diff --git a/.circleci/requirements.txt b/.circleci/requirements.txt index 5dece1fc8b..88c0aa4dda 100644 --- a/.circleci/requirements.txt +++ b/.circleci/requirements.txt @@ -10,6 +10,6 @@ anthropic orjson==3.9.15 pydantic==2.10.2 google-cloud-aiplatform==1.43.0 -fastapi-sso==0.10.0 +fastapi-sso==0.16.0 uvloop==0.21.0 mcp==1.5.0 # for MCP server diff --git a/docs/my-website/docs/completion/document_understanding.md b/docs/my-website/docs/completion/document_understanding.md index c101aa1aef..b4d4768186 100644 --- a/docs/my-website/docs/completion/document_understanding.md +++ b/docs/my-website/docs/completion/document_understanding.md @@ -80,11 +80,13 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \ -d '{ "model": "bedrock-model", "messages": [ - {"role": "user", "content": {"type": "text", "text": "What's this file about?"}}, - { - "type": "image_url", - "image_url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf", - } + {"role": "user", "content": [ + {"type": "text", "text": "What's this file about?"}, + { + "type": "image_url", + "image_url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf", + } + ]}, ] }' ``` @@ -135,6 +137,46 @@ response = completion( assert response is not None ``` + + +1. Setup config.yaml + +```yaml +model_list: + - model_name: bedrock-model + litellm_params: + model: bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0 + aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID + aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY + aws_region_name: os.environ/AWS_REGION_NAME +``` + +2. Start the proxy + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test it! + +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model": "bedrock-model", + "messages": [ + {"role": "user", "content": [ + {"type": "text", "text": "What's this file about?"}, + { + "type": "image_url", + "image_url": "data:application/pdf;base64...", + } + ]}, + ] +}' +``` + ## Checking if a model supports pdf input diff --git a/docs/my-website/docs/proxy/db_deadlocks.md b/docs/my-website/docs/proxy/db_deadlocks.md index 51052a0bc5..e649bdccc0 100644 --- a/docs/my-website/docs/proxy/db_deadlocks.md +++ b/docs/my-website/docs/proxy/db_deadlocks.md @@ -31,6 +31,15 @@ Each instance writes updates to redis A single instance will acquire a lock on the DB and flush all elements in the redis queue to the DB. +- 1 instance will attempt to acquire the lock for the DB update job +- The status of the lock is stored in redis +- If the instance acquires the lock to write to DB + - It will read all updates from redis + - Aggregate all updates into 1 transaction + - Write updates to DB + - Release the lock +- Note: Only 1 instance can acquire the lock at a time, this limits the number of instances that can write to the DB at once +

diff --git a/docs/my-website/img/deadlock_fix_1.png b/docs/my-website/img/deadlock_fix_1.png index 3dff86a4d2..df651f440c 100644 Binary files a/docs/my-website/img/deadlock_fix_1.png and b/docs/my-website/img/deadlock_fix_1.png differ diff --git a/docs/my-website/img/deadlock_fix_2.png b/docs/my-website/img/deadlock_fix_2.png index 345acff586..0f139d84e5 100644 Binary files a/docs/my-website/img/deadlock_fix_2.png and b/docs/my-website/img/deadlock_fix_2.png differ diff --git a/litellm/files/main.py b/litellm/files/main.py index cdc3115a6f..7516088f83 100644 --- a/litellm/files/main.py +++ b/litellm/files/main.py @@ -63,16 +63,17 @@ async def acreate_file( loop = asyncio.get_event_loop() kwargs["acreate_file"] = True - # Use a partial function to pass your keyword arguments - func = partial( - create_file, - file, - purpose, - custom_llm_provider, - extra_headers, - extra_body, + call_args = { + "file": file, + "purpose": purpose, + "custom_llm_provider": custom_llm_provider, + "extra_headers": extra_headers, + "extra_body": extra_body, **kwargs, - ) + } + + # Use a partial function to pass your keyword arguments + func = partial(create_file, **call_args) # Add the context to the function