"""
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
