargs.get("client", None)
# get logging object
litellm_logging_obj = cast(LiteLLMLoggingObj, kwargs.get("litellm_logging_obj"))
# get the litellm params
litellm_params = get_litellm_params(**kwargs)
# get the custom llm provider
model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(
model=model,
custom_llm_provider=litellm_params.get("custom_llm_provider", None),
api_base=litellm_params.get("api_base", None),
api_key=litellm_params.get("api_key", None),
)
# route to the correct provider w/ the params
try:
llm_provider = LlmProviders(custom_llm_provider)
image_variation_provider = LITELLM_IMAGE_VARIATION_PROVIDERS(llm_provider)
except ValueError:
raise ValueError(
f"Invalid image variation provider: {custom_llm_provider}. Supported providers are: {LITELLM_IMAGE_VARIATION_PROVIDERS}"
)
model_response = ImageResponse()
response: Optional[ImageResponse] = None
provider_config = ProviderConfigManager.get_provider_model_info(
model=model or "", # openai defaults to dall-e-2
provider=llm_provider,
)
if provider_config is None:
raise ValueError(
f"image variation provider has no known model info config - required for getting api keys, etc.: {custom_llm_provider}. Supported providers are: {LITELLM_IMAGE_VARIATION_PROVIDERS}"
)
api_key = provider_config.get_api_key(litellm_params.get("api_key", None))
api_base = provider_config.get_api_base(litellm_params.get("api_base", None))
if image_variation_provider == LITELLM_IMAGE_VARIATION_PROVIDERS.OPENAI:
if api_key is None:
raise ValueError("API key is required for OpenAI image variations")
if api_base is None:
raise ValueError("API base is required for OpenAI image variations")
response = openai_image_variations.image_variations(
model_response=model_response,
api_key=api_key,
api_base=api_base,
model=model,
image=image,
timeout=litellm_params.get("timeout", None),
custom_llm_provider=custom_llm_provider,
logging_obj=litellm_logging_obj,
optional_params={},
litellm_params=litellm_params,
)
elif image_variation_provider == LITELLM_IMAGE_VARIATION_PROVIDERS.TOPAZ:
if api_key is None:
raise ValueError("API key is required for Topaz image variations")
if api_base is None:
raise ValueError("API base is required for Topaz image variations")
response = base_llm_aiohttp_handler.image_variations(
model_response=model_response,
api_key=api_key,
api_base=api_base,
model=model,
image=image,
timeout=litellm_params.get("timeout", None),
custom_llm_provider=custom_llm_provider,
logging_obj=litellm_logging_obj,
optional_params={},
litellm_params=litellm_params,
client=client,
)
# return the response
if response is None:
raise ValueError(
f"Invalid image variation provider: {custom_llm_provider}. Supported providers are: {LITELLM_IMAGE_VARIATION_PROVIDERS}"
)
return response
##### Transcription #######################
@client
async def atranscription(*args, **kwargs) -> TranscriptionResponse:
"""
Calls openai + azure whisper endpoints.
Allows router to load balance between them
"""
loop = asyncio.get_event_loop()
model = args[0] if len(args) > 0 else kwargs["model"]
### PASS ARGS TO Image Generation ###
kwargs["atranscription"] = True
custom_llm_provider = None
try:
# Use a partial function to pass your keyword arguments
func = partial(transcription, *args, **kwargs)
# Add the context to the function
ctx = contextvars.copy_context()
func_with_context = partial(ctx.run, func)
_, custom_llm_provider, _, _ = get_llm_provider(
model=model, api_base=kwargs.get("api_base", None)
)
# Await normally
init_response = await loop.run_in_executor(None, func_with_context)
if isinstance(init_response, dict):
response = TranscriptionResponse(**init_response)
elif isinstance(init_response, TranscriptionResponse): ## CACHING SCENARIO
response = init_response
elif asyncio.iscoroutine(init_response):
response = await init_response # type: ignore
else:
# Call the synchronous function using run_in_executor
response = await loop.run_in_executor(None, func_with_context)
if not isinstance(response, TranscriptionResponse):
raise ValueError(
f"Invalid response from transcription provider, expected TranscriptionResponse, but got {type(response)}"
)
return response
except Exception as e:
custom_llm_provider = custom_llm_provider or "openai"
raise exception_type(
model=model,
custom_llm_provider=custom_llm_provider,
original_exception=e,
completion_kwargs=args,
extra_kwargs=kwargs,
)
@client
def transcription(
model: str,
file: FileTypes,
## OPTIONAL OPENAI PARAMS ##
language: Optional[str] = None,
prompt: Optional[str] = None,
response_format: Optional[
Literal["json", "text", "srt", "verbose_json", "vtt"]
] = None,
timestamp_granularities: Optional[List[Literal["word", "segment"]]] = None,
temperature: Optional[int] = None, # openai defaults this to 0
## LITELLM PARAMS ##
user: Optional[str] = None,
timeout=600, # default to 10 minutes
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
max_retries: Optional[int] = None,
custom_llm_provider=None,
**kwargs,
) -> Union[TranscriptionResponse, Coroutine[Any, Any, TranscriptionResponse]]:
"""
Calls openai + azure whisper endpoints.
