two version of R2R are hereHEADmaster

2 files changed, 1974 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/vertex_ai/gemini/transformation.py b/.venv/lib/python3.12/site-packages/litellm/llms/vertex_ai/gemini/transformation.py
new file mode 100644
index 00000000..d6bafc7c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/vertex_ai/gemini/transformation.py
@@ -0,0 +1,479 @@
+"""
+Transformation logic from OpenAI format to Gemini format. 
+
+Why separate file? Make it easy to see how transformation works
+"""
+
+import os
+from typing import TYPE_CHECKING, List, Literal, Optional, Tuple, Union, cast
+
+import httpx
+from pydantic import BaseModel
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.litellm_core_utils.prompt_templates.factory import (
+    convert_to_anthropic_image_obj,
+    convert_to_gemini_tool_call_invoke,
+    convert_to_gemini_tool_call_result,
+    response_schema_prompt,
+)
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.types.files import (
+    get_file_mime_type_for_file_type,
+    get_file_type_from_extension,
+    is_gemini_1_5_accepted_file_type,
+)
+from litellm.types.llms.openai import (
+    AllMessageValues,
+    ChatCompletionAssistantMessage,
+    ChatCompletionImageObject,
+    ChatCompletionTextObject,
+)
+from litellm.types.llms.vertex_ai import *
+from litellm.types.llms.vertex_ai import (
+    GenerationConfig,
+    PartType,
+    RequestBody,
+    SafetSettingsConfig,
+    SystemInstructions,
+    ToolConfig,
+    Tools,
+)
+
+from ..common_utils import (
+    _check_text_in_content,
+    get_supports_response_schema,
+    get_supports_system_message,
+)
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+
+    LiteLLMLoggingObj = _LiteLLMLoggingObj
+else:
+    LiteLLMLoggingObj = Any
+
+
+def _process_gemini_image(image_url: str, format: Optional[str] = None) -> PartType:
+    """
+    Given an image URL, return the appropriate PartType for Gemini
+    """
+
+    try:
+        # GCS URIs
+        if "gs://" in image_url:
+            # Figure out file type
+            extension_with_dot = os.path.splitext(image_url)[-1]  # Ex: ".png"
+            extension = extension_with_dot[1:]  # Ex: "png"
+
+            if not format:
+                file_type = get_file_type_from_extension(extension)
+
+                # Validate the file type is supported by Gemini
+                if not is_gemini_1_5_accepted_file_type(file_type):
+                    raise Exception(f"File type not supported by gemini - {file_type}")
+
+                mime_type = get_file_mime_type_for_file_type(file_type)
+            else:
+                mime_type = format
+            file_data = FileDataType(mime_type=mime_type, file_uri=image_url)
+
+            return PartType(file_data=file_data)
+        elif (
+            "https://" in image_url
+            and (image_type := format or _get_image_mime_type_from_url(image_url))
+            is not None
+        ):
+
+            file_data = FileDataType(file_uri=image_url, mime_type=image_type)
+            return PartType(file_data=file_data)
+        elif "http://" in image_url or "https://" in image_url or "base64" in image_url:
+            # https links for unsupported mime types and base64 images
+            image = convert_to_anthropic_image_obj(image_url, format=format)
+            _blob = BlobType(data=image["data"], mime_type=image["media_type"])
+            return PartType(inline_data=_blob)
+        raise Exception("Invalid image received - {}".format(image_url))
+    except Exception as e:
+        raise e
+
+
+def _get_image_mime_type_from_url(url: str) -> Optional[str]:
+    """
+    Get mime type for common image URLs
+    See gemini mime types: https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/image-understanding#image-requirements
+
+    Supported by Gemini:
+     - PNG (`image/png`)
+     - JPEG (`image/jpeg`)
+     - WebP (`image/webp`)
+    Example:
+        url = https://example.com/image.jpg
+        Returns: image/jpeg
+    """
+    url = url.lower()
+    if url.endswith((".jpg", ".jpeg")):
+        return "image/jpeg"
+    elif url.endswith(".png"):
+        return "image/png"
+    elif url.endswith(".webp"):
+        return "image/webp"
+    elif url.endswith(".mp4"):
+        return "video/mp4"
+    elif url.endswith(".pdf"):
+        return "application/pdf"
+    return None
+
+
+def _gemini_convert_messages_with_history(  # noqa: PLR0915
+    messages: List[AllMessageValues],
+) -> List[ContentType]:
+    """
+    Converts given messages from OpenAI format to Gemini format
+
+    - Parts must be iterable
+    - Roles must alternate b/w 'user' and 'model' (same as anthropic -> merge consecutive roles)
+    - Please ensure that function response turn comes immediately after a function call turn
+    """
+    user_message_types = {"user", "system"}
+    contents: List[ContentType] = []
+
+    last_message_with_tool_calls = None
+
+    msg_i = 0
+    tool_call_responses = []
+    try:
+        while msg_i < len(messages):
+            user_content: List[PartType] = []
+            init_msg_i = msg_i
+            ## MERGE CONSECUTIVE USER CONTENT ##
+            while (
+                msg_i < len(messages) and messages[msg_i]["role"] in user_message_types
+            ):
+                _message_content = messages[msg_i].get("content")
+                if _message_content is not None and isinstance(_message_content, list):
+                    _parts: List[PartType] = []
+                    for element in _message_content:
+                        if (
+                            element["type"] == "text"
+                            and "text" in element
+                            and len(element["text"]) > 0
+                        ):
+                            element = cast(ChatCompletionTextObject, element)
+                            _part = PartType(text=element["text"])
+                            _parts.append(_part)
+                        elif element["type"] == "image_url":
+                            element = cast(ChatCompletionImageObject, element)
+                            img_element = element
+                            format: Optional[str] = None
+                            if isinstance(img_element["image_url"], dict):
+                                image_url = img_element["image_url"]["url"]
+                                format = img_element["image_url"].get("format")
+                            else:
+                                image_url = img_element["image_url"]
+                            _part = _process_gemini_image(
+                                image_url=image_url, format=format
+                            )
+                            _parts.append(_part)
+                    user_content.extend(_parts)
+                elif (
+                    _message_content is not None
+                    and isinstance(_message_content, str)
+                    and len(_message_content) > 0
+                ):
+                    _part = PartType(text=_message_content)
+                    user_content.append(_part)
+
+                msg_i += 1
+
+            if user_content:
+                """
+                check that user_content has 'text' parameter.
+                    - Known Vertex Error: Unable to submit request because it must have a text parameter.
+                    - Relevant Issue: https://github.com/BerriAI/litellm/issues/5515
+                """
+                has_text_in_content = _check_text_in_content(user_content)
+                if has_text_in_content is False:
+                    verbose_logger.warning(
+                        "No text in user content. Adding a blank text to user content, to ensure Gemini doesn't fail the request. Relevant Issue - https://github.com/BerriAI/litellm/issues/5515"
+                    )
+                    user_content.append(
+                        PartType(text=" ")
+                    )  # add a blank text, to ensure Gemini doesn't fail the request.
