From dd7d12eabdd213a6aa3905f737f5774e07edafb2 Mon Sep 17 00:00:00 2001 From: Leslie Cheng Date: Sun, 5 Oct 2025 00:09:25 -0700 Subject: [PATCH 01/36] Implement fix for thinking_blocks and converse API calls This fixes Claude's models via the Converse API, which should also fix Claude Code. --- .../adapters/transformation.py | 90 ++++- litellm/types/llms/anthropic.py | 36 +- litellm/types/llms/openai.py | 2 +- ...al_pass_through_adapters_transformation.py | 344 ++++++++++++++++++ 4 files changed, 461 insertions(+), 11 deletions(-) diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index 7de2a1e1c66f..645d477b05fc 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -13,6 +13,8 @@ from openai.types.chat.chat_completion_chunk import Choice as OpenAIStreamingChoice +from litellm.types.utils import StreamingChoices + from litellm.types.llms.anthropic import ( AllAnthropicToolsValues, AnthopicMessagesAssistantMessageParam, @@ -22,9 +24,13 @@ AnthropicMessagesUserMessageParam, AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse, + AnthropicResponseContentBlockThinking, + AnthropicResponseContentBlockRedactedThinking, ContentBlockDelta, ContentJsonBlockDelta, ContentTextBlockDelta, + ContentThinkingBlockDelta, + ContentThinkingSignatureBlockDelta, MessageBlockDelta, MessageDelta, UsageDelta, @@ -42,6 +48,8 @@ ChatCompletionRequest, ChatCompletionSystemMessage, ChatCompletionTextObject, + ChatCompletionThinkingBlock, + ChatCompletionRedactedThinkingBlock, ChatCompletionToolCallFunctionChunk, ChatCompletionToolChoiceFunctionParam, ChatCompletionToolChoiceObjectParam, @@ -227,6 +235,7 @@ def translate_anthropic_messages_to_openai( # noqa: PLR0915 ## ASSISTANT MESSAGE ## assistant_message_str: Optional[str] = None tool_calls: List[ChatCompletionAssistantToolCall] = [] + thinking_blocks: List[Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]] = [] if m["role"] == "assistant": if isinstance(m.get("content"), str): assistant_message_str = str(m.get("content", "")) @@ -253,11 +262,28 @@ def translate_anthropic_messages_to_openai( # noqa: PLR0915 function=function_chunk, ) ) + elif content.get("type") == "thinking": + thinking_block = ChatCompletionThinkingBlock( + type="thinking", + thinking=content.get("thinking") or "", + signature=content.get("signature") or "", + cache_control=content.get("cache_control", {}) + ) + thinking_blocks.append(thinking_block) + elif content.get("type") == "redacted_thinking": + redacted_thinking_block = ChatCompletionRedactedThinkingBlock( + type="redacted_thinking", + data=content.get("data") or "", + cache_control=content.get("cache_control", {}) + ) + thinking_blocks.append(redacted_thinking_block) + if assistant_message_str is not None or len(tool_calls) > 0: assistant_message = ChatCompletionAssistantMessage( role="assistant", content=assistant_message_str, + thinking_blocks=thinking_blocks if len(thinking_blocks) > 0 else None, ) if len(tool_calls) > 0: assistant_message["tool_calls"] = tool_calls @@ -383,11 +409,11 @@ def translate_anthropic_to_openai( def _translate_openai_content_to_anthropic( self, choices: List[Choices] ) -> List[ - Union[AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse] + Union[AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse, AnthropicResponseContentBlockThinking, AnthropicResponseContentBlockRedactedThinking] ]: new_content: List[ Union[ - AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse + AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse, AnthropicResponseContentBlockThinking, AnthropicResponseContentBlockRedactedThinking ] ] = [] for choice in choices: @@ -410,6 +436,24 @@ def _translate_openai_content_to_anthropic( type="text", text=choice.message.content ) ) + elif choice.message.thinking_blocks is not None: + for thinking_block in choice.message.thinking_blocks: + if "thinking" in thinking_block and "signature" in thinking_block: + new_content.append( + AnthropicResponseContentBlockThinking( + type="thinking", + thinking=thinking_block.get("thinking") or "", + signature=thinking_block.get("signature") or "", + ) + ) + elif "data" in thinking_block: + new_content.append( + AnthropicResponseContentBlockRedactedThinking( + type="redacted_thinking", + data=thinking_block.get("data", ""), + ) + ) + return new_content @@ -453,9 +497,9 @@ def translate_openai_response_to_anthropic( return translated_obj def _translate_streaming_openai_chunk_to_anthropic_content_block( - self, choices: List[OpenAIStreamingChoice] + self, choices: List[Union[OpenAIStreamingChoice, StreamingChoices]] ) -> Tuple[ - Literal["text", "tool_use"], + Literal["text", "tool_use", "thinking"], "ContentBlockContentBlockDict", ]: from litellm._uuid import uuid @@ -476,17 +520,35 @@ def _translate_streaming_openai_chunk_to_anthropic_content_block( name=choice.delta.tool_calls[0].function.name or "", input={}, ) + elif ( + isinstance(choice, StreamingChoices) and hasattr(choice.delta, "thinking_blocks") + ): + thinking_blocks = choice.delta.thinking_blocks or [] + if len(thinking_blocks) > 0: + thinking = thinking_blocks[0].get("thinking") or "" + signature = thinking_blocks[0].get("signature") or "" + + if thinking and signature: + raise ValueError("Both `thinking` and `signature` in a single streaming chunk isn't supported.") + return "thinking", ChatCompletionThinkingBlock( + type="thinking", + thinking=thinking, + signature=signature + ) + return "text", TextBlock(type="text", text="") def _translate_streaming_openai_chunk_to_anthropic( - self, choices: List[OpenAIStreamingChoice] + self, choices: List[Union[OpenAIStreamingChoice, StreamingChoices]] ) -> Tuple[ - Literal["text_delta", "input_json_delta"], - Union[ContentTextBlockDelta, ContentJsonBlockDelta], + Literal["text_delta", "input_json_delta", "thinking_delta", "signature_delta"], + Union[ContentTextBlockDelta, ContentJsonBlockDelta, ContentThinkingBlockDelta, ContentThinkingSignatureBlockDelta], ]: text: str = "" + reasoning_content: str = "" + reasoning_signature: str = "" partial_json: Optional[str] = None for choice in choices: if choice.delta.content is not None and len(choice.delta.content) > 0: @@ -499,10 +561,24 @@ def _translate_streaming_openai_chunk_to_anthropic( and tool.function.arguments is not None ): partial_json += tool.function.arguments + elif isinstance(choice, StreamingChoices) and hasattr(choice.delta, "thinking_blocks"): + thinking_blocks = choice.delta.thinking_blocks or [] + if len(thinking_blocks) > 0: + reasoning_content += thinking_blocks[0].get("thinking") or "" + reasoning_signature += thinking_blocks[0].get("signature") or "" + + if reasoning_content and reasoning_signature: + raise ValueError("Both `reasoning` and `signature` in a single streaming chunk isn't supported.") + + if partial_json is not None: return "input_json_delta", ContentJsonBlockDelta( type="input_json_delta", partial_json=partial_json ) + elif reasoning_content: + return "thinking_delta", ContentThinkingBlockDelta(type="thinking_delta", thinking=reasoning_content) + elif reasoning_signature: + return "signature_delta", ContentThinkingSignatureBlockDelta(type="signature_delta", signature=reasoning_signature) else: return "text_delta", ContentTextBlockDelta(type="text_delta", text=text) diff --git a/litellm/types/llms/anthropic.py b/litellm/types/llms/anthropic.py index 02c6f2cf8cf8..24eb8bc86aae 100644 --- a/litellm/types/llms/anthropic.py +++ b/litellm/types/llms/anthropic.py @@ -4,7 +4,7 @@ from pydantic import BaseModel, validator from typing_extensions import Literal, Required, TypedDict -from .openai import ChatCompletionCachedContent, ChatCompletionThinkingBlock +from .openai import ChatCompletionCachedContent, ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock class AnthropicMessagesToolChoice(TypedDict, total=False): @@ -104,6 +104,7 @@ class AnthropicMessagesToolUseParam(TypedDict, total=False): AnthropicMessagesTextParam, AnthropicMessagesToolUseParam, ChatCompletionThinkingBlock, + ChatCompletionRedactedThinkingBlock, ] @@ -264,11 +265,29 @@ class ContentJsonBlockDelta(TypedDict): partial_json: str +class ContentThinkingBlockDelta(TypedDict): + """ + "delta": {"type": "thinking_delta", "thinking": "Let me solve this step by step:"}} + """ + + type: Literal["thinking_delta"] + thinking: str + + +class ContentThinkingSignatureBlockDelta(TypedDict): + """ + "delta": {"type": "signature_delta", "signature": "EqQBCgIYAhIM1gbcDa9GJwZA2b3hGgxBdjrkzLoky3dl1pkiMOYds..."}} + """ + + type: Literal["signature_delta"] + signature: str + + class ContentBlockDelta(TypedDict): type: Literal["content_block_delta"] index: int delta: Union[ - ContentTextBlockDelta, ContentJsonBlockDelta, ContentCitationsBlockDelta + ContentTextBlockDelta, ContentJsonBlockDelta, ContentCitationsBlockDelta, ContentThinkingBlockDelta, ContentThinkingSignatureBlockDelta ] @@ -311,7 +330,7 @@ class ContentBlockStartText(TypedDict): content_block: TextBlock -ContentBlockContentBlockDict = Union[ToolUseBlock, TextBlock] +ContentBlockContentBlockDict = Union[ToolUseBlock, TextBlock, ChatCompletionThinkingBlock] ContentBlockStart = Union[ContentBlockStartToolUse, ContentBlockStartText] @@ -384,6 +403,17 @@ class AnthropicResponseContentBlockToolUse(BaseModel): input: dict +class AnthropicResponseContentBlockThinking(BaseModel): + type: Literal["thinking"] + thinking: str + signature: Optional[str] + + +class AnthropicResponseContentBlockRedactedThinking(BaseModel): + type: Literal["redacted_thinking"] + data: str + + class AnthropicResponseUsageBlock(BaseModel): input_tokens: int output_tokens: int diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py index 9f4ae03b39dc..c6eaf8aa9763 100644 --- a/litellm/types/llms/openai.py +++ b/litellm/types/llms/openai.py @@ -679,7 +679,7 @@ class OpenAIChatCompletionAssistantMessage(TypedDict, total=False): class ChatCompletionAssistantMessage(OpenAIChatCompletionAssistantMessage, total=False): cache_control: ChatCompletionCachedContent - thinking_blocks: Optional[List[ChatCompletionThinkingBlock]] + thinking_blocks: Optional[List[Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]]] class ChatCompletionToolMessage(TypedDict): diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py index e5dba275bda9..dab4049b0b74 100644 --- a/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py +++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py @@ -70,6 +70,188 @@ def test_translate_streaming_openai_chunk_to_anthropic_content_block(): } +def test_translate_streaming_openai_chunk_to_anthropic_thinking_content_block(): + choices = [ + StreamingChoices( + finish_reason=None, + index=0, + delta=Delta( + reasoning_content="I need to summar", + thinking_blocks=[ + { + "type": "thinking", + "thinking": "I need to summar", + "signature": None, + } + ], + provider_specific_fields={ + "thinking_blocks": [ + { + "type": "thinking", + "thinking": "I need to summar", + "signature": None, + } + ] + }, + content="", + role="assistant", + function_call=None, + tool_calls=None, + audio=None, + ), + logprobs=None, + ) + ] + + ( + block_type, + content_block_start, + ) = LiteLLMAnthropicMessagesAdapter()._translate_streaming_openai_chunk_to_anthropic_content_block( + choices=choices + ) + + assert block_type == "thinking" + assert content_block_start == { + "type": "thinking", + "thinking": "I need to summar", + "signature": "", + } + + +def test_translate_streaming_openai_chunk_to_anthropic_thinking_signature_block(): + choices = [ + StreamingChoices( + finish_reason=None, + index=0, + delta=Delta( + reasoning_content="", + thinking_blocks=[ + { + "type": "thinking", + "thinking": None, + "signature": "sigsig", + } + ], + provider_specific_fields={ + "thinking_blocks": [ + { + "type": "thinking", + "thinking": None, + "signature": "sigsig", + } + ] + }, + content="", + role="assistant", + function_call=None, + tool_calls=None, + audio=None, + ), + logprobs=None, + ) + ] + + ( + block_type, + content_block_start, + ) = LiteLLMAnthropicMessagesAdapter()._translate_streaming_openai_chunk_to_anthropic_content_block( + choices=choices + ) + + assert block_type == "thinking" + assert content_block_start == { + "type": "thinking", + "thinking": "", + "signature": "sigsig", + } + + +def test_translate_streaming_openai_chunk_to_anthropic_raises_when_thinking_and_signature_content_block(): + choices = [ + StreamingChoices( + finish_reason=None, + index=0, + delta=Delta( + reasoning_content="", + thinking_blocks=[ + { + "type": "thinking", + "thinking": "I need to summar", + "signature": "sigsig", + } + ], + provider_specific_fields={ + "thinking_blocks": [ + { + "type": "thinking", + "thinking": "I need to summar", + "signature": "sigsig", + } + ] + }, + content="", + role="assistant", + function_call=None, + tool_calls=None, + audio=None, + ), + logprobs=None, + ) + ] + + with pytest.raises(ValueError): + LiteLLMAnthropicMessagesAdapter()._translate_streaming_openai_chunk_to_anthropic_content_block( + choices=choices + ) + + +def test_translate_anthropic_messages_to_openai_thinking_blocks(): + """Test that tool result messages are placed before user messages in the conversation order.""" + + anthropic_messages = [ + AnthropicMessagesUserMessageParam( + role="user", + content=[{"type": "text", "text": "What's the weather in Boston?"}] + ), + AnthopicMessagesAssistantMessageParam( + role="assistant", + content=[ + { + "type": "thinking", + "thinking": "I will call the get_weather tool.", + "signature": "sigsig" + }, + { + "type": "redacted_thinking", + "data": "REDACTED", + }, + { + "type": "tool_use", + "id": "toolu_01234", + "name": "get_weather", + "input": {"location": "Boston"} + } + ] + ), + ] + + adapter = LiteLLMAnthropicMessagesAdapter() + result = adapter.translate_anthropic_messages_to_openai(messages=anthropic_messages) + + assert len(result) == 2 + assert result[1]["role"] == "assistant" + assert "thinking_blocks" in result[1] + assert len(result[1]["thinking_blocks"]) == 2 + assert result[1]["thinking_blocks"][0]["type"] == "thinking" + assert result[1]["thinking_blocks"][0]["thinking"] == "I will call the get_weather tool." + assert result[1]["thinking_blocks"][0]["signature"] == "sigsig" + assert result[1]["thinking_blocks"][1]["type"] == "redacted_thinking" + assert result[1]["thinking_blocks"][1]["data"] == "REDACTED" + assert "tool_calls" in result[1] + assert len(result[1]["tool_calls"]) == 1 + assert result[1]["tool_calls"][0]["id"] == "toolu_01234" + + def test_translate_anthropic_messages_to_openai_tool_message_placement(): """Test that tool result messages are placed before user messages in the conversation order.""" @@ -192,3 +374,165 @@ def test_translate_streaming_openai_chunk_to_anthropic_with_partial_json(): assert type_of_content == "input_json_delta" assert content_block_delta["type"] == "input_json_delta" assert content_block_delta["partial_json"] == ': "San ' + + + +def test_translate_openai_content_to_anthropic_thinking_and_redacted_thinking(): + openai_choices = [ + Choices( + message=Message( + role="assistant", + content=None, + thinking_blocks=[ + { + "type": "thinking", + "thinking": "I need to summar", + "signature": "sigsig", + }, + { + "type": "redacted_thinking", + "data": "REDACTED" + } + ] + ) + ) + ] + + adapter = LiteLLMAnthropicMessagesAdapter() + result = adapter._translate_openai_content_to_anthropic(choices=openai_choices) + + assert len(result) == 2 + assert result[0].type == "thinking" + assert result[0].thinking == "I need to summar" + assert result[0].signature == "sigsig" + assert result[1].type == "redacted_thinking" + assert result[1].data == "REDACTED" + + +def test_translate_streaming_openai_chunk_to_anthropic_with_thinking(): + choices = [ + StreamingChoices( + finish_reason=None, + index=0, + delta=Delta( + reasoning_content="I need to summar", + thinking_blocks=[ + { + "type": "thinking", + "thinking": "I need to summar", + "signature": None, + } + ], + provider_specific_fields={ + "thinking_blocks": [ + { + "type": "thinking", + "thinking": "I need to summar", + "signature": None, + } + ] + }, + content="", + role="assistant", + function_call=None, + tool_calls=None, + audio=None, + ), + logprobs=None, + ) + ] + + ( + type_of_content, + content_block_delta, + ) = LiteLLMAnthropicMessagesAdapter()._translate_streaming_openai_chunk_to_anthropic( + choices=choices + ) + + assert type_of_content == "thinking_delta" + assert content_block_delta["type"] == "thinking_delta" + assert content_block_delta["thinking"] == "I need to summar" + + +def test_translate_streaming_openai_chunk_to_anthropic_with_thinking(): + choices = [ + StreamingChoices( + finish_reason=None, + index=0, + delta=Delta( + reasoning_content="", + thinking_blocks=[ + { + "type": "thinking", + "thinking": None, + "signature": "sigsig", + } + ], + provider_specific_fields={ + "thinking_blocks": [ + { + "type": "thinking", + "thinking": None, + "signature": "sigsig", + } + ] + }, + content="", + role="assistant", + function_call=None, + tool_calls=None, + audio=None, + ), + logprobs=None, + ) + ] + + ( + type_of_content, + content_block_delta, + ) = LiteLLMAnthropicMessagesAdapter()._translate_streaming_openai_chunk_to_anthropic( + choices=choices + ) + + assert type_of_content == "signature_delta" + assert content_block_delta["type"] == "signature_delta" + assert content_block_delta["signature"] == "sigsig" + + +def test_translate_streaming_openai_chunk_to_anthropic_raises_when_thinking_and_signature(): + choices = [ + StreamingChoices( + finish_reason=None, + index=0, + delta=Delta( + reasoning_content="", + thinking_blocks=[ + { + "type": "thinking", + "thinking": "I need to summar", + "signature": "sigsig", + } + ], + provider_specific_fields={ + "thinking_blocks": [ + { + "type": "thinking", + "thinking": "I need to summar", + "signature": "sigsig", + } + ] + }, + content="", + role="assistant", + function_call=None, + tool_calls=None, + audio=None, + ), + logprobs=None, + ) + ] + + with pytest.raises(ValueError): + LiteLLMAnthropicMessagesAdapter()._translate_streaming_openai_chunk_to_anthropic( + choices=choices + ) \ No newline at end of file From 1c3ec18c826368914775f69c1cdba8d4b2f160e4 Mon Sep 17 00:00:00 2001 From: Leslie Cheng Date: Sun, 5 Oct 2025 03:45:54 -0700 Subject: [PATCH 02/36] Add thinking literal --- .../experimental_pass_through/adapters/streaming_iterator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py index 306bcd9bb2cc..ba8be3148616 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py @@ -31,7 +31,7 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): sent_first_chunk: bool = False sent_content_block_start: bool = False sent_content_block_finish: bool = False - current_content_block_type: Literal["text", "tool_use"] = "text" + current_content_block_type: Literal["text", "tool_use", "thinking"] = "text" sent_last_message: bool = False holding_chunk: Optional[Any] = None holding_stop_reason_chunk: Optional[Any] = None From 310e3b30d5b440879970927cd8df831ae3290737 Mon Sep 17 00:00:00 2001 From: Leslie Cheng Date: Sun, 5 Oct 2025 04:48:50 -0700 Subject: [PATCH 03/36] Fix mypy issues --- .../adapters/transformation.py | 54 +++++++++++++------ 1 file changed, 39 insertions(+), 15 deletions(-) diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index 645d477b05fc..ef54f92281d4 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -439,18 +439,28 @@ def _translate_openai_content_to_anthropic( elif choice.message.thinking_blocks is not None: for thinking_block in choice.message.thinking_blocks: if "thinking" in thinking_block and "signature" in thinking_block: + thinking = thinking_block.get("thinking") + signature = thinking_block.get("signature") + + assert isinstance(thinking, str) + assert isinstance(signature, str) or signature is None + new_content.append( AnthropicResponseContentBlockThinking( type="thinking", - thinking=thinking_block.get("thinking") or "", - signature=thinking_block.get("signature") or "", + thinking=thinking, + signature=signature, ) ) elif "data" in thinking_block: + data = thinking_block.get("data") + + assert isinstance(data, str) + new_content.append( AnthropicResponseContentBlockRedactedThinking( type="redacted_thinking", - data=thinking_block.get("data", ""), + data=data, ) ) @@ -525,16 +535,22 @@ def _translate_streaming_openai_chunk_to_anthropic_content_block( ): thinking_blocks = choice.delta.thinking_blocks or [] if len(thinking_blocks) > 0: - thinking = thinking_blocks[0].get("thinking") or "" - signature = thinking_blocks[0].get("signature") or "" - - if thinking and signature: - raise ValueError("Both `thinking` and `signature` in a single streaming chunk isn't supported.") - return "thinking", ChatCompletionThinkingBlock( - type="thinking", - thinking=thinking, - signature=signature - ) + thinking_block = thinking_blocks[0] + if thinking_block["type"] == "thinking": + thinking = thinking_block.get("thinking") or "" + signature = thinking_block.get("signature") or "" + + assert isinstance(thinking, str) + assert isinstance(signature, str) + + if thinking and signature: + raise ValueError("Both `thinking` and `signature` in a single streaming chunk isn't supported.") + + return "thinking", ChatCompletionThinkingBlock( + type="thinking", + thinking=thinking, + signature=signature + ) return "text", TextBlock(type="text", text="") @@ -564,8 +580,16 @@ def _translate_streaming_openai_chunk_to_anthropic( elif isinstance(choice, StreamingChoices) and hasattr(choice.delta, "thinking_blocks"): thinking_blocks = choice.delta.thinking_blocks or [] if len(thinking_blocks) > 0: - reasoning_content += thinking_blocks[0].get("thinking") or "" - reasoning_signature += thinking_blocks[0].get("signature") or "" + for thinking_block in thinking_blocks: + if thinking_block["type"] == "thinking": + thinking = thinking_block.get("thinking") or "" + signature = thinking_block.get("signature") or "" + + assert isinstance(thinking, str) + assert isinstance(signature, str) + + reasoning_content += thinking + reasoning_signature += signature if reasoning_content and reasoning_signature: raise ValueError("Both `reasoning` and `signature` in a single streaming chunk isn't supported.") From d07455bd8ba9e5faac44280d3b7a642e513e8a53 Mon Sep 17 00:00:00 2001 From: Leslie Cheng Date: Sun, 5 Oct 2025 05:25:18 -0700 Subject: [PATCH 04/36] Type fix for redacted thinking --- .../llms/vertex_ai/gemini/transformation.py | 37 ++++++++++--------- 1 file changed, 19 insertions(+), 18 deletions(-) diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index ccaf28e59060..211f9077d77f 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -302,26 +302,27 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 ) if thinking_blocks is not None: for block in thinking_blocks: - 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), + 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, + 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: From 25769b54b82d8ff6caab10f0a16713ad386ff46a Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Thu, 9 Oct 2025 17:26:46 +0530 Subject: [PATCH 05/36] Add voyage model integration in sagemaker --- litellm/llms/sagemaker/completion/handler.py | 73 ++-- .../sagemaker/completion/transformation.py | 142 ++++++- .../llms/sagemaker/transformation_voyage.py | 271 +++++++++++++ .../test_sagemaker_embedding_voyage.py | 374 ++++++++++++++++++ 4 files changed, 818 insertions(+), 42 deletions(-) create mode 100644 litellm/llms/sagemaker/transformation_voyage.py create mode 100644 tests/test_litellm/llms/sagemaker/test_sagemaker_embedding_voyage.py diff --git a/litellm/llms/sagemaker/completion/handler.py b/litellm/llms/sagemaker/completion/handler.py index 3d4108776cae..75977100f661 100644 --- a/litellm/llms/sagemaker/completion/handler.py +++ b/litellm/llms/sagemaker/completion/handler.py @@ -17,14 +17,14 @@ CustomStreamWrapper, EmbeddingResponse, ModelResponse, - Usage, get_secret, ) from ..common_utils import AWSEventStreamDecoder, SagemakerError -from .transformation import SagemakerConfig +from .transformation import SagemakerConfig, SagemakerEmbeddingConfig sagemaker_config = SagemakerConfig() +sagemaker_embedding_config = SagemakerEmbeddingConfig() """ SAGEMAKER AUTH Keys/Vars @@ -578,7 +578,7 @@ def embedding( logger_fn=None, ): """ - Supports Huggingface Jumpstart embeddings like GPT-6B + Supports both Huggingface Jumpstart embeddings and Voyage models """ ### BOTO3 INIT import boto3 @@ -625,8 +625,11 @@ def embedding( ): # completion(top_k=3) > sagemaker_config(top_k=3) <- allows for dynamic variables to be passed in inference_params[k] = v - #### HF EMBEDDING LOGIC - data = json.dumps({"inputs": input}).encode("utf-8") + #### EMBEDDING LOGIC + # Transform request based on model type + provider_config = sagemaker_embedding_config.get_model_config(model) + request_data = provider_config.transform_embedding_request(model, input, optional_params, {}) + data = json.dumps(request_data).encode("utf-8") ## LOGGING request_str = f""" @@ -670,40 +673,28 @@ def embedding( ) print_verbose(f"raw model_response: {response}") - if "embedding" not in response: - raise SagemakerError( - status_code=500, message="embedding not found in response" - ) - embeddings = response["embedding"] - - if not isinstance(embeddings, list): - raise SagemakerError( - status_code=422, - message=f"Response not in expected format - {embeddings}", - ) - - output_data = [] - for idx, embedding in enumerate(embeddings): - output_data.append( - {"object": "embedding", "index": idx, "embedding": embedding} - ) - - model_response.object = "list" - model_response.data = output_data - model_response.model = model - - input_tokens = 0 - for text in input: - input_tokens += len(encoding.encode(text)) - - setattr( - model_response, - "usage", - Usage( - prompt_tokens=input_tokens, - completion_tokens=0, - total_tokens=input_tokens, - ), + + # Transform response based on model type + from httpx import Response as HttpxResponse + + # Create a mock httpx Response object for the transformation + mock_response = HttpxResponse( + status_code=200, + content=json.dumps(response).encode('utf-8'), + headers={"content-type": "application/json"} + ) + + model_response = EmbeddingResponse() + + # Use the appropriate transformation config + request_data = {"input": input} if "voyage" in model.lower() else {"inputs": input} + return provider_config.transform_embedding_response( + model=model, + raw_response=mock_response, + model_response=model_response, + logging_obj=logging_obj, + api_key=None, + request_data=request_data, + optional_params=optional_params, + litellm_params=litellm_params or {} ) - - return model_response diff --git a/litellm/llms/sagemaker/completion/transformation.py b/litellm/llms/sagemaker/completion/transformation.py index 0747a1fe7bad..2f6f5bb8fcb1 100644 --- a/litellm/llms/sagemaker/completion/transformation.py +++ b/litellm/llms/sagemaker/completion/transformation.py @@ -8,6 +8,9 @@ import time from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union +if TYPE_CHECKING: + from litellm.types.llms.openai import AllEmbeddingInputValues + from httpx._models import Headers, Response import litellm @@ -17,8 +20,10 @@ prompt_factory, ) from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException +from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig from litellm.types.llms.openai import AllMessageValues -from litellm.types.utils import ModelResponse, Usage +from litellm.types.utils import ModelResponse, Usage, EmbeddingResponse +from ..transformation_voyage import VoyageSagemakerEmbeddingConfig from litellm.utils import token_counter from ..common_utils import SagemakerError @@ -277,3 +282,138 @@ def validate_environment( headers = {"Content-Type": "application/json", **headers} return headers + + +class SagemakerEmbeddingConfig(BaseEmbeddingConfig): + """ + SageMaker embedding configuration factory for supporting embedding parameters + """ + + def __init__(self) -> None: + pass + + @classmethod + def get_model_config(cls, model: str) -> "BaseEmbeddingConfig": + """ + Factory method to get the appropriate embedding config based on model type + + Args: + model: The model name + + Returns: + Appropriate embedding config instance + """ + if "voyage" in model.lower(): + return VoyageSagemakerEmbeddingConfig() + else: + return cls() + + def get_supported_openai_params(self, model: str) -> List[str]: + # Check if this is an embedding model + if "voyage" in model.lower(): + return VoyageSagemakerEmbeddingConfig().get_supported_openai_params(model) + else: + return [] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + + return optional_params + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[dict, Headers] + ) -> BaseLLMException: + return SagemakerError( + message=error_message, status_code=status_code, headers=headers + ) + + def transform_embedding_request( + self, + model: str, + input: "AllEmbeddingInputValues", + optional_params: dict, + headers: dict, + ) -> dict: + """ + Transform embedding request for Hugging Face models on SageMaker + """ + # HF models expect "inputs" field (plural) + return {"inputs": input, **optional_params} + + def transform_embedding_response( + self, + model: str, + raw_response: Response, + model_response: "EmbeddingResponse", + logging_obj: Any, + api_key: Optional[str] = None, + request_data: dict = {}, + optional_params: dict = {}, + litellm_params: dict = {}, + ) -> "EmbeddingResponse": + """ + Transform embedding response for Hugging Face models on SageMaker + """ + try: + response_data = raw_response.json() + except Exception as e: + raise SagemakerError( + message=f"Failed to parse response: {str(e)}", + status_code=raw_response.status_code + ) + + if "embedding" not in response_data: + raise SagemakerError( + status_code=500, message="HF response missing 'embedding' field" + ) + embeddings = response_data["embedding"] + + if not isinstance(embeddings, list): + raise SagemakerError( + status_code=422, + message=f"HF response not in expected format - {embeddings}", + ) + + output_data = [] + for idx, embedding in enumerate(embeddings): + output_data.append( + {"object": "embedding", "index": idx, "embedding": embedding} + ) + + model_response.object = "list" + model_response.data = output_data + model_response.model = model + + # Calculate usage from request data + input_texts = request_data.get("inputs", []) + input_tokens = 0 + for text in input_texts: + input_tokens += len(text.split()) # Simple word count fallback + + model_response.usage = Usage( + prompt_tokens=input_tokens, + completion_tokens=0, + total_tokens=input_tokens, + ) + + return model_response + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[Any], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + """ + Validate environment for SageMaker embeddings + """ + return {"Content-Type": "application/json"} diff --git a/litellm/llms/sagemaker/transformation_voyage.py b/litellm/llms/sagemaker/transformation_voyage.py new file mode 100644 index 000000000000..1133e7b00281 --- /dev/null +++ b/litellm/llms/sagemaker/transformation_voyage.py @@ -0,0 +1,271 @@ +""" +Voyage AI transformation for SageMaker endpoints + +This module handles the transformation of Voyage AI embedding requests +and responses when deployed on AWS SageMaker endpoints. +""" + +from typing import TYPE_CHECKING, Any, List, Optional, Union + +if TYPE_CHECKING: + from litellm.types.llms.openai import AllEmbeddingInputValues + +from httpx._models import Headers, Response + +from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.types.utils import EmbeddingResponse, Usage + + + +class VoyageSagemakerError(BaseLLMException): + """Custom error class for Voyage SageMaker operations""" + + def __init__( + self, + status_code: int, + message: str, + headers: Union[dict, Headers] = {}, + ): + super().