Allows router to load balance between them
"""
litellm_call_id = kwargs.get("litellm_call_id", None)
proxy_server_request = kwargs.get("proxy_server_request", None)
model_info = kwargs.get("model_info", None)
metadata = kwargs.get("metadata", None)
atranscription = kwargs.get("atranscription", False)
atranscription = kwargs.get("atranscription", False)
litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore
extra_headers = kwargs.get("extra_headers", None)
kwargs.pop("tags", [])
drop_params = kwargs.get("drop_params", None)
client: Optional[
Union[
openai.AsyncOpenAI,
openai.OpenAI,
openai.AzureOpenAI,
openai.AsyncAzureOpenAI,
]
] = kwargs.pop("client", None)
if litellm_logging_obj:
litellm_logging_obj.model_call_details["client"] = str(client)
if max_retries is None:
max_retries = openai.DEFAULT_MAX_RETRIES
model_response = litellm.utils.TranscriptionResponse()
model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(
model=model, custom_llm_provider=custom_llm_provider, api_base=api_base
) # type: ignore
if dynamic_api_key is not None:
api_key = dynamic_api_key
optional_params = get_optional_params_transcription(
model=model,
language=language,
prompt=prompt,
response_format=response_format,
timestamp_granularities=timestamp_granularities,
temperature=temperature,
custom_llm_provider=custom_llm_provider,
drop_params=drop_params,
)
litellm_params_dict = get_litellm_params(**kwargs)
litellm_logging_obj.update_environment_variables(
model=model,
user=user,
optional_params={},
litellm_params={
"litellm_call_id": litellm_call_id,
"proxy_server_request": proxy_server_request,
"model_info": model_info,
"metadata": metadata,
"preset_cache_key": None,
"stream_response": {},
**kwargs,
},
custom_llm_provider=custom_llm_provider,
)
response: Optional[
Union[TranscriptionResponse, Coroutine[Any, Any, TranscriptionResponse]]
] = None
if custom_llm_provider == "azure":
# azure configs
api_base = api_base or litellm.api_base or get_secret_str("AZURE_API_BASE")
api_version = (
api_version or litellm.api_version or get_secret_str("AZURE_API_VERSION")
)
azure_ad_token = kwargs.pop("azure_ad_token", None) or get_secret_str(
"AZURE_AD_TOKEN"
)
api_key = (
api_key
or litellm.api_key
or litellm.azure_key
or get_secret_str("AZURE_API_KEY")
)
optional_params["extra_headers"] = extra_headers
response = azure_audio_transcriptions.audio_transcriptions(
model=model,
audio_file=file,
optional_params=optional_params,
model_response=model_response,
atranscription=atranscription,
client=client,
timeout=timeout,
logging_obj=litellm_logging_obj,
api_base=api_base,
api_key=api_key,
api_version=api_version,
azure_ad_token=azure_ad_token,
max_retries=max_retries,
litellm_params=litellm_params_dict,
)
elif (
custom_llm_provider == "openai"
or custom_llm_provider in litellm.openai_compatible_providers
):
api_base = (
api_base
or litellm.api_base
or get_secret("OPENAI_API_BASE")
or "https://api.openai.com/v1"
) # type: ignore
openai.organization = (
litellm.organization
or get_secret("OPENAI_ORGANIZATION")
or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
)
# set API KEY
api_key = api_key or litellm.api_key or litellm.openai_key or get_secret("OPENAI_API_KEY") # type: ignore
response = openai_audio_transcriptions.audio_transcriptions(
model=model,
audio_file=file,
optional_params=optional_params,
model_response=model_response,
atranscription=atranscription,
client=client,
timeout=timeout,
logging_obj=litellm_logging_obj,
max_retries=max_retries,
api_base=api_base,
api_key=api_key,
)
elif custom_llm_provider == "deepgram":
response = base_llm_http_handler.audio_transcriptions(
model=model,
audio_file=file,
optional_params=optional_params,
model_response=model_response,
atranscription=atranscription,
client=(
client
if client is not None
and (
isinstance(client, HTTPHandler)
or isinstance(client, AsyncHTTPHandler)
)
else None
),
timeout=timeout,
max_retries=max_retries,
logging_obj=litellm_logging_obj,
api_base=api_base,
api_key=api_key,
custom_llm_provider="deepgram",
headers={},
)
if response is None:
raise ValueError("Unmapped provider passed in. Unable to get the response.")