+                contents.append(ContentType(role="user", parts=user_content))
+            assistant_content = []
+            ## MERGE CONSECUTIVE ASSISTANT CONTENT ##
+            while msg_i < len(messages) and messages[msg_i]["role"] == "assistant":
+                if isinstance(messages[msg_i], BaseModel):
+                    msg_dict: Union[ChatCompletionAssistantMessage, dict] = messages[msg_i].model_dump()  # type: ignore
+                else:
+                    msg_dict = messages[msg_i]  # type: ignore
+                assistant_msg = ChatCompletionAssistantMessage(**msg_dict)  # type: ignore
+                _message_content = assistant_msg.get("content", None)
+                if _message_content is not None and isinstance(_message_content, list):
+                    _parts = []
+                    for element in _message_content:
+                        if isinstance(element, dict):
+                            if element["type"] == "text":
+                                _part = PartType(text=element["text"])
+                                _parts.append(_part)
+                    assistant_content.extend(_parts)
+                elif (
+                    _message_content is not None
+                    and isinstance(_message_content, str)
+                    and _message_content
+                ):
+                    assistant_text = _message_content  # either string or none
+                    assistant_content.append(PartType(text=assistant_text))  # type: ignore
+
+                ## HANDLE ASSISTANT FUNCTION CALL
+                if (
+                    assistant_msg.get("tool_calls", []) is not None
+                    or assistant_msg.get("function_call") is not None
+                ):  # support assistant tool invoke conversion
+                    assistant_content.extend(
+                        convert_to_gemini_tool_call_invoke(assistant_msg)
+                    )
+                    last_message_with_tool_calls = assistant_msg
+
+                msg_i += 1
+
+            if assistant_content:
+                contents.append(ContentType(role="model", parts=assistant_content))
+
+            ## APPEND TOOL CALL MESSAGES ##
+            tool_call_message_roles = ["tool", "function"]
+            if (
+                msg_i < len(messages)
+                and messages[msg_i]["role"] in tool_call_message_roles
+            ):
+                _part = convert_to_gemini_tool_call_result(
+                    messages[msg_i], last_message_with_tool_calls  # type: ignore
+                )
+                msg_i += 1
+                tool_call_responses.append(_part)
+            if msg_i < len(messages) and (
+                messages[msg_i]["role"] not in tool_call_message_roles
+            ):
+                if len(tool_call_responses) > 0:
+                    contents.append(ContentType(parts=tool_call_responses))
+                    tool_call_responses = []
+
+            if msg_i == init_msg_i:  # prevent infinite loops
+                raise Exception(
+                    "Invalid Message passed in - {}. File an issue https://github.com/BerriAI/litellm/issues".format(
+                        messages[msg_i]
+                    )
+                )
+        if len(tool_call_responses) > 0:
+            contents.append(ContentType(parts=tool_call_responses))
+        return contents
+    except Exception as e:
+        raise e
+
+
+def _transform_request_body(
+    messages: List[AllMessageValues],
+    model: str,
+    optional_params: dict,
+    custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"],
+    litellm_params: dict,
+    cached_content: Optional[str],
+) -> RequestBody:
+    """
+    Common transformation logic across sync + async Gemini /generateContent calls.
+    """
+    # Separate system prompt from rest of message
+    supports_system_message = get_supports_system_message(
+        model=model, custom_llm_provider=custom_llm_provider
+    )
+    system_instructions, messages = _transform_system_message(
+        supports_system_message=supports_system_message, messages=messages
+    )
+    # Checks for 'response_schema' support - if passed in
+    if "response_schema" in optional_params:
+        supports_response_schema = get_supports_response_schema(
+            model=model, custom_llm_provider=custom_llm_provider
+        )
+        if supports_response_schema is False:
+            user_response_schema_message = response_schema_prompt(
+                model=model, response_schema=optional_params.get("response_schema")  # type: ignore
+            )
+            messages.append({"role": "user", "content": user_response_schema_message})
+            optional_params.pop("response_schema")
+
+    # Check for any 'litellm_param_*' set during optional param mapping
+
+    remove_keys = []
+    for k, v in optional_params.items():
+        if k.startswith("litellm_param_"):
+            litellm_params.update({k: v})
+            remove_keys.append(k)
+
+    optional_params = {k: v for k, v in optional_params.items() if k not in remove_keys}
+
+    try:
+        if custom_llm_provider == "gemini":
+            content = litellm.GoogleAIStudioGeminiConfig()._transform_messages(
+                messages=messages
+            )
+        else:
+            content = litellm.VertexGeminiConfig()._transform_messages(
+                messages=messages
+            )
+        tools: Optional[Tools] = optional_params.pop("tools", None)
+        tool_choice: Optional[ToolConfig] = optional_params.pop("tool_choice", None)
+        safety_settings: Optional[List[SafetSettingsConfig]] = optional_params.pop(
+            "safety_settings", None
+        )  # type: ignore
+        config_fields = GenerationConfig.__annotations__.keys()
+
+        filtered_params = {
+            k: v for k, v in optional_params.items() if k in config_fields
+        }
+
+        generation_config: Optional[GenerationConfig] = GenerationConfig(
+            **filtered_params
+        )
+        data = RequestBody(contents=content)
+        if system_instructions is not None:
+            data["system_instruction"] = system_instructions
+        if tools is not None:
+            data["tools"] = tools
+        if tool_choice is not None:
+            data["toolConfig"] = tool_choice
+        if safety_settings is not None:
+            data["safetySettings"] = safety_settings
+        if generation_config is not None:
+            data["generationConfig"] = generation_config
+        if cached_content is not None:
+            data["cachedContent"] = cached_content
+    except Exception as e:
+        raise e
+
+    return data
+
+
+def sync_transform_request_body(
+    gemini_api_key: Optional[str],
+    messages: List[AllMessageValues],
+    api_base: Optional[str],
+    model: str,
+    client: Optional[HTTPHandler],
+    timeout: Optional[Union[float, httpx.Timeout]],
+    extra_headers: Optional[dict],
+    optional_params: dict,
+    logging_obj: LiteLLMLoggingObj,
+    custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"],
+    litellm_params: dict,
+) -> RequestBody:
+    from ..context_caching.vertex_ai_context_caching import ContextCachingEndpoints
+
+    context_caching_endpoints = ContextCachingEndpoints()
+
+    if gemini_api_key is not None:
+        messages, cached_content = context_caching_endpoints.check_and_create_cache(
+            messages=messages,
+            api_key=gemini_api_key,
+            api_base=api_base,
+            model=model,
+            client=client,
+            timeout=timeout,
+            extra_headers=extra_headers,
+            cached_content=optional_params.pop("cached_content", None),
+            logging_obj=logging_obj,
+        )
+    else:  # [TODO] implement context caching for gemini as well
+        cached_content = optional_params.pop("cached_content", None)
+
+    return _transform_request_body(
+        messages=messages,
+        model=model,
+        custom_llm_provider=custom_llm_provider,
+        litellm_params=litellm_params,
+        cached_content=cached_content,
+        optional_params=optional_params,
+    )
+
+
+async def async_transform_request_body(
+    gemini_api_key: Optional[str],
+    messages: List[AllMessageValues],
+    api_base: Optional[str],
+    model: str,
+    client: Optional[AsyncHTTPHandler],
+    timeout: Optional[Union[float, httpx.Timeout]],
+    extra_headers: Optional[dict],
+    optional_params: dict,
+    logging_obj: litellm.litellm_core_utils.litellm_logging.Logging,  # type: ignore
+    custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"],
+    litellm_params: dict,
+) -> RequestBody:
+    from ..context_caching.vertex_ai_context_caching import ContextCachingEndpoints
+
+    context_caching_endpoints = ContextCachingEndpoints()
+
+    if gemini_api_key is not None:
+        messages, cached_content = (
+            await context_caching_endpoints.async_check_and_create_cache(
+                messages=messages,
+                api_key=gemini_api_key,
+                api_base=api_base,
+                model=model,
+                client=client,
+                timeout=timeout,
+                extra_headers=extra_headers,
+                cached_content=optional_params.pop("cached_content", None),
+                logging_obj=logging_obj,
+            )
+        )
+    else:  # [TODO] implement context caching for gemini as well
+        cached_content = optional_params.pop("cached_content", None)
+
+    return _transform_request_body(
+        messages=messages,
+        model=model,
+        custom_llm_provider=custom_llm_provider,
+        litellm_params=litellm_params,
+        cached_content=cached_content,
+        optional_params=optional_params,
+    )
+
+
+def _transform_system_message(
+    supports_system_message: bool, messages: List[AllMessageValues]
+) -> Tuple[Optional[SystemInstructions], List[AllMessageValues]]:
+    """
+    Extracts the system message from the openai message list.
+
+    Converts the system message to Gemini format
+
+    Returns
+    - system_content_blocks: Optional[SystemInstructions] - the system message list in Gemini format.
+    - messages: List[AllMessageValues] - filtered list of messages in OpenAI format (transformed separately)
+    """
+    # Separate system prompt from rest of message
+    system_prompt_indices = []
+    system_content_blocks: List[PartType] = []
+    if supports_system_message is True:
+        for idx, message in enumerate(messages):
+            if message["role"] == "system":
+                _system_content_block: Optional[PartType] = None
+                if isinstance(message["content"], str):
+                    _system_content_block = PartType(text=message["content"])
+                elif isinstance(message["content"], list):
+                    system_text = ""
+                    for content in message["content"]:
+                        system_text += content.get("text") or ""
+                    _system_content_block = PartType(text=system_text)
+                if _system_content_block is not None:
+                    system_content_blocks.append(_system_content_block)
+                    system_prompt_indices.append(idx)
+        if len(system_prompt_indices) > 0:
+            for idx in reversed(system_prompt_indices):
+                messages.pop(idx)
+
+    if len(system_content_blocks) > 0:
+        return SystemInstructions(parts=system_content_blocks), messages
+
+    return None, messages
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/.venv/lib/python3.12/site-packages/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
new file mode 100644
index 00000000..9ac1b1ff
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
@@ -0,0 +1,1495 @@
+# What is this?
