# LiteLLM main module: public completion, embedding, streaming, and moderation entrypoints.
#
# +-----------------------------------------------+
# |                                               |
# |           Give Feedback / Get Help            |
# | https://github.com/BerriAI/litellm/issues/new |
# |                                               |
# +-----------------------------------------------+
#
#  Thank you ! We ❤️ you! - Krrish & Ishaan

import asyncio
import contextvars
import datetime
import inspect
import json
import os
import random
import sys
import time
import traceback
from collections.abc import AsyncIterator, Callable, Coroutine, Iterable, Mapping, Sequence
from concurrent import futures
from concurrent.futures import FIRST_COMPLETED, ThreadPoolExecutor, wait
from copy import deepcopy
from functools import partial
from types import MappingProxyType
from typing import TYPE_CHECKING, Any, Final, Literal, Optional, Protocol, Union, cast, get_args
from urllib.parse import urlsplit

from litellm._logging import _redact_string
from litellm._uuid import uuid

if TYPE_CHECKING:
    from aiohttp import ClientSession

import dotenv
import httpx
import openai
import tiktoken
from pydantic import BaseModel
from typing_extensions import overload

import litellm

# client must be imported from litellm as it's a decorator used at function definition time
from litellm import client

# Other utils are imported directly to avoid circular imports
from litellm.utils import (
    exception_type,
    get_litellm_params,
    get_optional_params,
    peek_reasoning_summary_aliases,
    strip_reasoning_summary_aliases_from_optional_params,
)

# Logging is imported lazily when needed to avoid loading litellm_logging at import time
if TYPE_CHECKING:
    from litellm.litellm_core_utils.litellm_logging import Logging
    from litellm.types.utils import TokenCountResponse

from litellm.constants import (
    AZURE_OPENAI_AUDIO_PROVIDERS,
    DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT,
    DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT,
)
from litellm.exceptions import LiteLLMUnknownProvider
from litellm.integrations.custom_logger import CustomLogger
from litellm.litellm_core_utils.asyncify import run_async_function
from litellm.litellm_core_utils.audio_utils.utils import (
    calculate_request_duration,
    get_audio_file_for_health_check,
)
from litellm.litellm_core_utils.chat_completion_agentic_loop import (
    maybe_run_chat_completion_agentic_loop,
)
from litellm.litellm_core_utils.completion_timeout import CompletionTimeout
from litellm.litellm_core_utils.dd_tracing import tracer
from litellm.litellm_core_utils.get_litellm_params import (
    FORWARDED_KWARGS_KEYS,
    OPTIONAL_KWARGS_KEYS,
)
from litellm.litellm_core_utils.get_provider_specific_headers import (
    ProviderSpecificHeaderUtils,
)
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.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.litellm_core_utils.request_timeout_resolver import (
    get_configured_request_timeout,
)
from litellm.llms.base_llm import BaseConfig, BaseImageGenerationConfig
from litellm.llms.base_llm.base_model_iterator import (
    convert_model_response_to_streaming,
)
from litellm.llms.bedrock.common_utils import BedrockModelInfo
from litellm.llms.cohere.common_utils import CohereModelInfo
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
from litellm.llms.openai.chat.gpt_5_transformation import OpenAIGPT5Config
from litellm.llms.openai_like.json_loader import JSONProviderRegistry
from litellm.llms.vertex_ai.common_utils import (
    VertexAIModelRoute,
    get_vertex_ai_model_route,
)
from litellm.realtime_api.main import _realtime_health_check
from litellm.secret_managers.main import get_secret_bool, get_secret_str
from litellm.types.completion import (
    _CompletionDispatchContext,
    _CompletionDispatchResult,
)
from litellm.types.router import GenericLiteLLMParams
from litellm.types.utils import (
    CustomPricingLiteLLMParams,
    ModelResponseStream,
    RawRequestTypedDict,
    StreamingChoices,
)
from litellm.utils import (
    Choices,
    CustomStreamWrapper,
    EmbeddingResponse,
    Message,
    ModelResponse,
    ProviderConfigManager,
    TextChoices,
    TextCompletionResponse,
    TextCompletionStreamWrapper,
    TranscriptionResponse,
    Usage,
    _get_model_info_helper,
    add_provider_specific_params_to_optional_params,
    async_mock_completion_streaming_obj,
    convert_to_model_response_object,
    create_pretrained_tokenizer,
    create_tokenizer,
    get_llm_provider,
    get_model_info,
    get_non_default_completion_params,
    get_non_default_transcription_params,
    get_optional_params_embeddings,
    get_optional_params_image_gen,
    get_optional_params_transcription,
    get_requester_metadata,
    get_secret,
    get_standard_openai_params,
    mock_completion_streaming_obj,
    pre_process_non_default_params,
    read_config_args,
    should_run_mock_completion,
    supports_httpx_timeout,
    token_counter,
    validate_and_fix_openai_messages,
    validate_and_fix_openai_tools,
    validate_and_fix_thinking_param,
    validate_chat_completion_tool_choice,
    validate_openai_optional_params,
)

from ._logging import verbose_logger
from .caching.caching import disable_cache, enable_cache, update_cache
from .litellm_core_utils.core_helpers import safe_deep_copy
from .litellm_core_utils.fallback_utils import (
    async_completion_with_fallbacks,
    completion_with_fallbacks,
)
from .litellm_core_utils.prompt_templates.common_utils import (
    add_system_prompt_to_messages,
    get_completion_messages,
    update_messages_with_model_file_ids,
)
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.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.anthropic.handler import AzureAnthropicChatCompletion
from .llms.azure_ai.embed import AzureAIEmbedding
from .llms.bedrock.chat import BedrockConverseLLM
from .llms.bedrock.embed.embedding import BedrockEmbedding
from .llms.bedrock.image_edit.handler import BedrockImageEdit
from .llms.bedrock.image_generation.image_handler import BedrockImageGeneration
from .llms.bytez.chat.transformation import BytezChatConfig
from .llms.clarifai.chat.transformation import ClarifaiConfig
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.embed.handler import DatabricksEmbeddingHandler
from .llms.deprecated_providers import aleph_alpha, palm
from .llms.gdc.chat.transformation import GDCGeminiConfig
from .llms.gemini.common_utils import get_api_key_from_env
from .llms.groq.chat.handler import GroqChatCompletion
from .llms.heroku.chat.transformation import HerokuChatConfig
from .llms.huggingface.embedding.handler import HuggingFaceEmbedding
from .llms.lemonade.chat.transformation import LemonadeChatConfig
from .llms.nlp_cloud.chat.handler import completion as nlp_cloud_chat_completion
from .llms.nvidia_riva.audio_transcription.handler import (
    NvidiaRivaAudioTranscription,
)
from .llms.nvidia_riva.audio_transcription.transformation import (
    NvidiaRivaAudioTranscriptionConfig,
)
from .llms.oci.chat.transformation import OCIChatConfig
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.ovhcloud.chat.transformation import OVHCloudChatConfig
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.sap.chat.handler import GenAIHubOrchestration
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.vertex_ai_partner_models.main import VertexAIPartnerModels
from .llms.vertex_ai.vertex_embeddings.embedding_handler import VertexEmbedding
from .llms.vertex_ai.vertex_gemma_models.main import VertexAIGemmaModels
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,
    OpenAIModerationResponse,
    OpenAIWebSearchOptions,
)
from .types.utils import (
    AdapterCompletionStreamWrapper,
    ChatCompletionMessageToolCall,
    CompletionTokensDetails,
    FileTypes,
    HiddenParams,
    LlmProviders,
    PromptTokensDetails,
    ProviderSpecificHeader,
    all_litellm_params,
)

####### ENVIRONMENT VARIABLES ###################
openai_chat_completions = OpenAIChatCompletion()
openai_text_completions: Final = OpenAITextCompletion()
openai_audio_transcriptions: Final = OpenAIAudioTranscription()
nvidia_riva_audio_transcriptions: Final = NvidiaRivaAudioTranscription()
openai_image_variations = OpenAIImageVariationsHandler()
groq_chat_completions: Final = GroqChatCompletion()
sap_gen_ai_hub_chat_completions: Final = GenAIHubOrchestration()
sap_gen_ai_hub_emb: Final = GenAIHubOrchestration()
azure_ai_embedding: Final = AzureAIEmbedding()
anthropic_chat_completions: Final = AnthropicChatCompletion()
azure_anthropic_chat_completions: Final = AzureAnthropicChatCompletion()
azure_chat_completions = AzureChatCompletion()
azure_o1_chat_completions: Final = AzureOpenAIO1ChatCompletion()
azure_text_completions: Final = AzureTextCompletion()
azure_audio_transcriptions: Final = AzureAudioTranscription()
huggingface_embed: Final = HuggingFaceEmbedding()
predibase_chat_completions: Final = PredibaseChatCompletion()
codestral_text_completions: Final = CodestralTextCompletion()
bedrock_converse_chat_completion: Final = BedrockConverseLLM()
bedrock_embedding: Final = BedrockEmbedding()
bedrock_image_generation = BedrockImageGeneration()
bedrock_image_edit = BedrockImageEdit()
vertex_chat_completion: Final = VertexLLM()
vertex_embedding: Final = VertexEmbedding()
vertex_multimodal_embedding: Final = VertexMultimodalEmbedding()
vertex_image_generation: Final = VertexImageGeneration()
google_batch_embeddings: Final = GoogleBatchEmbeddings()
vertex_partner_models_chat_completion: Final = VertexAIPartnerModels()
vertex_gemma_chat_completion: Final = VertexAIGemmaModels()
vertex_model_garden_chat_completion: Final = VertexAIModelGardenModels()
gdc_transformation: Final = GDCGeminiConfig()
# vertex_text_to_speech is now replaced by VertexAITextToSpeechConfig
sagemaker_llm: Final = SagemakerLLM()
watsonx_chat_completion: Final = WatsonXChatHandler()
openai_like_embedding: Final = OpenAILikeEmbeddingHandler()
openai_like_chat_completion: Final = OpenAILikeChatHandler()
databricks_embedding: Final = DatabricksEmbeddingHandler()
base_llm_http_handler = BaseLLMHTTPHandler()
base_llm_aiohttp_handler = BaseLLMAIOHTTPHandler()
sagemaker_chat_completion: Final = SagemakerChatHandler()
bytez_transformation: Final = BytezChatConfig()
heroku_transformation: Final = HerokuChatConfig()
oci_transformation: Final = OCIChatConfig()
ovhcloud_transformation: Final = OVHCloudChatConfig()
lemonade_transformation: Final = LemonadeChatConfig()

MOCK_RESPONSE_TYPE = str | Exception | dict | ModelResponse | ModelResponseStream
####### COMPLETION ENDPOINTS ################


class LiteLLM:
    def __init__(
        self,
        *,
        api_key=None,
        organization: str | None = None,
        base_url: str | None = None,
        timeout: float | None = 600,
        max_retries: int | None = litellm.num_retries,
        default_headers: Mapping[str, str] | None = None,
    ):
        self.params = locals()
        self.chat = Chat(self.params, router_obj=None)


class Chat:
    def __init__(self, params, router_obj: Any | None):
        self.params = params
        if self.params.get("acompletion", False) is True:
            self.params.pop("acompletion")
            self.completions: 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: Any | None):
        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: Any | None):
        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


@tracer.wrap()
@client
async def acompletion(
    model: str,
    # Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create
    messages: list = [],
    functions: list | None = None,
    function_call: str | None = None,
    timeout: float | None = None,
    temperature: float | None = None,
    top_p: float | None = None,
    n: int | None = None,
    stream: bool | None = None,
    stream_options: dict | None = None,
    stop=None,
    max_tokens: int | None = None,
    max_completion_tokens: int | None = None,
    modalities: list[ChatCompletionModality] | None = None,
    prediction: ChatCompletionPredictionContentParam | None = None,
    audio: ChatCompletionAudioParam | None = None,
    presence_penalty: float | None = None,
    frequency_penalty: float | None = None,
    logit_bias: dict | None = None,
    user: str | None = None,
    # openai v1.0+ new params
    response_format: dict | type[BaseModel] | None = None,
    seed: int | None = None,
    tools: list | None = None,
    tool_choice: str | dict | None = None,
    parallel_tool_calls: bool | None = None,
    logprobs: bool | None = None,
    top_logprobs: int | None = None,
    deployment_id=None,
    reasoning_effort: Literal["none", "minimal", "low", "medium", "high", "xhigh", "max", "default"] | None = None,
    verbosity: Literal["low", "medium", "high"] | None = None,
    safety_identifier: str | None = None,
    service_tier: str | None = None,
    store: bool | None = None,
    prompt_cache_key: str | None = None,
    # set api_base, api_version, api_key
    base_url: str | None = None,
    api_version: str | None = None,
    api_key: str | None = None,
    model_list: list | None = None,  # pass in a list of api_base,keys, etc.
    extra_headers: dict | None = None,
    # Optional liteLLM function params
    thinking: AnthropicThinkingParam | None = None,
    web_search_options: OpenAIWebSearchOptions | None = None,
    include_server_side_tool_invocations: bool | None = None,
    # Session management
    shared_session: Optional["ClientSession"] = None,
    # Per-request JSON schema validation (overrides litellm.enable_json_schema_validation)
    enable_json_schema_validation: bool | None = None,
    **kwargs,
) -> 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: Final = asyncio.get_event_loop()
    custom_llm_provider = kwargs.get("custom_llm_provider", None)

    ## PROMPT MANAGEMENT HOOKS ##
    #########################################################
    #########################################################
    litellm_logging_obj: Final = kwargs.get("litellm_logging_obj", None)

    from litellm.integrations.anthropic_cache_control_hook import (
        AnthropicCacheControlHook,
    )
    from litellm.types.llms.openai import AllMessageValues

    AnthropicCacheControlHook.maybe_seed_default_injection_points(
        non_default_params=kwargs,
        messages=cast(list[AllMessageValues], messages),  # cast-ok: acompletion types messages as a bare List
        model=model,
        custom_llm_provider=cast(str | None, custom_llm_provider),  # cast-ok: read from untyped kwargs
        tools=tools,
        enable_prompt_caching=cast(bool | None, kwargs.get("enable_prompt_caching")),  # cast-ok: untyped kwargs
        api_base=kwargs.get("api_base") or base_url,
    )

    if isinstance(litellm_logging_obj, LiteLLMLoggingObj) and (
        litellm_logging_obj.should_run_prompt_management_hooks(
            prompt_id=kwargs.get("prompt_id", None),
            non_default_params=kwargs,
            tools=tools,
        )
    ):
        (
            model,
            messages,
            _,
        ) = await litellm_logging_obj.async_get_chat_completion_prompt(
            model=model,
            messages=messages,
            non_default_params=kwargs,
            prompt_id=kwargs.get("prompt_id", None),
            prompt_variables=kwargs.get("prompt_variables", None),
            tools=tools,
            prompt_label=kwargs.get("prompt_label", None),
            prompt_version=kwargs.get("prompt_version", None),
            request_kwargs=kwargs,
        )
        #########################################################
        # if the chat completion logging hook removed all tools,
        # set tools to None
        # eg. in certain cases when users send vector stores as tools
        # we don't want the tools to go to the upstream llm
        # relevant issue: https://github.com/BerriAI/litellm/issues/11404
        #########################################################
        if tools is not None and len(tools) == 0:
            tools = None

    #########################################################
    #########################################################

    # Log shared session usage
    if shared_session is not None:
        verbose_logger.debug("🔄 SHARED SESSION: acompletion called with shared_session (ID: %s)", id(shared_session))
    else:
        verbose_logger.debug("🔄 NO SHARED SESSION: acompletion called without shared_session")

    # Adjusted to use explicit arguments instead of *args and **kwargs
    completion_kwargs: Final = {
        "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,
        "verbosity": verbosity,
        "safety_identifier": safety_identifier,
        "service_tier": service_tier,
        "store": store,
        "prompt_cache_key": prompt_cache_key,
        "extra_headers": extra_headers,
        "acompletion": True,  # assuming this is a required parameter
        "thinking": thinking,
        "web_search_options": web_search_options,
        "include_server_side_tool_invocations": include_server_side_tool_invocations,
        "shared_session": shared_session,
        "enable_json_schema_validation": enable_json_schema_validation,
    }
    if custom_llm_provider is None:
        _, custom_llm_provider, _, _ = get_llm_provider(
            model=model,
            custom_llm_provider=custom_llm_provider,
            api_base=kwargs.get("api_base") or base_url,
        )

    fallbacks = fallbacks or litellm.model_fallbacks
    if fallbacks is not None:
        response = await async_completion_with_fallbacks(**completion_kwargs, kwargs={"fallbacks": fallbacks, **kwargs})
        if response is None:
            raise Exception(
                "No response from fallbacks. Got none. Turn on `litellm.set_verbose=True` to see more details."
            )
        return response

    ### APPLY MOCK DELAY ###

    mock_delay: Final = kwargs.get("mock_delay")
    mock_response: Final = kwargs.get("mock_response")
    mock_tool_calls: Final = kwargs.get("mock_tool_calls")
    mock_timeout = kwargs.get("mock_timeout")
    if mock_delay and should_run_mock_completion(
        mock_response=mock_response,
        mock_tool_calls=mock_tool_calls,
        mock_timeout=mock_timeout,
    ):
        await asyncio.sleep(mock_delay)

    try:
        # Use a partial function to pass your keyword arguments
        kwargs.pop("acompletion", None)
        func: Final = partial(completion, **completion_kwargs, **kwargs)

        # Add the context to the function
        ctx: Final = contextvars.copy_context()
        func_with_context: Final = partial(ctx.run, func)

        init_response: Final = 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 = _model_response_from_cached_dict(init_response)
            response = init_response
        elif asyncio.iscoroutine(init_response):
            response = await _resolve_dispatched_chat_response(init_response)
        else:
            response = init_response

        if (
            custom_llm_provider == "text-completion-openai"
            or custom_llm_provider == "text-completion-codestral"
            or custom_llm_provider == "text-completion-inception"
        ) and isinstance(response, TextCompletionResponse):
            response = litellm.OpenAITextCompletionConfig().convert_to_chat_model_response_object(
                response_object=response,
                model_response_object=litellm.ModelResponse(),
            )
        # Provider-agnostic dispatch point for the chat-completions agentic loop
        # (code-interpreter interception, etc). Chat routing forks per provider
        # before this (OpenAI goes through the OpenAI SDK in openai.py, others
        # through the shared httpx handler), so a dispatch inside any single
        # provider handler would miss the others. Here is where every fork
        # reconverges, so the loop runs once for all providers. Responses needs
        # no equivalent: every provider already funnels through one shared
        # handler where the loop is dispatched.
        if isinstance(response, litellm.ModelResponse):
            looped: Final = await maybe_run_chat_completion_agentic_loop(
                response=response,
                model=model,
                messages=messages,
                optional_params={
                    k: v
                    for k, v in completion_kwargs.items()
                    if v is not None
                    and k
                    not in (
                        "model",
                        "messages",
                        "stream",
                        "acompletion",
                        "deployment_id",
                    )
                },
                kwargs=kwargs,
                logging_obj=kwargs.get("litellm_logging_obj"),
                custom_llm_provider=custom_llm_provider,
                stream=bool(stream),
            )
            if looped is not None:
                response = looped
        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 _resolve_dispatched_chat_response(
    pending: Coroutine[object, object, ModelResponse | CustomStreamWrapper],
) -> ModelResponse | CustomStreamWrapper:
    return await pending


def _model_response_from_cached_dict(cached_response_dict: Mapping[str, object]) -> ModelResponse:
    return ModelResponse(**cached_response_dict)


def _transcription_response_from_cached_dict(cached_response_dict: Mapping[str, object]) -> TranscriptionResponse:
    return TranscriptionResponse(**cached_response_dict)


