chore: initial public snapshot for github upload
This commit is contained in:
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@@ -0,0 +1,45 @@
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"""
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Cost calculator for Vertex AI Gemini.
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Used because there are differences in how Google AI Studio and Vertex AI Gemini handle web search requests.
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"""
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from litellm.types.utils import ModelInfo, Usage
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def cost_per_web_search_request(usage: "Usage", model_info: "ModelInfo") -> float:
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"""
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Calculate the cost of a web search request for Vertex AI Gemini.
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Vertex AI charges $35/1000 prompts, independent of the number of web search requests.
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For a single call, this is $35e-3 USD.
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Args:
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usage: The usage object for the web search request.
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model_info: The model info for the web search request.
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Returns:
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The cost of the web search request.
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"""
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from litellm.types.utils import PromptTokensDetailsWrapper
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# check if usage object has web search requests
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cost_per_llm_call_with_web_search = 35e-3
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makes_web_search_request = False
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if (
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usage is not None
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and usage.prompt_tokens_details is not None
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and isinstance(usage.prompt_tokens_details, PromptTokensDetailsWrapper)
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):
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makes_web_search_request = True
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# Calculate total cost
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if makes_web_search_request:
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return cost_per_llm_call_with_web_search
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else:
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return 0.0
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@@ -0,0 +1,888 @@
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"""
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Transformation logic from OpenAI format to Gemini format.
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Why separate file? Make it easy to see how transformation works
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"""
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import json
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import os
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from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Tuple, Union, cast
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import httpx
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from pydantic import BaseModel
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import litellm
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from litellm._logging import verbose_logger
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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_get_image_mime_type_from_url,
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)
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from litellm.litellm_core_utils.prompt_templates.factory import (
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convert_generic_image_chunk_to_openai_image_obj,
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convert_to_anthropic_image_obj,
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convert_to_gemini_tool_call_invoke,
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convert_to_gemini_tool_call_result,
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response_schema_prompt,
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)
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from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
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from litellm.types.files import (
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get_file_mime_type_for_file_type,
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get_file_type_from_extension,
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is_gemini_1_5_accepted_file_type,
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)
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from litellm.types.llms.openai import (
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AllMessageValues,
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ChatCompletionAssistantMessage,
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ChatCompletionAudioObject,
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ChatCompletionFileObject,
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ChatCompletionImageObject,
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ChatCompletionTextObject,
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ChatCompletionUserMessage,
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)
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from litellm.types.llms.vertex_ai import *
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from litellm.types.llms.vertex_ai import (
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GenerationConfig,
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PartType,
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RequestBody,
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SafetSettingsConfig,
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SystemInstructions,
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ToolConfig,
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Tools,
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)
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from litellm.types.utils import GenericImageParsingChunk, LlmProviders
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from ..common_utils import (
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_check_text_in_content,
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get_supports_response_schema,
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get_supports_system_message,
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)
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if TYPE_CHECKING:
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from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
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LiteLLMLoggingObj = _LiteLLMLoggingObj
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else:
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LiteLLMLoggingObj = Any
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def _convert_detail_to_media_resolution_enum(
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detail: Optional[str],
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) -> Optional[Dict[str, str]]:
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if detail == "low":
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return {"level": "MEDIA_RESOLUTION_LOW"}
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elif detail == "medium":
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return {"level": "MEDIA_RESOLUTION_MEDIUM"}
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elif detail == "high":
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return {"level": "MEDIA_RESOLUTION_HIGH"}
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elif detail == "ultra_high":
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return {"level": "MEDIA_RESOLUTION_ULTRA_HIGH"}
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return None
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def _get_highest_media_resolution(
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current: Optional[str], new_detail: Optional[str]
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) -> Optional[str]:
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"""
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Compare two media resolution values and return the highest one.
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Resolution hierarchy: ultra_high > high > medium > low > None
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"""
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resolution_priority = {"ultra_high": 4, "high": 3, "medium": 2, "low": 1}
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current_priority = resolution_priority.get(current, 0) if current else 0
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new_priority = resolution_priority.get(new_detail, 0) if new_detail else 0
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if new_priority > current_priority:
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return new_detail
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return current
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def _extract_max_media_resolution_from_messages(
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messages: List[AllMessageValues],
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) -> Optional[str]:
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"""
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Extract the highest media resolution (detail) from image content in messages.