+## httpx client for vertex ai calls
+## Initial implementation - covers gemini + image gen calls
+import json
+import uuid
+from copy import deepcopy
+from functools import partial
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    List,
+    Literal,
+    Optional,
+    Tuple,
+    Union,
+    cast,
+)
+
+import httpx  # type: ignore
+
+import litellm
+import litellm.litellm_core_utils
+import litellm.litellm_core_utils.litellm_logging
+from litellm import verbose_logger
+from litellm.litellm_core_utils.core_helpers import map_finish_reason
+from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
+from litellm.llms.custom_httpx.http_handler import (
+    AsyncHTTPHandler,
+    HTTPHandler,
+    get_async_httpx_client,
+)
+from litellm.types.llms.openai import (
+    AllMessageValues,
+    ChatCompletionResponseMessage,
+    ChatCompletionToolCallChunk,
+    ChatCompletionToolCallFunctionChunk,
+    ChatCompletionToolParamFunctionChunk,
+    ChatCompletionUsageBlock,
+)
+from litellm.types.llms.vertex_ai import (
+    VERTEX_CREDENTIALS_TYPES,
+    Candidates,
+    ContentType,
+    FunctionCallingConfig,
+    FunctionDeclaration,
+    GenerateContentResponseBody,
+    HttpxPartType,
+    LogprobsResult,
+    ToolConfig,
+    Tools,
+)
+from litellm.types.utils import (
+    ChatCompletionTokenLogprob,
+    ChoiceLogprobs,
+    GenericStreamingChunk,
+    PromptTokensDetailsWrapper,
+    TopLogprob,
+    Usage,
+)
+from litellm.utils import CustomStreamWrapper, ModelResponse
+
+from ....utils import _remove_additional_properties, _remove_strict_from_schema
+from ..common_utils import VertexAIError, _build_vertex_schema
+from ..vertex_llm_base import VertexBase
+from .transformation import (
+    _gemini_convert_messages_with_history,
+    async_transform_request_body,
+    sync_transform_request_body,
+)
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+
+    LoggingClass = LiteLLMLoggingObj
+else:
+    LoggingClass = Any
+
+
+class VertexAIBaseConfig:
+    def get_mapped_special_auth_params(self) -> dict:
+        """
+        Common auth params across bedrock/vertex_ai/azure/watsonx
+        """
+        return {"project": "vertex_project", "region_name": "vertex_location"}
+
+    def map_special_auth_params(self, non_default_params: dict, optional_params: dict):
+        mapped_params = self.get_mapped_special_auth_params()
+
+        for param, value in non_default_params.items():
+            if param in mapped_params:
+                optional_params[mapped_params[param]] = value
+        return optional_params
+
+    def get_eu_regions(self) -> List[str]:
+        """
+        Source: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/locations#available-regions
+        """
+        return [
+            "europe-central2",
+            "europe-north1",
+            "europe-southwest1",
+            "europe-west1",
+            "europe-west2",
+            "europe-west3",
+            "europe-west4",
+            "europe-west6",
+            "europe-west8",
+            "europe-west9",
+        ]
+
+    def get_us_regions(self) -> List[str]:
+        """
+        Source: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/locations#available-regions
+        """
+        return [
+            "us-central1",
+            "us-east1",
+            "us-east4",
+            "us-east5",
+            "us-south1",
+            "us-west1",
+            "us-west4",
+            "us-west5",
+        ]
+
+
+class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
+    """
+    Reference: https://cloud.google.com/vertex-ai/docs/generative-ai/chat/test-chat-prompts
+    Reference: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference
+
+    The class `VertexAIConfig` provides configuration for the VertexAI's API interface. Below are the parameters:
+
+    - `temperature` (float): This controls the degree of randomness in token selection.
+
+    - `max_output_tokens` (integer): This sets the limitation for the maximum amount of token in the text output. In this case, the default value is 256.
+
+    - `top_p` (float): The tokens are selected from the most probable to the least probable until the sum of their probabilities equals the `top_p` value. Default is 0.95.
+
+    - `top_k` (integer): The value of `top_k` determines how many of the most probable tokens are considered in the selection. For example, a `top_k` of 1 means the selected token is the most probable among all tokens. The default value is 40.
+
+    - `response_mime_type` (str): The MIME type of the response. The default value is 'text/plain'.
+
+    - `candidate_count` (int): Number of generated responses to return.
+
+    - `stop_sequences` (List[str]): The set of character sequences (up to 5) that will stop output generation. If specified, the API will stop at the first appearance of a stop sequence. The stop sequence will not be included as part of the response.
+
+    - `frequency_penalty` (float): This parameter is used to penalize the model from repeating the same output. The default value is 0.0.
+
+    - `presence_penalty` (float): This parameter is used to penalize the model from generating the same output as the input. The default value is 0.0.
+
+    - `seed` (int): The seed value is used to help generate the same output for the same input. The default value is None.
+
+    Note: Please make sure to modify the default parameters as required for your use case.
+    """
+
+    temperature: Optional[float] = None
+    max_output_tokens: Optional[int] = None
+    top_p: Optional[float] = None
+    top_k: Optional[int] = None
+    response_mime_type: Optional[str] = None
+    candidate_count: Optional[int] = None
+    stop_sequences: Optional[list] = None
+    frequency_penalty: Optional[float] = None
+    presence_penalty: Optional[float] = None
+    seed: Optional[int] = None
+
+    def __init__(
+        self,
+        temperature: Optional[float] = None,
+        max_output_tokens: Optional[int] = None,
+        top_p: Optional[float] = None,
+        top_k: Optional[int] = None,
+        response_mime_type: Optional[str] = None,
+        candidate_count: Optional[int] = None,
+        stop_sequences: Optional[list] = None,
+        frequency_penalty: Optional[float] = None,
+        presence_penalty: Optional[float] = None,
+        seed: Optional[int] = None,
+    ) -> None:
+        locals_ = locals().copy()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return super().get_config()
+
+    def get_supported_openai_params(self, model: str) -> List[str]:
+        return [
+            "temperature",
+            "top_p",
+            "max_tokens",
+            "max_completion_tokens",
+            "stream",
+            "tools",
+            "functions",
+            "tool_choice",
+            "response_format",
+            "n",
+            "stop",
+            "frequency_penalty",
+            "presence_penalty",
+            "extra_headers",
+            "seed",
+            "logprobs",
+        ]
+
+    def map_tool_choice_values(
+        self, model: str, tool_choice: Union[str, dict]
+    ) -> Optional[ToolConfig]:
+        if tool_choice == "none":
+            return ToolConfig(functionCallingConfig=FunctionCallingConfig(mode="NONE"))
+        elif tool_choice == "required":
+            return ToolConfig(functionCallingConfig=FunctionCallingConfig(mode="ANY"))
+        elif tool_choice == "auto":
+            return ToolConfig(functionCallingConfig=FunctionCallingConfig(mode="AUTO"))
+        elif isinstance(tool_choice, dict):
+            # only supported for anthropic + mistral models - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html
+            name = tool_choice.get("function", {}).get("name", "")
+            return ToolConfig(
+                functionCallingConfig=FunctionCallingConfig(
+                    mode="ANY", allowed_function_names=[name]
+                )
+            )
+        else:
+            raise litellm.utils.UnsupportedParamsError(
+                message="VertexAI doesn't support tool_choice={}. Supported tool_choice values=['auto', 'required', json object]. To drop it from the call, set `litellm.drop_params = True.".format(
+                    tool_choice
+                ),
+                status_code=400,
+            )
+
+    def _map_function(self, value: List[dict]) -> List[Tools]:
+        gtool_func_declarations = []
+        googleSearch: Optional[dict] = None
+        googleSearchRetrieval: Optional[dict] = None
+        code_execution: Optional[dict] = None
+        # remove 'additionalProperties' from tools
+        value = _remove_additional_properties(value)
+        # remove 'strict' from tools
+        value = _remove_strict_from_schema(value)
+
+        for tool in value:
+            openai_function_object: Optional[ChatCompletionToolParamFunctionChunk] = (
+                None
+            )
+            if "function" in tool:  # tools list
+                _openai_function_object = ChatCompletionToolParamFunctionChunk(  # type: ignore
+                    **tool["function"]
+                )
+
+                if (
+                    "parameters" in _openai_function_object
+                    and _openai_function_object["parameters"] is not None
+                ):  # OPENAI accepts JSON Schema, Google accepts OpenAPI schema.