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: str | Exception,
    model: str,
    custom_llm_provider: str | None = 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),
            message=getattr(mock_response, "text", str(mock_response)),
            llm_provider=getattr(mock_response, "llm_provider", custom_llm_provider or "openai"),
            model=model,
            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"),
            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"),
            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"),
            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,
            request=httpx.Request(method="POST", url="https://api.openai.com/v1/"),
        )


def _handle_mock_timeout(
    mock_timeout: bool | None,
    timeout: float | str | httpx.Timeout | None,
    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: bool | None,
    timeout: float | str | httpx.Timeout | None,
    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: 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: 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)


class _AdmissionReservation(BaseModel):
    input_tokens: int | None = None


class _AdmissionMetadata(BaseModel):
    user_api_key_budget_reservation: _AdmissionReservation | None = None


def admission_input_tokens(kwargs: Mapping[str, object]) -> int | None:
    reservations: Final = (
        _AdmissionMetadata.model_validate(kwargs.get(key) or {}).user_api_key_budget_reservation
        for key in ("litellm_metadata", "metadata")
    )
    return next(
        (
            reservation.input_tokens
            for reservation in reservations
            if reservation and reservation.input_tokens is not None
        ),
        None,
    )


def admitted_prompt_token_counter(prompt_tokens: int | None) -> Callable[[], int] | None:
    if prompt_tokens is None:
        return None
    return lambda: prompt_tokens


def mock_completion(
    model: str,
    messages: list,
    stream: bool | None = False,
    n: int | None = None,
    mock_response: MOCK_RESPONSE_TYPE | None = "This is a mock request",
    mock_tool_calls: list | None = None,
    mock_timeout: bool | None = False,
    logging=None,
    custom_llm_provider=None,
    timeout: float | str | httpx.Timeout | None = None,
    prompt_tokens: int | None = 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:
        is_acompletion: Final = kwargs.get("acompletion") or False
        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",
            )

        if isinstance(mock_response, str) or isinstance(mock_response, Exception):
            _handle_mock_potential_exceptions(
                mock_response=mock_response,
                model=model,
                custom_llm_provider=custom_llm_provider,
            )

        mock_response = cast(
            str | dict | ModelResponse | ModelResponseStream, mock_response
        )  # after this point, mock_response is a string, dict, ModelResponse, or ModelResponseStream
        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: Final = kwargs.get("mock_delay", None)
        if time_delay is not None and not is_acompletion:
            time.sleep(time_delay)

        if isinstance(mock_response, dict):
            return ModelResponse(**mock_response)

        if isinstance(mock_response, ModelResponse):
            if not stream:
                return mock_response
            # convert to ModelResponseStream
            mock_response = convert_model_response_to_streaming(mock_response)

        model_response: ModelResponse | ModelResponseStream = ModelResponse()

        if stream is True:
            model_response = ModelResponseStream()
            count_prompt_tokens: Final = admitted_prompt_token_counter(prompt_tokens)
            # 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, prompt_tokens=prompt_tokens
                    ),
                    model=model,
                    custom_llm_provider="openai",
                    logging_obj=logging,
                    count_prompt_tokens=count_prompt_tokens,
                )
            return CustomStreamWrapper(
                completion_stream=mock_completion_streaming_obj(
                    model_response, mock_response=mock_response, model=model, n=n, prompt_tokens=prompt_tokens
                ),
                model=model,
                custom_llm_provider="openai",
                logging_obj=logging,
                count_prompt_tokens=count_prompt_tokens,
            )
        if isinstance(mock_response, litellm.MockException):
            raise mock_response
        # At this point, mock_response must be a string (all other types have been handled or returned early)
        mock_response = cast(str, mock_response)

        if n is None:
            model_response.choices[0].message.content = mock_response
        else:
            _all_choices: Final = []
            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
        model_response.created = int(time.time())
        model_response.model = model

        if mock_tool_calls:
            model_response.choices[0].message.tool_calls = [
                ChatCompletionMessageToolCall(**tool_call) for tool_call in mock_tool_calls
            ]

        usage_prompt_tokens: Final = (
            prompt_tokens if prompt_tokens is not None else DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT
        )
        setattr(
            model_response,
            "usage",
            Usage(
                prompt_tokens=usage_prompt_tokens,
                completion_tokens=DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT,
                total_tokens=usage_prompt_tokens + DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT,
            ),
        )

        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(f"Mock completion response failed - {e}")


_OPENAI_DEFAULT_API_BASE: Final = "https://api.openai.com/v1"
_OPENAI_API_HOST: Final = "api.openai.com"


def _is_openai_backed_api_base(api_base: str) -> bool:
    hostname: Final = urlsplit(api_base).hostname
    return hostname is not None and (hostname == _OPENAI_API_HOST or hostname.endswith(f".{_OPENAI_API_HOST}"))


def _resolve_openai_api_base(api_base: str | None) -> str:
    """Effective OpenAI base a chat request will hit: arg > global > env > default. The bridge gate
    and the ``_complete_custom_openai`` chat handler MUST resolve this identically, or a custom base
    set via ``litellm.api_base`` or ``OPENAI_BASE_URL``/``OPENAI_API_BASE`` is invisible to the gate,
    which then misreads it as the default OpenAI endpoint and bridges a request the backend can't serve."""
    return (
        api_base
        or litellm.api_base
        or get_secret_str("OPENAI_BASE_URL")
        or get_secret_str("OPENAI_API_BASE")
        or _OPENAI_DEFAULT_API_BASE
    )


def responses_api_bridge_check(
    model: str,
    custom_llm_provider: str,
    web_search_options: OpenAIWebSearchOptions | None = None,
    tools: Sequence[Mapping[str, object]] | None = None,
    reasoning_effort: str | Mapping[str, object] | None = None,
    reasoning_summary: object | None = None,
    api_base: str | None = None,
) -> tuple[dict, str]:
    model_info: dict[str, object] = {}

    # Global flag: route ALL OpenAI chat completions through Responses API.
    # Returns early with minimal model_info; callers only inspect the "mode" key.
    if litellm.route_all_chat_openai_to_responses and custom_llm_provider == "openai":
        model = model.replace("responses/", "")
        model_info["mode"] = "responses"
        return model_info, model

    try:
        model_info = cast(
            dict,
            _get_model_info_helper(model=model, custom_llm_provider=custom_llm_provider),
        )
        if model_info.get("mode") is None and model.startswith("responses/"):
            model = model.replace("responses/", "")
            mode = "responses"
            model_info["mode"] = mode

        if web_search_options is not None and custom_llm_provider == "xai":
            model_info["mode"] = "responses"
            model = model.replace("responses/", "")

    except Exception as e:
        verbose_logger.debug("Error getting model info: %s", e)

        if model.startswith("responses/"):  # handle azure models - `azure/responses/<deployment-name>`
            model = model.replace("responses/", "")
            mode = "responses"
            model_info["mode"] = mode

    # OpenAI/Azure GPT-5 chat-completions that need Responses-only fields (e.g.
    # ``reasoningSummary`` in ``extra_body``) must be bridged; Chat Completions rejects
    # those keys.
    #
    # - gpt-5.4+: FUNCTION tools with reasoning active must be bridged. OpenAI enables
    #   reasoning by default for these models (unset reasoning_effort means medium
    #   server-side), and Chat Completions rejects function tools whenever reasoning is
    #   on ("Function tools with reasoning_effort are not supported ... use
    #   /v1/responses or set reasoning_effort to 'none'"), so only an explicit
    #   ``"none"`` keeps the request chat-servable. Custom (grammar) tools are served
    #   natively by Chat Completions with reasoning on, so custom-only requests stay on
    #   chat and keep their native custom tool_call response shape.
    # - The UNSET-effort arm only fires against endpoints known to enforce that
    #   constraint (any api.openai.com host, or Azure OpenAI where api_base is
    #   always set): chat-only OpenAI-compatible backends registered under the openai
    #   provider with a custom api_base and gpt-5.4+ model names serve tools without
    #   reasoning fine and have no /responses route, so they keep pre-existing
    #   behavior (bridge only on an explicit reasoning_effort).
    # - Older GPT-5 names (e.g. ``gpt-5``, ``gpt-5.1``): bridge only when a reasoning
    #   summary alias is present with ``reasoning_effort`` (tools alone stay on chat).
    has_function_tool: Final = any(
        (tool.get("type") == "function" if isinstance(tool, dict) else getattr(tool, "type", None) == "function")
        for tool in (tools or ())
    )
    if isinstance(reasoning_effort, dict):
        reasoning_active = reasoning_effort.get("effort") != "none" or reasoning_effort.get("summary") is not None
    else:
        reasoning_active = reasoning_effort != "none"
    # The reasoning+tools constraint is enforced by the real OpenAI backend behind any api.openai.com
    # host (the default URL or a PrivateLink hostname such as <region>.privatelink.api.openai.com) and
    # by Azure OpenAI. Resolve the effective base arg>global>env>default exactly as the chat handler
    # does, so a custom base set via litellm.api_base or OPENAI_BASE_URL/OPENAI_API_BASE isn't misread
    # as the default and bridged to a /responses route it lacks. A whitespace-only base collapses to
    # the default too.
    resolved_api_base: Final = _resolve_openai_api_base(api_base).strip()
    on_constraint_enforcing_endpoint: Final = (
        custom_llm_provider == "azure" or resolved_api_base == "" or _is_openai_backed_api_base(resolved_api_base)
    )
    if (
        custom_llm_provider in ("openai", "azure")
        and model_info.get("mode") != "responses"
        and OpenAIGPT5Config.is_model_gpt_5_model(model)
        and not OpenAIGPT5Config.is_model_gpt_5_search_model(model)
        and (
            (reasoning_effort is not None and reasoning_summary is not None)
            or (
                OpenAIGPT5Config.is_model_gpt_5_4_plus_model(model)
                and has_function_tool
                and reasoning_active
                and (reasoning_effort is not None or on_constraint_enforcing_endpoint)
            )
        )
    ):
        model_info["mode"] = "responses"
        model = model.replace("responses/", "")

    return model_info, model


def _should_allow_input_examples(custom_llm_provider: str | None, model: str) -> bool:
    if custom_llm_provider == "anthropic":
        return True
    if custom_llm_provider == "azure_ai" or custom_llm_provider == "bedrock" or custom_llm_provider == "vertex_ai":
        return "claude" in model.lower()
    return False


def _drop_input_examples_from_tool(tool: dict) -> dict:
    tool_copy: Final = tool.copy()
    tool_copy.pop("input_examples", None)
    function = tool_copy.get("function")
    if isinstance(function, dict):
        function = function.copy()
        function.pop("input_examples", None)
        tool_copy["function"] = function
    return tool_copy


def _drop_input_examples_from_tools(
    tools: list[dict] | None,
) -> list[dict] | None:
    if tools is None:
        return None
    cleaned_tools: Final[list[dict]] = []
    for tool in tools:
        if isinstance(tool, dict):
            cleaned_tools.append(_drop_input_examples_from_tool(tool))
        else:
            cleaned_tools.append(tool)
    return cleaned_tools


class _ProxyAuthHeadersProvider(Protocol):
    def get_auth_headers(self) -> Mapping[str, str]: ...


def _proxy_auth_headers(proxy_auth: _ProxyAuthHeadersProvider) -> Mapping[str, str]:
    return proxy_auth.get_auth_headers()


def _provider_config_items(config: Mapping[str, object]) -> Iterable[tuple[str, object]]:
    return config.items()


def _locals_snapshot(values: Mapping[str, object]) -> Mapping[str, object]:
    return values


def _build_custom_pricing_entry(
    custom_llm_provider: str,
    kwargs: dict,
    model_info: dict | None = None,
) -> dict:
    """Build a complete model cost entry from kwargs and model_info.

    Collects all CustomPricingLiteLLMParams fields present in kwargs and
    merges metadata from model_info (mode, supports_prompt_caching, max_tokens)
    so that register_model() receives the full pricing configuration.
    """
    entry: Final[dict] = {"litellm_provider": custom_llm_provider}

    for field_name in CustomPricingLiteLLMParams.model_fields:
        value = kwargs.get(field_name)
        if value is not None:
            entry[field_name] = value

    if model_info and isinstance(model_info, dict):
        for key in ("mode", "supports_prompt_caching", "max_tokens"):
            if key in model_info and model_info[key] is not None:
                entry.setdefault(key, model_info[key])

    return entry


def _get_router_deployment_id(kwargs: dict) -> str | None:
    for metadata_key in ("litellm_metadata", "metadata"):
        metadata = kwargs.get(metadata_key) or {}
        if not isinstance(metadata, dict):
            continue
        deployment_model_info = metadata.get("model_info") or {}
        if not isinstance(deployment_model_info, dict):
            continue
        deployment_id = deployment_model_info.get("id")
        if deployment_id is not None:
            return str(deployment_id)
    return None


def _register_custom_pricing_for_request(
    model: str,
    custom_llm_provider: str,
    kwargs: dict,
    model_info: dict | None,
) -> None:
    """Register per-request custom pricing in litellm.model_cost.

    Router-originated requests (identified by the deployment id the router puts
    in metadata) get their full pricing registered under that unique id only;
    the shared ``{provider}/{model}`` key receives the entry with pricing fields
    stripped, mirroring Router._create_deployment. This keeps one deployment's
    pricing overrides (e.g. a zero-cost wildcard) from clobbering built-in
    pricing used by sibling deployments of the same backend model. Direct SDK
    calls keep the legacy behavior of registering the shared key with pricing.
    """
    entry: Final = _build_custom_pricing_entry(
        custom_llm_provider=custom_llm_provider,
        kwargs=kwargs,
        model_info=model_info,
    )
    shared_key: Final = f"{custom_llm_provider}/{model}"
    deployment_id: Final = _get_router_deployment_id(kwargs)
    if deployment_id is None:
        litellm.register_model({shared_key: entry}, persist_across_reloads=False)
        return
    litellm.register_model(
        {
            deployment_id: entry,
            shared_key: CustomPricingLiteLLMParams.strip_custom_pricing_fields(entry),
        },
        persist_across_reloads=False,
        warning_display_name=shared_key,
    )


def _dispatch_metadata(ctx: _CompletionDispatchContext) -> Mapping[str, object] | None:
    return ctx.metadata


def _dispatch_client_http(ctx: _CompletionDispatchContext) -> HTTPHandler | AsyncHTTPHandler | None:
    return ctx.client


def _dispatch_client_azure(
    ctx: _CompletionDispatchContext,
) -> openai.AzureOpenAI | openai.AsyncAzureOpenAI | HTTPHandler | AsyncHTTPHandler | None:
    return ctx.client


def _dispatch_client_openai(ctx: _CompletionDispatchContext) -> openai.OpenAI | openai.AsyncOpenAI | None:
    return ctx.client


def _complete_azure(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    _azure_detection_model: Final = ctx._azure_detection_model
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    api_version = ctx.api_version
    client: Final = _dispatch_client_azure(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    extra_headers: Final = ctx.extra_headers
    headers = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    max_retries: Final = ctx.max_retries
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    timeout: Final = ctx.timeout

    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: Final = 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_str("AZURE_API_VERSION") or litellm.AZURE_DEFAULT_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: Final = optional_params.get("extra_body", {}).pop("azure_ad_token", None) or get_secret_str(
        "AZURE_AD_TOKEN"
    )

    azure_ad_token_provider_value: Final = litellm_params.get("azure_ad_token_provider", None)
    azure_ad_token_provider: Final = azure_ad_token_provider_value if callable(azure_ad_token_provider_value) else 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=_azure_detection_model):
        ## LOAD CONFIG - if set
        config = litellm.AzureOpenAIO1Config.get_config()
        for k, v in _provider_config_items(config):
            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,
            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 _provider_config_items(config):
            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,
            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,
            },
        )

    return response  # pyright: ignore[reportReturnType]  # provider SDK return type is broader than the dispatch contract


def _complete_azure_text(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    api_version = ctx.api_version
    client: Final = _dispatch_client_azure(ctx)
    extra_headers: Final = ctx.extra_headers
    headers = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    timeout: Final = ctx.timeout

    api_type: Final = get_secret_str("AZURE_API_TYPE") or "azure"

    api_base = api_base or litellm.api_base or get_secret_str("AZURE_API_BASE")

    if api_base is None:
        raise ValueError(
            "api_base is required for Azure OpenAI LLM provider. Either set it dynamically or set the AZURE_API_BASE environment variable."
        )

    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: Final = optional_params.get("extra_body", {}).pop("azure_ad_token", None) or get_secret_str(
        "AZURE_AD_TOKEN"
    )

    azure_ad_token_provider_value: Final = litellm_params.get("azure_ad_token_provider", None)
    azure_ad_token_provider: Final = azure_ad_token_provider_value if callable(azure_ad_token_provider_value) else None

    headers = headers or litellm.headers

    if extra_headers is not None:
        optional_params["extra_headers"] = extra_headers

    ## LOAD CONFIG - if set
    config: Final = litellm.AzureOpenAIConfig.get_config()
    for k, v in _provider_config_items(config):
        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: Final = azure_text_completions.completion(
        model=model,
        messages=messages,
        headers=headers,
        api_key=api_key,
        api_base=api_base,
        api_version=cast(str, 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,
            },
        )

    return response


def _complete_deepseek(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base: Final = ctx.api_base
    api_key: Final = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    provider_config: Final = ctx.provider_config
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    try:
        response: Final = 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,
            shared_session=shared_session,
            timeout=timeout,
            client=client,
            custom_llm_provider=custom_llm_provider,
            encoding=_get_encoding(),
            stream=stream,
            provider_config=provider_config,
        )
    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

    return response


def _complete_azure_ai(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    extra_headers: Final = ctx.extra_headers
    headers = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    from litellm.llms.azure_ai.common_utils import AzureFoundryModelInfo

    azure_ai_route: Final = AzureFoundryModelInfo.get_azure_ai_route(model)

    # Check if this is an agents route - model format: azure_ai/agents/<agent_id>
    if azure_ai_route == "agents":
        from litellm.llms.azure_ai.agents import AzureAIAgentsConfig

        api_base = AzureFoundryModelInfo.get_api_base(api_base)
        if api_base is None:
            raise ValueError(
                "Azure AI Agents requests require an api_base. Set `api_base` or the AZURE_AI_API_BASE env var."
            )
        api_key = AzureFoundryModelInfo.get_api_key(api_key)

        response = AzureAIAgentsConfig.completion(
            model=model,
            messages=messages,
            api_base=api_base,
            api_key=api_key,
            model_response=model_response,
            logging_obj=logging,
            optional_params=optional_params,
            litellm_params=litellm_params,
            timeout=timeout,
            acompletion=acompletion,
            stream=stream,
            headers=headers or litellm.headers,
        )

    # Check if this is a Claude model - route to Azure Anthropic handler
    elif "claude" in model.lower():
        # Use Azure Anthropic handler for Claude models
        api_base = AzureFoundryModelInfo.get_api_base(api_base)
        if api_base is None:
            raise ValueError(
                "Azure Anthropic requests require an api_base. Set `api_base` or the AZURE_AI_API_BASE env var."
            )
        api_key = AzureFoundryModelInfo.get_api_key(api_key)

        # Ensure the URL ends with /v1/messages for Anthropic
        if api_base:
            api_base = api_base.rstrip("/")
            if not api_base.endswith("/v1/messages"):
                if "/anthropic" in api_base:
                    parts: Final = api_base.split("/anthropic", 1)
                    api_base = parts[0] + "/anthropic"
                else:
                    api_base = api_base + "/anthropic"
                api_base = api_base + "/v1/messages"

        response = azure_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=_get_encoding(),
            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
    else:
        # Non-Claude models use standard Azure AI flow
        api_base = AzureFoundryModelInfo.get_api_base(api_base)
        # set API KEY
        api_key = AzureFoundryModelInfo.get_api_key(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,
                shared_session=shared_session,
                timeout=timeout,
                client=client,  # pass AsyncOpenAI, OpenAI client
                custom_llm_provider=custom_llm_provider,
                encoding=_get_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},
            )

    return response


def _complete_text_completion_openai(
    ctx: _CompletionDispatchContext,
) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_openai(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    text_completion: Final = ctx.text_completion
    timeout: Final = ctx.timeout

    openai.api_type = "openai"

    api_base = (
        api_base
        or litellm.api_base
        or get_secret("OPENAI_BASE_URL")
        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

    ## LOAD CONFIG - if set
    config: Final = litellm.OpenAITextCompletionConfig.get_config()
    for k, v in _provider_config_items(config):
        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

    ## COMPLETION CALL
    _response = openai_text_completions.completion(
        model=model,
        messages=messages,
        headers=headers,
        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,
    )

    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},
        )
    return _response  # pyright: ignore[reportReturnType]  # provider SDK return type is broader than the dispatch contract


def _complete_fireworks_ai(
    ctx: _CompletionDispatchContext,
) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base: Final = ctx.api_base
    api_key: Final = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    provider_config: Final = ctx.provider_config
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout
    optional_params: Final = (
        provider_config.map_extra_body_params(optional_params=ctx.optional_params, model=model)
        if isinstance(provider_config, litellm.FireworksAIConfig)
        else ctx.optional_params
    )

    try:
        response: Final = 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,
            shared_session=shared_session,
            timeout=timeout,
            client=client,
            custom_llm_provider=custom_llm_provider,
            encoding=_get_encoding(),
            stream=stream,
            provider_config=provider_config,
        )
    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

    return response


def _complete_together_ai(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base: Final = ctx.api_base
    api_key: Final = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    provider_config: Final = ctx.provider_config
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    try:
        response: Final = 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,
            shared_session=shared_session,
            timeout=timeout,
            client=client,
            custom_llm_provider=custom_llm_provider,
            encoding=_get_encoding(),
            stream=stream,
            provider_config=provider_config,
        )
    except Exception as e:
        logging.post_call(
            input=messages,
            api_key=api_key,
            original_response=str(e),
            additional_args=MappingProxyType({"headers": headers}),
        )
        raise

    return response


def _complete_heroku(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base: Final = ctx.api_base
    api_key: Final = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    provider_config: Final = ctx.provider_config
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    try:
        response: Final = 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,
            shared_session=shared_session,
            timeout=timeout,
            client=client,
            custom_llm_provider=custom_llm_provider,
            encoding=_get_encoding(),
            stream=stream,
            provider_config=provider_config,
        )
    except Exception as e:
        logging.post_call(
            input=messages,
            api_key=api_key,
            original_response=str(e),
            additional_args={"headers": headers},
        )
        raise e

    return response


def _complete_ragflow(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base: Final = ctx.api_base
    api_key: Final = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    provider_config: Final = ctx.provider_config
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    try:
        response: Final = 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,
            shared_session=shared_session,
            timeout=timeout,
            client=client,
            custom_llm_provider=custom_llm_provider,
            encoding=_get_encoding(),
            stream=stream,
            provider_config=provider_config,
        )
    except Exception as e:
        logging.post_call(
            input=messages,
            api_key=api_key,
            original_response=str(e),
            additional_args={"headers": headers},
        )
        raise e

    return response


def _complete_xai(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base: Final = ctx.api_base
    api_key: Final = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    provider_config: Final = ctx.provider_config
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    try:
        response: Final = 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,
            shared_session=shared_session,
            timeout=timeout,
            client=client,
            custom_llm_provider=custom_llm_provider,
            encoding=_get_encoding(),
            stream=stream,
            provider_config=provider_config,
        )
    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

    return response


def _complete_groq(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    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: Final = litellm.GroqChatConfig.get_config()
    for k, v in _provider_config_items(config):
        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

    return 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,
        shared_session=shared_session,
        custom_llm_provider=custom_llm_provider,
        timeout=timeout,
        headers=headers,
        encoding=_get_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,
    )


def _complete_bedrock_mantle(
    ctx: _CompletionDispatchContext,
) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    api_base = api_base or litellm.api_base or get_secret("BEDROCK_MANTLE_API_BASE")
    api_key = api_key or litellm.api_key or get_secret("BEDROCK_MANTLE_API_KEY")
    headers = headers or litellm.headers
    config: Final = litellm.BedrockMantleChatConfig.get_config()
    for k, v in _provider_config_items(config):
        if k not in optional_params:
            optional_params[k] = v
    return 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,
        shared_session=shared_session,
        custom_llm_provider=custom_llm_provider,
        timeout=timeout,
        headers=headers,
        encoding=_get_encoding(),
        api_key=api_key,
        logging_obj=logging,
        client=client,
    )


def _complete_a2a(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    provider_config: Final = ctx.provider_config
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    (
        api_base,
        api_key,
        headers,
    ) = litellm.A2AConfig.resolve_agent_config_from_registry(
        model=model,
        api_base=api_base,
        api_key=api_key,
        headers=headers,
        optional_params=optional_params,
    )