return response
@client
async def aspeech(*args, **kwargs) -> HttpxBinaryResponseContent:
"""
Calls openai tts endpoints.
"""
loop = asyncio.get_event_loop()
model = args[0] if len(args) > 0 else kwargs["model"]
### PASS ARGS TO Image Generation ###
kwargs["aspeech"] = True
custom_llm_provider = kwargs.get("custom_llm_provider", None)
try:
# Use a partial function to pass your keyword arguments
func = partial(speech, *args, **kwargs)
# Add the context to the function
ctx = contextvars.copy_context()
func_with_context = partial(ctx.run, func)
_, custom_llm_provider, _, _ = get_llm_provider(
model=model, api_base=kwargs.get("api_base", None)
)
# Await normally
init_response = await loop.run_in_executor(None, func_with_context)
if asyncio.iscoroutine(init_response):
response = await init_response
else:
# Call the synchronous function using run_in_executor
response = await loop.run_in_executor(None, func_with_context)
return response # type: ignore
except Exception as e:
custom_llm_provider = custom_llm_provider or "openai"
raise exception_type(
model=model,
custom_llm_provider=custom_llm_provider,
original_exception=e,
completion_kwargs=args,
extra_kwargs=kwargs,
)
@client
def speech( # noqa: PLR0915
model: str,
input: str,
voice: Optional[Union[str, dict]] = None,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
organization: Optional[str] = None,
project: Optional[str] = None,
max_retries: Optional[int] = None,
metadata: Optional[dict] = None,
timeout: Optional[Union[float, httpx.Timeout]] = None,
response_format: Optional[str] = None,
speed: Optional[int] = None,
client=None,
headers: Optional[dict] = None,
custom_llm_provider: Optional[str] = None,
aspeech: Optional[bool] = None,
**kwargs,
) -> HttpxBinaryResponseContent:
user = kwargs.get("user", None)
litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None)
proxy_server_request = kwargs.get("proxy_server_request", None)
extra_headers = kwargs.get("extra_headers", None)
model_info = kwargs.get("model_info", None)
model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(
model=model, custom_llm_provider=custom_llm_provider, api_base=api_base
) # type: ignore
kwargs.pop("tags", [])
optional_params = {}
if response_format is not None:
optional_params["response_format"] = response_format
if speed is not None:
optional_params["speed"] = speed # type: ignore
if timeout is None:
timeout = litellm.request_timeout
if max_retries is None:
max_retries = litellm.num_retries or openai.DEFAULT_MAX_RETRIES
litellm_params_dict = get_litellm_params(**kwargs)
logging_obj = kwargs.get("litellm_logging_obj", None)
logging_obj.update_environment_variables(
model=model,
user=user,
optional_params={},
litellm_params={
"litellm_call_id": litellm_call_id,
"proxy_server_request": proxy_server_request,
"model_info": model_info,
"metadata": metadata,
"preset_cache_key": None,
"stream_response": {},
**kwargs,
},
custom_llm_provider=custom_llm_provider,
)
response: Optional[HttpxBinaryResponseContent] = None
if (
custom_llm_provider == "openai"
or custom_llm_provider in litellm.openai_compatible_providers
):
if voice is None or not (isinstance(voice, str)):
raise litellm.BadRequestError(
message="'voice' is required to be passed as a string for OpenAI TTS",
model=model,
llm_provider=custom_llm_provider,
)
api_base = (
api_base # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there
or litellm.api_base
or get_secret("OPENAI_API_BASE")
or "https://api.openai.com/v1"
) # type: ignore
# set API KEY
api_key = (
api_key
or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
or litellm.openai_key
or get_secret("OPENAI_API_KEY")
) # type: ignore
organization = (
organization
or litellm.organization
or get_secret("OPENAI_ORGANIZATION")
or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
) # type: ignore
project = (
project
or litellm.project
or get_secret("OPENAI_PROJECT")
or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
) # type: ignore
headers = headers or litellm.headers
response = openai_chat_completions.audio_speech(
model=model,
input=input,
voice=voice,