+                    _openai_function_object["parameters"] = _build_vertex_schema(
+                        _openai_function_object["parameters"]
+                    )
+
+                openai_function_object = _openai_function_object
+
+            elif "name" in tool:  # functions list
+                openai_function_object = ChatCompletionToolParamFunctionChunk(**tool)  # type: ignore
+
+            # check if grounding
+            if tool.get("googleSearch", None) is not None:
+                googleSearch = tool["googleSearch"]
+            elif tool.get("googleSearchRetrieval", None) is not None:
+                googleSearchRetrieval = tool["googleSearchRetrieval"]
+            elif tool.get("code_execution", None) is not None:
+                code_execution = tool["code_execution"]
+            elif openai_function_object is not None:
+                gtool_func_declaration = FunctionDeclaration(
+                    name=openai_function_object["name"],
+                )
+                _description = openai_function_object.get("description", None)
+                _parameters = openai_function_object.get("parameters", None)
+                if _description is not None:
+                    gtool_func_declaration["description"] = _description
+                if _parameters is not None:
+                    gtool_func_declaration["parameters"] = _parameters
+                gtool_func_declarations.append(gtool_func_declaration)
+            else:
+                # assume it's a provider-specific param
+                verbose_logger.warning(
+                    "Invalid tool={}. Use `litellm.set_verbose` or `litellm --detailed_debug` to see raw request."
+                )
+
+        _tools = Tools(
+            function_declarations=gtool_func_declarations,
+        )
+        if googleSearch is not None:
+            _tools["googleSearch"] = googleSearch
+        if googleSearchRetrieval is not None:
+            _tools["googleSearchRetrieval"] = googleSearchRetrieval
+        if code_execution is not None:
+            _tools["code_execution"] = code_execution
+        return [_tools]
+
+    def _map_response_schema(self, value: dict) -> dict:
+        old_schema = deepcopy(value)
+        if isinstance(old_schema, list):
+            for item in old_schema:
+                if isinstance(item, dict):
+                    item = _build_vertex_schema(parameters=item)
+        elif isinstance(old_schema, dict):
+            old_schema = _build_vertex_schema(parameters=old_schema)
+        return old_schema
+
+    def map_openai_params(
+        self,
+        non_default_params: Dict,
+        optional_params: Dict,
+        model: str,
+        drop_params: bool,
+    ) -> Dict:
+        for param, value in non_default_params.items():
+            if param == "temperature":
+                optional_params["temperature"] = value
+            if param == "top_p":
+                optional_params["top_p"] = value
+            if (
+                param == "stream" and value is True
+            ):  # sending stream = False, can cause it to get passed unchecked and raise issues
+                optional_params["stream"] = value
+            if param == "n":
+                optional_params["candidate_count"] = value
+            if param == "stop":
+                if isinstance(value, str):
+                    optional_params["stop_sequences"] = [value]
+                elif isinstance(value, list):
+                    optional_params["stop_sequences"] = value
+            if param == "max_tokens" or param == "max_completion_tokens":
+                optional_params["max_output_tokens"] = value
+            if param == "response_format" and isinstance(value, dict):  # type: ignore
+                # remove 'additionalProperties' from json schema
+                value = _remove_additional_properties(value)
+                # remove 'strict' from json schema
+                value = _remove_strict_from_schema(value)
+                if value["type"] == "json_object":
+                    optional_params["response_mime_type"] = "application/json"
+                elif value["type"] == "text":
+                    optional_params["response_mime_type"] = "text/plain"
+                if "response_schema" in value:
+                    optional_params["response_mime_type"] = "application/json"
+                    optional_params["response_schema"] = value["response_schema"]
+                elif value["type"] == "json_schema":  # type: ignore
+                    if "json_schema" in value and "schema" in value["json_schema"]:  # type: ignore
+                        optional_params["response_mime_type"] = "application/json"
+                        optional_params["response_schema"] = value["json_schema"]["schema"]  # type: ignore
+
+                if "response_schema" in optional_params and isinstance(
+                    optional_params["response_schema"], dict
+                ):
+                    optional_params["response_schema"] = self._map_response_schema(
+                        value=optional_params["response_schema"]
+                    )
+            if param == "frequency_penalty":
+                optional_params["frequency_penalty"] = value
+            if param == "presence_penalty":
+                optional_params["presence_penalty"] = value
+            if param == "logprobs":
+                optional_params["responseLogprobs"] = value
+            if (param == "tools" or param == "functions") and isinstance(value, list):
+                optional_params["tools"] = self._map_function(value=value)
+                optional_params["litellm_param_is_function_call"] = (
+                    True if param == "functions" else False
+                )
+            if param == "tool_choice" and (
+                isinstance(value, str) or isinstance(value, dict)
+            ):
+                _tool_choice_value = self.map_tool_choice_values(
+                    model=model, tool_choice=value  # type: ignore
+                )
+                if _tool_choice_value is not None:
+                    optional_params["tool_choice"] = _tool_choice_value
+            if param == "seed":
+                optional_params["seed"] = value
+
+        if litellm.vertex_ai_safety_settings is not None:
+            optional_params["safety_settings"] = litellm.vertex_ai_safety_settings
+        return optional_params
+
+    def get_mapped_special_auth_params(self) -> dict:
+        """
+        Common auth params across bedrock/vertex_ai/azure/watsonx
+        """
+        return {"project": "vertex_project", "region_name": "vertex_location"}
+
+    def map_special_auth_params(self, non_default_params: dict, optional_params: dict):
+        mapped_params = self.get_mapped_special_auth_params()
+
+        for param, value in non_default_params.items():
+            if param in mapped_params:
+                optional_params[mapped_params[param]] = value
+        return optional_params
+
+    def get_eu_regions(self) -> List[str]:
+        """
+        Source: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/locations#available-regions
+        """
+        return [
+            "europe-central2",
+            "europe-north1",
+            "europe-southwest1",
+            "europe-west1",
+            "europe-west2",
+            "europe-west3",
+            "europe-west4",
+            "europe-west6",
+            "europe-west8",
+            "europe-west9",
+        ]
+
+    def get_flagged_finish_reasons(self) -> Dict[str, str]:
+        """
+        Return Dictionary of finish reasons which indicate response was flagged
+
+        and what it means
+        """
+        return {
+            "SAFETY": "The token generation was stopped as the response was flagged for safety reasons. NOTE: When streaming the Candidate.content will be empty if content filters blocked the output.",
+            "RECITATION": "The token generation was stopped as the response was flagged for unauthorized citations.",
+            "BLOCKLIST": "The token generation was stopped as the response was flagged for the terms which are included from the terminology blocklist.",
+            "PROHIBITED_CONTENT": "The token generation was stopped as the response was flagged for the prohibited contents.",
+            "SPII": "The token generation was stopped as the response was flagged for Sensitive Personally Identifiable Information (SPII) contents.",
+        }
+
+    def translate_exception_str(self, exception_string: str):
+        if (
+            "GenerateContentRequest.tools[0].function_declarations[0].parameters.properties: should be non-empty for OBJECT type"
+            in exception_string
+        ):
+            return "'properties' field in tools[0]['function']['parameters'] cannot be empty if 'type' == 'object'. Received error from provider - {}".format(
+                exception_string
+            )
+        return exception_string
+
+    def get_assistant_content_message(
+        self, parts: List[HttpxPartType]
+    ) -> Optional[str]:
+        _content_str = ""
+        for part in parts:
+            if "text" in part:
+                _content_str += part["text"]
+        if _content_str:
+            return _content_str
+        return None
+
+    def _transform_parts(
+        self,
+        parts: List[HttpxPartType],
+        index: int,
+        is_function_call: Optional[bool],
+    ) -> Tuple[
+        Optional[ChatCompletionToolCallFunctionChunk],
+        Optional[List[ChatCompletionToolCallChunk]],