    # Fall back to environment variables and defaults
    api_base = api_base or litellm.api_base or get_secret_str("A2A_API_BASE")

    if api_base is None:
        raise Exception(
            "api_base is required for A2A provider. "
            "Either provide api_base parameter, set A2A_API_BASE environment variable, "
            "or register the agent in the proxy with model='a2a/<agent-name>'."
        )

    headers = headers or litellm.headers

    return 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,
        shared_session=shared_session,
        custom_llm_provider=custom_llm_provider,
        timeout=timeout,
        headers=headers,
        encoding=_get_encoding(),
        api_key=api_key,
        logging_obj=logging,
        client=client,
        provider_config=provider_config,
    )


def _complete_gigachat(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base: Final = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    provider_config: Final = ctx.provider_config
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    api_key = (
        api_key
        or litellm.api_key
        or litellm.gigachat_key
        or get_secret("GIGACHAT_API_KEY")
        or get_secret("GIGACHAT_CREDENTIALS")
    )

    headers = headers or litellm.headers or {}

    ## COMPLETION CALL
    try:
        response: Final = 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,
            shared_session=shared_session,
            timeout=timeout,
            client=client,
            custom_llm_provider=custom_llm_provider,
            encoding=_get_encoding(),
            stream=stream,
            provider_config=provider_config,
        )
    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

    return response


def _complete_sap(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base: Final = ctx.api_base
    api_key: Final = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    headers = headers or litellm.headers
    ## LOAD CONFIG - if set
    config: Final = litellm.GenAIHubOrchestrationConfig.get_config()
    for k, v in _provider_config_items(config):
        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

    return sap_gen_ai_hub_chat_completions.completion(
        model=model,
        messages=messages,
        headers=headers,
        model_response=model_response,
        acompletion=acompletion,
        logging_obj=logging,
        optional_params=optional_params,
        litellm_params=litellm_params,
        timeout=timeout,
        shared_session=shared_session,
        client=client,
        custom_llm_provider=custom_llm_provider,
        encoding=_get_encoding(),
        api_key=api_key,
        api_base=api_base,
        stream=stream,
    )


def _complete_aiohttp_openai(
    ctx: _CompletionDispatchContext,
) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    extra_headers: Final = ctx.extra_headers
    headers = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    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_BASE_URL")
        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
    return 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=_get_encoding(),
        stream=stream,
    )


def _complete_cometapi(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    provider_config: Final = ctx.provider_config
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    api_key = api_key or litellm.cometapi_key or get_secret_str("COMETAPI_KEY") or litellm.api_key

    api_base = api_base or litellm.api_base or get_secret_str("COMETAPI_API_BASE") or "https://api.cometapi.com/v1"

    ## COMPLETION CALL
    response: Final = 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,
        shared_session=shared_session,
        timeout=timeout,
        client=client,
        custom_llm_provider=custom_llm_provider,
        encoding=_get_encoding(),
        stream=stream,
        provider_config=provider_config,
    )

    ## LOGGING
    logging.post_call(input=messages, api_key=api_key, original_response=response)

    return response


def _complete_minimax(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    provider_config: Final = ctx.provider_config
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    api_key = api_key or get_secret_str("MINIMAX_API_KEY") or litellm.api_key

    api_base = api_base or litellm.api_base or get_secret_str("MINIMAX_API_BASE") or "https://api.minimax.io/v1"

    response: Final = base_llm_http_handler.completion(
        model=model,
        messages=messages,
        api_base=api_base,
        custom_llm_provider=custom_llm_provider,
        model_response=model_response,
        encoding=_get_encoding(),
        logging_obj=logging,
        optional_params=optional_params,
        timeout=timeout,
        litellm_params=litellm_params,
        shared_session=shared_session,
        acompletion=acompletion,
        stream=stream,
        api_key=api_key,
        headers=headers,
        client=client,
        provider_config=provider_config,
    )
    logging.post_call(input=messages, api_key=api_key, original_response=response)

    return response


def _complete_hosted_vllm(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key: Final = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    provider_config: Final = ctx.provider_config
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    api_base = api_base or litellm.api_base or get_secret_str("HOSTED_VLLM_API_BASE")

    response: Final = base_llm_http_handler.completion(
        model=model,
        messages=messages,
        api_base=api_base,
        custom_llm_provider=custom_llm_provider,
        model_response=model_response,
        encoding=_get_encoding(),
        logging_obj=logging,
        optional_params=optional_params,
        timeout=timeout,
        litellm_params=litellm_params,
        shared_session=shared_session,
        acompletion=acompletion,
        stream=stream,
        api_key=api_key,
        headers=headers,
        client=client,
        provider_config=provider_config,
    )
    logging.post_call(input=messages, api_key=api_key, original_response=response)

    return response


def _complete_custom_openai(
    ctx: _CompletionDispatchContext,
) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    custom_prompt_dict: Final = ctx.custom_prompt_dict
    extra_headers = ctx.extra_headers
    headers = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    metadata: Final = _dispatch_metadata(ctx)
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    organization = ctx.organization
    provider_config: Final = ctx.provider_config
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    # for deepinfra/perplexity/anyscale/groq/friendliai we check in get_llm_provider and pass in the api base from there
    api_base = _resolve_openai_api_base(api_base)
    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

    # Add GitHub Copilot headers (same as /responses endpoint does)
    if custom_llm_provider == "github_copilot":
        from litellm.llms.github_copilot.authenticator import Authenticator
        from litellm.llms.github_copilot.common_utils import (
            get_copilot_default_headers,
        )

        copilot_auth: Final = Authenticator()
        copilot_api_key: Final = copilot_auth.get_api_key()
        copilot_headers: Final = get_copilot_default_headers(copilot_api_key)
        if extra_headers:
            copilot_headers.update(extra_headers)
        extra_headers = copilot_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
        openai_metadata: Final = get_requester_metadata(metadata)
        if openai_metadata is not None:
            optional_params["metadata"] = openai_metadata

    ## LOAD CONFIG - if set
    config: Final = litellm.OpenAIConfig.get_config()
    for k, v in _provider_config_items(config):
        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
    use_base_llm_http_handler: Final = get_secret_bool("EXPERIMENTAL_OPENAI_BASE_LLM_HTTP_HANDLER")

    try:
        if use_base_llm_http_handler:
            response = base_llm_http_handler.completion(
                model=model,
                messages=messages,
                api_base=api_base,
                custom_llm_provider=custom_llm_provider,
                model_response=model_response,
                encoding=_get_encoding(),
                logging_obj=logging,
                optional_params=optional_params,
                timeout=timeout,
                litellm_params=litellm_params,
                shared_session=shared_session,
                acompletion=acompletion,
                stream=stream,
                api_key=api_key,
                headers=headers,
                client=client,
                provider_config=provider_config,
            )
        else:
            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,
                custom_prompt_dict=custom_prompt_dict,
                client=client,  # pass AsyncOpenAI, OpenAI client
                organization=organization,
                custom_llm_provider=custom_llm_provider,
                shared_session=shared_session,
            )
    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},
        )

    return response  # pyright: ignore[reportReturnType]  # provider SDK return type is broader than the dispatch contract


def _complete_mistral(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    provider_config: Final = ctx.provider_config
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    api_key = api_key or litellm.api_key or get_secret("MISTRAL_API_KEY")
    api_base = api_base or litellm.api_base or get_secret("MISTRAL_API_BASE") or "https://api.mistral.ai/v1"

    return base_llm_http_handler.completion(
        model=model,
        messages=messages,
        api_base=api_base,
        custom_llm_provider=custom_llm_provider,
        model_response=model_response,
        encoding=_get_encoding(),
        logging_obj=logging,
        optional_params=optional_params,
        timeout=timeout,
        litellm_params=litellm_params,
        shared_session=shared_session,
        acompletion=acompletion,
        stream=stream,
        api_key=api_key,
        headers=headers,
        client=client,
        provider_config=provider_config,
    )


def _complete_replicate(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key: Final = ctx.api_key
    custom_prompt_dict = ctx.custom_prompt_dict
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response = ctx.model_response
    optional_params: Final = ctx.optional_params

    replicate_key: Final = (
        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(
        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=_get_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,
        )

    return model_response


def _complete_anthropic_text(
    ctx: _CompletionDispatchContext,
) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    custom_prompt_dict = ctx.custom_prompt_dict
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    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 = cast(
        str | None,
        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",
    )

    # Check if we should disable automatic URL suffix appending
    disable_url_suffix: Final = get_secret_bool("LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX")
    if api_base is not None and not disable_url_suffix and not api_base.endswith("/v1/complete"):
        api_base += "/v1/complete"
    elif disable_url_suffix:
        verbose_logger.debug("LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX is set, skipping /v1/complete suffix")

    return 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,
        shared_session=shared_session,
        custom_llm_provider="anthropic_text",
        timeout=timeout,
        headers=headers,
        encoding=_get_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
    )


def _complete_anthropic(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    custom_prompt_dict = ctx.custom_prompt_dict
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    timeout: Final = ctx.timeout

    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 = cast(
        str | None,
        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",
    )

    # Check if we should disable automatic URL suffix appending
    disable_url_suffix: Final = get_secret_bool("LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX")
    if api_base is not None and not disable_url_suffix and not api_base.endswith("/v1/messages"):
        api_base += "/v1/messages"
    elif disable_url_suffix:
        verbose_logger.debug("LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX is set, skipping /v1/messages suffix")

    response: Final = 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=_get_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,
        )
    return response


def _complete_nlp_cloud(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key: Final = ctx.api_key
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params

    nlp_cloud_key: Final = 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=_get_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,
        )

    return response  # pyright: ignore[reportReturnType]  # provider SDK return type is broader than the dispatch contract


def _complete_aleph_alpha(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    api_base = ctx.api_base
    api_key: Final = ctx.api_key
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response = ctx.model_response
    optional_params: Final = ctx.optional_params

    aleph_alpha_key: Final = (
        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=_get_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,
        return CustomStreamWrapper(
            model_response,
            model,
            custom_llm_provider="aleph_alpha",
            logging_obj=logging,
        )
    return model_response  # pyright: ignore[reportReturnType]  # provider SDK return type is broader than the dispatch contract


def _complete_cohere_chat(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key: Final = ctx.api_key
    extra_headers: Final = ctx.extra_headers
    headers = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    provider_config: Final = ctx.provider_config
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    cohere_key: Final = (
        api_key
        or litellm.cohere_key
        or get_secret_str("COHERE_API_KEY")
        or get_secret_str("CO_API_KEY")
        or litellm.api_key
    )

    cohere_route: Final = CohereModelInfo.get_cohere_route(model)
    verbose_logger.debug("Cohere route: %s", cohere_route)
    # Set API base based on route
    if cohere_route == "v2":
        api_base = api_base or litellm.api_base or get_secret_str("COHERE_API_BASE") or "https://api.cohere.com/v2/chat"
        # Remove v2/ prefix from model name for the actual API call
        if "v2/" in model:
            model = model.replace("v2/", "")
    else:
        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)

    verbose_logger.debug("Model: %s, API Base: %s", model, api_base)
    verbose_logger.debug("Provider Config: %s", provider_config)
    return 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,
        shared_session=shared_session,
        custom_llm_provider="cohere_chat",
        timeout=timeout,
        headers=headers,
        encoding=_get_encoding(),
        api_key=cohere_key,
        provider_config=provider_config,
        logging_obj=logging,  # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements
    )


def _complete_maritalk(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    api_base = ctx.api_base
    api_key: Final = ctx.api_key
    custom_prompt_dict: Final = ctx.custom_prompt_dict
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params

    maritalk_key: Final = 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"

    return 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=_get_encoding(),
        api_key=maritalk_key,
        logging_obj=logging,
        custom_llm_provider="maritalk",
        custom_prompt_dict=custom_prompt_dict,
    )


def _complete_amazon_nova(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    api_base = ctx.api_base
    api_key = ctx.api_key
    custom_llm_provider: Final = ctx.custom_llm_provider
    custom_prompt_dict: Final = ctx.custom_prompt_dict
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    timeout: Final = ctx.timeout

    api_key = api_key or litellm.amazon_nova_api_key or get_secret_str("AMAZON_NOVA_API_KEY") or litellm.api_key
    api_base = (
        api_base or litellm.api_base or get_secret_str("AMAZON_NOVA_API_BASE") or "https://api.nova.amazon.com/v1"
    )
    return 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=_get_encoding(),
        api_key=api_key,
        logging_obj=logging,
        timeout=timeout,
        custom_llm_provider=custom_llm_provider,
        custom_prompt_dict=custom_prompt_dict,
    )


def _complete_huggingface(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base: Final = ctx.api_base
    api_key: Final = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    huggingface_key: Final = (
        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: Final = headers or litellm.headers
    return base_llm_http_handler.completion(
        model=model,
        messages=messages,
        headers=hf_headers,
        model_response=model_response,
        api_key=huggingface_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=_get_encoding(),
        stream=stream,
    )


def _complete_oci(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base: Final = ctx.api_base
    api_key: Final = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    return 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,
        client=client,
        custom_llm_provider=custom_llm_provider,
        encoding=_get_encoding(),
        stream=stream,
    )


def _complete_compactifai(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    provider_config: Final = ctx.provider_config
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    api_key = api_key or get_secret_str("COMPACTIFAI_API_KEY") or litellm.api_key

    api_base = api_base or "https://api.compactif.ai/v1"

    ## COMPLETION CALL
    return 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,
        client=client,
        custom_llm_provider=custom_llm_provider,
        encoding=_get_encoding(),
        stream=stream,
        provider_config=provider_config,
    )


def _complete_oobabooga(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    api_base: Final = ctx.api_base
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response = ctx.model_response
    optional_params: Final = ctx.optional_params

    model_response = oobabooga.completion(
        model=model,
        messages=messages,
        model_response=model_response,
        api_base=api_base,
        print_verbose=print_verbose,
        optional_params=optional_params,
        litellm_params=litellm_params,
        api_key=None,
        logger_fn=logger_fn,
        encoding=_get_encoding(),
        logging_obj=logging,
    )
    if "stream" in optional_params and optional_params["stream"] is True:
        # don't try to access stream object,
        return CustomStreamWrapper(
            model_response,
            model,
            custom_llm_provider="oobabooga",
            logging_obj=logging,
        )
    return model_response  # pyright: ignore[reportReturnType]  # provider SDK return type is broader than the dispatch contract


def _complete_databricks(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    headers = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    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: Final = 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="databricks",
            timeout=timeout,
            headers=headers,
            encoding=_get_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,
        )
    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},
        )

    return response


def _complete_datarobot(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base: Final = ctx.api_base
    api_key: Final = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    provider_config: Final = ctx.provider_config
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    return 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,
        client=client,
        custom_llm_provider=custom_llm_provider,
        encoding=_get_encoding(),
        stream=stream,
        provider_config=provider_config,
    )


def _complete_openrouter(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    headers = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    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_str("OPENROUTER_API_KEY")
        or get_secret_str("OR_API_KEY")
    )

    openrouter_site_url: Final = get_secret("OR_SITE_URL") or "https://litellm.ai"
    openrouter_app_name: Final = get_secret("OR_APP_NAME") or "liteLLM"

    openrouter_headers: Final = {
        "HTTP-Referer": openrouter_site_url,
        "X-Title": openrouter_app_name,
    }

    _headers: Final = headers or litellm.headers
    if _headers:
        openrouter_headers.update(_headers)

    headers = openrouter_headers

    ## Load Config
    config: Final = litellm.OpenrouterConfig.get_config()
    for k, v in _provider_config_items(config):
        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

    ## COMPLETION CALL
    response: Final = 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,
        shared_session=shared_session,
        custom_llm_provider="openrouter",
        timeout=timeout,
        headers=headers,
        encoding=_get_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)

    return response


def _complete_vercel_ai_gateway(
    ctx: _CompletionDispatchContext,
) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    headers = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    api_base = (
        api_base
        or litellm.api_base
        or get_secret_str("VERCEL_AI_GATEWAY_API_BASE")
        or "https://ai-gateway.vercel.sh/v1"
    )

    api_key = api_key or litellm.api_key or get_secret("VERCEL_AI_GATEWAY_API_KEY")

    vercel_site_url: Final = get_secret("VERCEL_SITE_URL") or "https://litellm.ai"
    vercel_app_name: Final = get_secret("VERCEL_APP_NAME") or "liteLLM"

    vercel_headers: Final = {
        "http-referer": vercel_site_url,
        "x-title": vercel_app_name,
    }

    _headers: Final = headers or litellm.headers
    if _headers:
        vercel_headers.update(_headers)

    headers = vercel_headers

    ## Load Config
    config: Final = litellm.VercelAIGatewayConfig.get_config()
    for k, v in _provider_config_items(config):
        if k == "extra_body":
            # we use openai 'extra_body' to pass vercel specific params - providerOptions
            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

    ## COMPLETION CALL
    response: Final = 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,
        shared_session=shared_session,
        custom_llm_provider="vercel_ai_gateway",
        timeout=timeout,
        headers=headers,
        encoding=_get_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)

    return response


def _complete_vertex_ai_beta(
    ctx: _CompletionDispatchContext,
) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key: Final = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    timeout: Final = ctx.timeout

    vertex_ai_project: Final = (
        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: Final = (
        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: Final = (
        optional_params.pop("vertex_credentials", None)
        or optional_params.pop("vertex_ai_credentials", None)
        or get_secret("VERTEXAI_CREDENTIALS")
    )

    gemini_api_key: Final = (
        api_key
        or get_api_key_from_env()
        or get_secret("PALM_API_KEY")  # older palm api key should also work
        or litellm.api_key
    )

    api_base = api_base or litellm.api_base or get_secret("GEMINI_API_BASE")
    new_params: Final = safe_deep_copy(optional_params or {})
    return vertex_chat_completion.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=_get_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=headers,
    )


def _complete_vertex_ai(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    custom_prompt_dict: Final = ctx.custom_prompt_dict
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response = ctx.model_response
    optional_params: Final = ctx.optional_params
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    vertex_ai_project: Final = (
        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: Final = (
        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: Final = (
        optional_params.pop("vertex_credentials", None)
        or optional_params.pop("vertex_ai_credentials", None)
        or get_secret("VERTEXAI_CREDENTIALS")
    )

    api_base = api_base or litellm.api_base or get_secret("VERTEXAI_API_BASE")

    new_params: Final = safe_deep_copy(optional_params or {})
    model_route: Final = get_vertex_ai_model_route(model=model, litellm_params=litellm_params)

    if model_route == VertexAIModelRoute.PARTNER_MODELS:
        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,
            logger_fn=logger_fn,
            encoding=_get_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 model_route == VertexAIModelRoute.GEMINI:
        model_response = vertex_chat_completion.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=_get_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=headers,
        )
    elif model_route == VertexAIModelRoute.GEMMA:
        # Vertex Gemma Models with custom prediction endpoint
        model_response = vertex_gemma_chat_completion.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=_get_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 model_route == VertexAIModelRoute.MODEL_GARDEN:
        # 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,
            logger_fn=logger_fn,
            encoding=_get_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 model_route == VertexAIModelRoute.AGENT_ENGINE:
        # Vertex AI Agent Engine (Reasoning Engines)
        from litellm.llms.vertex_ai.agent_engine.transformation import (
            VertexAgentEngineConfig,
        )

        vertex_agent_engine_config: Final = VertexAgentEngineConfig()

        # Update litellm_params with vertex credentials
        litellm_params["vertex_project"] = vertex_ai_project
        litellm_params["vertex_location"] = vertex_ai_location
        litellm_params["vertex_credentials"] = vertex_credentials

        model_response = base_llm_http_handler.completion(
            model=model,
            stream=stream,
            messages=messages,
            model_response=model_response,
            optional_params=new_params,
            litellm_params=litellm_params,
            encoding=_get_encoding(),
            api_key=None,
            api_base=api_base,
            logging_obj=logging,
            acompletion=acompletion,
            timeout=timeout,
            client=client,
            custom_llm_provider="vertex_ai",
            provider_config=vertex_agent_engine_config,
            headers=headers or {},
        )
    else:  # VertexAIModelRoute.NON_GEMINI
        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=_get_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:
            return CustomStreamWrapper(
                model_response,
                model,
                custom_llm_provider="vertex_ai",
                logging_obj=logging,
            )
    return model_response  # pyright: ignore[reportReturnType]  # provider SDK return type is broader than the dispatch contract


def _complete_predibase(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    custom_prompt_dict: Final = ctx.custom_prompt_dict
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    timeout: Final = ctx.timeout

    tenant_id: Final = (
        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: Final = 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=_get_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
    return _model_response


def _complete_text_completion_codestral(
    ctx: _CompletionDispatchContext,
) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    custom_prompt_dict: Final = ctx.custom_prompt_dict
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    optional_params: Final = ctx.optional_params
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    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: Final = litellm.TextCompletionResponse(stream=stream)