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This is used to set the global media_resolution in generation_config for
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Gemini 2.x models which don't support per-part media resolution.
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Args:
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messages: List of messages in OpenAI format
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Returns:
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The highest detail level found ("high", "low", or None)
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"""
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max_resolution: Optional[str] = None
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for msg in messages:
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content = msg.get("content")
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if isinstance(content, list):
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for item in content:
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if not isinstance(item, dict):
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continue
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detail: Optional[str] = None
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if item.get("type") == "image_url":
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image_url = item.get("image_url")
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if isinstance(image_url, dict):
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detail = image_url.get("detail")
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elif item.get("type") == "file":
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file_obj = item.get("file")
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if isinstance(file_obj, dict):
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detail = file_obj.get("detail")
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if detail:
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max_resolution = _get_highest_media_resolution(
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max_resolution, detail
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)
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return max_resolution
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def _apply_gemini_3_metadata(
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part: PartType,
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model: Optional[str],
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media_resolution_enum: Optional[Dict[str, str]],
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video_metadata: Optional[Dict[str, Any]],
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) -> PartType:
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"""
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Apply the unique media_resolution and video_metadata parameters of Gemini 3+
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"""
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if model is None:
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return part
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from .vertex_and_google_ai_studio_gemini import VertexGeminiConfig
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if not VertexGeminiConfig._is_gemini_3_or_newer(model):
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return part
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part_dict = dict(part)
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if media_resolution_enum is not None:
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part_dict["media_resolution"] = media_resolution_enum
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if video_metadata is not None:
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gemini_video_metadata = {}
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if "fps" in video_metadata:
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gemini_video_metadata["fps"] = video_metadata["fps"]
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if "start_offset" in video_metadata:
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gemini_video_metadata["startOffset"] = video_metadata["start_offset"]
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if "end_offset" in video_metadata:
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gemini_video_metadata["endOffset"] = video_metadata["end_offset"]
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if gemini_video_metadata:
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part_dict["video_metadata"] = gemini_video_metadata
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return cast(PartType, part_dict)
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def _process_gemini_media(
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image_url: str,
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format: Optional[str] = None,
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media_resolution_enum: Optional[Dict[str, str]] = None,
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model: Optional[str] = None,
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video_metadata: Optional[Dict[str, Any]] = None,
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) -> PartType:
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"""
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Given a media URL (image, audio, or video), return the appropriate PartType for Gemini
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By the way, actually video_metadata can only be used with videos; it cannot be used with images, audio, or files. However, I haven't made any special handling because vertex returns a parameter error.
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Args:
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image_url: The URL or base64 string of the media (image, audio, or video)
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format: The MIME type of the media
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media_resolution_enum: Media resolution level (for Gemini 3+)
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model: The model name (to check version compatibility)
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video_metadata: Video-specific metadata (fps, start_offset, end_offset)
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"""
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try:
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# GCS URIs
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if "gs://" in image_url:
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# Figure out file type
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extension_with_dot = os.path.splitext(image_url)[-1] # Ex: ".png"
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extension = extension_with_dot[1:] # Ex: "png"
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if not format:
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file_type = get_file_type_from_extension(extension)
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# Validate the file type is supported by Gemini
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if not is_gemini_1_5_accepted_file_type(file_type):
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raise Exception(f"File type not supported by gemini - {file_type}")
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mime_type = get_file_mime_type_for_file_type(file_type)
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else:
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mime_type = format
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file_data = FileDataType(mime_type=mime_type, file_uri=image_url)
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part: PartType = {"file_data": file_data}
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return _apply_gemini_3_metadata(
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part, model, media_resolution_enum, video_metadata
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)
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elif (
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"https://" in image_url
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and (image_type := format or _get_image_mime_type_from_url(image_url))
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is not None