+    ]:
+        function: Optional[ChatCompletionToolCallFunctionChunk] = None
+        _tools: List[ChatCompletionToolCallChunk] = []
+        for part in parts:
+            if "functionCall" in part:
+                _function_chunk = ChatCompletionToolCallFunctionChunk(
+                    name=part["functionCall"]["name"],
+                    arguments=json.dumps(part["functionCall"]["args"]),
+                )
+                if is_function_call is True:
+                    function = _function_chunk
+                else:
+                    _tool_response_chunk = ChatCompletionToolCallChunk(
+                        id=f"call_{str(uuid.uuid4())}",
+                        type="function",
+                        function=_function_chunk,
+                        index=index,
+                    )
+                    _tools.append(_tool_response_chunk)
+        if len(_tools) == 0:
+            tools: Optional[List[ChatCompletionToolCallChunk]] = None
+        else:
+            tools = _tools
+        return function, tools
+
+    def _transform_logprobs(
+        self, logprobs_result: Optional[LogprobsResult]
+    ) -> Optional[ChoiceLogprobs]:
+        if logprobs_result is None:
+            return None
+        if "chosenCandidates" not in logprobs_result:
+            return None
+        logprobs_list: List[ChatCompletionTokenLogprob] = []
+        for index, candidate in enumerate(logprobs_result["chosenCandidates"]):
+            top_logprobs: List[TopLogprob] = []
+            if "topCandidates" in logprobs_result and index < len(
+                logprobs_result["topCandidates"]
+            ):
+                top_candidates_for_index = logprobs_result["topCandidates"][index][
+                    "candidates"
+                ]
+
+                for options in top_candidates_for_index:
+                    top_logprobs.append(
+                        TopLogprob(
+                            token=options["token"], logprob=options["logProbability"]
+                        )
+                    )
+            logprobs_list.append(
+                ChatCompletionTokenLogprob(
+                    token=candidate["token"],
+                    logprob=candidate["logProbability"],
+                    top_logprobs=top_logprobs,
+                )
+            )
+        return ChoiceLogprobs(content=logprobs_list)
+
+    def _handle_blocked_response(
+        self,
+        model_response: ModelResponse,
+        completion_response: GenerateContentResponseBody,
+    ) -> ModelResponse:
+        # If set, the prompt was blocked and no candidates are returned. Rephrase your prompt
+        model_response.choices[0].finish_reason = "content_filter"
+
+        chat_completion_message: ChatCompletionResponseMessage = {
+            "role": "assistant",
+            "content": None,
+        }
+
+        choice = litellm.Choices(
+            finish_reason="content_filter",
+            index=0,
+            message=chat_completion_message,  # type: ignore
+            logprobs=None,
+            enhancements=None,
+        )
+
+        model_response.choices = [choice]
+
+        ## GET USAGE ##
+        usage = Usage(
+            prompt_tokens=completion_response["usageMetadata"].get(
+                "promptTokenCount", 0
+            ),
+            completion_tokens=completion_response["usageMetadata"].get(
+                "candidatesTokenCount", 0
+            ),
+            total_tokens=completion_response["usageMetadata"].get("totalTokenCount", 0),
+        )
+
+        setattr(model_response, "usage", usage)
+
+        return model_response
+
+    def _handle_content_policy_violation(
+        self,
+        model_response: ModelResponse,
+        completion_response: GenerateContentResponseBody,
+    ) -> ModelResponse:
+        ## CONTENT POLICY VIOLATION ERROR
+        model_response.choices[0].finish_reason = "content_filter"
+
+        _chat_completion_message = {
+            "role": "assistant",
+            "content": None,
+        }
+
+        choice = litellm.Choices(
+            finish_reason="content_filter",
+            index=0,
+            message=_chat_completion_message,
+            logprobs=None,
+            enhancements=None,
+        )
+
+        model_response.choices = [choice]
+
+        ## GET USAGE ##
+        usage = Usage(
+            prompt_tokens=completion_response["usageMetadata"].get(
+                "promptTokenCount", 0
+            ),
+            completion_tokens=completion_response["usageMetadata"].get(
+                "candidatesTokenCount", 0
+            ),
+            total_tokens=completion_response["usageMetadata"].get("totalTokenCount", 0),
+        )
+
+        setattr(model_response, "usage", usage)
+
+        return model_response
+
+    def _calculate_usage(
+        self,
+        completion_response: GenerateContentResponseBody,
+    ) -> Usage:
+        cached_tokens: Optional[int] = None
+        prompt_tokens_details: Optional[PromptTokensDetailsWrapper] = None
+        if "cachedContentTokenCount" in completion_response["usageMetadata"]:
+            cached_tokens = completion_response["usageMetadata"][
+                "cachedContentTokenCount"
+            ]
+
+        if cached_tokens is not None:
+            prompt_tokens_details = PromptTokensDetailsWrapper(
+                cached_tokens=cached_tokens,
+            )
+        ## GET USAGE ##
+        usage = Usage(
+            prompt_tokens=completion_response["usageMetadata"].get(
+                "promptTokenCount", 0
+            ),
+            completion_tokens=completion_response["usageMetadata"].get(
+                "candidatesTokenCount", 0
+            ),
+            total_tokens=completion_response["usageMetadata"].get("totalTokenCount", 0),
+            prompt_tokens_details=prompt_tokens_details,
+        )
+
+        return usage
+
+    def transform_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: ModelResponse,
+        logging_obj: LoggingClass,
+        request_data: Dict,
+        messages: List[AllMessageValues],
+        optional_params: Dict,
+        litellm_params: Dict,
+        encoding: Any,
+        api_key: Optional[str] = None,
+        json_mode: Optional[bool] = None,
+    ) -> ModelResponse:
+        ## LOGGING
+        logging_obj.post_call(
+            input=messages,
+            api_key="",
+            original_response=raw_response.text,
+            additional_args={"complete_input_dict": request_data},
+        )
+
+        ## RESPONSE OBJECT
+        try:
+            completion_response = GenerateContentResponseBody(**raw_response.json())  # type: ignore
+        except Exception as e:
+            raise VertexAIError(
+                message="Received={}, Error converting to valid response block={}. File an issue if litellm error - https://github.com/BerriAI/litellm/issues".format(
+                    raw_response.text, str(e)
+                ),
+                status_code=422,
+                headers=raw_response.headers,
+            )
+
+        ## GET MODEL ##
+        model_response.model = model
+
+        ## CHECK IF RESPONSE FLAGGED
+        if (
+            "promptFeedback" in completion_response
+            and "blockReason" in completion_response["promptFeedback"]
+        ):
+            return self._handle_blocked_response(
+                model_response=model_response,
+                completion_response=completion_response,
+            )
+
+        _candidates = completion_response.get("candidates")
+        if _candidates and len(_candidates) > 0:
+            content_policy_violations = (
+                VertexGeminiConfig().get_flagged_finish_reasons()
+            )
+            if (
+                "finishReason" in _candidates[0]
+                and _candidates[0]["finishReason"] in content_policy_violations.keys()
+            ):
+                return self._handle_content_policy_violation(
+                    model_response=model_response,
+                    completion_response=completion_response,
+                )
+
+        model_response.choices = []  # type: ignore
+
+        try:
+            ## CHECK IF GROUNDING METADATA IN REQUEST
+            grounding_metadata: List[dict] = []
+            safety_ratings: List = []
+            citation_metadata: List = []
+            ## GET TEXT ##
+            chat_completion_message: ChatCompletionResponseMessage = {
+                "role": "assistant"
+            }
+            chat_completion_logprobs: Optional[ChoiceLogprobs] = None
+            tools: Optional[List[ChatCompletionToolCallChunk]] = []
+            functions: Optional[ChatCompletionToolCallFunctionChunk] = None
+            if _candidates:
+                for idx, candidate in enumerate(_candidates):
+                    if "content" not in candidate:
+                        continue
+
+                    if "groundingMetadata" in candidate:
+                        grounding_metadata.append(candidate["groundingMetadata"])  # type: ignore
+
+                    if "safetyRatings" in candidate:
+                        safety_ratings.append(candidate["safetyRatings"])
+
+                    if "citationMetadata" in candidate:
+                        citation_metadata.append(candidate["citationMetadata"])
+                    if "parts" in candidate["content"]:
+                        chat_completion_message[
+                            "content"
+                        ] = VertexGeminiConfig().get_assistant_content_message(