ctx = contextvars.copy_context() @@ -92,7 +93,7 @@ async def acreate_file( def create_file( file: FileTypes, purpose: Literal["assistants", "batch", "fine-tune"], - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + custom_llm_provider: Optional[Literal["openai", "azure", "vertex_ai"]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -101,6 +102,8 @@ def create_file( Files are used to upload documents that can be used with features like Assistants, Fine-tuning, and Batch API. LiteLLM Equivalent of POST: POST https://api.openai.com/v1/files + + Specify either provider_list or custom_llm_provider. """ try: _is_async = kwargs.pop("acreate_file", False) is True @@ -120,7 +123,7 @@ def create_file( if ( timeout is not None and isinstance(timeout, httpx.Timeout) - and supports_httpx_timeout(custom_llm_provider) is False + and supports_httpx_timeout(cast(str, custom_llm_provider)) is False ): read_timeout = timeout.read or 600 timeout = read_timeout # default 10 min timeout diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 255cce7336..bf7ac1eb99 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -457,8 +457,12 @@ class Logging(LiteLLMLoggingBaseClass): non_default_params: dict, prompt_id: str, prompt_variables: Optional[dict], + prompt_management_logger: Optional[CustomLogger] = None, ) -> Tuple[str, List[AllMessageValues], dict]: - custom_logger = self.get_custom_logger_for_prompt_management(model) + custom_logger = ( + prompt_management_logger + or self.get_custom_logger_for_prompt_management(model) + ) if custom_logger: ( model, diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py index 4170d3c1e1..9ba1153c08 100644 --- a/litellm/litellm_core_utils/prompt_templates/common_utils.py +++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py @@ -7,6 +7,7 @@ from typing import Dict, List, Literal, Optional, Union, cast from litellm.types.llms.openai import ( AllMessageValues, ChatCompletionAssistantMessage, + ChatCompletionFileObject, ChatCompletionUserMessage, ) from litellm.types.utils import Choices, ModelResponse, StreamingChoices @@ -292,3 +293,58 @@ def get_completion_messages( messages, assistant_continue_message, ensure_alternating_roles ) return messages + + +def get_file_ids_from_messages(messages: List[AllMessageValues]) -> List[str]: + """ + Gets file ids from messages + """ + file_ids = [] + for message in messages: + if message.get("role") == "user": + content = message.get("content") + if content: + if isinstance(content, str): + continue + for c in content: + if c["type"] == "file": + file_object = cast(ChatCompletionFileObject, c) + file_object_file_field = file_object["file"] + file_id = file_object_file_field.get("file_id") + if file_id: + file_ids.append(file_id) + return file_ids + + +def update_messages_with_model_file_ids( + messages: List[AllMessageValues], + model_id: str, + model_file_id_mapping: Dict[str, Dict[str, str]], +) -> List[AllMessageValues]: + """ + Updates messages with model file ids. + + model_file_id_mapping: Dict[str, Dict[str, str]] = { + "litellm_proxy/file_id": { + "model_id": "provider_file_id" + } + } + """ + for message in messages: + if message.get("role") == "user": + content = message.get("content") + if content: + if isinstance(content, str): + continue + for c in content: + if c["type"] == "file": + file_object = cast(ChatCompletionFileObject, c) + file_object_file_field = file_object["file"] + file_id = file_object_file_field.get("file_id") + if file_id: + provider_file_id = ( + model_file_id_mapping.get(file_id, {}).get(model_id) + or file_id + ) + file_object_file_field["file_id"] = provider_file_id + return messages diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index 0673307929..e8d8456ed7 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -1300,20 +1300,37 @@ def convert_to_anthropic_tool_invoke( ] } """ - anthropic_tool_invoke = [ - AnthropicMessagesToolUseParam( + anthropic_tool_invoke = [] + + for tool in tool_calls: + if not