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):
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file_data = FileDataType(mime_type=image_type, file_uri=image_url)
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part = {"file_data": file_data}
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return _apply_gemini_3_metadata(
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part, model, media_resolution_enum, video_metadata
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)
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elif "http://" in image_url or "https://" in image_url or "base64" in image_url:
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image = convert_to_anthropic_image_obj(image_url, format=format)
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_blob: BlobType = {"data": image["data"], "mime_type": image["media_type"]}
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part = {"inline_data": cast(BlobType, _blob)}
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return _apply_gemini_3_metadata(
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part, model, media_resolution_enum, video_metadata
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)
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raise Exception("Invalid image received - {}".format(image_url))
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except Exception as e:
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raise e
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def _snake_to_camel(snake_str: str) -> str:
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"""Convert snake_case to camelCase"""
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components = snake_str.split("_")
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return components[0] + "".join(x.capitalize() for x in components[1:])
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def _camel_to_snake(camel_str: str) -> str:
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"""Convert camelCase to snake_case"""
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import re
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return re.sub(r"(?<!^)(?=[A-Z])", "_", camel_str).lower()
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def _get_equivalent_key(key: str, available_keys: set) -> Optional[str]:
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"""
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Get the equivalent key from available keys, checking both camelCase and snake_case variants
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"""
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if key in available_keys:
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return key
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# Try camelCase version
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camel_key = _snake_to_camel(key)
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if camel_key in available_keys:
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return camel_key
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# Try snake_case version
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snake_key = _camel_to_snake(key)
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if snake_key in available_keys:
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return snake_key
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return None
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||||
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def check_if_part_exists_in_parts(
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parts: List[PartType], part: PartType, excluded_keys: List[str] = []
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||||
) -> bool:
|
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"""
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||||
Check if a part exists in a list of parts
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Handles both camelCase and snake_case key variations (e.g., function_call vs functionCall)
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"""
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||||
keys_to_compare = set(part.keys()) - set(excluded_keys)
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for p in parts:
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p_keys = set(p.keys())
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# Check if all keys in part have equivalent values in p
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match_found = True
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for key in keys_to_compare:
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equivalent_key = _get_equivalent_key(key, p_keys)
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if equivalent_key is None or p.get(equivalent_key, None) != part.get(
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||||
key, None
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||||
):
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||||
match_found = False
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||||
break
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||||
|
||||
if match_found:
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return True
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||||
return False
|
||||
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||||
def _gemini_convert_messages_with_history( # noqa: PLR0915
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||||
messages: List[AllMessageValues],
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model: Optional[str] = None,
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||||
) -> List[ContentType]:
|
||||
"""
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||||
Converts given messages from OpenAI format to Gemini format
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||||
- Parts must be iterable
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||||
- Roles must alternate b/w 'user' and 'model' (same as anthropic -> merge consecutive roles)
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||||
- Please ensure that function response turn comes immediately after a function call turn
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||||
"""
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||||
user_message_types = {"user", "system"}
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||||
contents: List[ContentType] = []
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||||
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||||
last_message_with_tool_calls = None
|
||||
|
||||
msg_i = 0
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||||
tool_call_responses = []
|
||||
try:
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||||
while msg_i < len(messages):
|
||||
user_content: List[PartType] = []
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||||
init_msg_i = msg_i
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## MERGE CONSECUTIVE USER CONTENT ##
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||||
while (
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||||
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_idx, element in enumerate(_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
|
||||
media_resolution_enum: Optional[Dict[str, str]] = None
|
||||
if isinstance(img_element["image_url"], dict):
|
||||
image_url = img_element["image_url"]["url"]
|
||||
format = img_element["image_url"].get("format")
|
||||
detail = img_element["image_url"].get("detail")
|
||||
media_resolution_enum = (
|
||||
_convert_detail_to_media_resolution_enum(detail)
|
||||
)
|
||||
else:
|
||||
image_url = img_element["image_url"]
|
||||
_part = _process_gemini_media(
|
||||
image_url=image_url,
|
||||
format=format,
|
||||
media_resolution_enum=media_resolution_enum,
|
||||
model=model,
|
||||
)
|
||||
_parts.append(_part)
|
||||
elif element["type"] == "input_audio":
|
||||
audio_element = cast(ChatCompletionAudioObject, element)
|
||||
audio_data = audio_element["input_audio"].get("data")
|
||||
audio_format = audio_element["input_audio"].get("format")
|
||||
if audio_data is not None and audio_format is not None:
|
||||
audio_format_modified = (
|
||||
"audio/" + audio_format
|
||||
if audio_format.startswith("audio/") is False
|
||||
else audio_format
|
||||
) # Gemini expects audio/wav, audio/mp3, etc.