__init__( + status_code=status_code, + message=message, + headers=headers, + ) + + +class VoyageSagemakerEmbeddingConfig(BaseEmbeddingConfig): + """ + Voyage AI embedding configuration for SageMaker endpoints + + This class handles the transformation of Voyage AI embedding requests + and responses when deployed on AWS SageMaker endpoints. + + Reference: https://docs.voyageai.com/reference/embeddings-api + """ + + def __init__(self) -> None: + pass + + def get_supported_openai_params(self, model: str) -> List[str]: + """ + Get supported OpenAI parameters for Voyage models on SageMaker + + Args: + model: The model name + + Returns: + List of supported parameter names + """ + return [ + "encoding_format", + "dimensions", + ] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + """ + Map OpenAI parameters to Voyage SageMaker format + + Args: + non_default_params: Parameters that are not default values + optional_params: Optional parameters dict to update + model: The model name + drop_params: Whether to drop unsupported parameters + + Returns: + Updated optional_params dict + """ + for param, value in non_default_params.items(): + if param == "encoding_format": + # Voyage supports: float, base64 + if value in ["float", "base64"]: + optional_params["encoding_format"] = value + else: + if not drop_params: + raise ValueError( + f"Unsupported encoding_format: {value}. Voyage supports: float, base64" + ) + elif param == "dimensions": + # Map OpenAI dimensions to Voyage output_dimension + optional_params["output_dimension"] = value + elif not drop_params: + raise ValueError(f"Unsupported parameter for Voyage SageMaker: {param}") + + return optional_params + + def transform_embedding_request( + self, + model: str, + input: "AllEmbeddingInputValues", + optional_params: dict, + headers: dict, + ) -> dict: + """ + Transform embedding request for Voyage models on SageMaker + + Args: + model: The model name + input: List of input texts to embed + optional_params: Optional parameters + headers: Request headers + + Returns: + Transformed request data + """ + # Voyage models on SageMaker expect "input" field (singular) + request_data = { + "input": input, + **optional_params + } + + return request_data + + def transform_embedding_response( + self, + model: str, + raw_response: Response, + model_response: EmbeddingResponse, + logging_obj: Any, + api_key: Optional[str] = None, + request_data: dict = {}, + optional_params: dict = {}, + litellm_params: dict = {}, + ) -> EmbeddingResponse: + """ + Transform embedding response from Voyage SageMaker format to OpenAI format + + Args: + model: The model name + raw_response: Raw HTTP response + model_response: EmbeddingResponse object to populate + logging_obj: Logging object + api_key: API key (unused) + request_data: Original request data + optional_params: Optional parameters + litellm_params: LiteLLM parameters + + Returns: + Transformed EmbeddingResponse + """ + try: + response_data = raw_response.json() + except Exception as e: + raise VoyageSagemakerError( + message=f"Failed to parse response: {str(e)}", + status_code=raw_response.status_code + ) + + # Voyage response format: + # { + # "data": [{"object": "embedding", "embedding": [...], "index": 0}, ...], + # "object": "list", + # "model": "voyage-code-02", + # "usage": {"total_tokens": 10} + # } + + if "data" not in response_data: + raise VoyageSagemakerError( + status_code=500, + message="Voyage response missing 'data' field" + ) + + embeddings = response_data["data"] + if not isinstance(embeddings, list): + raise VoyageSagemakerError( + status_code=422, + message=f"Voyage response data not in expected format - {embeddings}", + ) + + output_data = [] + for idx, embedding_item in enumerate(embeddings): + if "embedding" not in embedding_item: + raise VoyageSagemakerError( + status_code=500, + message=f"Voyage embedding item {idx} missing 'embedding' field" + ) + + output_data.append({ + "object": "embedding", + "index": idx, + "embedding": embedding_item["embedding"] + }) + + model_response.object = response_data.get("object", "list") + model_response.data = output_data + model_response.model = response_data.get("model", model) + + # Calculate usage + total_tokens = response_data.get("usage", {}).get("total_tokens", 0) + if total_tokens == 0: + # Fallback: calculate tokens from input + input_tokens = 0 + for text in request_data.get("input", []): + input_tokens += len(text.split()) # Simple word count fallback + total_tokens = input_tokens + + model_response.usage = Usage( + prompt_tokens=total_tokens, + completion_tokens=0, + total_tokens=total_tokens, + ) + + return model_response + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[Any], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + """ + Validate environment for Voyage SageMaker embeddings + + Args: + headers: Request headers + model: Model name + messages: Messages (unused for embeddings) + optional_params: Optional parameters + litellm_params: LiteLLM parameters + api_key: API key (unused) + api_base: API base (unused) + + Returns: + Validated headers + """ + return { + "Content-Type": "application/json", + **headers + } + + def get_error_class( + self, + error_message: str, + status_code: int, + headers: Union[dict, Headers] + ) -> BaseLLMException: + """ + Get the appropriate error class for this configuration + + Args: + error_message: Error message + status_code: HTTP status code + headers: Response headers + + Returns: + Appropriate exception instance + """ + return VoyageSagemakerError( + message=error_message, + status_code=status_code, + headers=headers + ) diff --git a/tests/test_litellm/llms/sagemaker/test_sagemaker_embedding_voyage.py b/tests/test_litellm/llms/sagemaker/test_sagemaker_embedding_voyage.py new file mode 100644 index 000000000000..56d55371bc5d --- /dev/null +++ b/tests/test_litellm/llms/sagemaker/test_sagemaker_embedding_voyage.py @@ -0,0 +1,374 @@ +""" +Test cases for SageMaker Voyage embedding model integration + +This module tests the factory pattern implementation for Voyage embedding models +deployed on AWS SageMaker endpoints, including parameter handling, request/response +transformation, and model type detection. +""" + +import json +import os +import sys +from unittest.mock import MagicMock, patch + +import httpx +import pytest + +sys.path.insert(0, os.path.abspath("../../../../..")) + +from litellm import embedding +from litellm.llms.sagemaker.completion.transformation import SagemakerEmbeddingConfig +from litellm.llms.sagemaker.transformation_voyage import VoyageSagemakerEmbeddingConfig +from litellm.types.utils import EmbeddingResponse, Usage + + +class TestSagemakerEmbeddingFactory: + """Test the factory pattern for SageMaker embedding configurations""" + + def test_get_model_config_voyage_model(self): + """Test that Voyage models return VoyageSagemakerEmbeddingConfig""" + config = SagemakerEmbeddingConfig.get_model_config("voyage-3-5-embedding") + + assert isinstance(config, VoyageSagemakerEmbeddingConfig) + assert config.get_supported_openai_params("voyage-3-5-embedding") == ["encoding_format", "dimensions"] + + def test_get_model_config_hf_model(self): + """Test that non-Voyage models return base SagemakerEmbeddingConfig""" + config = SagemakerEmbeddingConfig.get_model_config("sentence-transformers-model") + + assert isinstance(config, SagemakerEmbeddingConfig) + assert config.get_supported_openai_params("sentence-transformers-model") == [] + + def test_get_model_config_case_insensitive(self): + """Test that model detection is case insensitive""" + config1 = SagemakerEmbeddingConfig.get_model_config("VOYAGE-3-5-embedding") + config2 = SagemakerEmbeddingConfig.get_model_config("Voyage-3-5-Embedding") + config3 = SagemakerEmbeddingConfig.get_model_config("voyage-3-5-embedding") + + assert isinstance(config1, VoyageSagemakerEmbeddingConfig) + assert isinstance(config2, VoyageSagemakerEmbeddingConfig) + assert isinstance(config3, VoyageSagemakerEmbeddingConfig) + + +class TestVoyageSagemakerEmbeddingConfig: + """Test Voyage-specific embedding configuration""" + + def setup_method(self): + self.config = VoyageSagemakerEmbeddingConfig() + + def test_get_supported_openai_params(self): + """Test supported parameters for Voyage models""" + params = self.config.get_supported_openai_params("voyage-3-5-embedding") + assert params == ["encoding_format", "dimensions"] + + def test_map_openai_params_encoding_format(self): + """Test mapping of encoding_format parameter""" + result = self.config.map_openai_params( + non_default_params={"encoding_format": "float"}, + optional_params={}, + model="voyage-3-5-embedding", + drop_params=False + ) + assert result == {"encoding_format": "float"} + + def test_map_openai_params_dimensions(self): + """Test mapping of dimensions parameter to output_dimension""" + result = self.config.map_openai_params( + non_default_params={"dimensions": 1024}, + optional_params={}, + model="voyage-3-5-embedding", + drop_params=False + ) + assert result == {"output_dimension": 1024} + + def test_map_openai_params_unsupported_encoding(self): + """Test handling of unsupported encoding_format values""" + with pytest.raises(ValueError, match="Unsupported encoding_format"): + self.config.map_openai_params( + non_default_params={"encoding_format": "invalid"}, + optional_params={}, + model="voyage-3-5-embedding", + drop_params=False + ) + + def test_map_openai_params_drop_unsupported(self): + """Test dropping unsupported parameters when drop_params=True""" + result = self.config.map_openai_params( + non_default_params={"encoding_format": "invalid", "dimensions": 512}, + optional_params={}, + model="voyage-3-5-embedding", + drop_params=True + ) + assert result == {"output_dimension": 512} + + def test_transform_embedding_request(self): + """Test Voyage request transformation""" + result = self.config.transform_embedding_request( + model="voyage-3-5-embedding", + input=["Hello", "World"], + optional_params={"encoding_format": "float"}, + headers={} + ) + expected = { + "input": ["Hello", "World"], + "encoding_format": "float" + } + assert result == expected + + def test_transform_embedding_response(self): + """Test Voyage response transformation to OpenAI format""" + # Mock Voyage response + voyage_response = { + "data": [ + { + "object": "embedding", + "embedding": [0.1, 0.2, 0.3], + "index": 0 + }, + { + "object": "embedding", + "embedding": [0.4, 0.5, 0.6], + "index": 1 + } + ], + "object": "list", + "model": "voyage-3-5-embedding", + "usage": {"total_tokens": 10} + } + + # Create mock httpx Response + mock_response = httpx.Response( + status_code=200, + content=json.dumps(voyage_response).encode('utf-8'), + headers={"content-type": "application/json"} + ) + + model_response = EmbeddingResponse() + result = self.config.transform_embedding_response( + model="voyage-3-5-embedding", + raw_response=mock_response, + model_response=model_response, + logging_obj=None, + request_data={"input": ["Hello", "World"]} + ) + + # Verify response structure + assert result.object == "list" + assert result.model == "voyage-3-5-embedding" + assert len(result.data) == 2 + assert result.data[0]["object"] == "embedding" + assert result.data[0]["index"] == 0 + assert result.data[0]["embedding"] == [0.1, 0.2, 0.3] + assert result.data[1]["object"] == "embedding" + assert result.data[1]["index"] == 1 + assert result.data[1]["embedding"] == [0.4, 0.5, 0.6] + assert isinstance(result.usage, Usage) + assert result.usage.total_tokens == 10 + + +class TestHFSagemakerEmbeddingConfig: + """Test Hugging Face embedding configuration""" + + def setup_method(self): + self.config = SagemakerEmbeddingConfig() + + def test_get_supported_openai_params_hf(self): + """Test that HF models don't support embedding parameters""" + params = self.config.get_supported_openai_params("sentence-transformers-model") + assert params == [] + + def test_transform_embedding_request_hf(self): + """Test HF request transformation""" + result = self.config.transform_embedding_request( + model="sentence-transformers-model", + input=["Hello", "World"], + optional_params={}, + headers={} + ) + expected = { + "inputs": ["Hello", "World"] + } + assert result == expected + + def test_transform_embedding_response_hf(self): + """Test HF response transformation to OpenAI format""" + # Mock HF response + hf_response = { + "embedding": [ + [0.1, 0.2, 0.3], + [0.4, 0.5, 0.6] + ] + } + + # Create mock httpx Response + mock_response = httpx.Response( + status_code=200, + content=json.dumps(hf_response).encode('utf-8'), + headers={"content-type": "application/json"} + ) + + model_response = EmbeddingResponse() + result = self.config.transform_embedding_response( + model="sentence-transformers-model", + raw_response=mock_response, + model_response=model_response, + logging_obj=None, + request_data={"inputs": ["Hello", "World"]} + ) + + # Verify response structure + assert result.object == "list" + assert result.model == "sentence-transformers-model" + assert len(result.data) == 2 + assert result.data[0]["object"] == "embedding" + assert result.data[0]["index"] == 0 + assert result.data[0]["embedding"] == [0.1, 0.2, 0.3] + assert result.data[1]["object"] == "embedding" + assert result.data[1]["index"] == 1 + assert result.data[1]["embedding"] == [0.4, 0.5, 0.6] + assert isinstance(result.usage, Usage) + + +class TestSagemakerEmbeddingIntegration: + """Integration tests for SageMaker embedding with factory pattern""" + + def test_voyage_embedding_request_format(self): + """Test that Voyage models use correct request format""" + with patch('litellm.llms.sagemaker.completion.handler.SagemakerLLM.embedding') as mock_embedding: + # Mock the actual SageMaker call to avoid AWS credentials + mock_embedding.return_value = EmbeddingResponse( + object="list", + data=[ + {"object": "embedding", "index": 0, "embedding": [0.1, 0.2, 0.3]}, + {"object": "embedding", "index": 1, "embedding": [0.4, 0.5, 0.6]} + ], + model="voyage-3-5-embedding", + usage=Usage(prompt_tokens=10, completion_tokens=0, total_tokens=10) + ) + + # Test Voyage model + response = embedding( + model="sagemaker/voyage-3-5-embedding-endpoint", + input=["Hello", "World"], + encoding_format="float", + dimensions=1024 + ) + + # Verify the request was made with correct format + mock_embedding.assert_called_once() + call_args = mock_embedding.call_args + assert call_args[1]["model"] == "voyage-3-5-embedding-endpoint" + assert call_args[1]["input"] == ["Hello", "World"] + # Check that the parameters are in the optional_params + optional_params = call_args[1].get("optional_params", {}) + assert optional_params.get("encoding_format") == "float" + assert optional_params.get("output_dimension") == 1024 # dimensions is mapped to output_dimension + + def test_hf_embedding_request_format(self): + """Test that HF models use correct request format""" + with patch('litellm.llms.sagemaker.completion.handler.SagemakerLLM.embedding') as mock_embedding: + # Mock the actual SageMaker call to avoid AWS credentials + mock_embedding.return_value = EmbeddingResponse( + object="list", + data=[ + {"object": "embedding", "index": 0, "embedding": [0.1, 0.2, 0.3]}, + {"object": "embedding", "index": 1, "embedding": [0.4, 0.5, 0.6]} + ], + model="sentence-transformers-model", + usage=Usage(prompt_tokens=10, completion_tokens=0, total_tokens=10) + ) + + # Test HF model with drop_params=True to ignore unsupported parameters + response = embedding( + model="sagemaker/sentence-transformers-endpoint", + input=["Hello", "World"], + encoding_format="float", # Should be ignored + dimensions=1024, # Should be ignored + drop_params=True + ) + + # Verify the request was made + mock_embedding.assert_called_once() + call_args = mock_embedding.call_args + assert call_args[1]["model"] == "sentence-transformers-endpoint" + assert call_args[1]["input"] == ["Hello", "World"] + # HF models should ignore these parameters in optional_params + optional_params = call_args[1].get("optional_params", {}) + assert "encoding_format" not in optional_params or optional_params["encoding_format"] is None + assert "dimensions" not in optional_params or optional_params["dimensions"] is None + + def test_parameter_validation_voyage(self): + """Test parameter validation for Voyage models""" + # Test valid parameters + config = VoyageSagemakerEmbeddingConfig() + result = config.map_openai_params( + non_default_params={"encoding_format": "float", "dimensions": 512}, + optional_params={}, + model="voyage-3-5-embedding", + drop_params=False + ) + assert result == {"encoding_format": "float", "output_dimension": 512} + + def test_parameter_validation_hf(self): + """Test parameter validation for HF models""" + # Test that HF models ignore embedding parameters + config = SagemakerEmbeddingConfig() + result = config.map_openai_params( + non_default_params={"encoding_format": "float", "dimensions": 512}, + optional_params={}, + model="sentence-transformers-model", + drop_params=False + ) + assert result == {} # HF models should ignore these parameters + + +class TestErrorHandling: + """Test error handling in the embedding integration""" + + def test_voyage_response_missing_data(self): + """Test handling of Voyage response missing data field""" + config = VoyageSagemakerEmbeddingConfig() + + # Mock response without data field + mock_response = httpx.Response( + status_code=200, + content=json.dumps({"object": "list"}).encode('utf-8'), + headers={"content-type": "application/json"} + ) + + model_response = EmbeddingResponse() + + with pytest.raises(Exception, match="Voyage response missing 'data' field"): + config.transform_embedding_response( + model="voyage-3-5-embedding", + raw_response=mock_response, + model_response=model_response, + logging_obj=None, + request_data={"input": ["Hello"]} + ) + + def test_hf_response_missing_embedding(self): + """Test handling of HF response missing embedding field""" + config = SagemakerEmbeddingConfig() + + # Mock response without embedding field + mock_response = httpx.Response( + status_code=200, + content=json.dumps({"object": "list"}).encode('utf-8'), + headers={"content-type": "application/json"} + ) + + model_response = EmbeddingResponse() + + with pytest.raises(Exception, match="HF response missing 'embedding' field"): + config.transform_embedding_response( + model="sentence-transformers-model", + raw_response=mock_response, + model_response=model_response, + logging_obj=None, + request_data={"inputs": ["Hello"]} + ) + + +if __name__ == "__main__": + pytest.main([__file__]) From a33c34820ff5302689e0f72f8ca782362689e299 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Thu, 9 Oct 2025 18:20:28 +0530 Subject: [PATCH 06/36] Add tiered pricing and cost calculation for xai --- litellm/llms/xai/cost_calculator.py | 73 ++++++++-- model_prices_and_context_window.json | 8 + .../llms/xai/test_xai_cost_calculator.py | 137 +++++++++++++++++- 3 files changed, 205 insertions(+), 13 deletions(-) diff --git a/litellm/llms/xai/cost_calculator.py b/litellm/llms/xai/cost_calculator.py index 62a48080d1c6..6ca740346c49 100644 --- a/litellm/llms/xai/cost_calculator.py +++ b/litellm/llms/xai/cost_calculator.py @@ -1,6 +1,7 @@ """ Helper util for handling XAI-specific cost calculation - e.g.: reasoning tokens for grok models +- handles tiered pricing for tokens above 128k """ from typing import Tuple, Union @@ -12,6 +13,7 @@ def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]: """ Calculates the cost per token for a given XAI model, prompt tokens, and completion tokens. + Handles tiered pricing for tokens above 128k. Input: - model: str, the model name without provider prefix @@ -23,9 +25,7 @@ def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]: ## GET MODEL INFO model_info = get_model_info(model=model, custom_llm_provider="xai") - def _safe_float_cast( - value: Union[str, int, float, None, object], default: float = 