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Diffstat (limited to '.venv/lib/python3.12/site-packages/litellm/llms/gemini')
3 files changed, 154 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/gemini/chat/transformation.py b/.venv/lib/python3.12/site-packages/litellm/llms/gemini/chat/transformation.py new file mode 100644 index 00000000..fbc1916d --- /dev/null +++ b/.venv/lib/python3.12/site-packages/litellm/llms/gemini/chat/transformation.py @@ -0,0 +1,132 @@ +from typing import Dict, List, Optional + +import litellm +from litellm.litellm_core_utils.prompt_templates.factory import ( + convert_generic_image_chunk_to_openai_image_obj, + convert_to_anthropic_image_obj, +) +from litellm.types.llms.openai import AllMessageValues +from litellm.types.llms.vertex_ai import ContentType, PartType + +from ...vertex_ai.gemini.transformation import _gemini_convert_messages_with_history +from ...vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexGeminiConfig + + +class GoogleAIStudioGeminiConfig(VertexGeminiConfig): + """ + Reference: https://ai.google.dev/api/rest/v1beta/GenerationConfig + + The class `GoogleAIStudioGeminiConfig` provides configuration for the Google AI Studio's Gemini 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'. Other values - `application/json`. + + - `response_schema` (dict): Optional. Output response schema of the generated candidate text when response mime type can have schema. Schema can be objects, primitives or arrays and is a subset of OpenAPI schema. If set, a compatible response_mime_type must also be set. Compatible mimetypes: application/json: Schema for JSON response. + + - `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. + + 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 + response_schema: Optional[dict] = None + candidate_count: Optional[int] = None + stop_sequences: Optional[list] = 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, + response_schema: Optional[dict] = None, + candidate_count: Optional[int] = None, + stop_sequences: Optional[list] = 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", + "tool_choice", + "functions", + "response_format", + "n", + "stop", + "logprobs", + "frequency_penalty", + ] + + def map_openai_params( + self, + non_default_params: Dict, + optional_params: Dict, + model: str, + drop_params: bool, + ) -> Dict: + + if litellm.vertex_ai_safety_settings is not None: + optional_params["safety_settings"] = litellm.vertex_ai_safety_settings + return super().map_openai_params( + model=model, + non_default_params=non_default_params, + optional_params=optional_params, + drop_params=drop_params, + ) + + def _transform_messages( + self, messages: List[AllMessageValues] + ) -> List[ContentType]: + """ + Google AI Studio Gemini does not support image urls in messages. + """ + for message in messages: + _message_content = message.get("content") + if _message_content is not None and isinstance(_message_content, list): + _parts: List[PartType] = [] + for element in _message_content: + if element.get("type") == "image_url": + img_element = element + _image_url: Optional[str] = None + format: Optional[str] = None + if isinstance(img_element.get("image_url"), dict): + _image_url = img_element["image_url"].get("url") # type: ignore + format = img_element["image_url"].get("format") # type: ignore + else: + _image_url = img_element.get("image_url") # type: ignore + if _image_url and "https://" in _image_url: + image_obj = convert_to_anthropic_image_obj( + _image_url, format=format + ) + img_element["image_url"] = ( # type: ignore + convert_generic_image_chunk_to_openai_image_obj( + image_obj + ) + ) + return _gemini_convert_messages_with_history(messages=messages) diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/gemini/context_caching/README.md b/.venv/lib/python3.12/site-packages/litellm/llms/gemini/context_caching/README.md new file mode 100644 index 00000000..fce0b9d4 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/litellm/llms/gemini/context_caching/README.md @@ -0,0 +1 @@ +[Go here for the Gemini Context Caching code](../../vertex_ai/context_caching/)
\ No newline at end of file diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/gemini/cost_calculator.py b/.venv/lib/python3.12/site-packages/litellm/llms/gemini/cost_calculator.py new file mode 100644 index 00000000..5497640d --- /dev/null +++ b/.venv/lib/python3.12/site-packages/litellm/llms/gemini/cost_calculator.py @@ -0,0 +1,21 @@ +""" +This file is used to calculate the cost of the Gemini API. + +Handles the context caching for Gemini API. +""" + +from typing import Tuple + +from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token +from litellm.types.utils import Usage + + +def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]: + """ + Calculates the cost per token for a given model, prompt tokens, and completion tokens. + + Follows the same logic as Anthropic's cost per token calculation. + """ + return generic_cost_per_token( + model=model, usage=usage, custom_llm_provider="gemini" + ) |