|
||||
openai_image_str = (
|
||||
convert_generic_image_chunk_to_openai_image_obj(
|
||||
image_chunk=GenericImageParsingChunk(
|
||||
type="base64",
|
||||
media_type=audio_format_modified,
|
||||
data=audio_data,
|
||||
)
|
||||
)
|
||||
)
|
||||
_part = _process_gemini_media(
|
||||
image_url=openai_image_str,
|
||||
format=audio_format_modified,
|
||||
model=model,
|
||||
)
|
||||
_parts.append(_part)
|
||||
elif element["type"] == "file":
|
||||
file_element = cast(ChatCompletionFileObject, element)
|
||||
file_id = file_element["file"].get("file_id")
|
||||
format = file_element["file"].get("format")
|
||||
file_data = file_element["file"].get("file_data")
|
||||
detail = file_element["file"].get("detail")
|
||||
video_metadata = file_element["file"].get("video_metadata")
|
||||
passed_file = file_id or file_data
|
||||
if passed_file is None:
|
||||
raise Exception(
|
||||
"Unknown file type. Please pass in a file_id or file_data"
|
||||
)
|
||||
|
||||
# Convert detail to media_resolution_enum
|
||||
media_resolution_enum = (
|
||||
_convert_detail_to_media_resolution_enum(detail)
|
||||
)
|
||||
|
||||
try:
|
||||
_part = _process_gemini_media(
|
||||
image_url=passed_file,
|
||||
format=format,
|
||||
model=model,
|
||||
media_resolution_enum=media_resolution_enum,
|
||||
video_metadata=video_metadata,
|
||||
)
|
||||
_parts.append(_part)
|
||||
except Exception:
|
||||
raise Exception(
|
||||
"Unable to determine mime type for file_id: {}, set this explicitly using message[{}].content[{}].file.format".format(
|
||||
file_id, msg_i, element_idx
|
||||
)
|
||||
)
|
||||
user_content.extend(_parts)
|
||||
elif _message_content is not None and isinstance(_message_content, str):
|
||||
_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)
|
||||
reasoning_content = assistant_msg.get("reasoning_content", None)
|
||||
thinking_blocks = assistant_msg.get("thinking_blocks")
|
||||
if reasoning_content is not None:
|
||||
assistant_content.append(
|
||||
PartType(thought=True, text=reasoning_content)
|
||||
)
|
||||
if thinking_blocks is not None:
|
||||
for block in thinking_blocks:
|
||||
if block["type"] == "thinking":
|
||||
block_thinking_str = block.get("thinking")
|
||||
block_signature = block.get("signature")
|
||||
if (
|
||||
block_thinking_str is not None
|
||||
and block_signature is not None
|
||||
):
|
||||
try:
|
||||
assistant_content.append(
|
||||
PartType(
|
||||
thoughtSignature=block_signature,
|
||||
**json.loads(block_thinking_str),
|
||||
)
|
||||
)
|
||||
except Exception:
|
||||
assistant_content.append(
|
||||
PartType(
|
||||
thoughtSignature=block_signature,
|
||||
text=block_thinking_str,
|
||||
)
|
||||
)
|
||||
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):
|
||||
assistant_text = _message_content
|
||||
# Check if message has thought_signatures in provider_specific_fields
|
||||
provider_specific_fields = assistant_msg.get(
|
||||
"provider_specific_fields"
|
||||
)
|
||||
thought_signatures = None
|
||||
if provider_specific_fields and isinstance(
|
||||
provider_specific_fields, dict
|
||||
):
|
||||
thought_signatures = provider_specific_fields.get(
|
||||
"thought_signatures"
|
||||
)
|
||||
|
||||
# If we have thought signatures, add them to the part
|
||||
if (
|
||||
thought_signatures
|
||||
and isinstance(thought_signatures, list)
|
||||
and len(thought_signatures) > 0
|
||||
):
|
||||
# Use the first signature for the text part (Gemini expects one signature per part)
|
||||
assistant_content.append(PartType(text=assistant_text, thoughtSignature=thought_signatures[0])) # type: ignore
|
||||
else:
|
||||
assistant_content.append(PartType(text=assistant_text)) # type: ignore
|
||||
|
||||
## HANDLE ASSISTANT IMAGES FIELD
|
||||
# Process images field if present (for generated images from assistant)
|
||||
assistant_images = assistant_msg.get("images")
|
||||
if assistant_images is not None and isinstance(assistant_images, list):