optional_params=optional_params,
api_key=api_key,
api_base=api_base,
organization=organization,
project=project,
max_retries=max_retries,
timeout=timeout,
client=client, # pass AsyncOpenAI, OpenAI client
aspeech=aspeech,
)
elif custom_llm_provider == "azure":
# azure configs
if voice is None or not (isinstance(voice, str)):
raise litellm.BadRequestError(
message="'voice' is required to be passed as a string for Azure TTS",
model=model,
llm_provider=custom_llm_provider,
)
api_base = api_base or litellm.api_base or get_secret("AZURE_API_BASE") # type: ignore
api_version = api_version or litellm.api_version or get_secret("AZURE_API_VERSION") # type: ignore
api_key = (
api_key
or litellm.api_key
or litellm.azure_key
or get_secret("AZURE_OPENAI_API_KEY")
or get_secret("AZURE_API_KEY")
) # type: ignore
azure_ad_token: Optional[str] = optional_params.get("extra_body", {}).pop( # type: ignore
"azure_ad_token", None
) or get_secret(
"AZURE_AD_TOKEN"
)
azure_ad_token_provider = kwargs.get("azure_ad_token_provider", None)
if extra_headers:
optional_params["extra_headers"] = extra_headers
response = azure_chat_completions.audio_speech(
model=model,
input=input,
voice=voice,
optional_params=optional_params,
api_key=api_key,
api_base=api_base,
api_version=api_version,
azure_ad_token=azure_ad_token,
azure_ad_token_provider=azure_ad_token_provider,
organization=organization,
max_retries=max_retries,
timeout=timeout,
client=client, # pass AsyncOpenAI, OpenAI client
aspeech=aspeech,
litellm_params=litellm_params_dict,
)
elif custom_llm_provider == "vertex_ai" or custom_llm_provider == "vertex_ai_beta":
generic_optional_params = GenericLiteLLMParams(**kwargs)
api_base = generic_optional_params.api_base or ""
vertex_ai_project = (
generic_optional_params.vertex_project
or litellm.vertex_project
or get_secret_str("VERTEXAI_PROJECT")
)
vertex_ai_location = (
generic_optional_params.vertex_location
or litellm.vertex_location
or get_secret_str("VERTEXAI_LOCATION")
)
vertex_credentials = (
generic_optional_params.vertex_credentials
or get_secret_str("VERTEXAI_CREDENTIALS")
)
if voice is not None and not isinstance(voice, dict):
raise litellm.BadRequestError(
message=f"'voice' is required to be passed as a dict for Vertex AI TTS, passed in voice={voice}",
model=model,
llm_provider=custom_llm_provider,
)
response = vertex_text_to_speech.audio_speech(
_is_async=aspeech,
vertex_credentials=vertex_credentials,
vertex_project=vertex_ai_project,
vertex_location=vertex_ai_location,
timeout=timeout,
api_base=api_base,
model=model,
input=input,
voice=voice,
optional_params=optional_params,
kwargs=kwargs,
logging_obj=logging_obj,
)
if response is None:
raise Exception(
"Unable to map the custom llm provider={} to a known provider={}.".format(
custom_llm_provider, litellm.provider_list
)
)
return response
##### Health Endpoints #######################
async def ahealth_check_wildcard_models(
model: str, custom_llm_provider: str, model_params: dict
) -> dict:
# this is a wildcard model, we need to pick a random model from the provider
cheapest_models = pick_cheapest_chat_models_from_llm_provider(
custom_llm_provider=custom_llm_provider, n=3
)
if len(cheapest_models) == 0:
raise Exception(
f"Unable to health check wildcard model for provider {custom_llm_provider}. Add a model on your config.yaml or contribute here - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json"
)
if len(cheapest_models) > 1:
fallback_models = cheapest_models[
1:
] # Pick the last 2 models from the shuffled list
else:
fallback_models = None
model_params["model"] = cheapest_models[0]
model_params["fallbacks"] = fallback_models
model_params["max_tokens"] = 1
await acompletion(**model_params)
return {}
async def ahealth_check(
model_params: dict,
mode: Optional[
Literal[
"chat",
"completion",
"embedding",
"audio_speech",
"audio_transcription",
"image_generation",
"batch",
"rerank",
"realtime",
]
] = "chat",
prompt: Optional[str] = None,
input: Optional[List] = None,
):
"""
Support health checks for different providers. Return remaining rate limit, etc.