    _model_response: Final = codestral_text_completions.completion(
        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=_get_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  # pyright: ignore[reportReturnType]  # provider SDK return type is broader than the dispatch contract
    return _model_response  # pyright: ignore[reportReturnType]  # provider SDK return type is broader than the dispatch contract


def _complete_text_completion_inception(
    ctx: _CompletionDispatchContext,
) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_openai(ctx)
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    text_completion: Final = ctx.text_completion
    timeout: Final = ctx.timeout

    passed_api_base: Final = api_base or optional_params.pop("api_base", None) or optional_params.pop("base_url", None)
    api_base = passed_api_base or get_secret_str("INCEPTION_API_BASE") or "https://api.inceptionlabs.ai/v1"
    # FIM is served at `/v1/fim/completions`; the OpenAI client appends
    # `/completions`, so point it at the `/v1/fim` base.
    api_base = api_base.rstrip("/")
    if not api_base.endswith("/fim"):
        api_base += "/fim"

    # Don't forward the server-managed Inception key to a caller-supplied
    # api_base; only resolve it for the default/server base, or when the
    # caller passes their own key.
    if passed_api_base is None or api_key:
        api_key = api_key or litellm.inception_key or get_secret_str("INCEPTION_API_KEY")

    _response = openai_text_completions.completion(
        model=model,
        messages=messages,
        model_response=model_response,
        print_verbose=print_verbose,
        api_key=api_key,
        custom_llm_provider="text-completion-inception",
        api_base=api_base,
        acompletion=acompletion,
        client=client,
        logging_obj=logging,
        optional_params=optional_params,
        litellm_params=litellm_params,
        logger_fn=logger_fn,
        timeout=timeout,
    )

    if optional_params.get("stream", False) is False and acompletion is False and text_completion is False:
        _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.post_call(
            input=messages,
            api_key=api_key,
            original_response=_response,
            additional_args={"headers": headers},
        )
    return _response  # pyright: ignore[reportReturnType]  # provider SDK return type is broader than the dispatch contract


def _complete_sagemaker_chat(
    ctx: _CompletionDispatchContext,
) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base: Final = ctx.api_base
    api_key: Final = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    return 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=_get_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,
    )


def _complete_sagemaker(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    custom_prompt_dict: Final = ctx.custom_prompt_dict
    hf_model_name: Final = ctx.hf_model_name
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params

    return 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=_get_encoding(),
        logging_obj=logging,
        acompletion=acompletion,
    )


def _complete_bedrock(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base: Final = ctx.api_base
    api_key: Final = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_prompt_dict = ctx.custom_prompt_dict
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    provider_config = ctx.provider_config
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    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: Final = optional_params.pop("aws_bedrock_client")
        creds: Final = 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: Final = BedrockModelInfo.get_bedrock_route(model)
    if bedrock_route == "claude_platform":
        provider_config = ProviderConfigManager.get_provider_chat_config(
            model=model,
            provider=LlmProviders.BEDROCK,
        )
        model = BedrockModelInfo.get_claude_platform_model(model)
        return 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,
            shared_session=shared_session,
            custom_llm_provider="bedrock",
            timeout=timeout,
            headers=headers,
            encoding=_get_encoding(),
            api_key=api_key,
            logging_obj=logging,
            client=client,
            provider_config=provider_config,
        )
    elif 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,
            logger_fn=logger_fn,
            encoding=_get_encoding(),
            logging_obj=logging,
            extra_headers=headers,  # Use merged headers instead of original extra_headers
            timeout=timeout,
            acompletion=acompletion,
            client=client,
            api_base=api_base,
            api_key=api_key,
        )
    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=_get_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=_get_encoding(),
            api_key=api_key,
            logging_obj=logging,
            client=client,
        )

    return response


def _complete_watsonx(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base: Final = ctx.api_base
    api_key: Final = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_prompt_dict: Final = ctx.custom_prompt_dict
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    timeout: Final = ctx.timeout

    return 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,
        custom_prompt_dict=custom_prompt_dict,
        client=client,  # pass AsyncOpenAI, OpenAI client
        encoding=_get_encoding(),
        custom_llm_provider="watsonx",
    )


def _complete_watsonx_text(
    ctx: _CompletionDispatchContext,
) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    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: Final = optional_params.pop(
        "wx_credentials",
        optional_params.pop("watsonx_credentials", None),  # follow {provider}_credentials, same as vertex ai
    )

    token: str | None = 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

    return 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,
        shared_session=shared_session,
        custom_llm_provider="watsonx_text",
        timeout=timeout,
        headers=headers,
        encoding=_get_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,
    )


def _complete_vllm(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    custom_prompt_dict = ctx.custom_prompt_dict
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response = ctx.model_response
    optional_params: Final = ctx.optional_params

    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=_get_encoding(),
        logging_obj=logging,
    )

    if "stream" in optional_params and optional_params["stream"] is True:  ## [BETA]
        # don't try to access stream object,
        return CustomStreamWrapper(
            model_response,
            model,
            custom_llm_provider="vllm",
            logging_obj=logging,
        )

    ## RESPONSE OBJECT
    return model_response


def _complete_ollama(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key: Final = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    api_base = litellm.api_base or api_base or get_secret("OLLAMA_API_BASE") or "http://localhost:11434"
    if api_key is not None and "Authorization" not in headers:
        headers["Authorization"] = f"Bearer {api_key}"

    return 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,
        shared_session=shared_session,
        custom_llm_provider="ollama",
        timeout=timeout,
        headers=headers,
        encoding=_get_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,
    )


def _complete_ollama_chat(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    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
    if api_key is not None and "Authorization" not in headers:
        headers["Authorization"] = f"Bearer {api_key}"

    return 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,
        shared_session=shared_session,
        custom_llm_provider="ollama_chat",
        timeout=timeout,
        headers=headers,
        encoding=_get_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,
    )


def _complete_triton(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key: Final = ctx.api_key
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    api_base = litellm.api_base or api_base
    return 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,
        shared_session=shared_session,
        custom_llm_provider=custom_llm_provider,
        timeout=timeout,
        headers=headers,
        encoding=_get_encoding(),
        api_key=api_key,
        logging_obj=logging,
    )


def _complete_cloudflare(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    custom_prompt_dict = ctx.custom_prompt_dict
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    api_key = api_key or litellm.cloudflare_api_key or litellm.api_key or get_secret("CLOUDFLARE_API_KEY")
    api_base = api_base or litellm.api_base or get_secret("CLOUDFLARE_API_BASE")

    custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
    return 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,
        shared_session=shared_session,
        custom_llm_provider="cloudflare",
        timeout=timeout,
        headers=headers,
        encoding=_get_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
    )


def _complete_petals(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    api_base = ctx.api_base
    client: Final = _dispatch_client_http(ctx)
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response = ctx.model_response
    optional_params: Final = ctx.optional_params
    stream = ctx.stream

    api_base = api_base or litellm.api_base

    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=_get_encoding(),
        logging_obj=logging,
        client=client,
    )
    if stream is True:  ## [BETA]
        # Fake streaming for petals
        resp_string: Final = model_response["choices"][0]["message"]["content"]
        return CustomStreamWrapper(
            resp_string,
            model,
            custom_llm_provider="petals",
            logging_obj=logging,
        )
    return model_response


def _complete_snowflake(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base: Final = ctx.api_base
    api_key: Final = ctx.api_key
    injected_client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout
    client: Final = (
        injected_client if injected_client is not None else (HTTPHandler(timeout=timeout) if stream is False else None)
    )  # Keep this here, otherwise, the httpx.client closes and streaming is impossible

    try:
        response: Final = 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,
            shared_session=shared_session,
            timeout=timeout,
            client=client,
            custom_llm_provider=custom_llm_provider,
            encoding=_get_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

    return response


def _complete_gradient_ai(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key: Final = ctx.api_key
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    api_base = litellm.api_base or api_base
    return 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,
        shared_session=shared_session,
        custom_llm_provider="gradient_ai",
        timeout=timeout,
        headers=headers,
        encoding=_get_encoding(),
        api_key=api_key,
        logging_obj=logging,
    )


def _complete_gdc(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    api_key = api_key or litellm.gdc_key or get_secret_str("GDC_API_KEY") or litellm.api_key
    api_base = api_base or litellm.gdc_api_base or get_secret_str("GDC_API_BASE") or litellm.api_base

    return 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,
        client=client,
        custom_llm_provider=custom_llm_provider,
        encoding=_get_encoding(),
        stream=stream,
        provider_config=gdc_transformation,
    )


def _complete_bytez(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base: Final = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    api_key = api_key or litellm.bytez_key or get_secret_str("BYTEZ_API_KEY") or litellm.api_key

    response: Final = 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,
        client=client,
        custom_llm_provider=custom_llm_provider,
        encoding=_get_encoding(),
        stream=stream,
        provider_config=bytez_transformation,
    )

    return response


def _complete_lemonade(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base: Final = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    api_key = api_key or litellm.lemonade_key or get_secret_str("LEMONADE_API_KEY") or litellm.api_key

    response: Final = 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,
        client=client,
        custom_llm_provider=custom_llm_provider,
        encoding=_get_encoding(),
        stream=stream,
        provider_config=lemonade_transformation,
    )

    return response


def _complete_ovhcloud(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers: Final = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    api_key = api_key or litellm.ovhcloud_key or get_secret_str("OVHCLOUD_API_KEY") or litellm.api_key

    api_base = (
        api_base
        or litellm.api_base
        or get_secret_str("OVHCLOUD_API_BASE")
        or "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1"
    )

    response: Final = 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,
        client=client,
        custom_llm_provider=custom_llm_provider,
        encoding=_get_encoding(),
        stream=stream,
        provider_config=ovhcloud_transformation,
    )

    return response


def _custom_api_first_output(resp: httpx.Response | None) -> str:
    return resp.json()["data"][0]["output"][0]


def _complete_custom(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    api_base: Final = ctx.api_base
    headers: Final = ctx.headers
    kwargs: Final = ctx.kwargs
    max_tokens: Final = ctx.max_tokens
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    temperature: Final = ctx.temperature
    top_p: Final = ctx.top_p

    url: Final = 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: Final = " ".join([message["content"] for message in messages])
    resp: Final = litellm.module_level_client.post(
        url,
        headers=headers,
        json={
            "model": model,
            "params": {
                "prompt": [prompt],
                "max_tokens": max_tokens,
                "temperature": temperature,
                "top_p": top_p,
                "top_k": kwargs.get("top_k"),
            },
            **kwargs.get("extra_body", {}),
        },
    )
    """
    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: Final = _custom_api_first_output(resp)
    ## RESPONSE OBJECT
    model_response.choices[0].message.content = string_response
    model_response.created = int(time.time())
    model_response.model = model
    return model_response


def _complete_custom_providers(
    ctx: _CompletionDispatchContext,
) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base: Final = ctx.api_base
    api_key: Final = ctx.api_key
    client: Final = ctx.client
    custom_llm_provider: Final = ctx.custom_llm_provider
    custom_prompt_dict: Final = ctx.custom_prompt_dict
    headers = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logger_fn: Final = ctx.logger_fn
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    custom_handler: CustomLLM | None = 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: Final = custom_chat_llm_router(async_fn=acompletion, stream=stream, custom_llm=custom_handler)

    headers = headers or litellm.headers or {}

    ## CALL FUNCTION
    response: Final = 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,
        custom_prompt_dict=custom_prompt_dict,
        client=client,  # pass AsyncOpenAI, OpenAI client
        encoding=_get_encoding(),
    )
    if stream is True:
        return CustomStreamWrapper(
            completion_stream=response,
            model=model,
            custom_llm_provider=custom_llm_provider,
            logging_obj=logging,
        )

    return response  # pyright: ignore[reportReturnType]  # provider SDK return type is broader than the dispatch contract


def _complete_langgraph(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    from litellm.llms.langgraph.chat.transformation import LangGraphConfig

    (
        api_base,
        api_key,
    ) = LangGraphConfig()._get_openai_compatible_provider_info(
        api_base=api_base or litellm.api_base,
        api_key=api_key or litellm.api_key,
    )

    headers = headers or litellm.headers

    return 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,
        shared_session=shared_session,
        custom_llm_provider=custom_llm_provider,
        timeout=timeout,
        headers=headers,
        encoding=_get_encoding(),
        api_key=api_key,
        logging_obj=logging,
        client=client,
    )


def _complete_langflow(ctx: _CompletionDispatchContext) -> _CompletionDispatchResult:
    acompletion: Final = ctx.acompletion
    api_base = ctx.api_base
    api_key = ctx.api_key
    client: Final = _dispatch_client_http(ctx)
    custom_llm_provider: Final = ctx.custom_llm_provider
    headers = ctx.headers
    litellm_params: Final = ctx.litellm_params
    logging: Final = ctx.logging
    messages: Final = ctx.messages
    model: Final = ctx.model
    model_response: Final = ctx.model_response
    optional_params: Final = ctx.optional_params
    shared_session: Final = ctx.shared_session
    stream: Final = ctx.stream
    timeout: Final = ctx.timeout

    from litellm.llms.langflow.chat.transformation import LangFlowConfig

    (
        api_base,
        api_key,
    ) = LangFlowConfig()._get_openai_compatible_provider_info(
        api_base=api_base or litellm.api_base,
        api_key=api_key or litellm.api_key,
    )

    headers = headers or litellm.headers

    return 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,
        shared_session=shared_session,
        custom_llm_provider=custom_llm_provider,
        timeout=timeout,
        headers=headers,
        encoding=_get_encoding(),
        api_key=api_key,
        logging_obj=logging,
        client=client,
    )


@tracer.wrap()
@client
def completion(
    model: str,
    # Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create
    messages: list = [],
    timeout: float | str | httpx.Timeout | None = None,
    temperature: float | None = None,
    top_p: float | None = None,
    n: int | None = None,
    stream: bool | None = None,
    stream_options: dict | None = None,
    stop=None,
    max_completion_tokens: int | None = None,
    max_tokens: int | None = None,
    modalities: list[ChatCompletionModality] | None = None,
    prediction: ChatCompletionPredictionContentParam | None = None,
    audio: ChatCompletionAudioParam | None = None,
    presence_penalty: float | None = None,
    frequency_penalty: float | None = None,
    logit_bias: dict | None = None,
    user: str | None = None,
    # openai v1.0+ new params
    reasoning_effort: Literal["none", "minimal", "low", "medium", "high", "xhigh", "max", "default"] | None = None,
    verbosity: Literal["low", "medium", "high"] | None = None,
    response_format: dict | type[BaseModel] | None = None,
    seed: int | None = None,
    tools: list | None = None,
    tool_choice: str | dict | None = None,
    logprobs: bool | None = None,
    top_logprobs: int | None = None,
    parallel_tool_calls: bool | None = None,
    web_search_options: OpenAIWebSearchOptions | None = None,
    include_server_side_tool_invocations: bool | None = None,
    deployment_id=None,
    extra_headers: dict | None = None,
    safety_identifier: str | None = None,
    service_tier: str | None = None,
    store: bool | None = None,
    prompt_cache_key: str | None = None,
    # soon to be deprecated params by OpenAI
    functions: list | None = None,
    function_call: str | None = None,
    # set api_base, api_version, api_key
    base_url: str | None = None,
    api_version: str | None = None,
    api_key: str | None = None,
    model_list: list | None = None,  # pass in a list of api_base,keys, etc.
    # Optional liteLLM function params
    thinking: AnthropicThinkingParam | None = None,
    # Session management
    shared_session: Optional["ClientSession"] = None,
    # Per-request JSON schema validation (overrides litellm.enable_json_schema_validation)
    enable_json_schema_validation: bool | None = None,
    **kwargs,
) -> 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)
    tools = validate_and_fix_openai_tools(tools=tools)
    # validate tool_choice
    tool_choice = validate_chat_completion_tool_choice(tool_choice=tool_choice)
    # validate optional params
    stop = validate_openai_optional_params(stop=stop)
    thinking = validate_and_fix_thinking_param(thinking=thinking)

    ######### unpacking kwargs #####################
    args: Final = _locals_snapshot(locals())

    # Set by the responses->completion fallback so completion() does not bridge
    # back to the Responses API: that round-trip mutually recurses forever for a
    # model whose model_cost mode is "responses" but whose provider has no
    # Responses API config (get_provider_responses_api_config -> None).
    skip_responses_api_bridge: Final = kwargs.pop("_skip_responses_api_bridge", False)

    skip_mcp_handler: Final = kwargs.pop("_skip_mcp_handler", False)
    if not skip_mcp_handler and tools:
        from litellm.responses.mcp.chat_completions_handler import acompletion_with_mcp
        from litellm.responses.mcp.litellm_proxy_mcp_handler import (
            LiteLLM_Proxy_MCP_Handler,
        )
        from litellm.types.llms.openai import ToolParam

        # Check if MCP tools are present (following responses pattern)
        # Cast tools to Optional[Iterable[ToolParam]] for type checking
        tools_for_mcp: Final = cast(Iterable[ToolParam] | None, tools)
        if LiteLLM_Proxy_MCP_Handler._should_use_litellm_mcp_gateway(tools=tools_for_mcp):
            return acompletion_with_mcp(  # pyright: ignore[reportReturnType]  # MCP path returns a coroutine that acompletion() awaits; completion()'s sync return type omits it
                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,
                reasoning_effort=reasoning_effort,
                verbosity=verbosity,
                safety_identifier=safety_identifier,
                service_tier=service_tier,
                store=store,
                prompt_cache_key=prompt_cache_key,
                base_url=base_url,
                api_version=api_version,
                api_key=api_key,
                model_list=model_list,
                extra_headers=extra_headers,
                thinking=thinking,
                web_search_options=web_search_options,
                shared_session=shared_session,
                enable_json_schema_validation=enable_json_schema_validation,
                **kwargs,
            )
    api_base = kwargs.get("api_base", None)
    mock_response: Final[MOCK_RESPONSE_TYPE | None] = kwargs.get("mock_response", None)
    mock_tool_calls: Final = kwargs.get("mock_tool_calls", None)
    mock_timeout: Final = cast(bool | None, kwargs.get("mock_timeout", None))
    force_timeout: Final = kwargs.get("force_timeout", 600)  ## deprecated
    logger_fn: Final = kwargs.get("logger_fn", None)
    verbose: Final = kwargs.get("verbose", False)
    custom_llm_provider = kwargs.get("custom_llm_provider", None)
    litellm_logging_obj: Final = kwargs.get("litellm_logging_obj", None)
    id: Final = kwargs.get("id", None)
    metadata: Final = kwargs.get("metadata", None)
    model_info: Final = kwargs.get("model_info", None)
    proxy_server_request: Final = kwargs.get("proxy_server_request", None)
    fallbacks = kwargs.get("fallbacks", None)
    provider_specific_header: Final = cast(
        ProviderSpecificHeader | Sequence[ProviderSpecificHeader] | None,
        kwargs.get("provider_specific_header", None),
    )
    headers = kwargs.get("headers", None) or extra_headers

    ensure_alternating_roles: Final[bool | None] = kwargs.get("ensure_alternating_roles", None)
    user_continue_message: Final[ChatCompletionUserMessage | None] = kwargs.get("user_continue_message", None)
    assistant_continue_message: ChatCompletionAssistantMessage | None = kwargs.get("assistant_continue_message", None)
    headers = {} if headers is None else dict(headers)
    if extra_headers is not None:
        headers.update(extra_headers)
    # Inject proxy auth headers if configured
    if litellm.proxy_auth is not None:
        try:
            proxy_headers: Final = _proxy_auth_headers(litellm.proxy_auth)
            headers.update(proxy_headers)
        except Exception as e:
            verbose_logger.warning("Failed to get proxy auth headers: %s", e)
    num_retries: Final = 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: Final = kwargs.get("cooldown_time", None)
    context_window_fallback_dict: Final = kwargs.get("context_window_fallback_dict", None)
    organization: Final = kwargs.get("organization", None)
    ### VERIFY SSL ###
    ssl_verify: Final = kwargs.get("ssl_verify", None)
    ### CUSTOM MODEL COST ###
    input_cost_per_token: Final = kwargs.get("input_cost_per_token", None)
    output_cost_per_token: Final = kwargs.get("output_cost_per_token", None)
    input_cost_per_second: Final = kwargs.get("input_cost_per_second", None)
    output_cost_per_second: Final = kwargs.get("output_cost_per_second", None)
    ### CUSTOM PROMPT TEMPLATE ###
    initial_prompt_value: Final = kwargs.get("initial_prompt_value", None)
    roles: Final = kwargs.get("roles", None)
    final_prompt_value: Final = kwargs.get("final_prompt_value", None)
    bos_token: Final = kwargs.get("bos_token", None)
    eos_token: Final = kwargs.get("eos_token", None)
    preset_cache_key: Final = kwargs.get("preset_cache_key", None)
    hf_model_name: Final = kwargs.get("hf_model_name", None)
    supports_system_message: Final = kwargs.get("supports_system_message", None)
    base_model: Final = kwargs.get("base_model", None) or (
        model_info.get("base_model") if isinstance(model_info, dict) else None
    )
    ### DISABLE FLAGS ###
    disable_add_transform_inline_image_block: Final = kwargs.get("disable_add_transform_inline_image_block", None)
    ### TEXT COMPLETION CALLS ###
    text_completion: Final = kwargs.get("text_completion", False)
    atext_completion: Final = kwargs.get("atext_completion", False)
    ### ASYNC CALLS ###
    acompletion: Final = kwargs.get("acompletion", False)
    client: Final = kwargs.get("client", None)
    ### Admin Controls ###
    no_log: Final = kwargs.get("no-log", False)
    ### PROMPT MANAGEMENT ###
    prompt_id: Final = cast(str | None, kwargs.get("prompt_id", None))
    prompt_variables: Final = cast(dict | None, kwargs.get("prompt_variables", None))
    litellm_system_prompt: Final = kwargs.get("litellm_system_prompt", 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: Final = get_non_default_completion_params(kwargs=kwargs)
    litellm_params: dict[str, object] = {}  # used to prevent unbound var errors
    ## PROMPT MANAGEMENT HOOKS ##

    from litellm.integrations.anthropic_cache_control_hook import (
        AnthropicCacheControlHook,
    )
    from litellm.types.llms.openai import AllMessageValues