+                            parts=candidate["content"]["parts"]
+                        )
+
+                        functions, tools = self._transform_parts(
+                            parts=candidate["content"]["parts"],
+                            index=candidate.get("index", idx),
+                            is_function_call=litellm_params.get(
+                                "litellm_param_is_function_call"
+                            ),
+                        )
+
+                    if "logprobsResult" in candidate:
+                        chat_completion_logprobs = self._transform_logprobs(
+                            logprobs_result=candidate["logprobsResult"]
+                        )
+
+                    if tools:
+                        chat_completion_message["tool_calls"] = tools
+
+                    if functions is not None:
+                        chat_completion_message["function_call"] = functions
+                    choice = litellm.Choices(
+                        finish_reason=candidate.get("finishReason", "stop"),
+                        index=candidate.get("index", idx),
+                        message=chat_completion_message,  # type: ignore
+                        logprobs=chat_completion_logprobs,
+                        enhancements=None,
+                    )
+
+                    model_response.choices.append(choice)
+
+            usage = self._calculate_usage(completion_response=completion_response)
+            setattr(model_response, "usage", usage)
+
+            ## ADD GROUNDING METADATA ##
+            setattr(model_response, "vertex_ai_grounding_metadata", grounding_metadata)
+            model_response._hidden_params[
+                "vertex_ai_grounding_metadata"
+            ] = (  # older approach - maintaining to prevent regressions
+                grounding_metadata
+            )
+
+            ## ADD SAFETY RATINGS ##
+            setattr(model_response, "vertex_ai_safety_results", safety_ratings)
+            model_response._hidden_params["vertex_ai_safety_results"] = (
+                safety_ratings  # older approach - maintaining to prevent regressions
+            )
+
+            ## ADD CITATION METADATA ##
+            setattr(model_response, "vertex_ai_citation_metadata", citation_metadata)
+            model_response._hidden_params["vertex_ai_citation_metadata"] = (
+                citation_metadata  # older approach - maintaining to prevent regressions
+            )
+
+        except Exception as e:
+            raise VertexAIError(
+                message="Received={}, Error converting to valid response block={}. File an issue if litellm error - https://github.com/BerriAI/litellm/issues".format(
+                    completion_response, str(e)
+                ),
+                status_code=422,
+                headers=raw_response.headers,
+            )
+
+        return model_response
+
+    def _transform_messages(
+        self, messages: List[AllMessageValues]
+    ) -> List[ContentType]:
+        return _gemini_convert_messages_with_history(messages=messages)
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Union[Dict, httpx.Headers]
+    ) -> BaseLLMException:
+        return VertexAIError(
+            message=error_message, status_code=status_code, headers=headers
+        )
+
+    def transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: Dict,
+        litellm_params: Dict,
+        headers: Dict,
+    ) -> Dict:
+        raise NotImplementedError(
+            "Vertex AI has a custom implementation of transform_request. Needs sync + async."
+        )
+
+    def validate_environment(
+        self,
+        headers: Optional[Dict],
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: Dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> Dict:
+        default_headers = {
+            "Content-Type": "application/json",
+        }
+        if api_key is not None:
+            default_headers["Authorization"] = f"Bearer {api_key}"
+        if headers is not None:
+            default_headers.update(headers)
+
+        return default_headers
+
+
+async def make_call(
+    client: Optional[AsyncHTTPHandler],
+    api_base: str,
+    headers: dict,
+    data: str,
+    model: str,
+    messages: list,
+    logging_obj,
+):
+    if client is None:
+        client = get_async_httpx_client(
+            llm_provider=litellm.LlmProviders.VERTEX_AI,
+        )
+
+    try:
+        response = await client.post(api_base, headers=headers, data=data, stream=True)
+        response.raise_for_status()
+    except httpx.HTTPStatusError as e:
+        exception_string = str(await e.response.aread())
+        raise VertexAIError(
+            status_code=e.response.status_code,
+            message=VertexGeminiConfig().translate_exception_str(exception_string),
+            headers=e.response.headers,
+        )
+    if response.status_code != 200 and response.status_code != 201:
+        raise VertexAIError(
+            status_code=response.status_code,
+            message=response.text,
+            headers=response.headers,
+        )
+
+    completion_stream = ModelResponseIterator(
+        streaming_response=response.aiter_lines(), sync_stream=False
+    )
+    # LOGGING
+    logging_obj.post_call(
+        input=messages,
+        api_key="",
+        original_response="first stream response received",
+        additional_args={"complete_input_dict": data},
+    )
+
+    return completion_stream
+
+
+def make_sync_call(
+    client: Optional[HTTPHandler],  # module-level client
+    gemini_client: Optional[HTTPHandler],  # if passed by user
+    api_base: str,
+    headers: dict,
+    data: str,
+    model: str,
+    messages: list,
+    logging_obj,
+):
+    if gemini_client is not None:
+        client = gemini_client
+    if client is None:
+        client = HTTPHandler()  # Create a new client if none provided
+
+    response = client.post(api_base, headers=headers, data=data, stream=True)
+
+    if response.status_code != 200 and response.status_code != 201:
+        raise VertexAIError(
+            status_code=response.status_code,
+            message=str(response.read()),
+            headers=response.headers,
+        )
+
+    completion_stream = ModelResponseIterator(
+        streaming_response=response.iter_lines(), sync_stream=True
+    )
+
+    # LOGGING
+    logging_obj.post_call(
+        input=messages,
+        api_key="",
+        original_response="first stream response received",
+        additional_args={"complete_input_dict": data},
+    )
+
+    return completion_stream
+
+
+class VertexLLM(VertexBase):
+    def __init__(self) -> None:
+        super().__init__()
+
+    async def async_streaming(
+        self,
+        model: str,
+        custom_llm_provider: Literal[
+            "vertex_ai", "vertex_ai_beta", "gemini"
+        ],  # if it's vertex_ai or gemini (google ai studio)
+        messages: list,
+        model_response: ModelResponse,
+        print_verbose: Callable,
+        data: dict,
+        timeout: Optional[Union[float, httpx.Timeout]],
+        encoding,
+        logging_obj,
+        stream,
+        optional_params: dict,
+        litellm_params=None,
+        logger_fn=None,
+        api_base: Optional[str] = None,
+        client: Optional[AsyncHTTPHandler] = None,
+        vertex_project: Optional[str] = None,
+        vertex_location: Optional[str] = None,
+        vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES] = None,
+        gemini_api_key: Optional[str] = None,
+        extra_headers: Optional[dict] = None,
+    ) -> CustomStreamWrapper:
+        request_body = await async_transform_request_body(**data)  # type: ignore
+
+        should_use_v1beta1_features = self.is_using_v1beta1_features(
+            optional_params=optional_params
+        )
+
+        _auth_header, vertex_project = await self._ensure_access_token_async(
+            credentials=vertex_credentials,
+            project_id=vertex_project,
+            custom_llm_provider=custom_llm_provider,
+        )
+
+        auth_header, api_base = self._get_token_and_url(
+            model=model,
+            gemini_api_key=gemini_api_key,
+            auth_header=_auth_header,
+            vertex_project=vertex_project,
+            vertex_location=vertex_location,
+            vertex_credentials=vertex_credentials,
+            stream=stream,
+            custom_llm_provider=custom_llm_provider,
+            api_base=api_base,
+            should_use_v1beta1_features=should_use_v1beta1_features,
+        )
+
+        headers = VertexGeminiConfig().validate_environment(
+            api_key=auth_header,
+            headers=extra_headers,
+            model=model,
+            messages=messages,
+            optional_params=optional_params,
+        )
+
+        ## LOGGING
+        logging_obj.pre_call(
+            input=messages,
+            api_key="",
+            additional_args={
+                "complete_input_dict": data,
+                "api_base": api_base,
+                "headers": headers,
+            },
+        )
+
+        request_body_str = json.dumps(request_body)
+        streaming_response = CustomStreamWrapper(
+            completion_stream=None,
+            make_call=partial(
+                make_call,
+                client=client,
+                api_base=api_base,
+                headers=headers,
+                data=request_body_str,
+                model=model,
+                messages=messages,