Returns:
{
"x-ratelimit-remaining-requests": int,
"x-ratelimit-remaining-tokens": int,
"x-ms-region": str,
}
"""
# Map modes to their corresponding health check calls
litellm_logging_obj = Logging(
model="",
messages=[],
stream=False,
call_type="acompletion",
litellm_call_id="1234",
start_time=datetime.datetime.now(),
function_id="1234",
log_raw_request_response=True,
)
try:
model: Optional[str] = model_params.get("model", None)
if model is None:
raise Exception("model not set")
if model in litellm.model_cost and mode is None:
mode = litellm.model_cost[model].get("mode")
model, custom_llm_provider, _, _ = get_llm_provider(model=model)
if model in litellm.model_cost and mode is None:
mode = litellm.model_cost[model].get("mode")
model_params["cache"] = {
"no-cache": True
} # don't used cached responses for making health check calls
mode = mode or "chat"
if "*" in model:
return await ahealth_check_wildcard_models(
model=model,
custom_llm_provider=custom_llm_provider,
model_params=model_params,
)
model_params["litellm_logging_obj"] = litellm_logging_obj
mode_handlers = {
"chat": lambda: litellm.acompletion(
**model_params,
),
"completion": lambda: litellm.atext_completion(
**_filter_model_params(model_params),
prompt=prompt or "test",
),
"embedding": lambda: litellm.aembedding(
**_filter_model_params(model_params),
input=input or ["test"],
),
"audio_speech": lambda: litellm.aspeech(
**_filter_model_params(model_params),
input=prompt or "test",
voice="alloy",
),
"audio_transcription": lambda: litellm.atranscription(
**_filter_model_params(model_params),
file=get_audio_file_for_health_check(),
),
"image_generation": lambda: litellm.aimage_generation(
**_filter_model_params(model_params),
prompt=prompt,
),
"rerank": lambda: litellm.arerank(
**_filter_model_params(model_params),
query=prompt or "",
documents=["my sample text"],
),
"realtime": lambda: _realtime_health_check(
model=model,
custom_llm_provider=custom_llm_provider,
api_base=model_params.get("api_base", None),
api_key=model_params.get("api_key", None),
api_version=model_params.get("api_version", None),
),
}
if mode in mode_handlers:
_response = await mode_handlers[mode]()
# Only process headers for chat mode
_response_headers: dict = (
getattr(_response, "_hidden_params", {}).get("headers", {}) or {}
)
return _create_health_check_response(_response_headers)
else:
raise Exception(
f"Mode {mode} not supported. See modes here: https://docs.litellm.ai/docs/proxy/health"
)
except Exception as e:
stack_trace = traceback.format_exc()
if isinstance(stack_trace, str):
stack_trace = stack_trace[:1000]
if mode is None:
return {
"error": f"error:{str(e)}. Missing `mode`. Set the `mode` for the model - https://docs.litellm.ai/docs/proxy/health#embedding-models \nstacktrace: {stack_trace}"
}
error_to_return = str(e) + "\nstack trace: " + stack_trace
raw_request_typed_dict = litellm_logging_obj.model_call_details.get(
"raw_request_typed_dict"
)
return {
"error": error_to_return,
"raw_request_typed_dict": raw_request_typed_dict,
}
####### HELPER FUNCTIONS ################
## Set verbose to true -> ```litellm.set_verbose = True```
def print_verbose(print_statement):
try:
verbose_logger.debug(print_statement)
if litellm.set_verbose:
print(print_statement) # noqa
except Exception:
pass
def config_completion(**kwargs):
if litellm.config_path is not 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'`"
)
def stream_chunk_builder_text_completion(
chunks: list, messages: Optional[List] = None
) -> TextCompletionResponse:
id = chunks[0]["id"]
object = chunks[0]["object"]
created = chunks[0]["created"]
model = chunks[0]["model"]
system_fingerprint = chunks[0].get("system_fingerprint", None)
finish_reason = chunks[-1]["choices"][0]["finish_reason"]
logprobs = chunks[-1]["choices"][0]["logprobs"]
response = {
"id": id,
"object": object,
"created": created,
"model": model,
"system_fingerprint": system_fingerprint,
"choices": [
{
"text": None,
"index": 0,
"logprobs": logprobs,
"finish_reason": finish_reason,
}
],
"usage": {
"prompt_tokens": None,
"completion_tokens": None,
"total_tokens": None,