    AnthropicCacheControlHook.maybe_seed_default_injection_points(
        non_default_params=non_default_params,
        messages=cast(list[AllMessageValues], messages),  # cast-ok: completion types messages as a bare List
        model=model,
        custom_llm_provider=cast(str | None, kwargs.get("custom_llm_provider")),  # cast-ok: untyped kwargs
        tools=tools,
        enable_prompt_caching=cast(bool | None, kwargs.get("enable_prompt_caching")),  # cast-ok: untyped kwargs
        api_base=kwargs.get("api_base") or base_url,
    )

    if isinstance(litellm_logging_obj, LiteLLMLoggingObj) and (
        litellm_logging_obj.should_run_prompt_management_hooks(
            prompt_id=prompt_id, non_default_params=non_default_params
        )
    ):
        (
            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,
            prompt_label=kwargs.get("prompt_label", None),
            prompt_version=kwargs.get("prompt_version", None),
            request_kwargs=kwargs,
        )

    ### LITELLM SYSTEM PROMPT ###
    if litellm_system_prompt:
        messages = add_system_prompt_to_messages(
            messages=messages,
            system_prompt=litellm_system_prompt,
            merge_with_first_system=True,
        )

    try:
        if base_url is not None:
            api_base = base_url
        is_router_call: Final = any("model_group" in (kwargs.get(k) or ()) for k in ("metadata", "litellm_metadata"))
        if is_router_call:
            max_retries = 0
        elif num_retries is not None:
            max_retries = num_retries
        logging: Final[LiteLLMLoggingObj] = cast(LiteLLMLoggingObj, litellm_logging_obj)
        fallbacks = fallbacks or litellm.model_fallbacks
        if fallbacks is not None:
            return completion_with_fallbacks(  # pyright: ignore[reportReturnType]  # fallback runner is untyped; resolves to ModelResponse|CustomStreamWrapper at runtime
                **args
            )
        if model_list is not None:
            deployments: Final = [m["litellm_params"] for m in model_list if m["model_name"] == model]
            return litellm.batch_completion_models(  # pyright: ignore[reportReturnType]  # batch path returns a list of responses, outside completion()'s single-response return type
                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: Final = 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"
        _supplemental_provider_params: Final = {k: kwargs[k] for k in OPTIONAL_KWARGS_KEYS if k in kwargs}
        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,
            litellm_params=(
                GenericLiteLLMParams(**_supplemental_provider_params) if _supplemental_provider_params else None
            ),
        )

        ## RESPONSES API BRIDGE LOGIC ## - check early and normalize model name
        responses_api_model_info, model = responses_api_bridge_check(
            model=model,
            custom_llm_provider=custom_llm_provider,
            web_search_options=web_search_options,
            api_base=api_base,
        )

        if not _should_allow_input_examples(custom_llm_provider=custom_llm_provider, model=model):
            tools = _drop_input_examples_from_tools(tools=tools)

        if provider_specific_header is not None:
            headers.update(
                ProviderSpecificHeaderUtils.get_provider_specific_headers(
                    provider_specific_header=provider_specific_header,
                    custom_llm_provider=custom_llm_provider,
                )
            )

        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 = CompletionTimeout.resolve(
            timeout,
            kwargs,
            custom_llm_provider,
            global_timeout=get_configured_request_timeout(),
            supports_httpx_timeout=supports_httpx_timeout,
        )

        ### REGISTER CUSTOM MODEL PRICING -- IF GIVEN ###
        if (
            input_cost_per_token is not None and output_cost_per_token is not None
        ) or input_cost_per_second is not None:
            _register_custom_pricing_for_request(
                model=model,
                custom_llm_provider=custom_llm_provider,
                kwargs=kwargs,
                model_info=model_info,
            )
        ### BUILD CUSTOM PROMPT TEMPLATE -- IF GIVEN ###
        custom_prompt_dict = {}
        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

        messages = update_messages_with_model_file_ids(
            messages=messages,
            model_id=(kwargs.get("model_info") or {}).get("id", None),
            model_file_id_mapping=cast(
                dict[str, dict[str, str]],
                kwargs.get("model_file_id_mapping") or {},
            ),
        )

        provider_config: BaseConfig | None = 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),
                base_model=base_model,
            )

        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
        bridges_to_responses_api: Final = (
            responses_api_model_info.get("mode") == "responses" and not skip_responses_api_bridge
        )
        allowed_openai_params: Final[list[str] | None] = (
            [*(kwargs.get("allowed_openai_params") or []), "reasoning_effort"]
            if bridges_to_responses_api
            else kwargs.get("allowed_openai_params")
        )
        optional_param_args: Final = {
            "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,
            "verbosity": verbosity,
            "thinking": thinking,
            "web_search_options": web_search_options,
            "include_server_side_tool_invocations": (
                include_server_side_tool_invocations
                if include_server_side_tool_invocations is not None
                else kwargs.get("include_server_side_tool_invocations")
            ),
            "safety_identifier": safety_identifier,
            "service_tier": service_tier,
            "store": store,
            "prompt_cache_key": prompt_cache_key,
            "allowed_openai_params": allowed_openai_params,
            "base_model": base_model,
        }
        optional_params = get_optional_params(**optional_param_args, **non_default_params)
        processed_non_default_params: Final = pre_process_non_default_params(
            model=model,
            passed_params=optional_param_args,
            special_params=non_default_params,
            custom_llm_provider=custom_llm_provider,
            additional_drop_params=kwargs.get("additional_drop_params"),
            remove_sensitive_keys=True,
            add_provider_specific_params=True,
            provider_config=provider_config,
        )

        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: Final = 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"),
            litellm_session_id=kwargs.get("litellm_session_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),
            use_litellm_proxy=kwargs.get("use_litellm_proxy", False),
            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"),
            azure_scope=kwargs.get("azure_scope"),
            max_retries=max_retries,
            timeout=timeout,
            litellm_request_debug=kwargs.get("litellm_request_debug", False),
            tpm=kwargs.get("tpm"),
            rpm=kwargs.get("rpm"),
            use_xai_oauth=kwargs.get("use_xai_oauth", False),
            gigachat_scope=kwargs.get("gigachat_scope"),
            gigachat_auth_url=kwargs.get("gigachat_auth_url"),
            gigachat_access_token=kwargs.get("gigachat_access_token"),
            **{key: kwargs[key] for key in FORWARDED_KWARGS_KEYS if key in kwargs},
        )
        cast(LiteLLMLoggingObj, logging).update_environment_variables(
            model=model,
            user=user,
            optional_params=processed_non_default_params,  # [IMPORTANT] - using processed_non_default_params ensures consistent params logged to langfuse for finetuning / eval datasets.
            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,
                prompt_tokens=admission_input_tokens(
                    cast(Mapping[str, object], kwargs)  # cast-ok: completion's **kwargs is untyped
                ),
            )

        ## RESPONSES API BRIDGE LOGIC ## - check if model has 'mode: responses' in litellm.model_cost map
        # Only run the second bridge check if the first one didn't already
        # detect responses mode (e.g. via the "responses/" prefix).  The second
        # check handles cases like gpt-5.4+ with tools+reasoning_effort or
        # reasoningSummary/reasoning_summary without tools (AI SDK) that the first
        # (early) check doesn't cover.
        _reasoning_summary_for_bridge: Final = peek_reasoning_summary_aliases(optional_params)
        if responses_api_model_info.get("mode") != "responses":
            responses_api_model_info, model = responses_api_bridge_check(
                model=model,
                custom_llm_provider=custom_llm_provider,
                web_search_options=web_search_options,
                tools=tools,
                reasoning_effort=reasoning_effort,
                reasoning_summary=_reasoning_summary_for_bridge,
                api_base=api_base,
            )

        # Use base_model (the true underlying model) for Azure model-type
        # detection when the deployment name differs from the model name.
        _azure_detection_model: Final = base_model or model

        if responses_api_model_info.get("mode") == "responses" and not skip_responses_api_bridge:
            from litellm.completion_extras import responses_api_bridge

            optional_params, rs_val = strip_reasoning_summary_aliases_from_optional_params(optional_params)

            if isinstance(reasoning_effort, dict) and "summary" in reasoning_effort:
                optional_params["reasoning_effort"] = reasoning_effort
            elif rs_val is not None:
                eff: Final = optional_params.get("reasoning_effort", reasoning_effort)
                if isinstance(eff, dict):
                    optional_params["reasoning_effort"] = {**eff, "summary": rs_val}
                elif eff is not None:
                    optional_params["reasoning_effort"] = {
                        "effort": eff,
                        "summary": rs_val,
                    }
                else:
                    optional_params["reasoning_effort"] = {"summary": rs_val}

            return responses_api_bridge.completion(  # pyright: ignore[reportReturnType]  # bridge returns a coroutine on the acompletion path; awaited by the async caller
                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,  # pass AsyncOpenAI, OpenAI client
                custom_llm_provider=custom_llm_provider,
                encoding=_get_encoding(),
                stream=stream,
            )
        elif (custom_llm_provider == "openai" and OpenAIGPT5Config.is_model_gpt_5_model(model)) or (
            custom_llm_provider == "azure"
            and litellm.AzureOpenAIGPT5Config.is_model_gpt_5_model(_azure_detection_model)
        ):
            optional_params, _ = strip_reasoning_summary_aliases_from_optional_params(optional_params)

        _dispatch_ctx: Final = _CompletionDispatchContext(
            _azure_detection_model=_azure_detection_model,
            acompletion=acompletion,
            api_base=api_base,
            api_key=api_key,
            api_version=api_version,
            client=client,
            custom_llm_provider=custom_llm_provider,
            custom_prompt_dict=custom_prompt_dict,
            extra_headers=extra_headers,
            headers=headers,
            hf_model_name=hf_model_name,
            kwargs=kwargs,
            litellm_params=litellm_params,
            logger_fn=logger_fn,
            logging=logging,
            max_retries=max_retries,
            max_tokens=max_tokens,
            messages=messages,
            metadata=metadata,
            model=model,
            model_response=model_response,
            optional_params=optional_params,
            organization=organization,
            provider_config=provider_config,
            shared_session=shared_session,
            stream=stream,
            temperature=temperature,
            text_completion=text_completion,
            timeout=timeout,
            top_p=top_p,
        )
        if custom_llm_provider == "azure":
            # azure configs
            ## check dynamic params ##
            response = _complete_azure(_dispatch_ctx)
        elif custom_llm_provider == "azure_text":
            # azure configs
            response = _complete_azure_text(_dispatch_ctx)
        elif custom_llm_provider == "deepseek":
            ## COMPLETION CALL

            response = _complete_deepseek(_dispatch_ctx)

        elif custom_llm_provider == "azure_ai":
            response = _complete_azure_ai(_dispatch_ctx)
        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
        ):
            response = _complete_text_completion_openai(_dispatch_ctx)
        elif custom_llm_provider == "fireworks_ai":
            ## COMPLETION CALL
            response = _complete_fireworks_ai(_dispatch_ctx)
        elif custom_llm_provider == "together_ai":
            response = _complete_together_ai(_dispatch_ctx)
        elif custom_llm_provider == "heroku":
            response = _complete_heroku(_dispatch_ctx)

        elif custom_llm_provider == "ragflow":
            ## COMPLETION CALL - RAGFlow uses HTTP handler to support custom URL paths
            response = _complete_ragflow(_dispatch_ctx)
        elif custom_llm_provider == "xai":
            ## COMPLETION CALL
            response = _complete_xai(_dispatch_ctx)
        elif custom_llm_provider == "groq":
            response = _complete_groq(_dispatch_ctx)
        elif custom_llm_provider == "bedrock_mantle":
            response = _complete_bedrock_mantle(_dispatch_ctx)
        elif custom_llm_provider == "a2a":
            # A2A (Agent-to-Agent) Protocol
            # Resolve agent configuration from registry if model format is "a2a/<agent-name>"
            response = _complete_a2a(_dispatch_ctx)
        elif custom_llm_provider == "gigachat":
            # GigaChat - Sber AI's LLM (Russia)
            response = _complete_gigachat(_dispatch_ctx)

        elif custom_llm_provider == "sap":
            response = _complete_sap(_dispatch_ctx)
        elif custom_llm_provider == "aiohttp_openai":
            # NEW aiohttp provider for 10-100x higher RPS
            response = _complete_aiohttp_openai(_dispatch_ctx)
        elif custom_llm_provider == "cometapi":
            response = _complete_cometapi(_dispatch_ctx)
        elif custom_llm_provider == "minimax":
            response = _complete_minimax(_dispatch_ctx)
        elif custom_llm_provider == "hosted_vllm":
            response = _complete_hosted_vllm(_dispatch_ctx)
        elif (
            # A known OpenAI model name only decides the route when nothing else
            # resolved a provider. get_llm_provider() already maps these names to
            # "openai", so a different value here was asked for explicitly (or came
            # from a register_model entry), and the provider config built for it
            # would be handed to the OpenAI handler.
            (model in litellm.open_ai_chat_completion_models and custom_llm_provider in (None, "openai"))
            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 == "baseten"
            or custom_llm_provider == "sambanova"
            or custom_llm_provider == "volcengine"
            or custom_llm_provider == "anyscale"
            or custom_llm_provider == "openai"
            or custom_llm_provider == "nebius"
            or custom_llm_provider == "wandb"
            or custom_llm_provider == "clarifai"
            or custom_llm_provider in litellm.openai_compatible_providers
            or JSONProviderRegistry.exists(custom_llm_provider)  # JSON-configured 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
            response = _complete_custom_openai(_dispatch_ctx)

        elif custom_llm_provider == "mistral":
            response = _complete_mistral(_dispatch_ctx)
        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")
            response = _complete_replicate(_dispatch_ctx)
        elif "clarifai" in model or custom_llm_provider == "clarifai" or model in litellm.clarifai_models:
            pass  # Deprecated - handled in the openai compatible provider section above
        elif custom_llm_provider == "anthropic_text":
            response = _complete_anthropic_text(_dispatch_ctx)
        elif custom_llm_provider == "anthropic":
            response = _complete_anthropic(_dispatch_ctx)
        elif custom_llm_provider == "nlp_cloud":
            response = _complete_nlp_cloud(_dispatch_ctx)
        elif custom_llm_provider == "aleph_alpha":
            response = _complete_aleph_alpha(_dispatch_ctx)
        elif custom_llm_provider == "cohere_chat" or custom_llm_provider == "cohere":
            response = _complete_cohere_chat(_dispatch_ctx)
        elif custom_llm_provider == "maritalk":
            response = _complete_maritalk(_dispatch_ctx)
        elif custom_llm_provider == "amazon_nova":
            response = _complete_amazon_nova(_dispatch_ctx)
        elif custom_llm_provider == "huggingface":
            response = _complete_huggingface(_dispatch_ctx)
        elif custom_llm_provider == "oci":
            response = _complete_oci(_dispatch_ctx)
        elif custom_llm_provider == "compactifai":
            response = _complete_compactifai(_dispatch_ctx)
        elif custom_llm_provider == "oobabooga":
            response = _complete_oobabooga(_dispatch_ctx)
        elif custom_llm_provider == "databricks":
            response = _complete_databricks(_dispatch_ctx)

        elif custom_llm_provider == "datarobot":
            response = _complete_datarobot(_dispatch_ctx)
        elif custom_llm_provider == "openrouter":
            response = _complete_openrouter(_dispatch_ctx)
        elif custom_llm_provider == "vercel_ai_gateway":
            response = _complete_vercel_ai_gateway(_dispatch_ctx)
        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":
            response = _complete_vertex_ai_beta(_dispatch_ctx)

        elif custom_llm_provider == "vertex_ai":
            response = _complete_vertex_ai(_dispatch_ctx)
        elif custom_llm_provider == "predibase":
            response = _complete_predibase(_dispatch_ctx)
        elif custom_llm_provider == "text-completion-codestral":
            response = _complete_text_completion_codestral(_dispatch_ctx)
        elif custom_llm_provider == "text-completion-inception":
            response = _complete_text_completion_inception(_dispatch_ctx)
        elif custom_llm_provider in ("sagemaker_chat", "sagemaker_nova"):
            # boto3 reads keys from .env
            # sagemaker_chat: HF Messages API endpoints
            # sagemaker_nova: Nova models on SageMaker (OpenAI-compatible)
            response = _complete_sagemaker_chat(_dispatch_ctx)
        elif custom_llm_provider == "sagemaker":
            # boto3 reads keys from .env
            response = _complete_sagemaker(_dispatch_ctx)
        elif custom_llm_provider == "bedrock":
            # boto3 reads keys from .env
            response = _complete_bedrock(_dispatch_ctx)
        elif custom_llm_provider == "watsonx":
            response = _complete_watsonx(_dispatch_ctx)
        elif custom_llm_provider == "watsonx_text":
            response = _complete_watsonx_text(_dispatch_ctx)
        elif custom_llm_provider == "vllm":
            response = _complete_vllm(_dispatch_ctx)
        elif custom_llm_provider == "ollama":
            response = _complete_ollama(_dispatch_ctx)

        elif custom_llm_provider == "ollama_chat":
            response = _complete_ollama_chat(_dispatch_ctx)

        elif custom_llm_provider == "triton":
            response = _complete_triton(_dispatch_ctx)
        elif custom_llm_provider == "cloudflare":
            response = _complete_cloudflare(_dispatch_ctx)

        elif custom_llm_provider == "petals" or model in litellm.petals_models:
            response = _complete_petals(_dispatch_ctx)
        elif custom_llm_provider == "snowflake" or model in litellm.snowflake_models:
            response = _complete_snowflake(_dispatch_ctx)
        elif custom_llm_provider == "gradient_ai":
            response = _complete_gradient_ai(_dispatch_ctx)

        elif custom_llm_provider == "gdc":
            response = _complete_gdc(_dispatch_ctx)
        elif custom_llm_provider == "bytez":
            response = _complete_bytez(_dispatch_ctx)
        elif custom_llm_provider == "lemonade":
            response = _complete_lemonade(_dispatch_ctx)

        elif custom_llm_provider == "ovhcloud" or model in litellm.ovhcloud_models:
            response = _complete_ovhcloud(_dispatch_ctx)

        elif custom_llm_provider == "custom":
            response = _complete_custom(_dispatch_ctx)

        elif custom_llm_provider in litellm._custom_providers:  # Assume custom LLM provider
            # Get the Custom Handler
            response = _complete_custom_providers(_dispatch_ctx)

        elif custom_llm_provider == "langgraph":
            # LangGraph - Agent Runtime Provider
            response = _complete_langgraph(_dispatch_ctx)

        elif custom_llm_provider == "langflow":
            # LangFlow - Visual AI Agent Platform
            response = _complete_langflow(_dispatch_ctx)

        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: Final = kwargs.pop("num_retries", 3)
    # reset retries in .completion()
    kwargs["max_retries"] = 0
    kwargs["num_retries"] = 0
    retry_strategy: Final[Literal["exponential_backoff_retry", "constant_retry"]] = kwargs.pop(
        "retry_strategy", "constant_retry"
    )
    original_function: Final = 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: Final = kwargs.pop("num_retries", 3)
    kwargs["max_retries"] = 0
    kwargs["num_retries"] = 0
    retry_strategy: Final = kwargs.pop("retry_strategy", "constant_retry")
    original_function: Final = kwargs.pop("original_function", completion)
    if retry_strategy == "exponential_backoff_retry":
        retryer = tenacity.AsyncRetrying(
            wait=tenacity.wait_exponential(multiplier=1, max=10),
            stop=tenacity.stop_after_attempt(num_retries),
            reraise=True,
        )
    else:
        retryer = tenacity.AsyncRetrying(stop=tenacity.stop_after_attempt(num_retries), reraise=True)
    return await retryer(original_function, *args, **kwargs)


def responses_with_retries(*args, **kwargs):
    """
    Executes a litellm.responses() with retries
    """
    try:
        import tenacity
    except Exception as e:
        raise Exception(f"tenacity import failed please run `pip install tenacity`. Error{e}")

    from litellm.responses.main import responses

    num_retries: Final = kwargs.pop("num_retries", 3)
    # reset retries in .responses()
    kwargs["max_retries"] = 0
    kwargs["num_retries"] = 0
    retry_strategy: Final[Literal["exponential_backoff_retry", "constant_retry"]] = kwargs.pop(
        "retry_strategy", "constant_retry"
    )
    original_function: Final = kwargs.pop("original_function", responses)
    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 aresponses_with_retries(*args, **kwargs):
    """
    Executes a litellm.aresponses() with retries
    """
    try:
        import tenacity
    except Exception as e:
        raise Exception(f"tenacity import failed please run `pip install tenacity`. Error{e}")

    from litellm.responses.main import aresponses

    num_retries: Final = kwargs.pop("num_retries", 3)
    kwargs["max_retries"] = 0
    kwargs["num_retries"] = 0
    retry_strategy: Final = kwargs.pop("retry_strategy", "constant_retry")
    original_function: Final = kwargs.pop("original_function", aresponses)
    if retry_strategy == "exponential_backoff_retry":
        retryer = tenacity.AsyncRetrying(
            wait=tenacity.wait_exponential(multiplier=1, max=10),
            stop=tenacity.stop_after_attempt(num_retries),
            reraise=True,
        )
    else:
        retryer = tenacity.AsyncRetrying(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: Final = asyncio.get_event_loop()
    model: Final = args[0] if len(args) > 0 else kwargs["model"]
    ### PASS ARGS TO Embedding ###
    kwargs["aembedding"] = True
    custom_llm_provider = kwargs.get("custom_llm_provider", None)
    try:
        # Use a partial function to pass your keyword arguments
        func: Final = partial(embedding, *args, **kwargs)

        # Add the context to the function
        ctx: Final = contextvars.copy_context()
        func_with_context: Final = partial(ctx.run, func)

        _, custom_llm_provider, _, _ = get_llm_provider(
            model=model,
            custom_llm_provider=custom_llm_provider,
            api_base=kwargs.get("api_base", None),
        )

        # Await normally
        init_response: Final = await loop.run_in_executor(None, func_with_context)

        response: EmbeddingResponse | None = 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
        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,
        )


# fmt: off

# Overload for when aembedding=True (returns coroutine)
@overload
def embedding(
    model,
    input=[],
    # Optional params
    dimensions: int | None = None,
    encoding_format: str | None = None,
    timeout: float = 600,  # default to 10 minutes
    # set api_base, api_version, api_key
    api_base: str | None = None,
    api_version: str | None = None,
    api_key: str | None = None,
    api_type: str | None = None,
    caching: bool = False,
    user: str | None = None,
    custom_llm_provider=None,
    litellm_call_id=None,
    logger_fn=None,
    *,
    aembedding: Literal[True],
    **kwargs,
) -> Coroutine[object, object, EmbeddingResponse]: 
    ...