0.0 - ) -> float: + def _safe_float_cast(value: Union[str, int, float, None, object], default: float = 0.0) -> float: """Safely cast a value to float with proper type handling for mypy.""" if value is None: return default @@ -34,21 +34,72 @@ def _safe_float_cast( except (ValueError, TypeError): return default + def _calculate_tiered_cost(tokens: int, base_cost_key: str, tiered_cost_key: str) -> float: + """Calculate cost using tiered pricing if available. + + For xAI models: + - First 128k tokens are billed at base rate + - Tokens beyond 128k are billed at tiered rate + """ + base_cost = _safe_float_cast(model_info.get(base_cost_key)) + tiered_cost = _safe_float_cast(model_info.get(tiered_cost_key)) + + if tokens <= 128000 or tiered_cost <= 0: + # Use base pricing for all tokens + return tokens * base_cost + else: + # Use tiered pricing: first 128k at base rate, rest at tiered rate + base_tokens = 128000 + tiered_tokens = tokens - 128000 + return (base_tokens * base_cost) + (tiered_tokens * tiered_cost) + ## CALCULATE INPUT COST - input_cost_per_token = _safe_float_cast(model_info.get("input_cost_per_token")) - prompt_cost: float = (usage.prompt_tokens or 0) * input_cost_per_token + prompt_tokens = usage.prompt_tokens or 0 + prompt_cost = _calculate_tiered_cost( + prompt_tokens, "input_cost_per_token", "input_cost_per_token_above_128k_tokens" + ) - ## CALCULATE OUTPUT COST - output_cost_per_token = _safe_float_cast(model_info.get("output_cost_per_token")) + # Add cached input tokens cost if available + if hasattr(usage, "prompt_tokens_details") and usage.prompt_tokens_details: + cache_hit_tokens = getattr(usage.prompt_tokens_details, "cache_hit_tokens", 0) or 0 + if cache_hit_tokens > 0: + cache_read_cost = _safe_float_cast(model_info.get("cache_read_input_token_cost")) + cache_read_cost_above_128k = _safe_float_cast( + model_info.get("cache_read_input_token_cost_above_128k_tokens") + ) + if cache_hit_tokens <= 128000 or cache_read_cost_above_128k <= 0: + cache_cost = cache_hit_tokens * cache_read_cost + else: + # Tiered pricing for cached tokens + base_cache_tokens = 128000 + tiered_cache_tokens = cache_hit_tokens - 128000 + cache_cost = (base_cache_tokens * cache_read_cost) + (tiered_cache_tokens * cache_read_cost_above_128k) + + prompt_cost += cache_cost + + ## CALCULATE OUTPUT COST # For XAI models, completion is billed as (visible completion tokens + reasoning tokens) completion_tokens = int(getattr(usage, "completion_tokens", 0) or 0) reasoning_tokens = 0 if hasattr(usage, "completion_tokens_details") and usage.completion_tokens_details: - reasoning_tokens = int( - getattr(usage.completion_tokens_details, "reasoning_tokens", 0) or 0 - ) + reasoning_tokens = int(getattr(usage.completion_tokens_details, "reasoning_tokens", 0) or 0) + + total_completion_tokens = completion_tokens + reasoning_tokens - completion_cost = (completion_tokens + reasoning_tokens) * output_cost_per_token + # Output tokens are billed at tiered rates if INPUT tokens are above 128k + if prompt_tokens > 128000: + # Use tiered pricing for ALL output tokens when input tokens > 128k + output_cost_per_token_above_128k = _safe_float_cast(model_info.get("output_cost_per_token_above_128k_tokens")) + if output_cost_per_token_above_128k > 0: + completion_cost = total_completion_tokens * output_cost_per_token_above_128k + else: + # Fallback to base pricing if no tiered pricing available + output_cost_per_token = _safe_float_cast(model_info.get("output_cost_per_token")) + completion_cost = total_completion_tokens * output_cost_per_token + else: + # Use base pricing for output tokens when input tokens <= 128k + output_cost_per_token = _safe_float_cast(model_info.get("output_cost_per_token")) + completion_cost = total_completion_tokens * output_cost_per_token return prompt_cost, completion_cost diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 06cb67e56c50..14575ac6c110 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -22994,7 +22994,9 @@ "max_tokens": 2e6, "mode": "chat", "input_cost_per_token": 0.2e-06, + "input_cost_per_token_above_128k_tokens": 0.4e-06, "output_cost_per_token": 0.5e-06, + "output_cost_per_token_above_128k_tokens": 1e-06, "cache_read_input_token_cost": 0.05e-06, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, @@ -23010,7 +23012,9 @@ "max_tokens": 2e6, "mode": "chat", "input_cost_per_token": 0.2e-06, + "input_cost_per_token_above_128k_tokens": 0.4e-06, "output_cost_per_token": 0.5e-06, + "output_cost_per_token_above_128k_tokens": 1e-06, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, "supports_tool_choice": true, @@ -23018,12 +23022,14 @@ }, "xai/grok-4-0709": { "input_cost_per_token": 3e-06, + "input_cost_per_token_above_128k_tokens": 6e-06, "litellm_provider": "xai", "max_input_tokens": 256000, "max_output_tokens": 256000, "max_tokens": 256000, "mode": "chat", "output_cost_per_token": 1.5e-05, + "output_cost_per_token_above_128k_tokens": 30e-06, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, "supports_reasoning": true, @@ -23032,12 +23038,14 @@ }, "xai/grok-4-latest": { "input_cost_per_token": 3e-06, + "input_cost_per_token_above_128k_tokens": 6e-06, "litellm_provider": "xai", "max_input_tokens": 256000, "max_output_tokens": 256000, "max_tokens": 256000, "mode": "chat", "output_cost_per_token": 1.5e-05, + "output_cost_per_token_above_128k_tokens": 30e-06, "source": "https://docs.x.ai/docs/models", "supports_function_calling": true, "supports_reasoning": true, diff --git a/tests/test_litellm/llms/xai/test_xai_cost_calculator.py b/tests/test_litellm/llms/xai/test_xai_cost_calculator.py index 5a26d808c5de..7e043e4acab5 100644 --- a/tests/test_litellm/llms/xai/test_xai_cost_calculator.py +++ b/tests/test_litellm/llms/xai/test_xai_cost_calculator.py @@ -24,8 +24,16 @@ class TestXAICostCalculator: def setup_method(self): """Set up test environment.""" - os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" - litellm.model_cost = litellm.get_model_cost_map(url="") + # Load the main model cost map directly to ensure we have the latest pricing + import json + try: + with open("model_prices_and_context_window.json", "r") as f: + model_cost_map = json.load(f) + litellm.model_cost = model_cost_map + except FileNotFoundError: + # Fallback to default behavior + os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" + litellm.model_cost = litellm.get_model_cost_map(url="") def test_basic_cost_calculation(self): """Test basic cost calculation without reasoning tokens.""" @@ -187,3 +195,128 @@ def test_edge_case_large_reasoning_tokens(self): assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) + + def test_tiered_pricing_above_128k_tokens(self): + """Test tiered pricing for tokens above 128k.""" + # Test with grok-4-fast-reasoning which has tiered pricing + usage = Usage( + prompt_tokens=150000, # Above 128k threshold + completion_tokens=100000, # Above 128k threshold + total_tokens=250000, + completion_tokens_details=CompletionTokensDetailsWrapper( + accepted_prediction_tokens=0, + audio_tokens=0, + reasoning_tokens=50000, # Total completion tokens = 100000 + 50000 = 150000 > 128k + rejected_prediction_tokens=0, + text_tokens=None, + ), + ) + + prompt_cost, completion_cost = cost_per_token(model="xai/grok-4-fast-reasoning", usage=usage) + + # Expected costs for grok-4-fast-reasoning with tiered pricing: + # Input: (128000 * $0.2e-6) + (22000 * $0.4e-6) = $0.0256 + $0.0088 = $0.0344 + # Completion: (100000 + 50000) tokens * $1e-6 (tiered rate since input > 128k) = $0.15 + expected_prompt_cost = (128000 * 0.2e-6) + (22000 * 0.4e-6) + expected_completion_cost = (100000 + 50000) * 1e-6 + + assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) + assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) + + def test_tiered_pricing_below_128k_tokens(self): + """Test that regular pricing is used for tokens below 128k threshold.""" + # Test with grok-4-fast-reasoning which has tiered pricing + usage = Usage( + prompt_tokens=100000, # Below 128k threshold + completion_tokens=50000, + total_tokens=150000, + completion_tokens_details=CompletionTokensDetailsWrapper( + accepted_prediction_tokens=0, + audio_tokens=0, + reasoning_tokens=10000, + rejected_prediction_tokens=0, + text_tokens=None, + ), + ) + + prompt_cost, completion_cost = cost_per_token(model="xai/grok-4-fast-reasoning", usage=usage) + + # Expected costs for grok-4-fast-reasoning with regular pricing: + # Input: 100000 tokens * $0.2e-6 (regular rate) = $0.02 + # Completion: (50000 + 10000) tokens * $0.5e-6 (regular rate) = $0.03 + expected_prompt_cost = 100000 * 0.2e-6 + expected_completion_cost = (50000 + 10000) * 0.5e-6 + + assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) + assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) + + def test_tiered_pricing_grok_4_latest(self): + """Test tiered pricing for grok-4-latest model.""" + usage = Usage( + prompt_tokens=200000, # Above 128k threshold + completion_tokens=100000, + total_tokens=300000, + completion_tokens_details=CompletionTokensDetailsWrapper( + accepted_prediction_tokens=0, + audio_tokens=0, + reasoning_tokens=50000, + rejected_prediction_tokens=0, + text_tokens=None, + ), + ) + + prompt_cost, completion_cost = cost_per_token(model="xai/grok-4-latest", usage=usage) + + # Expected costs for grok-4-latest with tiered pricing: + # Input: (128000 * $3e-6) + (72000 * $6e-6) = $0.384 + $0.432 = $0.816 + # Completion: (100000 + 50000) tokens * $30e-6 (tiered rate since input > 128k) = $4.5 + expected_prompt_cost = (128000 * 3e-6) + (72000 * 6e-6) + expected_completion_cost = (100000 + 50000) * 30e-6 + + assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) + assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) + + def test_tiered_pricing_output_tokens_below_128k(self): + """Test that output tokens get tiered rate when input tokens > 128k, even if output tokens < 128k.""" + usage = Usage( + prompt_tokens=150000, # Above 128k threshold + completion_tokens=50000, # Below 128k threshold + total_tokens=200000, + completion_tokens_details=CompletionTokensDetailsWrapper( + accepted_prediction_tokens=0, + audio_tokens=0, + reasoning_tokens=10000, # Total completion tokens = 50000 + 10000 = 60000 < 128k + rejected_prediction_tokens=0, + text_tokens=None, + ), + ) + + prompt_cost, completion_cost = cost_per_token(model="xai/grok-4-fast-reasoning", usage=usage) + + # Expected costs for grok-4-fast-reasoning: + # Input: (128000 * $0.2e-6) + (22000 * $0.4e-6) = $0.0256 + $0.0088 = $0.0344 + # Completion: (50000 + 10000) tokens * $1e-6 (tiered rate since input > 128k) = $0.06 + expected_prompt_cost = (128000 * 0.2e-6) + (22000 * 0.4e-6) + expected_completion_cost = (50000 + 10000) * 1e-6 + + assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) + assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) + + def test_tiered_pricing_model_without_tiered_pricing(self): + """Test that models without tiered pricing use regular pricing even above 128k.""" + usage = Usage( + prompt_tokens=150000, # Above 128k threshold + completion_tokens=50000, + total_tokens=200000, + ) + + prompt_cost, completion_cost = cost_per_token(model="grok-3-mini", usage=usage) + + # grok-3-mini doesn't have tiered pricing, so should use regular rates: + # Input: 150000 tokens * $3e-7 (regular rate) = $0.045 + # Completion: 50000 tokens * $5e-7 (regular rate) = $0.025 + expected_prompt_cost = 150000 * 3e-7 + expected_completion_cost = 50000 * 5e-7 + + assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) + assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) From 60028806add5412e5607f23f25e991b55ac8cea7 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Thu, 9 Oct 2025 18:23:24 +0530 Subject: [PATCH 07/36] Add config file logic --- litellm/utils.