+                logging_obj=logging_obj,
+            ),
+            model=model,
+            custom_llm_provider="vertex_ai_beta",
+            logging_obj=logging_obj,
+        )
+        return streaming_response
+
+    async def async_completion(
+        self,
+        model: str,
+        messages: list,
+        model_response: ModelResponse,
+        print_verbose: Callable,
+        data: dict,
+        custom_llm_provider: Literal[
+            "vertex_ai", "vertex_ai_beta", "gemini"
+        ],  # if it's vertex_ai or gemini (google ai studio)
+        timeout: Optional[Union[float, httpx.Timeout]],
+        encoding,
+        logging_obj,
+        stream,
+        optional_params: dict,
+        litellm_params: dict,
+        logger_fn=None,
+        api_base: Optional[str] = None,
+        client: Optional[AsyncHTTPHandler] = None,
+        vertex_project: Optional[str] = None,
+        vertex_location: Optional[str] = None,
+        vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES] = None,
+        gemini_api_key: Optional[str] = None,
+        extra_headers: Optional[dict] = None,
+    ) -> Union[ModelResponse, CustomStreamWrapper]:
+        should_use_v1beta1_features = self.is_using_v1beta1_features(
+            optional_params=optional_params
+        )
+
+        _auth_header, vertex_project = await self._ensure_access_token_async(
+            credentials=vertex_credentials,
+            project_id=vertex_project,
+            custom_llm_provider=custom_llm_provider,
+        )
+
+        auth_header, api_base = self._get_token_and_url(
+            model=model,
+            gemini_api_key=gemini_api_key,
+            auth_header=_auth_header,
+            vertex_project=vertex_project,
+            vertex_location=vertex_location,
+            vertex_credentials=vertex_credentials,
+            stream=stream,
+            custom_llm_provider=custom_llm_provider,
+            api_base=api_base,
+            should_use_v1beta1_features=should_use_v1beta1_features,
+        )
+
+        headers = VertexGeminiConfig().validate_environment(
+            api_key=auth_header,
+            headers=extra_headers,
+            model=model,
+            messages=messages,
+            optional_params=optional_params,
+        )
+
+        request_body = await async_transform_request_body(**data)  # type: ignore
+        _async_client_params = {}
+        if timeout:
+            _async_client_params["timeout"] = timeout
+        if client is None or not isinstance(client, AsyncHTTPHandler):
+            client = get_async_httpx_client(
+                params=_async_client_params, llm_provider=litellm.LlmProviders.VERTEX_AI
+            )
+        else:
+            client = client  # type: ignore
+        ## LOGGING
+        logging_obj.pre_call(
+            input=messages,
+            api_key="",
+            additional_args={
+                "complete_input_dict": request_body,
+                "api_base": api_base,
+                "headers": headers,
+            },
+        )
+
+        try:
+            response = await client.post(
+                api_base, headers=headers, json=cast(dict, request_body)
+            )  # type: ignore
+            response.raise_for_status()
+        except httpx.HTTPStatusError as err:
+            error_code = err.response.status_code
+            raise VertexAIError(
+                status_code=error_code,
+                message=err.response.text,
+                headers=err.response.headers,
+            )
+        except httpx.TimeoutException:
+            raise VertexAIError(
+                status_code=408,
+                message="Timeout error occurred.",
+                headers=None,
+            )
+
+        return VertexGeminiConfig().transform_response(
+            model=model,
+            raw_response=response,
+            model_response=model_response,
+            logging_obj=logging_obj,
+            api_key="",
+            request_data=cast(dict, request_body),
+            messages=messages,
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+            encoding=encoding,
+        )
+
+    def completion(
+        self,
+        model: str,
+        messages: list,
+        model_response: ModelResponse,
+        print_verbose: Callable,
+        custom_llm_provider: Literal[
+            "vertex_ai", "vertex_ai_beta", "gemini"
+        ],  # if it's vertex_ai or gemini (google ai studio)
+        encoding,
+        logging_obj,
+        optional_params: dict,
+        acompletion: bool,
+        timeout: Optional[Union[float, httpx.Timeout]],
+        vertex_project: Optional[str],
+        vertex_location: Optional[str],
+        vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
+        gemini_api_key: Optional[str],
+        litellm_params: dict,
+        logger_fn=None,
+        extra_headers: Optional[dict] = None,
+        client: Optional[Union[AsyncHTTPHandler, HTTPHandler]] = None,
+        api_base: Optional[str] = None,
+    ) -> Union[ModelResponse, CustomStreamWrapper]:
+        stream: Optional[bool] = optional_params.pop("stream", None)  # type: ignore
+
+        transform_request_params = {
+            "gemini_api_key": gemini_api_key,
+            "messages": messages,
+            "api_base": api_base,
+            "model": model,
+            "client": client,
+            "timeout": timeout,
+            "extra_headers": extra_headers,
+            "optional_params": optional_params,
+            "logging_obj": logging_obj,
+            "custom_llm_provider": custom_llm_provider,
+            "litellm_params": litellm_params,
+        }
+
+        ### ROUTING (ASYNC, STREAMING, SYNC)
+        if acompletion:
+            ### ASYNC STREAMING
+            if stream is True:
+                return self.async_streaming(
+                    model=model,
+                    messages=messages,
+                    api_base=api_base,
+                    model_response=model_response,
+                    print_verbose=print_verbose,
+                    encoding=encoding,
+                    logging_obj=logging_obj,
+                    optional_params=optional_params,
+                    stream=stream,
+                    litellm_params=litellm_params,
+                    logger_fn=logger_fn,
+                    timeout=timeout,
+                    client=client,  # type: ignore
+                    data=transform_request_params,
+                    vertex_project=vertex_project,
+                    vertex_location=vertex_location,
+                    vertex_credentials=vertex_credentials,
+                    gemini_api_key=gemini_api_key,
+                    custom_llm_provider=custom_llm_provider,
+                    extra_headers=extra_headers,
+                )
+            ### ASYNC COMPLETION
+            return self.async_completion(
+                model=model,
+                messages=messages,
+                data=transform_request_params,  # type: ignore
+                api_base=api_base,
+                model_response=model_response,
+                print_verbose=print_verbose,
+                encoding=encoding,
+                logging_obj=logging_obj,
+                optional_params=optional_params,
+                stream=stream,
+                litellm_params=litellm_params,
+                logger_fn=logger_fn,
+                timeout=timeout,
+                client=client,  # type: ignore
+                vertex_project=vertex_project,
+                vertex_location=vertex_location,
+                vertex_credentials=vertex_credentials,
+                gemini_api_key=gemini_api_key,
+                custom_llm_provider=custom_llm_provider,
+                extra_headers=extra_headers,
+            )
+
+        should_use_v1beta1_features = self.is_using_v1beta1_features(
+            optional_params=optional_params
+        )
+
+        _auth_header, vertex_project = self._ensure_access_token(
+            credentials=vertex_credentials,
+            project_id=vertex_project,
+            custom_llm_provider=custom_llm_provider,
+        )
+
+        auth_header, url = self._get_token_and_url(
+            model=model,
+            gemini_api_key=gemini_api_key,
+            auth_header=_auth_header,
+            vertex_project=vertex_project,
+            vertex_location=vertex_location,
+            vertex_credentials=vertex_credentials,
+            stream=stream,
+            custom_llm_provider=custom_llm_provider,
+            api_base=api_base,
+            should_use_v1beta1_features=should_use_v1beta1_features,
+        )
+        headers = VertexGeminiConfig().validate_environment(
+            api_key=auth_header,
+            headers=extra_headers,
+            model=model,
+            messages=messages,
+            optional_params=optional_params,
+        )
+
+        ## TRANSFORMATION ##
+        data = sync_transform_request_body(**transform_request_params)
+
+        ## LOGGING
+        logging_obj.pre_call(
+            input=messages,
+            api_key="",
+            additional_args={
+                "complete_input_dict": data,
+                "api_base": url,
+                "headers": headers,
+            },
+        )
+
+        ## SYNC STREAMING CALL ##
+        if stream is True:
+            request_data_str = json.dumps(data)
+            streaming_response = CustomStreamWrapper(
+                completion_stream=None,
+                make_call=partial(
+                    make_sync_call,
+                    gemini_client=(