},
}
content_list = []
for chunk in chunks:
choices = chunk["choices"]
for choice in choices:
if (
choice is not None
and hasattr(choice, "text")
and choice.get("text") is not None
):
_choice = choice.get("text")
content_list.append(_choice)
# Combine the "content" strings into a single string || combine the 'function' strings into a single string
combined_content = "".join(content_list)
# Update the "content" field within the response dictionary
response["choices"][0]["text"] = combined_content
if len(combined_content) > 0:
pass
else:
pass
# # Update usage information if needed
try:
response["usage"]["prompt_tokens"] = token_counter(
model=model, messages=messages
)
except (
Exception
): # don't allow this failing to block a complete streaming response from being returned
print_verbose("token_counter failed, assuming prompt tokens is 0")
response["usage"]["prompt_tokens"] = 0
response["usage"]["completion_tokens"] = token_counter(
model=model,
text=combined_content,
count_response_tokens=True, # count_response_tokens is a Flag to tell token counter this is a response, No need to add extra tokens we do for input messages
)
response["usage"]["total_tokens"] = (
response["usage"]["prompt_tokens"] + response["usage"]["completion_tokens"]
)
return TextCompletionResponse(**response)
def stream_chunk_builder( # noqa: PLR0915
chunks: list, messages: Optional[list] = None, start_time=None, end_time=None
) -> Optional[Union[ModelResponse, TextCompletionResponse]]:
try:
if chunks is None:
raise litellm.APIError(
status_code=500,
message="Error building chunks for logging/streaming usage calculation",
llm_provider="",
model="",
)
if not chunks:
return None
processor = ChunkProcessor(chunks, messages)
chunks = processor.chunks
### BASE-CASE ###
if len(chunks) == 0:
return None
## Route to the text completion logic
if isinstance(
chunks[0]["choices"][0], litellm.utils.TextChoices
): # route to the text completion logic
return stream_chunk_builder_text_completion(
chunks=chunks, messages=messages
)
model = chunks[0]["model"]
# Initialize the response dictionary
response = processor.build_base_response(chunks)
tool_call_chunks = [
chunk
for chunk in chunks
if len(chunk["choices"]) > 0
and "tool_calls" in chunk["choices"][0]["delta"]
and chunk["choices"][0]["delta"]["tool_calls"] is not None
]
if len(tool_call_chunks) > 0:
tool_calls_list = processor.get_combined_tool_content(tool_call_chunks)
_choice = cast(Choices, response.choices[0])
_choice.message.content = None
_choice.message.tool_calls = tool_calls_list
function_call_chunks = [
chunk
for chunk in chunks
if len(chunk["choices"]) > 0
and "function_call" in chunk["choices"][0]["delta"]
and chunk["choices"][0]["delta"]["function_call"] is not None
]
if len(function_call_chunks) > 0:
_choice = cast(Choices, response.choices[0])
_choice.message.content = None
_choice.message.function_call = (
processor.get_combined_function_call_content(function_call_chunks)
)
content_chunks = [
chunk
for chunk in chunks
if len(chunk["choices"]) > 0
and "content" in chunk["choices"][0]["delta"]
and chunk["choices"][0]["delta"]["content"] is not None
]
if len(content_chunks) > 0:
response["choices"][0]["message"]["content"] = (
processor.get_combined_content(content_chunks)
)
audio_chunks = [
chunk
for chunk in chunks
if len(chunk["choices"]) > 0
and "audio" in chunk["choices"][0]["delta"]
and chunk["choices"][0]["delta"]["audio"] is not None
]
if len(audio_chunks) > 0:
_choice = cast(Choices, response.choices[0])
_choice.message.audio = processor.get_combined_audio_content(audio_chunks)
completion_output = get_content_from_model_response(response)
usage = processor.calculate_usage(
chunks=chunks,
model=model,
completion_output=completion_output,
messages=messages,
)
setattr(response, "usage", usage)
return response
except Exception as e:
verbose_logger.exception(
"litellm.main.py::stream_chunk_builder() - Exception occurred - {}".format(
str(e)
)
)
raise litellm.APIError(
status_code=500,
message="Error building chunks for logging/streaming usage calculation",
llm_provider="",
model="",
)