|
||||
for image_item in assistant_images:
|
||||
if isinstance(image_item, dict):
|
||||
image_url_obj = image_item.get("image_url")
|
||||
if isinstance(image_url_obj, dict):
|
||||
assistant_image_url = image_url_obj.get("url")
|
||||
format = image_url_obj.get("format")
|
||||
detail = image_url_obj.get("detail")
|
||||
media_resolution_enum = (
|
||||
_convert_detail_to_media_resolution_enum(detail)
|
||||
)
|
||||
if assistant_image_url:
|
||||
_part = _process_gemini_media(
|
||||
image_url=assistant_image_url,
|
||||
format=format,
|
||||
media_resolution_enum=media_resolution_enum,
|
||||
model=model,
|
||||
)
|
||||
assistant_content.append(_part)
|
||||
|
||||
## 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
|
||||
gemini_tool_call_parts = convert_to_gemini_tool_call_invoke(
|
||||
assistant_msg, model=model
|
||||
)
|
||||
## check if gemini_tool_call already exists in assistant_content
|
||||
for gemini_tool_call_part in gemini_tool_call_parts:
|
||||
if not check_if_part_exists_in_parts(
|
||||
assistant_content,
|
||||
gemini_tool_call_part,
|
||||
excluded_keys=["thoughtSignature"],
|
||||
):
|
||||
assistant_content.append(gemini_tool_call_part)
|
||||
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
|
||||
# Handle both single part and list of parts (for Computer Use with images)
|
||||
if isinstance(_part, list):
|
||||
tool_call_responses.extend(_part)
|
||||
else:
|
||||
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(role="user", 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(role="user", parts=tool_call_responses))
|
||||
|
||||
if len(contents) == 0:
|
||||
verbose_logger.warning(
|
||||
"""
|
||||
No contents in messages. Contents are required. See
|
||||
https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.publishers.models/generateContent#request-body.
|
||||
If the original request did not comply to OpenAI API requirements it should have failed by now,
|
||||
but LiteLLM does not check for missing messages.
|
||||
Setting an empty content to prevent an 400 error.
|
||||
Relevant Issue - https://github.com/BerriAI/litellm/issues/9733
|
||||
"""
|
||||
)
|
||||
contents.append(ContentType(role="user", parts=[PartType(text=" ")]))
|
||||
return contents
|
||||
except Exception as e:
|
||||
raise e
|
||||
|
||||
|
||||
# Keys that LiteLLM consumes internally and must never be forwarded to the
|
||||
_LITELLM_INTERNAL_EXTRA_BODY_KEYS: frozenset = frozenset({"cache", "tags"})
|
||||
|
||||
|
||||
def _pop_and_merge_extra_body(data: RequestBody, optional_params: dict) -> None:
|
||||
"""Pop extra_body from optional_params and shallow-merge into data, deep-merging dict values."""
|
||||
extra_body: Optional[dict] = optional_params.pop("extra_body", None)
|
||||
if extra_body is not None:
|
||||
data_dict: dict = data # type: ignore[assignment]
|
||||
for k, v in extra_body.items():
|
||||
if k in _LITELLM_INTERNAL_EXTRA_BODY_KEYS:
|
||||
continue
|
||||
if (
|
||||
k in data_dict
|
||||
and isinstance(data_dict[k], dict)
|
||||
and isinstance(v, dict)
|
||||
):
|
||||
data_dict[k].update(v)
|
||||
else:
|
||||
data_dict[k] = v
|
||||
|
||||
|
||||
def _transform_request_body( # noqa: PLR0915
|
||||
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, model=model
|
||||
)
|
||||
else:
|
||||
content = litellm.VertexGeminiConfig()._transform_messages(
|
||||
messages=messages, model=model
|
||||
)
|
||||
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
|
||||
# Drop output_config as it's not supported by Vertex AI
|
||||
optional_params.pop("output_config", None)
|
||||
config_fields = GenerationConfig.__annotations__.keys()
|
||||
|
||||
# If the LiteLLM client sends Gemini-supported parameter "labels", add it
|
||||
# as "labels" field to the request sent to the Gemini backend.
|
||||
labels: Optional[dict[str, str]] = optional_params.pop("labels", None)