# Overload for when aembedding=False or not specified (returns EmbeddingResponse)
@overload
def embedding(
    model,
    input=[],
    # Optional params
    dimensions: int | None = None,
    encoding_format: str | None = None,
    timeout: float = 600,  # default to 10 minutes
    # set api_base, api_version, api_key
    api_base: str | None = None,
    api_version: str | None = None,
    api_key: str | None = None,
    api_type: str | None = None,
    caching: bool = False,
    user: str | None = None,
    custom_llm_provider=None,
    litellm_call_id=None,
    logger_fn=None,
    *,
    aembedding: Literal[False] = False,
    **kwargs,
) -> EmbeddingResponse: 
    ...

# fmt: on


@client
def embedding(
    model,
    input=[],
    # Optional params
    dimensions: int | None = None,
    encoding_format: str | None = None,
    timeout: float = 600,  # default to 10 minutes
    # set api_base, api_version, api_key
    api_base: str | None = None,
    api_version: str | None = None,
    api_key: str | None = None,
    api_type: str | None = None,
    caching: bool = False,
    user: str | None = None,
    custom_llm_provider=None,
    litellm_call_id=None,
    logger_fn=None,
    **kwargs,
) -> EmbeddingResponse | Coroutine[object, object, 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: Final = kwargs.get("azure", None)
    client: Final = kwargs.pop("client", None)
    shared_session: Final = kwargs.get("shared_session", None)
    max_retries: Final = kwargs.get("max_retries", None)
    litellm_logging_obj: Final[LiteLLMLoggingObj] = kwargs.get("litellm_logging_obj")
    mock_response: Final[list[float] | None] = kwargs.get("mock_response", None)
    azure_ad_token_provider: Final = kwargs.get("azure_ad_token_provider", None)
    aembedding: Final[bool | None] = kwargs.get("aembedding", None)
    extra_headers: Final = kwargs.get("extra_headers", None)
    headers = kwargs.get("headers", None) or extra_headers
    if headers is None:
        headers = {}
    if extra_headers is not None:
        headers.update(extra_headers)
    # Inject proxy auth headers if configured
    if litellm.proxy_auth is not None:
        try:
            proxy_headers: Final = _proxy_auth_headers(litellm.proxy_auth)
            headers.update(proxy_headers)
        except Exception as e:
            verbose_logger.warning("Failed to get proxy auth headers: %s", e)
    ### CUSTOM MODEL COST ###
    input_cost_per_token: Final = kwargs.get("input_cost_per_token", None)
    output_cost_per_token: Final = kwargs.get("output_cost_per_token", None)
    input_cost_per_second: Final = kwargs.get("input_cost_per_second", None)
    openai_params: Final = [
        "user",
        "dimensions",
        "request_timeout",
        "api_base",
        "api_version",
        "api_key",
        "deployment_id",
        "organization",
        "base_url",
        "default_headers",
        "timeout",
        "max_retries",
        "encoding_format",
    ]
    litellm_params: Final = [
        "aembedding",
        "extra_headers",
    ] + all_litellm_params

    default_params: Final = openai_params + litellm_params
    non_default_params: Final = {
        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

    allowed_openai_params: Final[list[str] | None] = kwargs.get("allowed_openai_params", None)
    optional_params: Final = get_optional_params_embeddings(
        model=model,
        user=user,
        dimensions=dimensions,
        encoding_format=encoding_format,
        custom_llm_provider=custom_llm_provider,
        allowed_openai_params=allowed_openai_params,
        **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) or input_cost_per_second is not None:
        _register_custom_pricing_for_request(
            model=model,
            custom_llm_provider=custom_llm_provider,
            kwargs=kwargs,
            model_info=kwargs.get("model_info"),
        )

    litellm_params_dict: Final = get_litellm_params(**kwargs)

    logging: Final[LiteLLMLoggingObj] = litellm_logging_obj
    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: EmbeddingResponse | Coroutine[object, object, EmbeddingResponse] | None = 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: Final = 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 custom_llm_provider == "github_copilot":
            api_key = api_key or litellm.api_key
            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=litellm_params_dict,
            )
        elif (
            custom_llm_provider == "openai"
            or custom_llm_provider == "together_ai"
            or custom_llm_provider == "nvidia_nim"
            or custom_llm_provider == "litellm_proxy"
            or (model in litellm.open_ai_embedding_models and custom_llm_provider is None)
        ):
            api_base = (
                api_base
                or litellm.api_base
                or get_secret_str("OPENAI_BASE_URL")
                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 headers is not None and headers != {}:
                optional_params["extra_headers"] = headers

            requested_encoding_format: Final = (
                encoding_format
                or optional_params.get("encoding_format")
                or get_secret_str("LITELLM_DEFAULT_EMBEDDING_ENCODING_FORMAT")
            )
            if requested_encoding_format is None or requested_encoding_format.strip().lower() == "none":
                optional_params.pop("encoding_format", None)
            else:
                optional_params["encoding_format"] = requested_encoding_format

            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,
                shared_session=shared_session,
            )
        elif custom_llm_provider == "databricks":
            api_base = api_base or litellm.api_base or get_secret("DATABRICKS_API_BASE")

            # set API KEY
            api_key = api_key or litellm.api_key or litellm.databricks_key or get_secret("DATABRICKS_API_KEY")

            ## 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 == "hosted_vllm":
            api_base = api_base or litellm.api_base or get_secret_str("HOSTED_VLLM_API_BASE")

            # set API KEY
            if api_key is None:
                api_key = litellm.api_key or get_secret_str("HOSTED_VLLM_API_KEY")

            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=litellm_params_dict,
                headers=headers or {},
            )
        elif (
            custom_llm_provider == "openai_like"
            or custom_llm_provider == "llamafile"
            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")

            if headers is not None and headers != {}:
                optional_params["extra_headers"] = headers

            ## 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 == "oci":
            if headers is None:
                headers = {}
            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=litellm_params_dict,
                headers=headers,
            )
        elif custom_llm_provider == "cohere" or custom_llm_provider == "cohere_chat":
            cohere_key: Final = (
                api_key
                or litellm.cohere_key
                or get_secret_str("COHERE_API_KEY")
                or get_secret_str("CO_API_KEY")
                or litellm.api_key
            )

            # Use the merged headers variable (already merged at the top of the function)
            # Don't overwrite it with just extra_headers
            if headers is None:
                headers = {}

            response = base_llm_http_handler.embedding(
                model=model,
                input=input,
                custom_llm_provider=custom_llm_provider,
                api_base=api_base,
                api_key=cohere_key,
                logging_obj=logging,
                timeout=timeout,
                model_response=EmbeddingResponse(),
                optional_params=optional_params,
                client=client,
                aembedding=aembedding,
                litellm_params=litellm_params_dict,
                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_str("OPENROUTER_API_KEY")
                or get_secret_str("OR_API_KEY")
            )

            openrouter_site_url: Final = get_secret("OR_SITE_URL") or "https://litellm.ai"
            openrouter_app_name: Final = get_secret("OR_APP_NAME") or "liteLLM"

            openrouter_headers: Final = {
                "HTTP-Referer": openrouter_site_url,
                "X-Title": openrouter_app_name,
            }

            _headers: Final = headers or litellm.headers
            if _headers:
                openrouter_headers.update(_headers)

            headers = openrouter_headers

            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=litellm_params_dict,
                headers=headers,
            )
        elif custom_llm_provider == "vercel_ai_gateway":
            api_base = (
                api_base
                or litellm.api_base
                or get_secret_str("VERCEL_AI_GATEWAY_API_BASE")
                or "https://ai-gateway.vercel.sh/v1"
            )

            api_key = (
                api_key
                or litellm.api_key
                or get_secret_str("VERCEL_AI_GATEWAY_API_KEY")
                or get_secret_str("VERCEL_OIDC_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,
                client=client,
                aembedding=aembedding,
                litellm_params=litellm_params_dict,
                headers=headers,
            )
        elif custom_llm_provider == "huggingface":
            api_key = api_key or litellm.huggingface_key or get_secret("HUGGINGFACE_API_KEY") or litellm.api_key
            response = huggingface_embed.embedding(
                model=model,
                input=input,
                encoding=sys.modules[__name__].encoding,
                api_key=api_key,
                api_base=api_base,
                logging_obj=logging,
                model_response=EmbeddingResponse(),
                optional_params=optional_params,
                client=client,
                aembedding=aembedding,
                litellm_params=litellm_params_dict,
                headers=headers,
            )
        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=_get_encoding(),
                logging_obj=logging,
                optional_params=optional_params,
                model_response=EmbeddingResponse(),
                client=client,
                timeout=timeout,
                aembedding=aembedding,
                litellm_params=litellm_params_dict,
                api_base=api_base,
                print_verbose=print_verbose,
                extra_headers=headers,
                api_key=api_key,
            )
        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: Final = api_key or get_api_key_from_env() or litellm.api_key

            api_base = api_base or litellm.api_base or get_secret_str("GEMINI_API_BASE")

            response = google_batch_embeddings.batch_embeddings(
                model=model,
                input=input,
                encoding=_get_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,
                api_base=api_base,
                client=client,
                extra_headers=headers,
            )

        elif custom_llm_provider == "vertex_ai":
            vertex_ai_project: Final = (
                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: Final = (
                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: Final = (
                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")
            )

            api_base = (
                api_base or litellm.api_base or get_secret_str("VERTEXAI_API_BASE") or get_secret_str("VERTEX_API_BASE")
            )

            try:
                model_info: Final = get_model_info(model=model, custom_llm_provider="vertex_ai")
                uses_embed_content = model_info.get("uses_embed_content", False)
            except Exception:
                uses_embed_content = False

            if uses_embed_content:
                response = google_batch_embeddings.batch_embeddings(
                    model=model,
                    input=input,
                    encoding=_get_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",
                    api_key=None,
                    api_base=api_base,
                    client=client,
                    extra_headers=headers,
                )
            elif (
                "image" in optional_params
                or "video" in optional_params
                or model in vertex_multimodal_embedding.SUPPORTED_MULTIMODAL_EMBEDDING_MODELS
            ):
                response = vertex_multimodal_embedding.multimodal_embedding(
                    model=model,
                    input=input,
                    encoding=_get_encoding(),
                    logging_obj=logging,
                    optional_params=optional_params,
                    litellm_params=litellm_params_dict,
                    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",
                    client=client,
                    api_base=api_base,
                )
            else:
                response = vertex_embedding.embedding(
                    model=model,
                    input=input,
                    encoding=_get_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,
                    api_base=api_base,
                    client=client,
                    litellm_params=litellm_params_dict,
                )
        elif custom_llm_provider == "oobabooga":
            response = oobabooga.embedding(
                model=model,
                input=input,
                encoding=_get_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"

            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,
                    llm_provider="ollama",
                )
            ollama_embeddings_fn: Final = ollama.ollama_aembeddings if aembedding is True else ollama.ollama_embeddings
            response = ollama_embeddings_fn(
                api_base=api_base,
                model=model,
                prompts=input,
                encoding=_get_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=_get_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 == "nebius":
            api_key = api_key or litellm.api_key or get_secret_str("NEBIUS_API_KEY")
            api_base = api_base or litellm.api_base or get_secret_str("NEBIUS_API_BASE") or "api.studio.nebius.ai/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 == "wandb":
            api_key = api_key or litellm.api_key or get_secret_str("WANDB_API_KEY")
            api_base = (
                api_base or litellm.api_base or get_secret_str("WANDB_API_BASE") or "https://api.inference.wandb.ai/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 == "sambanova":
            api_key = api_key or litellm.api_key or get_secret_str("SAMBANOVA_API_KEY")
            api_base = (
                api_base or litellm.api_base or get_secret_str("SAMBANOVA_API_BASE") or "https://api.sambanova.ai/v1"
            )
            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 == "voyage" or custom_llm_provider == "infinity":
            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: Final = 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 == "sap":
            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 == "azure_ai":
            from litellm.llms.azure_ai.common_utils import get_azure_ai_entra_token

            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 get_secret_str("AZURE_AI_API_KEY")
                or get_azure_ai_entra_token(litellm_params=litellm_params_dict)
            )

            ## 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,
            )
        elif custom_llm_provider == "jina_ai":
            if isinstance(input, str):
                transformed_input = [input]
            else:
                transformed_input = input
            response = base_llm_http_handler.embedding(
                model=model,
                input=transformed_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 == "volcengine":
            volcengine_key: Final = (
                api_key or litellm.api_key or get_secret_str("ARK_API_KEY") or get_secret_str("VOLCENGINE_API_KEY")
            )
            if volcengine_key is None:
                raise ValueError(
                    "Missing API key for Volcengine. Set ARK_API_KEY or VOLCENGINE_API_KEY environment variable or pass api_key parameter."
                )
            if extra_headers is not None and isinstance(extra_headers, dict):
                headers = extra_headers
            else:
                headers = {}
            response = base_llm_http_handler.embedding(
                model=model,
                input=input,
                timeout=timeout,
                custom_llm_provider=custom_llm_provider,
                logging_obj=logging,
                api_base=api_base,
                optional_params=optional_params,
                litellm_params={},
                model_response=EmbeddingResponse(),
                api_key=volcengine_key,
                client=client,
                aembedding=aembedding,
                headers=headers,
            )
        elif custom_llm_provider in ("dashscope", "qwencloud", "qwen_ai_platform"):
            from litellm.llms.dashscope.common_utils import (
                missing_dashscope_family_key_message,
                resolve_dashscope_family_api_key,
            )

            dashscope_key: Final = resolve_dashscope_family_api_key(
                custom_llm_provider=custom_llm_provider,
                api_key=api_key or litellm.api_key,
            )
            if dashscope_key is None:
                raise ValueError(missing_dashscope_family_key_message(custom_llm_provider))
            if extra_headers is not None and isinstance(extra_headers, dict):
                headers = extra_headers
            else:
                headers = {}
            response = base_llm_http_handler.embedding(
                model=model,
                input=input,
                timeout=timeout,
                custom_llm_provider=custom_llm_provider,
                logging_obj=logging,
                api_base=api_base,
                optional_params=optional_params,
                litellm_params={},
                model_response=EmbeddingResponse(),
                api_key=dashscope_key,
                client=client,
                aembedding=aembedding,
                headers=headers,
            )
        elif custom_llm_provider == "ovhcloud":
            api_key = api_key or litellm.api_key or get_secret_str("OVHCLOUD_API_KEY")
            api_base = (
                api_base
                or litellm.api_base
                or get_secret_str("OVHCLOUD_API_BASE")
                or "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1"
            )
            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 == "cometapi":
            api_key = api_key or litellm.cometapi_key or get_secret_str("COMETAPI_KEY") or litellm.api_key
            api_base = (
                api_base or litellm.api_base or get_secret_str("COMETAPI_API_BASE") or "https://api.cometapi.com/v1"
            )
            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 in litellm._custom_providers:
            custom_handler: CustomLLM | None = 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)

            handler_fn: Final = custom_handler.embedding if not aembedding else custom_handler.aembedding

            response = handler_fn(
                model=model,
                input=input,
                logging_obj=logging,
                api_base=api_base,
                api_key=api_key,
                timeout=timeout,
                optional_params=optional_params,
                model_response=EmbeddingResponse(),
                print_verbose=print_verbose,
                litellm_params=litellm_params_dict,
            )
        elif custom_llm_provider == "snowflake":
            api_key = api_key or get_secret_str("SNOWFLAKE_JWT")
            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 == "gigachat":
            api_key = (
                api_key
                or litellm.api_key
                or litellm.gigachat_key
                or get_secret_str("GIGACHAT_CREDENTIALS")
                or get_secret_str("GIGACHAT_API_KEY")
            )
            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={"ssl_verify": kwargs.get("ssl_verify", None)},
            )
        elif custom_llm_provider == "perplexity":
            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={},
            )
        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 ################
async def _resolve_dispatched_text_completion_response(
    pending: Coroutine[
        object,
        object,
        TextCompletionResponse | ModelResponse | CustomStreamWrapper | TextCompletionStreamWrapper,
    ],
) -> TextCompletionResponse | ModelResponse | CustomStreamWrapper | TextCompletionStreamWrapper:
    return await pending


async def _resolve_pending_chat_response(pending: Coroutine[object, object, ModelResponse]) -> ModelResponse:
    return await pending


@client
async def atext_completion(*args, **kwargs) -> TextCompletionResponse | TextCompletionStreamWrapper:
    """
    Implemented to handle async streaming for the text completion endpoint
    """
    loop: Final = asyncio.get_event_loop()
    model: Final = 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: Final = partial(text_completion, *args, **kwargs)

        # Add the context to the function
        ctx: Final = contextvars.copy_context()
        func_with_context: Final = partial(ctx.run, func)

        init_response: Final = 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 _resolve_dispatched_text_completion_response(init_response)
        else:
            response = init_response

        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 _resolve_pending_chat_response(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(
    prompt: str | list[str | list[str | list[int]]],  # Required: The prompt(s) to generate completions for.
    model: str | None = None,  # Optional: either `model` or `engine` can be set
    best_of: int | None = None,  # Optional: Generates best_of completions server-side.
    echo: bool | None = None,  # Optional: Echo back the prompt in addition to the completion.
    frequency_penalty: float | None = None,  # Optional: Penalize new tokens based on their existing frequency.
    logit_bias: dict[int, int] | None = None,  # Optional: Modify the likelihood of specified tokens.
    logprobs: int | None = None,  # Optional: Include the log probabilities on the most likely tokens.
    max_tokens: int | None = None,  # Optional: The maximum number of tokens to generate in the completion.
    n: int | None = None,  # Optional: How many completions to generate for each prompt.
    presence_penalty: float
    | None = None,  # Optional: Penalize new tokens based on whether they appear in the text so far.
    stop: str | list[str] | None = None,  # Optional: Sequences where the API will stop generating further tokens.
    stream: bool | None = None,  # Optional: Whether to stream back partial progress.
    stream_options: dict | None = None,
    suffix: str | None = None,  # Optional: The suffix that comes after a completion of inserted text.
    temperature: float | None = None,  # Optional: Sampling temperature to use.
    top_p: float | None = None,  # Optional: Nucleus sampling parameter.
    user: str | None = None,  # Optional: A unique identifier representing your end-user.
    # set api_base, api_version, api_key
    api_base: str | None = None,
    api_version: str | None = None,
    api_key: str | None = None,
    model_list: list | None = None,  # pass in a list of api_base,keys, etc.
    # Optional liteLLM function params
    custom_llm_provider: str | None = 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: Final = 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: Final[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,
        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: Final = 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: Final = [None for x in prompt]  # init responses

                def process_prompt(i, individual_prompt):
                    decoded_prompt: Final = tokenizer.decode(individual_prompt)
                    all_params: Final = {**kwargs, **optional_params}
                    response: Final[TextCompletionResponse] = text_completion(
                        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: Final = [
                        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