+                        client
+                        if client is not None and isinstance(client, HTTPHandler)
+                        else None
+                    ),
+                    api_base=url,
+                    data=request_data_str,
+                    model=model,
+                    messages=messages,
+                    logging_obj=logging_obj,
+                    headers=headers,
+                ),
+                model=model,
+                custom_llm_provider="vertex_ai_beta",
+                logging_obj=logging_obj,
+            )
+
+            return streaming_response
+        ## COMPLETION CALL ##
+
+        if client is None or isinstance(client, AsyncHTTPHandler):
+            _params = {}
+            if timeout is not None:
+                if isinstance(timeout, float) or isinstance(timeout, int):
+                    timeout = httpx.Timeout(timeout)
+                _params["timeout"] = timeout
+            client = HTTPHandler(**_params)  # type: ignore
+        else:
+            client = client
+
+        try:
+            response = client.post(url=url, headers=headers, json=data)  # type: ignore
+            response.raise_for_status()
+        except httpx.HTTPStatusError as err:
+            error_code = err.response.status_code
+            raise VertexAIError(
+                status_code=error_code,
+                message=err.response.text,
+                headers=err.response.headers,
+            )
+        except httpx.TimeoutException:
+            raise VertexAIError(
+                status_code=408,
+                message="Timeout error occurred.",
+                headers=None,
+            )
+
+        return VertexGeminiConfig().transform_response(
+            model=model,
+            raw_response=response,
+            model_response=model_response,
+            logging_obj=logging_obj,
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+            api_key="",
+            request_data=data,  # type: ignore
+            messages=messages,
+            encoding=encoding,
+        )
+
+
+class ModelResponseIterator:
+    def __init__(self, streaming_response, sync_stream: bool):
+        self.streaming_response = streaming_response
+        self.chunk_type: Literal["valid_json", "accumulated_json"] = "valid_json"
+        self.accumulated_json = ""
+        self.sent_first_chunk = False
+
+    def chunk_parser(self, chunk: dict) -> GenericStreamingChunk:
+        try:
+            processed_chunk = GenerateContentResponseBody(**chunk)  # type: ignore
+
+            text = ""
+            tool_use: Optional[ChatCompletionToolCallChunk] = None
+            finish_reason = ""
+            usage: Optional[ChatCompletionUsageBlock] = None
+            _candidates: Optional[List[Candidates]] = processed_chunk.get("candidates")
+            gemini_chunk: Optional[Candidates] = None
+            if _candidates and len(_candidates) > 0:
+                gemini_chunk = _candidates[0]
+
+            if (
+                gemini_chunk
+                and "content" in gemini_chunk
+                and "parts" in gemini_chunk["content"]
+            ):
+                if "text" in gemini_chunk["content"]["parts"][0]:
+                    text = gemini_chunk["content"]["parts"][0]["text"]
+                elif "functionCall" in gemini_chunk["content"]["parts"][0]:
+                    function_call = ChatCompletionToolCallFunctionChunk(
+                        name=gemini_chunk["content"]["parts"][0]["functionCall"][
+                            "name"
+                        ],
+                        arguments=json.dumps(
+                            gemini_chunk["content"]["parts"][0]["functionCall"]["args"]
+                        ),
+                    )
+                    tool_use = ChatCompletionToolCallChunk(
+                        id=str(uuid.uuid4()),
+                        type="function",
+                        function=function_call,
+                        index=0,
+                    )
+
+            if gemini_chunk and "finishReason" in gemini_chunk:
+                finish_reason = map_finish_reason(
+                    finish_reason=gemini_chunk["finishReason"]
+                )
+                ## DO NOT SET 'is_finished' = True
+                ## GEMINI SETS FINISHREASON ON EVERY CHUNK!
+
+            if "usageMetadata" in processed_chunk:
+                usage = ChatCompletionUsageBlock(
+                    prompt_tokens=processed_chunk["usageMetadata"].get(
+                        "promptTokenCount", 0
+                    ),
+                    completion_tokens=processed_chunk["usageMetadata"].get(
+                        "candidatesTokenCount", 0
+                    ),
+                    total_tokens=processed_chunk["usageMetadata"].get(
+                        "totalTokenCount", 0
+                    ),
+                )
+
+            returned_chunk = GenericStreamingChunk(
+                text=text,
+                tool_use=tool_use,
+                is_finished=False,
+                finish_reason=finish_reason,
+                usage=usage,
+                index=0,
+            )
+            return returned_chunk
+        except json.JSONDecodeError:
+            raise ValueError(f"Failed to decode JSON from chunk: {chunk}")
+
+    # Sync iterator
+    def __iter__(self):
+        self.response_iterator = self.streaming_response
+        return self
+
+    def handle_valid_json_chunk(self, chunk: str) -> GenericStreamingChunk:
+        chunk = chunk.strip()
+        try:
+            json_chunk = json.loads(chunk)
+
+        except json.JSONDecodeError as e:
+            if (
+                self.sent_first_chunk is False
+            ):  # only check for accumulated json, on first chunk, else raise error. Prevent real errors from being masked.
+                self.chunk_type = "accumulated_json"
+                return self.handle_accumulated_json_chunk(chunk=chunk)
+            raise e
+
+        if self.sent_first_chunk is False:
+            self.sent_first_chunk = True
+
+        return self.chunk_parser(chunk=json_chunk)
+
+    def handle_accumulated_json_chunk(self, chunk: str) -> GenericStreamingChunk:
+        chunk = litellm.CustomStreamWrapper._strip_sse_data_from_chunk(chunk) or ""
+        message = chunk.replace("\n\n", "")
+
+        # Accumulate JSON data
+        self.accumulated_json += message
+
+        # Try to parse the accumulated JSON
+        try:
+            _data = json.loads(self.accumulated_json)
+            self.accumulated_json = ""  # reset after successful parsing
+            return self.chunk_parser(chunk=_data)
+        except json.JSONDecodeError:
+            # If it's not valid JSON yet, continue to the next event
+            return GenericStreamingChunk(
+                text="",
+                is_finished=False,
+                finish_reason="",
+                usage=None,
+                index=0,
+                tool_use=None,
+            )
+
+    def _common_chunk_parsing_logic(self, chunk: str) -> GenericStreamingChunk:
+        try:
+            chunk = litellm.CustomStreamWrapper._strip_sse_data_from_chunk(chunk) or ""
+            if len(chunk) > 0:
+                """
+                Check if initial chunk valid json
+                - if partial json -> enter accumulated json logic
+                - if valid - continue
+                """
+                if self.chunk_type == "valid_json":
+                    return self.handle_valid_json_chunk(chunk=chunk)
+                elif self.chunk_type == "accumulated_json":
+                    return self.handle_accumulated_json_chunk(chunk=chunk)
+
+            return GenericStreamingChunk(
+                text="",
+                is_finished=False,
+                finish_reason="",
+                usage=None,
+                index=0,
+                tool_use=None,
+            )
+        except Exception:
+            raise
+
+    def __next__(self):
+        try:
+            chunk = self.response_iterator.__next__()
+        except StopIteration:
+            if self.chunk_type == "accumulated_json" and self.accumulated_json:
+                return self.handle_accumulated_json_chunk(chunk="")
+            raise StopIteration
+        except ValueError as e:
+            raise RuntimeError(f"Error receiving chunk from stream: {e}")
+
+        try:
+            return self._common_chunk_parsing_logic(chunk=chunk)
+        except StopIteration:
+            raise StopIteration
+        except ValueError as e:
+            raise RuntimeError(f"Error parsing chunk: {e},\nReceived chunk: {chunk}")
+
+    # Async iterator
+    def __aiter__(self):
+        self.async_response_iterator = self.streaming_response.__aiter__()
+        return self
+
+    async def __anext__(self):
+        try:
+            chunk = await self.async_response_iterator.__anext__()
+        except StopAsyncIteration:
+            if self.chunk_type == "accumulated_json" and self.accumulated_json:
+                return self.handle_accumulated_json_chunk(chunk="")
+            raise StopAsyncIteration
+        except ValueError as e:
+            raise RuntimeError(f"Error receiving chunk from stream: {e}")
+
+        try:
+            return self._common_chunk_parsing_logic(chunk=chunk)
+        except StopAsyncIteration:
+            raise StopAsyncIteration
+        except ValueError as e:
+            raise RuntimeError(f"Error parsing chunk: {e},\nReceived chunk: {chunk}")