|
||||
# If the LiteLLM client sends OpenAI-supported parameter "metadata", add it
|
||||
# as "labels" field to the request sent to the Gemini backend.
|
||||
if labels is None and "metadata" in litellm_params:
|
||||
metadata = litellm_params["metadata"]
|
||||
if metadata is not None and "requester_metadata" in metadata:
|
||||
rm = metadata["requester_metadata"]
|
||||
labels = {k: v for k, v in rm.items() if isinstance(v, str)}
|
||||
|
||||
filtered_params = {
|
||||
k: v
|
||||
for k, v in optional_params.items()
|
||||
if _get_equivalent_key(k, set(config_fields))
|
||||
}
|
||||
|
||||
generation_config: Optional[GenerationConfig] = GenerationConfig(
|
||||
**filtered_params
|
||||
)
|
||||
|
||||
# For Gemini 2.x models, add media_resolution to generation_config (global)
|
||||
# Gemini 3+ supports per-part media_resolution, but 2.x only supports global
|
||||
# Gemini 1.x does not support mediaResolution at all
|
||||
if "gemini-2" in model:
|
||||
max_media_resolution = _extract_max_media_resolution_from_messages(messages)
|
||||
if max_media_resolution:
|
||||
media_resolution_value = _convert_detail_to_media_resolution_enum(
|
||||
max_media_resolution
|
||||
)
|
||||
if media_resolution_value and generation_config is not None:
|
||||
generation_config["mediaResolution"] = media_resolution_value[
|
||||
"level"
|
||||
]
|
||||
|
||||
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 and len(generation_config) > 0:
|
||||
data["generationConfig"] = generation_config
|
||||
if cached_content is not None:
|
||||
data["cachedContent"] = cached_content
|
||||
# Only add labels for Vertex AI endpoints (not Google GenAI/AI Studio) and only if non-empty
|
||||
if labels and custom_llm_provider != LlmProviders.GEMINI:
|
||||
data["labels"] = labels
|
||||
_pop_and_merge_extra_body(data, optional_params)
|
||||
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,
|
||||
vertex_project: Optional[str],
|
||||
vertex_location: Optional[str],
|
||||
vertex_auth_header: Optional[str],
|
||||
) -> RequestBody:
|
||||
from ..context_caching.vertex_ai_context_caching import ContextCachingEndpoints
|
||||
|
||||
context_caching_endpoints = ContextCachingEndpoints()
|
||||
|
||||
(
|
||||
messages,
|
||||
optional_params,
|
||||
cached_content,
|
||||
) = context_caching_endpoints.check_and_create_cache(
|
||||
messages=messages,
|
||||
optional_params=optional_params,
|
||||
api_key=gemini_api_key or "dummy",
|
||||
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,
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
vertex_project=vertex_project,
|
||||
vertex_location=vertex_location,
|
||||
vertex_auth_header=vertex_auth_header,
|
||||
)
|
||||
|
||||
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,
|
||||
vertex_project: Optional[str],
|
||||
vertex_location: Optional[str],
|
||||
vertex_auth_header: Optional[str],
|
||||
) -> RequestBody:
|
||||
from ..context_caching.vertex_ai_context_caching import ContextCachingEndpoints
|
||||
|
||||
context_caching_endpoints = ContextCachingEndpoints()
|
||||
|
||||
(
|
||||
messages,
|
||||
optional_params,
|
||||
cached_content,
|
||||
) = await context_caching_endpoints.async_check_and_create_cache(
|
||||
messages=messages,
|
||||
optional_params=optional_params,
|
||||
api_key=gemini_api_key or "dummy",
|
||||
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,
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
vertex_project=vertex_project,
|
||||
vertex_location=vertex_location,
|
||||
vertex_auth_header=vertex_auth_header,
|
||||
)
|
||||
|
||||
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 _default_user_message_when_system_message_passed() -> ChatCompletionUserMessage:
|
||||
"""
|
||||
Returns a default user message when a "system" message is passed in gemini fails.
|
||||
|
||||
This adds a blank user message to the messages list, to ensure that gemini doesn't fail the request.
|
||||
"""
|
||||
return ChatCompletionUserMessage(content=".", role="user")
|
||||
|
||||
|
||||
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:
|
||||
#########################################################
|
||||
# If no messages are passed in, add a blank user message
|
||||
# Relevant Issue - https://github.com/BerriAI/litellm/issues/13769
|
||||
#########################################################
|
||||
if len(messages) == 0:
|
||||
messages.append(_default_user_message_when_system_message_passed())
|
||||
#########################################################
|
||||
return SystemInstructions(parts=system_content_blocks), messages
|
||||
|
||||
return None, messages
|
||||
File diff suppressed because it is too large
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Reference in New Issue
Block a user