                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) or isinstance(prompt[0], int))
    ):
        # Support for token IDs as prompt (list of integers or list of lists of integers)
        messages = [{"role": "user", "content": prompt}]
    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) -> BaseModel | AdapterCompletionStreamWrapper | None:
    """
    Implemented to handle async calls for adapter_completion()
    """
    try:
        translation_obj: CustomLogger | None = None
        for item in litellm.adapters:
            if item["id"] == adapter_id:
                translation_obj = item["adapter"]

        if translation_obj is None:
            raise ValueError(
                f"No matching adapter given. Received 'adapter_id'={adapter_id}, litellm.adapters={litellm.adapters}"
            )

        new_kwargs: Final = translation_obj.translate_completion_input_params(kwargs=kwargs)

        response: Final[ModelResponse | CustomStreamWrapper] = await acompletion(**new_kwargs)
        translated_response: BaseModel | AdapterCompletionStreamWrapper | None = 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


async def aadapter_generate_content(
    **kwargs,
) -> dict[str, object] | AsyncIterator[bytes]:
    from litellm.google_genai.adapters.handler import GenerateContentToCompletionHandler

    coro: Final = cast(
        Coroutine[object, object, dict[str, object] | AsyncIterator[bytes]],
        GenerateContentToCompletionHandler.generate_content_handler(**kwargs, _is_async=True),
    )
    return await coro


def adapter_completion(*, adapter_id: str, **kwargs) -> BaseModel | AdapterCompletionStreamWrapper | None:
    translation_obj: CustomLogger | None = None
    for item in litellm.adapters:
        if item["id"] == adapter_id:
            translation_obj = item["adapter"]

    if translation_obj is None:
        raise ValueError(
            f"No matching adapter given. Received 'adapter_id'={adapter_id}, litellm.adapters={litellm.adapters}"
        )

    new_kwargs: Final = translation_obj.translate_completion_input_params(kwargs=kwargs)

    response: Final[ModelResponse | CustomStreamWrapper] = completion(**new_kwargs)
    translated_response: BaseModel | AdapterCompletionStreamWrapper | None = 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: str | None = None, api_key: str | None = None, **kwargs) -> OpenAIModerationResponse:
    # 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")

    # Extract api_base from kwargs
    api_base: Final = kwargs.get("api_base", None)

    openai_client = kwargs.get("client", None)
    if openai_client is None:
        if api_base is not None:
            openai_client = openai.OpenAI(api_key=api_key, base_url=api_base)
        else:
            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)

    response_dict: Final[dict] = response.model_dump()
    return litellm.utils.LiteLLMResponseObjectHandler.convert_to_moderation_response(
        response_object=response_dict,
    )


@client
async def amoderation(
    input: str,
    model: str | None = None,
    api_key: str | None = None,
    custom_llm_provider: str | None = None,
    **kwargs,
) -> OpenAIModerationResponse:
    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")
    optional_params: Final = GenericLiteLLMParams(**kwargs)
    litellm_logging_obj: Final[LiteLLMLoggingObj | None] = kwargs.get("litellm_logging_obj", None)
    _dynamic_api_base = None
    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

    openai_client: Final = 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(
            is_async=True,
            api_key=api_key,
            api_base=optional_params.api_base or _dynamic_api_base,
        )
    else:
        _openai_client = openai_client

    # update litellm_logging_obj with environment variables
    custom_llm_provider = custom_llm_provider or litellm.LlmProviders.OPENAI.value
    if litellm_logging_obj is not None:
        litellm_logging_obj.update_environment_variables(
            model=model,
            user=kwargs.get("user", None),
            optional_params={},
            litellm_params={
                **kwargs,
            },
            custom_llm_provider=custom_llm_provider,
        )
        moderation_request: Final = {"input": input, "model": model}  # mutable-ok: logged as the raw request body
        litellm_logging_obj.pre_call(
            input=input,
            api_key=api_key,
            additional_args={  # mutable-ok: loggers isinstance-check this payload as a dict
                "complete_input_dict": moderation_request,
                "api_base": str(_openai_client.base_url),
            },
        )

    if model is not None:
        response = await _openai_client.moderations.create(input=input, model=model)
    else:
        response = await _openai_client.moderations.create(input=input)
    response_dict: Final[dict] = response.model_dump()
    return litellm.utils.LiteLLMResponseObjectHandler.convert_to_moderation_response(
        response_object=response_dict,
    )


##### Transcription #######################


@client
async def atranscription(*args, **kwargs) -> TranscriptionResponse:
    """
    Calls openai + azure whisper endpoints.

    Allows router to load balance between them
    """
    loop: Final = asyncio.get_event_loop()
    model: Final = args[0] if len(args) > 0 else kwargs["model"]
    ### PASS ARGS TO Image Generation ###
    kwargs["atranscription"] = True
    file: Final = kwargs.get("file", None)
    custom_llm_provider = None
    try:
        # Use a partial function to pass your keyword arguments
        func: Final = partial(transcription, *args, **kwargs)

        # Add the context to the function
        ctx: Final = contextvars.copy_context()
        func_with_context: Final = partial(ctx.run, func)

        _, custom_llm_provider, _, _ = get_llm_provider(model=model, api_base=kwargs.get("api_base", None))

        # Await normally
        init_response: Final = await loop.run_in_executor(None, func_with_context)
        if isinstance(init_response, dict):
            response = _transcription_response_from_cached_dict(init_response)
        elif isinstance(init_response, TranscriptionResponse):  ## CACHING SCENARIO
            response = init_response
        elif 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)
        if not isinstance(response, TranscriptionResponse):
            raise ValueError(
                f"Invalid response from transcription provider, expected TranscriptionResponse, but got {type(response)}"
            )

        # Store duration in _hidden_params for cost calculation without
        # exposing it in the response body. Adding duration to the response
        # tricks the OpenAI SDK's "best match deserialization" into thinking
        # a plain Transcription is a TranscriptionVerbose/Diarized type.
        if response is not None and not isinstance(response, Coroutine) and file is not None:
            existing_duration: Final = getattr(response, "duration", None)
            if existing_duration is None:
                calculated_duration: Final = calculate_request_duration(file)
                if calculated_duration is not None:
                    response._hidden_params["audio_transcription_duration"] = calculated_duration

        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: str | None = None,
    prompt: str | None = None,
    response_format: Literal["json", "text", "srt", "verbose_json", "vtt"] | None = None,
    timestamp_granularities: list[Literal["word", "segment"]] | None = None,
    temperature: int | None = None,  # openai defaults this to 0
    ## LITELLM PARAMS ##
    user: str | None = None,
    timeout=600,  # default to 10 minutes
    api_key: str | None = None,
    api_base: str | None = None,
    api_version: str | None = None,
    max_retries: int | None = None,
    custom_llm_provider=None,
    **kwargs,
) -> TranscriptionResponse | Coroutine[object, object, TranscriptionResponse]:
    """
    Calls openai + azure whisper endpoints.

    Allows router to load balance between them
    """
    litellm_call_id: Final = kwargs.get("litellm_call_id", None)
    proxy_server_request: Final = kwargs.get("proxy_server_request", None)
    model_info: Final = kwargs.get("model_info", None)
    metadata: Final = kwargs.get("metadata", None)
    atranscription: Final = kwargs.pop("atranscription", False)
    litellm_logging_obj: Final[LiteLLMLoggingObj] = kwargs.get("litellm_logging_obj")
    extra_headers: Final = kwargs.get("extra_headers", None)
    shared_session: Final = kwargs.get("shared_session", None)
    kwargs.pop("tags", [])
    non_default_params: Final = get_non_default_transcription_params(kwargs)

    client: openai.AsyncOpenAI | openai.OpenAI | openai.AzureOpenAI | openai.AsyncAzureOpenAI | None = 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: Final = 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,
        api_key=api_key,
    )

    api_key = dynamic_api_key if dynamic_api_key is not None else api_key

    optional_params: Final = 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,
        **non_default_params,
    )

    litellm_params_dict: Final = 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: TranscriptionResponse | Coroutine[object, object, TranscriptionResponse] | None = None

    provider_config: Final = ProviderConfigManager.get_provider_audio_transcription_config(
        model=model,
        provider=LlmProviders(custom_llm_provider),
    )

    if custom_llm_provider in AZURE_OPENAI_AUDIO_PROVIDERS and provider_config is None:
        # 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: Final = 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,
            custom_llm_provider=custom_llm_provider,
        )
    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_BASE_URL")
            or get_secret("OPENAI_API_BASE")
            or "https://api.openai.com/v1"
        )
        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")
        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,
            provider_config=provider_config,
            litellm_params=litellm_params_dict,
            shared_session=shared_session,
        )
    elif custom_llm_provider == "nvidia_riva":
        # NVIDIA Riva is gRPC-based, not HTTP. It has its own dedicated handler
        # rather than going through base_llm_http_handler.
        response = nvidia_riva_audio_transcriptions.audio_transcriptions(
            model=model,
            audio_file=file,
            optional_params=optional_params,
            litellm_params=litellm_params_dict,
            model_response=model_response,
            atranscription=atranscription,
            timeout=timeout,
            logging_obj=litellm_logging_obj,
            api_base=api_base,
            api_key=api_key,
            provider_config=(
                provider_config if isinstance(provider_config, NvidiaRivaAudioTranscriptionConfig) else None
            ),
        )
    elif custom_llm_provider == "soniox":
        from litellm.llms.soniox.audio_transcription.handler import (
            SonioxAudioTranscriptionHandler,
        )

        response = SonioxAudioTranscriptionHandler().audio_transcriptions(
            model=model,
            audio_file=file,
            optional_params=optional_params,
            litellm_params=litellm_params_dict,
            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,
            headers=extra_headers,
            provider_config=provider_config,
        )
    elif custom_llm_provider == "bedrock":
        from litellm.llms.bedrock.audio_transcription import BedrockAudioTranscriptionRustDispatch

        dispatch: Final = BedrockAudioTranscriptionRustDispatch()
        if atranscription:
            response = dispatch.async_audio_transcriptions(
                model=model,
                audio_file=file,
                api_key=api_key,
                api_base=api_base,
                custom_llm_provider=custom_llm_provider,
                extra_headers=extra_headers,
                optional_params=optional_params,
                timeout=timeout,
            )
        else:
            response = dispatch.audio_transcriptions(
                model=model,
                audio_file=file,
                api_key=api_key,
                api_base=api_base,
                custom_llm_provider=custom_llm_provider,
                extra_headers=extra_headers,
                optional_params=optional_params,
                timeout=timeout,
            )
    elif provider_config is not None:
        response = base_llm_http_handler.audio_transcriptions(
            model=model,
            audio_file=file,
            optional_params=optional_params,
            litellm_params=litellm_params_dict,
            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=custom_llm_provider,
            headers={},
            provider_config=provider_config,
            shared_session=shared_session,
        )

    # Store duration in _hidden_params for cost calculation without
    # exposing it in the response body (see sync path comment above).
    if response is not None and not isinstance(response, Coroutine):
        existing_duration: Final = getattr(response, "duration", None)
        if existing_duration is None:
            calculated_duration: Final = calculate_request_duration(file)
            if calculated_duration is not None:
                response._hidden_params["audio_transcription_duration"] = calculated_duration

    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: Final = asyncio.get_event_loop()
    model: Final = 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: Final = partial(speech, *args, **kwargs)

        # Add the context to the function
        ctx: Final = contextvars.copy_context()
        func_with_context: Final = partial(ctx.run, func)

        _, custom_llm_provider, _, _ = get_llm_provider(model=model, api_base=kwargs.get("api_base", None))

        # Await normally
        init_response: Final = 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
    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(
    model: str,
    input: str,
    voice: str | dict | None = None,
    api_key: str | None = None,
    api_base: str | None = None,
    api_version: str | None = None,
    organization: str | None = None,
    project: str | None = None,
    max_retries: int | None = None,
    metadata: dict | None = None,
    timeout: float | httpx.Timeout | None = None,
    response_format: str | None = None,
    speed: int | None = None,
    instructions: str | None = None,
    client=None,
    headers: dict | None = None,
    custom_llm_provider: str | None = None,
    aspeech: bool | None = None,
    **kwargs,
) -> HttpxBinaryResponseContent | Coroutine[object, object, HttpxBinaryResponseContent]:
    user: Final = kwargs.get("user", None)
    litellm_call_id: Final[str | None] = kwargs.get("litellm_call_id", None)
    proxy_server_request: Final = kwargs.get("proxy_server_request", None)
    extra_headers: Final = kwargs.get("extra_headers", None)
    model_info: Final = kwargs.get("model_info", None)
    shared_session: Final = kwargs.get("shared_session", 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
    )
    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
    if instructions is not None:
        optional_params["instructions"] = instructions

    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: Final = get_litellm_params(metadata=metadata, api_key=api_key or dynamic_api_key, **kwargs)

    # Get provider-specific text-to-speech config and map parameters
    text_to_speech_provider_config = ProviderConfigManager.get_provider_text_to_speech_config(
        model=model,
        provider=litellm.LlmProviders(custom_llm_provider),
    )

    # Map OpenAI params to provider-specific params if config exists
    if text_to_speech_provider_config is not None:
        voice, optional_params = text_to_speech_provider_config.map_openai_params(
            model=model,
            optional_params=optional_params,
            voice=voice,
            drop_params=False,
            kwargs=kwargs,
        )

    logging_obj: Final[LiteLLMLoggingObj] = cast(LiteLLMLoggingObj, kwargs.get("litellm_logging_obj"))
    logging_obj.update_environment_variables(
        model=model,
        user=user,
        optional_params=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: HttpxBinaryResponseContent | Coroutine[object, object, HttpxBinaryResponseContent] | None = None
    if custom_llm_provider == "openai" or (
        custom_llm_provider in litellm.openai_compatible_providers
        and custom_llm_provider not in AZURE_OPENAI_AUDIO_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_BASE_URL")
            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 we check in get_llm_provider and pass in the api key from there
            or litellm.openai_key
            or get_secret("OPENAI_API_KEY")
        )

        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
        )

        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
        )

        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,
            logging_obj=logging_obj,
            client=client,  # pass AsyncOpenAI, OpenAI client
            aspeech=aspeech,
            shared_session=shared_session,
        )
    elif custom_llm_provider in AZURE_OPENAI_AUDIO_PROVIDERS:
        # Check if this is Azure Speech Service (Cognitive Services TTS)
        if model.startswith("speech/"):
            from litellm.llms.azure.text_to_speech.transformation import (
                AzureAVATextToSpeechConfig,
            )

            # Azure AVA (Cognitive Services) Text-to-Speech
            if text_to_speech_provider_config is None:
                raise litellm.BadRequestError(
                    message="Azure Speech Service configuration not found",
                    model=model,
                    llm_provider=custom_llm_provider,
                )

            # Cast to specific Azure config type to access dispatch method
            azure_config: Final = cast(AzureAVATextToSpeechConfig, text_to_speech_provider_config)

            response = azure_config.dispatch_text_to_speech(
                model=model,
                input=input,
                voice=voice,
                optional_params=optional_params,
                litellm_params_dict=litellm_params_dict,
                logging_obj=logging_obj,
                timeout=timeout,
                extra_headers=extra_headers,
                base_llm_http_handler=base_llm_http_handler,
                aspeech=aspeech or False,
                api_base=api_base,
                api_key=api_key,
                **kwargs,
            )
        else:
            # Azure OpenAI TTS
            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")

            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: Final[str | None] = optional_params.get("extra_body", {}).pop(
                "azure_ad_token", None
            ) or get_secret("AZURE_AD_TOKEN")
            azure_ad_token_provider: Final = 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,
                logging_obj=logging_obj,
                client=client,  # pass AsyncOpenAI, OpenAI client
                aspeech=aspeech,
                litellm_params=litellm_params_dict,
            )
    elif custom_llm_provider == "elevenlabs":
        from litellm.llms.elevenlabs.text_to_speech.transformation import (
            ElevenLabsTextToSpeechConfig,
        )

        if text_to_speech_provider_config is None:
            text_to_speech_provider_config = ElevenLabsTextToSpeechConfig()

        elevenlabs_config: Final = cast(ElevenLabsTextToSpeechConfig, text_to_speech_provider_config)

        voice_id = voice if isinstance(voice, str) else None
        if voice_id is None or not voice_id.strip():
            raise litellm.BadRequestError(
                message="'voice' must resolve to an ElevenLabs voice id for ElevenLabs TTS",
                model=model,
                llm_provider=custom_llm_provider,
            )
        voice_id = voice_id.strip()

        query_params: Final = kwargs.pop(ElevenLabsTextToSpeechConfig.ELEVENLABS_QUERY_PARAMS_KEY, None)
        if isinstance(query_params, dict):
            litellm_params_dict[ElevenLabsTextToSpeechConfig.ELEVENLABS_QUERY_PARAMS_KEY] = query_params

        litellm_params_dict[ElevenLabsTextToSpeechConfig.ELEVENLABS_VOICE_ID_KEY] = voice_id

        if api_base is not None:
            litellm_params_dict["api_base"] = api_base
        if api_key is not None:
            litellm_params_dict["api_key"] = api_key

        response = base_llm_http_handler.text_to_speech_handler(
            model=model,
            input=input,
            voice=voice_id,
            text_to_speech_provider_config=elevenlabs_config,
            text_to_speech_optional_params=optional_params,
            custom_llm_provider=custom_llm_provider,
            litellm_params=litellm_params_dict,
            logging_obj=logging_obj,
            timeout=timeout,
            extra_headers=extra_headers,
            client=client,
            _is_async=aspeech or False,
        )
    elif custom_llm_provider == "vertex_ai" or custom_llm_provider == "vertex_ai_beta":
        from litellm.llms.vertex_ai.text_to_speech.transformation import (
            VertexAILyriaTextToSpeechConfig,
            VertexAITextToSpeechConfig,
        )

        generic_optional_params: Final = GenericLiteLLMParams(**kwargs)

        # Handle Gemini models separately (they use speech_to_completion_bridge)
        if "gemini" in model:
            from .endpoints.speech.speech_to_completion_bridge.handler import (
                speech_to_completion_bridge_handler,
            )

            return speech_to_completion_bridge_handler.speech(
                model=model,
                input=input,
                voice=voice,
                optional_params=optional_params,
                litellm_params=litellm_params_dict,
                headers=headers or {},
                logging_obj=logging_obj,
                custom_llm_provider=custom_llm_provider,
            )

        # Vertex AI Text-to-Speech (Google Cloud TTS)
        if text_to_speech_provider_config is None:
            text_to_speech_provider_config = (  # rebind-ok: model metadata selects the Vertex TTS implementation
                VertexAILyriaTextToSpeechConfig()
                if VertexAILyriaTextToSpeechConfig.is_lyria_model(model)
                else VertexAITextToSpeechConfig()
            )

        # Cast to specific Vertex AI config type to access dispatch method
        vertex_config: Final = cast(VertexAITextToSpeechConfig, text_to_speech_provider_config)

        # Store Vertex AI specific params in litellm_params_dict
        litellm_params_dict.update(
            {
                "vertex_project": generic_optional_params.vertex_project,
                "vertex_location": generic_optional_params.vertex_location,
                "vertex_credentials": generic_optional_params.vertex_credentials,
            }
        )

        response = vertex_config.dispatch_text_to_speech(
            model=model,
            input=input,
            voice=voice,
            optional_params=optional_params,
            litellm_params_dict=litellm_params_dict,
            logging_obj=logging_obj,
            timeout=timeout,
            extra_headers=headers,
            base_llm_http_handler=base_llm_http_handler,
            aspeech=aspeech or False,
            api_base=generic_optional_params.api_base,
            api_key=None,  # Vertex AI uses OAuth, not API key
            **kwargs,
        )
    elif custom_llm_provider == "gemini":
        from .endpoints.speech.speech_to_completion_bridge.handler import (
            speech_to_completion_bridge_handler,
        )

        return speech_to_completion_bridge_handler.speech(
            model=model,
            input=input,
            voice=voice,
            optional_params=optional_params,
            litellm_params=litellm_params_dict,
            headers=headers or {},
            logging_obj=logging_obj,
            custom_llm_provider=custom_llm_provider,
        )
    elif custom_llm_provider == "runwayml":
        from litellm.llms.runwayml.text_to_speech.transformation import (
            RunwayMLTextToSpeechConfig,
        )

        # RunwayML Text-to-Speech
        if text_to_speech_provider_config is None:
            raise litellm.BadRequestError(
                message="RunwayML Text-to-Speech configuration not found",
                model=model,
                llm_provider=custom_llm_provider,
            )

        # Cast to specific RunwayML config type to access dispatch method
        runwayml_config: Final = cast(RunwayMLTextToSpeechConfig, text_to_speech_provider_config)

        response = runwayml_config.dispatch_text_to_speech(
            model=model,
            input=input,
            voice=voice,
            optional_params=optional_params,
            litellm_params_dict=litellm_params_dict,
            logging_obj=logging_obj,
            timeout=timeout,
            extra_headers=extra_headers,
            base_llm_http_handler=base_llm_http_handler,
            aspeech=aspeech or False,
            api_base=api_base,
            api_key=api_key,
            **kwargs,
        )
    elif custom_llm_provider == "minimax":
        from litellm.llms.minimax.text_to_speech.transformation import (
            MinimaxTextToSpeechConfig,
        )

        # MiniMax Text-to-Speech
        if text_to_speech_provider_config is None:
            text_to_speech_provider_config = MinimaxTextToSpeechConfig()

        minimax_config: Final = cast(MinimaxTextToSpeechConfig, text_to_speech_provider_config)

        if api_base is not None:
            litellm_params_dict["api_base"] = api_base
        if api_key is not None:
            litellm_params_dict["api_key"] = api_key