get_attribute_or_key(tool, "type") == "function": + continue + + _anthropic_tool_use_param = AnthropicMessagesToolUseParam( type="tool_use", - id=get_attribute_or_key(tool, "id"), - name=get_attribute_or_key(get_attribute_or_key(tool, "function"), "name"), + id=cast(str, get_attribute_or_key(tool, "id")), + name=cast( + str, + get_attribute_or_key(get_attribute_or_key(tool, "function"), "name"), + ), input=json.loads( get_attribute_or_key( get_attribute_or_key(tool, "function"), "arguments" ) ), ) - for tool in tool_calls - if get_attribute_or_key(tool, "type") == "function" - ] + + _content_element = add_cache_control_to_content( + anthropic_content_element=_anthropic_tool_use_param, + orignal_content_element=dict(tool), + ) + + if "cache_control" in _content_element: + _anthropic_tool_use_param["cache_control"] = _content_element[ + "cache_control" + ] + + anthropic_tool_invoke.append(_anthropic_tool_use_param) return anthropic_tool_invoke @@ -1324,6 +1341,7 @@ def add_cache_control_to_content( AnthropicMessagesImageParam, AnthropicMessagesTextParam, AnthropicMessagesDocumentParam, + AnthropicMessagesToolUseParam, ChatCompletionThinkingBlock, ], orignal_content_element: Union[dict, AllMessageValues], diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py index 1ca2bfe45e..abe5966d31 100644 --- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py +++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py @@ -1,6 +1,6 @@ import base64 import time -from typing import Any, Dict, List, Optional, Union +from typing import Any, Dict, List, Optional, Union, cast from litellm.types.llms.openai import ( ChatCompletionAssistantContentValue, @@ -9,7 +9,9 @@ from litellm.types.llms.openai import ( from litellm.types.utils import ( ChatCompletionAudioResponse, ChatCompletionMessageToolCall, + Choices, CompletionTokensDetails, + CompletionTokensDetailsWrapper, Function, FunctionCall, ModelResponse, @@ -203,14 +205,14 @@ class ChunkProcessor: ) def get_combined_content( - self, chunks: List[Dict[str, Any]] + self, chunks: List[Dict[str, Any]], delta_key: str = "content" ) -> ChatCompletionAssistantContentValue: content_list: List[str] = [] for chunk in chunks: choices = chunk["choices"] for choice in choices: delta = choice.get("delta", {}) - content = delta.get("content", "") + content = delta.get(delta_key, "") if content is None: continue # openai v1.0.0 sets content = None for chunks content_list.append(content) @@ -221,6 +223,11 @@ class ChunkProcessor: # Update the "content" field within the response dictionary return combined_content + def get_combined_reasoning_content( + self, chunks: List[Dict[str, Any]] + ) -> ChatCompletionAssistantContentValue: + return self.get_combined_content(chunks, delta_key="reasoning_content") + def get_combined_audio_content( self, chunks: List[Dict[str, Any]] ) -> ChatCompletionAudioResponse: @@ -296,12 +303,27 @@ class ChunkProcessor: "prompt_tokens_details": prompt_tokens_details, } + def count_reasoning_tokens(self, response: ModelResponse) -> int: + reasoning_tokens = 0 + for choice in response.choices: + if ( + hasattr(cast(Choices, choice).message, "reasoning_content") + and cast(Choices, choice).message.reasoning_content is not None + ): + reasoning_tokens += token_counter( + text=cast(Choices, choice).message.reasoning_content, + count_response_tokens=True, + ) + + return reasoning_tokens + def calculate_usage( self, chunks: List[Union[Dict[str, Any], ModelResponse]], model: str, completion_output: str, messages: Optional[List] = None, + reasoning_tokens: Optional[int] = None, ) -> Usage: """ Calculate usage for the given chunks. @@ -382,6 +404,19 @@ class ChunkProcessor: ) # for anthropic if completion_tokens_details is not None: returned_usage.completion_tokens_details = completion_tokens_details + + if reasoning_tokens is not None: + if returned_usage.completion_tokens_details is None: + returned_usage.completion_tokens_details = ( + CompletionTokensDetailsWrapper(reasoning_tokens=reasoning_tokens) + ) + elif ( + returned_usage.completion_tokens_details is not None + and returned_usage.completion_tokens_details.reasoning_tokens is None + ): + returned_usage.completion_tokens_details.reasoning_tokens = ( + reasoning_tokens + ) if prompt_tokens_details is not None: returned_usage.prompt_tokens_details = prompt_tokens_details diff --git a/litellm/llms/anthropic/chat/handler.py b/litellm/llms/anthropic/chat/handler.py index 7625292e6e..c29a98b217 100644 --- a/litellm/llms/anthropic/chat/handler.py +++ b/litellm/llms/anthropic/chat/handler.py @@ -21,7 +21,6 @@ from litellm.llms.custom_httpx.http_handler import ( get_async_httpx_client, ) from litellm.types.llms.anthropic import ( - AnthropicChatCompletionUsageBlock, ContentBlockDelta, ContentBlockStart, ContentBlockStop, @@ -32,13 +31,13 @@ from litellm.types.llms.anthropic import ( from litellm.types.llms.openai import ( ChatCompletionThinkingBlock, ChatCompletionToolCallChunk, - ChatCompletionUsageBlock, ) from litellm.types.utils import ( Delta, GenericStreamingChunk, ModelResponseStream, StreamingChoices, + Usage, ) from litellm.utils import CustomStreamWrapper, ModelResponse, ProviderConfigManager @@ -487,10 +486,8 @@ class ModelResponseIterator: return True return False - def _handle_usage( - self, anthropic_usage_chunk: Union[dict, UsageDelta] - ) -> AnthropicChatCompletionUsageBlock: - usage_block = AnthropicChatCompletionUsageBlock( + def _handle_usage(self, anthropic_usage_chunk: Union[dict, UsageDelta]) -> Usage: + usage_block = Usage( prompt_tokens=anthropic_usage_chunk.get("input_tokens", 0), completion_tokens=anthropic_usage_chunk.get("output_tokens", 0), total_tokens=anthropic_usage_chunk.get("input_tokens", 0) @@ -581,7 +578,7 @@ class ModelResponseIterator: text = "" tool_use: Optional[ChatCompletionToolCallChunk] = None finish_reason = "" - usage: Optional[ChatCompletionUsageBlock] = None + usage: Optional[Usage] = None provider_specific_fields: Dict[str, Any] = {} reasoning_content: Optional[str] = None thinking_blocks: Optional[List[ChatCompletionThinkingBlock]] = None diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index 64702b4f26..d4ae425554 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -33,9 +33,16 @@ from litellm.types.llms.openai import ( ChatCompletionToolCallFunctionChunk, ChatCompletionToolParam, ) +from litellm.types.utils import CompletionTokensDetailsWrapper from litellm.types.utils import Message as LitellmMessage from litellm.types.utils import PromptTokensDetailsWrapper -from litellm.utils import ModelResponse, Usage, add_dummy_tool, has_tool_call_blocks +from litellm.utils import ( + ModelResponse, + Usage, + add_dummy_tool, + has_tool_call_blocks, + token_counter, +) from ..common_utils import AnthropicError, process_anthropic_headers @@ -772,6 +779,15 @@ class AnthropicConfig(BaseConfig): prompt_tokens_details = PromptTokensDetailsWrapper( cached_tokens=cache_read_input_tokens ) + completion_token_details = ( + CompletionTokensDetailsWrapper( + reasoning_tokens=token_counter( + text=reasoning_content, count_response_tokens=True + ) + ) + if reasoning_content + else None + ) total_tokens = prompt_tokens + completion_tokens usage = Usage( prompt_tokens=prompt_tokens, @@ -780,6 +796,7 @@ class AnthropicConfig(BaseConfig): prompt_tokens_details=prompt_tokens_details, cache_creation_input_tokens=cache_creation_input_tokens, cache_read_input_tokens=cache_read_input_tokens, + completion_tokens_details=completion_token_details, ) setattr(model_response, "usage", usage) # type: ignore diff --git a/litellm/llms/azure/files/handler.py b/litellm/llms/azure/files/handler.py index 5e105374b2..50c122ccf2 100644 --- a/litellm/llms/azure/files/handler.py +++ b/litellm/llms/azure/files/handler.py @@ -28,11 +28,11 @@ class AzureOpenAIFilesAPI(BaseAzureLLM): self, create_file_data: CreateFileRequest, openai_client: AsyncAzureOpenAI, - ) -> FileObject: + ) -> OpenAIFileObject: verbose_logger.debug("create_file_data=%s", create_file_data) response = await openai_client.files.create(**create_file_data) verbose_logger.debug("create_file_response=%s", response) - return response + return OpenAIFileObject(**response.model_dump()) def create_file( self, @@ -66,7 +66,7 @@ class AzureOpenAIFilesAPI(BaseAzureLLM): raise ValueError( "AzureOpenAI client is not an instance of AsyncAzureOpenAI. Make sure you passed an AsyncAzureOpenAI client." ) - return self.acreate_file( # type: ignore + return self.acreate_file( create_file_data=create_file_data, openai_client=openai_client ) response = cast(AzureOpenAI, openai_client).files.create(**create_file_data) diff --git a/litellm/llms/openrouter/chat/transformation.py b/litellm/llms/openrouter/chat/transformation.py index 452921f551..77f402a131 100644 --- a/litellm/llms/openrouter/chat/transformation.py +++ b/litellm/llms/openrouter/chat/transformation.py @@ -1,17 +1,18 @@ """ -Support for OpenAI's `/v1/chat/completions` endpoint. +Support for OpenAI's `/v1/chat/completions` endpoint. Calls done in OpenAI/openai.py as OpenRouter is openai-compatible. Docs: https://openrouter.ai/docs/parameters """ -from typing import Any, AsyncIterator, Iterator, Optional, Union +from typing import Any, AsyncIterator, Iterator, List, Optional, Union import httpx from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.types.llms.openai import AllMessageValues from litellm.types.llms.openrouter import OpenRouterErrorMessage from litellm.types.utils import ModelResponse, ModelResponseStream @@ -47,6 +48,27 @@ class OpenrouterConfig(OpenAIGPTConfig): ] = extra_body # openai client supports `extra_body` param return mapped_openai_params + def transform_request( + self, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Transform the overall request to be sent to the API. + + Returns: + dict: The transformed request. Sent as the body of the API call. + """ + extra_body = optional_params.pop("extra_body", {}) + response = super().transform_request( + model, messages, optional_params, litellm_params, headers + ) + response.update(extra_body) + return response + def get_error_class( self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] ) -> BaseLLMException: diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py index 860dec9eb2..36382831c6 100644 --- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py +++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py @@ -676,6 +676,66 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): return usage + def _process_candidates(self, _candidates, model_response, litellm_params): + """Helper method to process candidates and extract metadata""" + grounding_metadata: List[dict] = [] + safety_ratings: List = [] + citation_metadata: List = [] + chat_completion_message: ChatCompletionResponseMessage = {"role": "assistant"} + chat_completion_logprobs: Optional[ChoiceLogprobs] = None + tools: Optional[List[ChatCompletionToolCallChunk]] = [] + functions: Optional[ChatCompletionToolCallFunctionChunk] = None + + 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"] + ) + # Handle avgLogprobs for Gemini Flash 2.0 + elif "avgLogprobs" in candidate: + chat_completion_logprobs = candidate["avgLogprobs"] + + 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) + + return grounding_metadata, safety_ratings, citation_metadata + def transform_response( self, model: str, @@ -725,9 +785,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): _candidates = completion_response.get("candidates") if _candidates and len(_candidates) > 0: - content_policy_violations = ( - VertexGeminiConfig().get_flagged_finish_reasons() - ) + content_policy_violations = VertexGeminiConfig().get_flagged_finish_reasons() if ( "finishReason" in _candidates[0] and _candidates[0]["finishReason"] in content_policy_violations.keys() @@ -740,88 +798,25 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): 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 + grounding_metadata, safety_ratings, citation_metadata = [], [], [] 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) + grounding_metadata, safety_ratings, citation_metadata = self._process_candidates( + _candidates, model_response, litellm_params + ) usage = self._calculate_usage(completion_response=completion_response) - setattr(model_response, "usage", usage) - ## ADD GROUNDING METADATA ## + ## ADD METADATA TO RESPONSE ## 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 ## + model_response._hidden_params["vertex_ai_grounding_metadata"] = grounding_metadata + 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 - + 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 + model_response._hidden_params["vertex_ai_citation_metadata"] = citation_metadata # older approach - maintaining to prevent regressions except Exception as e: raise VertexAIError( @@ -1029,7 +1024,7 @@ class VertexLLM(VertexBase): input=messages, api_key="", additional_args={ - "complete_input_dict": data, + "complete_input_dict": request_body, "api_base": api_base, "headers": headers, }, diff --git a/litellm/main.py b/litellm/main.py index 5d058c0c44..dcc277343e 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -110,7 +110,10 @@ from .litellm_core_utils.fallback_utils import ( async_completion_with_fallbacks, completion_with_fallbacks, ) -from .litellm_core_utils.prompt_templates.common_utils import get_completion_messages +from .litellm_core_utils.prompt_templates.common_utils import ( + get_completion_messages, + update_messages_with_model_file_ids, +) from .litellm_core_utils.prompt_templates.factory import ( custom_prompt, function_call_prompt, @@ -449,7 +452,7 @@ async def acompletion( fallbacks = fallbacks or litellm.model_fallbacks if fallbacks is not None: response = await async_completion_with_fallbacks( - **completion_kwargs, kwargs={"fallbacks": fallbacks} + **completion_kwargs, kwargs={"fallbacks": fallbacks, **kwargs} ) if response is None: raise Exception( @@ -953,7 +956,6 @@ def completion( # type: ignore # noqa: PLR0915 non_default_params = get_non_default_completion_params(kwargs=kwargs) litellm_params = {} # used to prevent unbound var errors ## PROMPT MANAGEMENT HOOKS ## - if isinstance(litellm_logging_obj, LiteLLMLoggingObj) and prompt_id is not None: ( model, @@ -1068,6 +1070,15 @@ def completion( # type: ignore # noqa: PLR0915 if eos_token: custom_prompt_dict[model]["eos_token"] = eos_token + if kwargs.get("model_file_id_mapping"): + messages = update_messages_with_model_file_ids( + messages=messages, + model_id=kwargs.get("model_info", {}).get("id", None), + model_file_id_mapping=cast( + Dict[str, Dict[str, str]], kwargs.get("model_file_id_mapping") + ), + ) + provider_config: Optional[BaseConfig] = None if custom_llm_provider is not None and custom_llm_provider in [ provider.value for provider in LlmProviders @@ -5799,6 +5810,19 @@ def stream_chunk_builder( # noqa: PLR0915 "content" ] = processor.get_combined_content(content_chunks) + reasoning_chunks = [ + chunk + for chunk in chunks + if len(chunk["choices"]) > 0 + and "reasoning_content" in chunk["choices"][0]["delta"] + and chunk["choices"][0]["delta"]["reasoning_content"] is not None + ] + + if len(reasoning_chunks) > 0: + response["choices"][0]["message"][ + "reasoning_content" + ] = processor.get_combined_reasoning_content(reasoning_chunks) + audio_chunks = [ chunk for chunk in chunks @@ -5813,11 +5837,14 @@ def stream_chunk_builder( # noqa: PLR0915 completion_output = get_content_from_model_response(response) + reasoning_tokens = processor.count_reasoning_tokens(response) + usage = processor.calculate_usage( chunks=chunks, model=model, completion_output=completion_output, messages=messages, + reasoning_tokens=reasoning_tokens, ) setattr(response, "usage", usage) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 28f8acd21c..4c56210625 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -4650,6 +4650,31 @@ "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#gemini-2.0-flash", "supports_tool_choice": true }, + "gemini-2.0-flash": { + "max_tokens": 8192, + "max_input_tokens": 1048576, + "max_output_tokens": 8192, + "max_images_per_prompt": 3000, + "max_videos_per_prompt": 10, + "max_video_length": 1, + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_pdf_size_mb": 30, + "input_cost_per_audio_token": 0.0000007, + "input_cost_per_token": 0.0000001, + "output_cost_per_token": 0.0000004, + "litellm_provider": "vertex_ai-language-models", + "mode": "chat", + "supports_system_messages": true, + "supports_function_calling": true, + "supports_vision": true, + "supports_response_schema": true, + "supports_audio_output": true, + "supports_audio_input": true, + "supported_modalities": ["text", "image", "audio", "video"], + "supports_tool_choice": true, + "source": "https://ai.google.dev/pricing#2_0flash" + }, "gemini-2.0-flash-lite": { "max_input_tokens": 1048576, "max_output_tokens": 8192, diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/250-282480f9afa56ac6.js b/litellm/proxy/_experimental/out/_next/static/chunks/250-282480f9afa56ac6.js new file mode 100644 index 0000000000..5645a08f11 --- /dev/null +++ b/litellm/proxy/_experimental/out/_next/static/chunks/250-282480f9afa56ac6.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[250],{19250:function(e,t,o){o.d(t,{$I:function(){return q},AZ:function(){return L},Au:function(){return eu},BL:function(){return eR},Br:function(){return F},E9:function(){return eU},EG:function(){return eD},EY:function(){return eq},Eb:function(){return C},FC:function(){return en},Gh:function(){return eF},H1:function(){return v},H2:function(){return n},Hx:function(){return ey},I1:function(){return j},It:function(){return x},J$:function(){return ee},K8:function(){return d},K_:function(){return eH},LY:function(){return eG},Lp:function(){return eO},N3:function(){return eE},N8:function(){return Q},NL:function(){return eY},NV:function(){return f},Nc:function(){return eb},O3:function(){return eI},OD:function(){return em},OU:function(){return el},Of:function(){return b},Og:function(){return y},Ov:function(){return E},PT:function(){return D},Qg:function(){return ej},RQ:function(){return _},Rg:function(){return K},Sb:function(){return ev},So:function(){return Y},Tj:function(){return eX},VA:function(){return G},Vt:function(){return eL},W_:function(){return I},X:function(){return et},XO:function(){return k},Xd:function(){return eg},Xm:function(){return S},YU:function(){return ez},Zr:function(){return m},a6:function(){return B},ao:function(){return eZ},b1:function(){return es},cq:function(){return A},cu:function(){return ex},eH:function(){return H},eZ:function(){return eN},fP:function(){return $},g:function(){return eK},gX:function(){return eC},h3:function(){return ea},hT:function(){return e_},hy:function(){return u},ix:function(){return M},j2:function(){return eo},jA:function(){return eM},jE:function(){return eJ},kK:function(){return p},kn:function(){return Z},lP:function(){return h},lU:function(){return e0},lg:function(){return ek},mR:function(){return W},m_:function(){return R},mp:function(){return eV},n$:function(){return ew},nd:function(){return eW},o6:function(){return X},oC:function(){return eT},pf:function(){return eA},qI:function(){return g},qk:function(){return eQ},qm:function(){return w},r6:function(){return O},rs:function(){return N},s0:function(){return z},sN:function(){return eB},t$:function(){return P},t0:function(){return ef},t3:function(){return e$},tB:function(){return e1},tN:function(){return ec},u5:function(){return er},um:function(){return eS},v9:function(){return ep},vh:function(){return eP},wX:function(){return T},wd:function(){return ei},xA:function(){return eh},xX:function(){return J},zg:function(){return ed}});var r=o(20347),a=o(41021);let n=null;console.log=function(){};let c=0,s=e=>new Promise(t=>setTimeout(t,e)),l=async e=>{let t=Date.now();t-c>6e4?(e.includes("Authentication Error - Expired Key")&&(a.ZP.info("UI Session Expired. 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