        # Convert voice to string if it's a dict (minimax handler expects Optional[str])
        voice_str: str | None = None
        if isinstance(voice, str):
            voice_str = voice
        elif isinstance(voice, dict):
            # Extract voice_id from dict if needed
            voice_str = voice.get("voice_id") or voice.get("id") or voice.get("name")

        response = base_llm_http_handler.text_to_speech_handler(
            model=model,
            input=input,
            voice=voice_str,
            text_to_speech_provider_config=minimax_config,
            text_to_speech_optional_params=optional_params,
            custom_llm_provider=custom_llm_provider,
            litellm_params=litellm_params_dict,
            logging_obj=logging_obj,
            timeout=timeout,
            extra_headers=extra_headers,
            client=client,
            _is_async=aspeech or False,
        )
    elif custom_llm_provider == "mistral":
        from litellm.llms.mistral.audio_speech.transformation import (
            MistralTextToSpeechConfig,
        )

        mistral_tts_config: Final = text_to_speech_provider_config or MistralTextToSpeechConfig()

        if api_base is not None:
            litellm_params_dict["api_base"] = api_base
        if api_key is not None:
            litellm_params_dict["api_key"] = api_key

        mistral_voice: Final[str | None] = voice if isinstance(voice, str) else None

        response = base_llm_http_handler.text_to_speech_handler(
            model=model,
            input=input,
            voice=mistral_voice,
            text_to_speech_provider_config=mistral_tts_config,
            text_to_speech_optional_params=optional_params,
            custom_llm_provider=custom_llm_provider,
            litellm_params=litellm_params_dict,
            logging_obj=logging_obj,
            timeout=timeout,
            extra_headers=extra_headers,
            client=client,
            _is_async=aspeech or False,
        )
    elif custom_llm_provider == "aws_polly":
        from litellm.llms.aws_polly.text_to_speech.transformation import (
            AWSPollyTextToSpeechConfig,
        )

        # AWS Polly Text-to-Speech
        if text_to_speech_provider_config is None:
            text_to_speech_provider_config = AWSPollyTextToSpeechConfig()

        # Cast to specific AWS Polly config type to access dispatch method
        aws_polly_config: Final = cast(AWSPollyTextToSpeechConfig, text_to_speech_provider_config)

        response = aws_polly_config.dispatch_text_to_speech(
            model=model,
            input=input,
            voice=voice,
            optional_params=optional_params,
            litellm_params_dict=litellm_params_dict,
            logging_obj=logging_obj,
            timeout=timeout,
            extra_headers=extra_headers,
            base_llm_http_handler=base_llm_http_handler,
            aspeech=aspeech or False,
            api_base=api_base,
            api_key=api_key,
            **kwargs,
        )

    if response is None:
        raise Exception(
            f"Unable to map the custom llm provider={custom_llm_provider} to a known provider={litellm.provider_list}."
        )
    return response


##### Health Endpoints #######################


async def ahealth_check(
    model_params: dict,
    mode: str | None = "chat",
    prompt: str | None = None,
    input: list | None = 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,
        }
    """
    from litellm.litellm_core_utils.cached_imports import get_litellm_logging_class
    from litellm.litellm_core_utils.health_check_helpers import HealthCheckHelpers

    # Use cached import helper to lazy-load Logging class (only loads when function is called)
    Logging: Final = get_litellm_logging_class()

    # Map modes to their corresponding health check calls
    #########################################################
    # Init request with tracking information
    #########################################################
    litellm_logging_obj: Final = Logging(
        model="",
        messages=[],
        stream=False,
        call_type="acompletion",
        litellm_call_id=str(uuid.uuid4()),
        start_time=datetime.datetime.now(),
        function_id=str(uuid.uuid4()),
        log_raw_request_response=True,
    )
    model_params["litellm_logging_obj"] = litellm_logging_obj
    model_params = HealthCheckHelpers._update_model_params_with_health_check_tracking_information(
        model_params=model_params
    )
    #########################################################
    try:
        model: str | None = 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")

        custom_llm_provider_from_params: Final = model_params.get("custom_llm_provider", None)
        api_base_from_params: Final = model_params.get("api_base", None)
        api_key_from_params: Final = model_params.get("api_key", None)

        model, custom_llm_provider, _, _ = get_llm_provider(
            model=model,
            custom_llm_provider=custom_llm_provider_from_params,
            api_base=api_base_from_params,
            api_key=api_key_from_params,
        )
        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 HealthCheckHelpers.ahealth_check_wildcard_models(
                model=model,
                custom_llm_provider=custom_llm_provider,
                model_params=model_params,
                litellm_logging_obj=litellm_logging_obj,
            )

        mode_handlers: Final = HealthCheckHelpers.get_mode_handlers(
            model=model,
            custom_llm_provider=custom_llm_provider,
            model_params=model_params,
            prompt=prompt,
            input=input,
        )

        if mode in mode_handlers:
            _response: Final = await mode_handlers[mode]()
            # Only process headers for chat mode
            _response_headers: Final[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 = _redact_string(traceback.format_exc())
        if isinstance(stack_trace, str):
            stack_trace = stack_trace[:1000]

        if mode is None:
            return {
                "error": f"error:{e}. Missing `mode`. Set the `mode` for the model - https://docs.litellm.ai/docs/proxy/health#embedding-models  \nstacktrace: {stack_trace}",
                "exception": e,
            }

        error_to_return: Final = str(e) + "\nstack trace: " + stack_trace

        raw_request_typed_dict: Final = litellm_logging_obj.model_call_details.get("raw_request_typed_dict")

        return {
            "error": error_to_return,
            "raw_request_typed_dict": raw_request_typed_dict,
            "exception": e,
        }


####### 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: T201
    except Exception:
        pass


def config_completion(**kwargs):
    if litellm.config_path is not None:
        config_args: Final = 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: Sequence | None = None) -> TextCompletionResponse:
    id: Final = chunks[0]["id"]
    object: Final = chunks[0]["object"]
    created: Final = chunks[0]["created"]
    model: Final = chunks[0]["model"]
    system_fingerprint: Final = chunks[0].get("system_fingerprint", None)
    finish_reason: Final = chunks[-1]["choices"][0]["finish_reason"]
    logprobs: Final = chunks[-1]["choices"][0]["logprobs"]

    content_list: Final = []
    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: Final = "".join(content_list)

    try:
        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")
        prompt_tokens = 0
    completion_tokens: Final = 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: Final = {
        "id": id,
        "object": object,
        "created": created,
        "model": model,
        "system_fingerprint": system_fingerprint,
        "choices": [
            {
                "text": combined_content,
                "index": 0,
                "logprobs": logprobs,
                "finish_reason": finish_reason,
            }
        ],
        "usage": {
            "prompt_tokens": prompt_tokens,
            "completion_tokens": completion_tokens,
            "total_tokens": prompt_tokens + completion_tokens,
        },
    }
    return TextCompletionResponse(**response)


_CALCULATOR_PRICED_REPORTED_COST_PROVIDERS: Final = frozenset({LlmProviders.XAI.value})


def _reported_cost_is_priced_by_calculator(logging_obj: Optional["Logging"]) -> bool:
    if logging_obj is None:
        return False
    provider: Final[object] = logging_obj.model_call_details.get("custom_llm_provider")
    return provider in _CALCULATOR_PRICED_REPORTED_COST_PROVIDERS


def _stream_builder_response_cost(response: ModelResponse, logging_obj: Optional["Logging"]) -> float | None:
    usage_cost: Final = getattr(getattr(response, "usage", None), "cost", None)
    if isinstance(usage_cost, (int, float)) and not _reported_cost_is_priced_by_calculator(logging_obj):
        return float(usage_cost)
    if logging_obj is not None:
        return None
    provider_hint: Final = response._hidden_params.get(  # pyright: ignore[reportPrivateUsage]  # no public accessor
        "custom_llm_provider"
    )
    try:
        return litellm.completion_cost(completion_response=response, custom_llm_provider=provider_hint)
    except Exception:
        return _stream_builder_model_map_cost(response)


def _joined_streamed_citations(streamed_citations: "tuple[object, ...]") -> "list[object]":
    if all(isinstance(citation, list) for citation in streamed_citations):
        return list(streamed_citations)  # mutable-ok: JSON list field
    return [list(streamed_citations)]  # mutable-ok: JSON list field


def _stream_builder_model_map_cost(response: ModelResponse) -> float | None:
    model_name: Final = response.model
    usage: Final = getattr(response, "usage", None)
    if not model_name or not isinstance(usage, Usage):
        return None
    try:
        prompt_cost, completion_tokens_cost = litellm.cost_per_token(model=model_name, usage_object=usage)
        return prompt_cost + completion_tokens_cost
    except Exception:  # noqa: BLE001  # cost_per_token raises bare Exception for unpriceable models
        return None


def _set_stream_builder_response_cost(response: ModelResponse, logging_obj: Optional["Logging"]) -> None:
    response_cost: Final = _stream_builder_response_cost(response, logging_obj)
    if response_cost is None:
        return
    hidden_params: Final = response._hidden_params  # pyright: ignore[reportPrivateUsage]  # no public accessor
    hidden_params["response_cost"] = response_cost


def _stamp_streaming_usage_cost(usage: Usage, response: ModelResponse, logging_obj: Optional["Logging"]) -> None:
    if logging_obj is None:
        return
    if isinstance(getattr(usage, "cost", None), (int, float)):
        return
    computed_cost: Final = logging_obj._response_cost_calculator(result=response)
    if isinstance(computed_cost, (int, float)) and computed_cost > 0:
        setattr(usage, "cost", computed_cost)


def stream_chunk_builder(
    chunks: list,
    messages: Sequence | None = None,
    start_time=None,
    end_time=None,
    logging_obj: Optional["Logging"] = None,
    count_prompt_tokens: Callable[[], int] | None = None,
) -> ModelResponse | TextCompletionResponse | None:
    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: Final = ChunkProcessor(chunks, messages)
        chunks = processor.chunks

        ### BASE-CASE ###
        if len(chunks) == 0:
            return None
        ## Route to the text completion logic
        first_chunk_with_choices: Final = next((c for c in chunks if c.get("choices")), None)
        if first_chunk_with_choices is not None and isinstance(
            first_chunk_with_choices["choices"][0], litellm.utils.TextChoices
        ):  # route to the text completion logic
            return stream_chunk_builder_text_completion(chunks=chunks, messages=messages)

        model: Final = chunks[0]["model"]
        # Initialize the response dictionary
        response: Final = processor.build_base_response(chunks)

        # Fast path for the common text-only streaming case:
        # avoid repeated multi-pass list scans over chunks.
        simple_content_parts: Final[list[str]] = []
        is_simple_text_stream = True
        for chunk in chunks:
            if not chunk.get("choices"):
                continue

            choice = chunk["choices"][0]
            delta_obj = choice.get("delta", {}) if isinstance(choice, dict) else getattr(choice, "delta", {})
            if isinstance(delta_obj, dict):
                delta = delta_obj
            elif hasattr(delta_obj, "model_dump"):
                delta = cast(dict[str, Any], delta_obj.model_dump())
            else:
                delta = {}

            if (
                delta.get("tool_calls") is not None
                or delta.get("function_call") is not None
                or delta.get("reasoning_content") is not None
                or delta.get("thinking_blocks") is not None
                or delta.get("annotations") is not None
                or delta.get("audio") is not None
                or delta.get("images") is not None
                or delta.get("provider_specific_fields") is not None
            ):
                is_simple_text_stream = False
                break

            content = delta.get("content")
            if isinstance(content, str) and content:
                simple_content_parts.append(content)

        if is_simple_text_stream:
            if simple_content_parts:
                response["choices"][0]["message"]["content"] = "".join(simple_content_parts)
            completion_output = get_content_from_model_response(response)
            usage = processor.calculate_usage(
                chunks=chunks,
                model=model,
                completion_output=completion_output,
                messages=messages,
                reasoning_tokens=0,
                count_prompt_tokens=count_prompt_tokens,
            )
            setattr(response, "usage", usage)

            # Propagate provider_specific_fields from chunk hidden params when present.
            for chunk in reversed(chunks):
                if isinstance(chunk, dict):
                    hidden = chunk.get("_hidden_params")
                else:
                    hidden = getattr(chunk, "_hidden_params", None)
                if isinstance(hidden, dict) and "provider_specific_fields" in hidden:
                    response._hidden_params.setdefault("provider_specific_fields", {}).update(
                        hidden["provider_specific_fields"]
                    )
                    break

            _stamp_streaming_usage_cost(usage, response, logging_obj)
            _set_stream_builder_response_cost(response, logging_obj)

            processor.apply_provider_assembled_streaming_metadata(response, chunks, logging_obj)
            return response

        tool_call_chunks: Final = [
            chunk
            for chunk in chunks
            if chunk.get("choices")
            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: Final = 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: Final = [
            chunk
            for chunk in chunks
            if chunk.get("choices")
            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: Final = [
            chunk
            for chunk in chunks
            if chunk.get("choices")
            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)

        thinking_blocks: Final = [
            chunk
            for chunk in chunks
            if chunk.get("choices")
            and "thinking_blocks" in chunk["choices"][0]["delta"]
            and chunk["choices"][0]["delta"]["thinking_blocks"] is not None
        ]

        if len(thinking_blocks) > 0:
            response["choices"][0]["message"]["thinking_blocks"] = processor.get_combined_thinking_content(
                thinking_blocks
            )

        reasoning_chunks: Final = [
            chunk
            for chunk in chunks
            if chunk.get("choices")
            and "reasoning_content" in chunk["choices"][0]["delta"]
            and chunk["choices"][0]["delta"]["reasoning_content"] is not None
        ]

        if len(reasoning_chunks) > 0:
            response["choices"][0]["message"]["reasoning_content"] = processor.get_combined_reasoning_content(
                reasoning_chunks
            )

        annotation_chunks: Final = [
            chunk
            for chunk in chunks
            if chunk.get("choices")
            and "annotations" in chunk["choices"][0]["delta"]
            and chunk["choices"][0]["delta"]["annotations"] is not None
        ]

        if len(annotation_chunks) > 0:
            # Merge annotations from ALL chunks — providers may spread
            # them across multiple streaming chunks or send them only in
            # the final chunk.
            all_annotations: Final[list] = []
            for ac in annotation_chunks:
                all_annotations.extend(ac["choices"][0]["delta"]["annotations"])
            response["choices"][0]["message"]["annotations"] = all_annotations

        audio_chunks: Final = [
            chunk
            for chunk in chunks
            if chunk.get("choices")
            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)

        # Handle image chunks from models like gemini-2.5-flash-image
        # See: https://github.com/BerriAI/litellm/issues/19478
        image_chunks: Final = [
            chunk
            for chunk in chunks
            if chunk.get("choices")
            and "images" in chunk["choices"][0]["delta"]
            and chunk["choices"][0]["delta"]["images"] is not None
        ]

        if len(image_chunks) > 0:
            # Images come complete in a single chunk, collect all images from all chunks
            all_images: Final = []
            for chunk in image_chunks:
                all_images.extend(chunk["choices"][0]["delta"]["images"])
            response["choices"][0]["message"]["images"] = all_images

        # Combine provider_specific_fields from streaming chunks (e.g., web_search_results, citations)
        # See: https://github.com/BerriAI/litellm/issues/17737
        provider_specific_chunks: Final = [
            chunk
            for chunk in chunks
            if chunk.get("choices")
            and "provider_specific_fields" in chunk["choices"][0]["delta"]
            and chunk["choices"][0]["delta"]["provider_specific_fields"] is not None
        ]

        if len(provider_specific_chunks) > 0:
            provider_field_dicts: Final = tuple(
                fields
                for chunk in provider_specific_chunks
                for fields in (chunk["choices"][0]["delta"]["provider_specific_fields"],)
                if isinstance(fields, dict)
            )
            streamed_citations: Final = tuple(
                fields["citation"] for fields in provider_field_dicts if fields.get("citation") is not None
            )
            citation_fields: Final = (
                {"citations": _joined_streamed_citations(streamed_citations)}  # mutable-ok: JSON dict field
                if streamed_citations
                else {}  # mutable-ok: JSON dict field
            )
            combined_provider_fields: Final = {  # mutable-ok: Message.provider_specific_fields is a plain dict field
                key: value
                for fields in (citation_fields, *provider_field_dicts)
                for key, value in fields.items()
                if key != "citation"
            }

            if combined_provider_fields:
                _choice = cast(Choices, response.choices[0])
                _choice.message.provider_specific_fields = combined_provider_fields

        completion_output = get_content_from_model_response(response)

        reasoning_tokens: Final = processor.count_reasoning_tokens(response)

        usage = processor.calculate_usage(
            chunks=chunks,
            model=model,
            completion_output=completion_output,
            messages=messages,
            reasoning_tokens=reasoning_tokens,
            count_prompt_tokens=count_prompt_tokens,
        )

        setattr(response, "usage", usage)

        # Propagate provider_specific_fields from the last chunk (contains provider
        # metadata like traffic_type set during streaming)
        for chunk in reversed(chunks):
            if isinstance(chunk, dict):
                hidden = chunk.get("_hidden_params")
            else:
                hidden = getattr(chunk, "_hidden_params", None)
            if isinstance(hidden, dict) and "provider_specific_fields" in hidden:
                response._hidden_params.setdefault("provider_specific_fields", {}).update(
                    hidden["provider_specific_fields"]
                )
                break

        _stamp_streaming_usage_cost(usage, response, logging_obj)
        _set_stream_builder_response_cost(response, logging_obj)

        processor.apply_provider_assembled_streaming_metadata(response, chunks, logging_obj)
        return response
    except Exception as e:
        verbose_logger.exception("litellm.main.py::stream_chunk_builder() - Exception occurred - %s", e)
        raise litellm.APIError(
            status_code=500,
            message="Error building chunks for logging/streaming usage calculation",
            llm_provider="",
            model="",
        )


########## Token Counting API ##########


async def acount_tokens(
    model: str,
    messages: list[dict[str, object]] | None = None,
    tools: list[dict[str, Any]] | None = None,
    system: str | None = None,
    api_key: str | None = None,
    api_base: str | None = None,
) -> "TokenCountResponse":
    """
    Count tokens for a given model and messages using provider-specific APIs.

    Routes to the appropriate provider's token counting API (OpenAI, Anthropic, etc.)
    for exact token counts. Falls back to local tiktoken-based counting for unsupported providers.

    Args:
        model: The model identifier (e.g., "openai/gpt-4o", "anthropic/claude-3-5-sonnet-20241022")
        messages: The messages to count tokens for (standard chat format)
        tools: Optional tools/functions to include in token count
        system: Optional system message/instructions
        api_key: Optional API key (falls back to environment variable)
        api_base: Optional custom API base URL

    Returns:
        TokenCountResponse with total_tokens and metadata
    """
    from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
    from litellm.types.utils import LlmProviders, TokenCountResponse
    from litellm.utils import ProviderConfigManager

    # Determine provider from model string
    (
        resolved_model,
        custom_llm_provider,
        dynamic_api_key,
        dynamic_api_base,
    ) = get_llm_provider(
        model=model,
        api_base=api_base,
        api_key=api_key,
    )

    # Use dynamic key/base if not explicitly provided
    if api_key is None:
        api_key = dynamic_api_key
    if api_base is None:
        api_base = dynamic_api_base

    # Build deployment dict for the token counter
    deployment: Final[dict[str, object]] = {
        "litellm_params": {
            "model": model,
            "api_key": api_key,
            "api_base": api_base,
        }
    }

    # Try to get provider-specific token counter
    try:
        llm_provider_enum: Final = LlmProviders(custom_llm_provider)
        provider_model_info = ProviderConfigManager.get_provider_model_info(model=model, provider=llm_provider_enum)

        if provider_model_info is not None:
            token_counter_instance: Final = provider_model_info.get_token_counter()
            if token_counter_instance is not None and token_counter_instance.should_use_token_counting_api(
                custom_llm_provider
            ):
                result: Final = await token_counter_instance.count_tokens(
                    model_to_use=resolved_model,
                    messages=messages,
                    contents=None,
                    deployment=deployment,
                    request_model=model,
                    tools=tools,
                    system=system,
                )
                if result is not None and not result.error:
                    return result
    except Exception as e:
        verbose_logger.debug("Provider token counting failed for model=%s, falling back to local: %s", model, e)

    # Fallback to local tiktoken-based token counting
    fallback_messages = messages or []
    if system and fallback_messages:
        fallback_messages = [{"role": "system", "content": system}] + fallback_messages
    local_count: Final = litellm.token_counter(
        model=model,
        messages=fallback_messages,
        tools=tools,
    )

    return TokenCountResponse(
        total_tokens=local_count,
        request_model=model,
        model_used=resolved_model,
        tokenizer_type="local_tokenizer",
    )


# Cache for encoding to avoid repeated __getattr__ calls
_encoding_cache: tiktoken.Encoding | None = None


def _load_module_encoding() -> tiktoken.Encoding:
    import sys

    return sys.modules[__name__].encoding


def _get_encoding() -> tiktoken.Encoding:
    """Get encoding, loading it lazily if needed."""
    global _encoding_cache
    if _encoding_cache is None:
        # Access via module to trigger __getattr__ if not cached
        _encoding_cache = _load_module_encoding()
    return _encoding_cache


def _load_default_encoding() -> tiktoken.Encoding:
    from litellm._lazy_imports import _get_default_encoding

    return _get_default_encoding()


def __getattr__(name: str) -> tiktoken.Encoding:
    """Lazy import handler for main module"""
    if name == "encoding":
        # Use _get_default_encoding which properly sets TIKTOKEN_CACHE_DIR
        # before loading tiktoken, ensuring the local cache is used
        # instead of downloading from the internet
        _encoding: Final = _load_default_encoding()
        # Cache it in the module's __dict__ for subsequent accesses
        import sys

        sys.modules[__name__].__dict__["encoding"] = _encoding
        global _encoding_cache
        _encoding_cache = _encoding
        return _encoding
    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")