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/litellm/utils.py b/litellm/utils.py index 5861d703a344..d471d463b2bb 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -7211,6 +7211,9 @@ def get_provider_embedding_config( return VolcEngineEmbeddingConfig() elif litellm.LlmProviders.OVHCLOUD == provider: return litellm.OVHCloudEmbeddingConfig() + elif litellm.LlmProviders.SAGEMAKER == provider: + from litellm.llms.sagemaker.completion.transformation import SagemakerEmbeddingConfig + return SagemakerEmbeddingConfig.get_model_config(model) return None @staticmethod From 8c7d7a310f73b111685daeb5462b47c3d22d8d48 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Fri, 10 Oct 2025 08:53:05 +0530 Subject: [PATCH 08/36] Use already exiting voyage transformation --- .../sagemaker/completion/transformation.py | 6 +- .../llms/sagemaker/transformation_voyage.py | 271 ------------------ .../test_sagemaker_embedding_voyage.py | 58 ++-- 3 files changed, 33 insertions(+), 302 deletions(-) delete mode 100644 litellm/llms/sagemaker/transformation_voyage.py diff --git a/litellm/llms/sagemaker/completion/transformation.py b/litellm/llms/sagemaker/completion/transformation.py index 2f6f5bb8fcb1..c860e9b88c57 100644 --- a/litellm/llms/sagemaker/completion/transformation.py +++ b/litellm/llms/sagemaker/completion/transformation.py @@ -23,7 +23,7 @@ from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import ModelResponse, Usage, EmbeddingResponse -from ..transformation_voyage import VoyageSagemakerEmbeddingConfig +from litellm.llms.voyage.embedding.transformation import VoyageEmbeddingConfig from litellm.utils import token_counter from ..common_utils import SagemakerError @@ -304,14 +304,14 @@ def get_model_config(cls, model: str) -> "BaseEmbeddingConfig": Appropriate embedding config instance """ if "voyage" in model.lower(): - return VoyageSagemakerEmbeddingConfig() + return VoyageEmbeddingConfig() else: return cls() def get_supported_openai_params(self, model: str) -> List[str]: # Check if this is an embedding model if "voyage" in model.lower(): - return VoyageSagemakerEmbeddingConfig().get_supported_openai_params(model) + return VoyageEmbeddingConfig().get_supported_openai_params(model) else: return [] diff --git a/litellm/llms/sagemaker/transformation_voyage.py b/litellm/llms/sagemaker/transformation_voyage.py deleted file mode 100644 index 1133e7b00281..000000000000 --- a/litellm/llms/sagemaker/transformation_voyage.py +++ /dev/null @@ -1,271 +0,0 @@ -""" -Voyage AI transformation for SageMaker endpoints - -This module handles the transformation of Voyage AI embedding requests -and responses when deployed on AWS SageMaker endpoints. -""" - -from typing import TYPE_CHECKING, Any, List, Optional, Union - -if TYPE_CHECKING: - from litellm.types.llms.openai import AllEmbeddingInputValues - -from httpx._models import Headers, Response - -from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig -from litellm.llms.base_llm.chat.transformation import BaseLLMException -from litellm.types.utils import EmbeddingResponse, Usage - - - -class VoyageSagemakerError(BaseLLMException): - """Custom error class for Voyage SageMaker operations""" - - def __init__( - self, - status_code: int, - message: str, - headers: Union[dict, Headers] = {}, - ): - super().__init__( - status_code=status_code, - message=message, - headers=headers, - ) - - -class VoyageSagemakerEmbeddingConfig(BaseEmbeddingConfig): - """ - Voyage AI embedding configuration for SageMaker endpoints - - This class handles the transformation of Voyage AI embedding requests - and responses when deployed on AWS SageMaker endpoints. - - Reference: https://docs.voyageai.com/reference/embeddings-api - """ - - def __init__(self) -> None: - pass - - def get_supported_openai_params(self, model: str) -> List[str]: - """ - Get supported OpenAI parameters for Voyage models on SageMaker - - Args: - model: The model name - - Returns: - List of supported parameter names - """ - return [ - "encoding_format", - "dimensions", - ] - - def map_openai_params( - self, - non_default_params: dict, - optional_params: dict, - model: str, - drop_params: bool, - ) -> dict: - """ - Map OpenAI parameters to Voyage SageMaker format - - Args: - non_default_params: Parameters that are not default values - optional_params: Optional parameters dict to update - model: The model name - drop_params: Whether to drop unsupported parameters - - Returns: - Updated optional_params dict - """ - for param, value in non_default_params.items(): - if param == "encoding_format": - # Voyage supports: float, base64 - if value in ["float", "base64"]: - optional_params["encoding_format"] = value - else: - if not drop_params: - raise ValueError( - f"Unsupported encoding_format: {value}. Voyage supports: float, base64" - ) - elif param == "dimensions": - # Map OpenAI dimensions to Voyage output_dimension - optional_params["output_dimension"] = value - elif not drop_params: - raise ValueError(f"Unsupported parameter for Voyage SageMaker: {param}") - - return optional_params - - def transform_embedding_request( - self, - model: str, - input: "AllEmbeddingInputValues", - optional_params: dict, - headers: dict, - ) -> dict: - """ - Transform embedding request for Voyage models on SageMaker - - Args: - model: The model name - input: List of input texts to embed - optional_params: Optional parameters - headers: Request headers - - Returns: - Transformed request data - """ - # Voyage models on SageMaker expect "input" field (singular) - request_data = { - "input": input, - **optional_params - } - - return request_data - - def transform_embedding_response( - self, - model: str, - raw_response: Response, - model_response: EmbeddingResponse, - logging_obj: Any, - api_key: Optional[str] = None, - request_data: dict = {}, - optional_params: dict = {}, - litellm_params: dict = {}, - ) -> EmbeddingResponse: - """ - Transform embedding response from Voyage SageMaker format to OpenAI format - - Args: - model: The model name - raw_response: Raw HTTP response - model_response: EmbeddingResponse object to populate - logging_obj: Logging object - api_key: API key (unused) - request_data: Original request data - optional_params: Optional parameters - litellm_params: LiteLLM parameters - - Returns: - Transformed EmbeddingResponse - """ - try: - response_data = raw_response.json() - except Exception as e: - raise VoyageSagemakerError( - message=f"Failed to parse response: {str(e)}", - status_code=raw_response.status_code - ) - - # Voyage response format: - # { - # "data": [{"object": "embedding", "embedding": [...], "index": 0}, ...], - # "object": "list", - # "model": "voyage-code-02", - # "usage": {"total_tokens": 10} - # } - - if "data" not in response_data: - raise VoyageSagemakerError( - status_code=500, - message="Voyage response missing 'data' field" - ) - - embeddings = response_data["data"] - if not isinstance(embeddings, list): - raise VoyageSagemakerError( - status_code=422, - message=f"Voyage response data not in expected format - {embeddings}", - ) - - output_data = [] - for idx, embedding_item in enumerate(embeddings): - if "embedding" not in embedding_item: - raise VoyageSagemakerError( - status_code=500, - message=f"Voyage embedding item {idx} missing 'embedding' field" - ) - - output_data.append({ - "object": "embedding", - "index": idx, - "embedding": embedding_item["embedding"] - }) - - model_response.object = response_data.get("object", "list") - model_response.data = output_data - model_response.model = response_data.get("model", model) - - # Calculate usage - total_tokens = response_data.get("usage", {}).get("total_tokens", 0) - if total_tokens == 0: - # Fallback: calculate tokens from input - input_tokens = 0 - for text in request_data.get("input", []): - input_tokens += len(text.split()) # Simple word count fallback - total_tokens = input_tokens - - model_response.usage = Usage( - prompt_tokens=total_tokens, - completion_tokens=0, - total_tokens=total_tokens, - ) - - return model_response - - def validate_environment( - self, - headers: dict, - model: str, - messages: List[Any], - optional_params: dict, - litellm_params: dict, - api_key: Optional[str] = None, - api_base: Optional[str] = None, - ) -> dict: - """ - Validate environment for Voyage SageMaker embeddings - - Args: - headers: Request headers - model: Model name - messages: Messages (unused for embeddings) - optional_params: Optional parameters - litellm_params: LiteLLM parameters - api_key: API key (unused) - api_base: API base (unused) - - Returns: - Validated headers - """ - return { - "Content-Type": "application/json", - **headers - } - - def get_error_class( - self, - error_message: str, - status_code: int, - headers: Union[dict, Headers] - ) -> BaseLLMException: - """ - Get the appropriate error class for this configuration - - Args: - error_message: Error message - status_code: HTTP status code - headers: Response headers - - Returns: - Appropriate exception instance - """ - return VoyageSagemakerError( - message=error_message, - status_code=status_code, - headers=headers - ) diff --git a/tests/test_litellm/llms/sagemaker/test_sagemaker_embedding_voyage.py b/tests/test_litellm/llms/sagemaker/test_sagemaker_embedding_voyage.py index 56d55371bc5d..105b2bdd9f0c 100644 --- a/tests/test_litellm/llms/sagemaker/test_sagemaker_embedding_voyage.py +++ b/tests/test_litellm/llms/sagemaker/test_sagemaker_embedding_voyage.py @@ -18,7 +18,7 @@ from litellm import embedding from litellm.llms.sagemaker.completion.transformation import SagemakerEmbeddingConfig -from litellm.llms.sagemaker.transformation_voyage import VoyageSagemakerEmbeddingConfig +from litellm.llms.voyage.embedding.transformation import VoyageEmbeddingConfig from litellm.types.utils import EmbeddingResponse, Usage @@ -26,10 +26,10 @@ class TestSagemakerEmbeddingFactory: """Test the factory pattern for SageMaker embedding configurations""" def test_get_model_config_voyage_model(self): - """Test that Voyage models return VoyageSagemakerEmbeddingConfig""" + """Test that Voyage models return VoyageEmbeddingConfig""" config = SagemakerEmbeddingConfig.get_model_config("voyage-3-5-embedding") - assert isinstance(config, VoyageSagemakerEmbeddingConfig) + assert isinstance(config, VoyageEmbeddingConfig) assert config.get_supported_openai_params("voyage-3-5-embedding") == ["encoding_format", "dimensions"] def test_get_model_config_hf_model(self): @@ -45,16 +45,16 @@ def test_get_model_config_case_insensitive(self): config2 = SagemakerEmbeddingConfig.get_model_config("Voyage-3-5-Embedding") config3 = SagemakerEmbeddingConfig.get_model_config("voyage-3-5-embedding") - assert isinstance(config1, VoyageSagemakerEmbeddingConfig) - assert isinstance(config2, VoyageSagemakerEmbeddingConfig) - assert isinstance(config3, VoyageSagemakerEmbeddingConfig) + assert isinstance(config1, VoyageEmbeddingConfig) + assert isinstance(config2, VoyageEmbeddingConfig) + assert isinstance(config3, VoyageEmbeddingConfig) -class TestVoyageSagemakerEmbeddingConfig: +class TestVoyageEmbeddingConfig: """Test Voyage-specific embedding configuration""" def setup_method(self): - self.config = VoyageSagemakerEmbeddingConfig() + self.config = VoyageEmbeddingConfig() def test_get_supported_openai_params(self): """Test supported parameters for Voyage models""" @@ -82,24 +82,24 @@ def test_map_openai_params_dimensions(self): assert result == {"output_dimension": 1024} def test_map_openai_params_unsupported_encoding(self): - """Test handling of unsupported encoding_format values""" - with pytest.raises(ValueError, match="Unsupported encoding_format"): - self.config.map_openai_params( - non_default_params={"encoding_format": "invalid"}, - optional_params={}, - model="voyage-3-5-embedding", - drop_params=False - ) + """Test handling of unsupported encoding_format values - VoyageEmbeddingConfig passes through without validation""" + result = self.config.map_openai_params( + non_default_params={"encoding_format": "invalid"}, + optional_params={}, + model="voyage-3-5-embedding", + drop_params=False + ) + assert result == {"encoding_format": "invalid"} def test_map_openai_params_drop_unsupported(self): - """Test dropping unsupported parameters when drop_params=True""" + """Test that VoyageEmbeddingConfig doesn't drop parameters based on drop_params flag""" result = self.config.map_openai_params( non_default_params={"encoding_format": "invalid", "dimensions": 512}, optional_params={}, model="voyage-3-5-embedding", drop_params=True ) - assert result == {"output_dimension": 512} + assert result == {"encoding_format": "invalid", "output_dimension": 512} def test_transform_embedding_request(self): """Test Voyage request transformation""" @@ -111,6 +111,7 @@ def test_transform_embedding_request(self): ) expected = { "input": ["Hello", "World"], + "model": "voyage-3-5-embedding", "encoding_format": "float" } assert result == expected @@ -300,7 +301,7 @@ def test_hf_embedding_request_format(self): def test_parameter_validation_voyage(self): """Test parameter validation for Voyage models""" # Test valid parameters - config = VoyageSagemakerEmbeddingConfig() + config = VoyageEmbeddingConfig() result = config.map_openai_params( non_default_params={"encoding_format": "float", "dimensions": 512}, optional_params={}, @@ -327,7 +328,7 @@ class TestErrorHandling: def test_voyage_response_missing_data(self): """Test handling of Voyage response missing data field""" - config = VoyageSagemakerEmbeddingConfig() + config = VoyageEmbeddingConfig() # Mock response without data field mock_response = httpx.Response( @@ -338,14 +339,15 @@ def test_voyage_response_missing_data(self): model_response = EmbeddingResponse() - with pytest.raises(Exception, match="Voyage response missing 'data' field"): - config.transform_embedding_response( - model="voyage-3-5-embedding", - raw_response=mock_response, - model_response=model_response, - logging_obj=None, - request_data={"input": ["Hello"]} - ) + # VoyageEmbeddingConfig doesn't validate for missing data field, it just sets it to None + result = config.transform_embedding_response( + model="voyage-3-5-embedding", + raw_response=mock_response, + model_response=model_response, + logging_obj=None, + request_data={"input": ["Hello"]} + ) + assert result.data is None def test_hf_response_missing_embedding(self): """Test handling of HF response missing embedding field""" From d0e26e28bbc38bf1c0ef7befa93fac86386bfbb5 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Fri, 10 Oct 2025 17:54:12 +0530 Subject: [PATCH 09/36] Use generic cost calculator --- litellm/llms/xai/cost_calculator.py | 97 ++++--------------- .../llms/xai/test_xai_cost_calculator.py | 12 +-- 2 files changed, 26 insertions(+), 83 deletions(-) diff --git a/litellm/llms/xai/cost_calculator.py b/litellm/llms/xai/cost_calculator.py index 6ca740346c49..e78721ef6e9e 100644 --- a/litellm/llms/xai/cost_calculator.py +++ b/litellm/llms/xai/cost_calculator.py @@ -1,19 +1,19 @@ """ Helper util for handling XAI-specific cost calculation -- e.g.: reasoning tokens for grok models -- handles tiered pricing for tokens above 128k +- Uses the generic cost calculator which already handles tiered pricing correctly +- Handles XAI-specific reasoning token billing (billed as part of completion tokens) """ -from typing import Tuple, Union +from typing import Tuple from litellm.types.utils import Usage -from litellm.utils import get_model_info +from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]: """ Calculates the cost per token for a given XAI model, prompt tokens, and completion tokens. - Handles tiered pricing for tokens above 128k. + Uses the generic cost calculator for all pricing logic, with XAI-specific reasoning token handling. Input: - model: str, the model name without provider prefix @@ -22,63 +22,7 @@ def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]: Returns: Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd """ - ## GET MODEL INFO - model_info = get_model_info(model=model, custom_llm_provider="xai") - - def _safe_float_cast(value: Union[str, int, float, None, object], default: float = 0.0) -> float: - """Safely cast a value to float with proper type handling for mypy.""" - if value is None: - return default - try: - return float(value) # type: ignore - except (ValueError, TypeError): - return default - - def _calculate_tiered_cost(tokens: int, base_cost_key: str, tiered_cost_key: str) -> float: - """Calculate cost using tiered pricing if available. - - For xAI models: - - First 128k tokens are billed at base rate - - Tokens beyond 128k are billed at tiered rate - """ - base_cost = _safe_float_cast(model_info.get(base_cost_key)) - tiered_cost = _safe_float_cast(model_info.get(tiered_cost_key)) - - if tokens <= 128000 or tiered_cost <= 0: - # Use base pricing for all tokens - return tokens * base_cost - else: - # Use tiered pricing: first 128k at base rate, rest at tiered rate - base_tokens = 128000 - tiered_tokens = tokens - 128000 - return (base_tokens * base_cost) + (tiered_tokens * tiered_cost) - - ## CALCULATE INPUT COST - prompt_tokens = usage.prompt_tokens or 0 - prompt_cost = _calculate_tiered_cost( - prompt_tokens, "input_cost_per_token", "input_cost_per_token_above_128k_tokens" - ) - - # Add cached input tokens cost if available - if hasattr(usage, "prompt_tokens_details") and usage.prompt_tokens_details: - cache_hit_tokens = getattr(usage.prompt_tokens_details, "cache_hit_tokens", 0) or 0 - if cache_hit_tokens > 0: - cache_read_cost = _safe_float_cast(model_info.get("cache_read_input_token_cost")) - cache_read_cost_above_128k = _safe_float_cast( - model_info.get("cache_read_input_token_cost_above_128k_tokens") - ) - - if cache_hit_tokens <= 128000 or cache_read_cost_above_128k <= 0: - cache_cost = cache_hit_tokens * cache_read_cost - else: - # Tiered pricing for cached tokens - base_cache_tokens = 128000 - tiered_cache_tokens = cache_hit_tokens - 128000 - cache_cost = (base_cache_tokens * cache_read_cost) + (tiered_cache_tokens * cache_read_cost_above_128k) - - prompt_cost += cache_cost - - ## CALCULATE OUTPUT COST + # XAI-specific completion cost calculation # For XAI models, completion is billed as (visible completion tokens + reasoning tokens) completion_tokens = int(getattr(usage, "completion_tokens", 0) or 0) reasoning_tokens = 0 @@ -86,20 +30,19 @@ def _calculate_tiered_cost(tokens: int, base_cost_key: str, tiered_cost_key: str reasoning_tokens = int(getattr(usage.completion_tokens_details, "reasoning_tokens", 0) or 0) total_completion_tokens = completion_tokens + reasoning_tokens - - # Output tokens are billed at tiered rates if INPUT tokens are above 128k - if prompt_tokens > 128000: - # Use tiered pricing for ALL output tokens when input tokens > 128k - output_cost_per_token_above_128k = _safe_float_cast(model_info.get("output_cost_per_token_above_128k_tokens")) - if output_cost_per_token_above_128k > 0: - completion_cost = total_completion_tokens * output_cost_per_token_above_128k - else: - # Fallback to base pricing if no tiered pricing available - output_cost_per_token = _safe_float_cast(model_info.get("output_cost_per_token")) - completion_cost = total_completion_tokens * output_cost_per_token - else: - # Use base pricing for output tokens when input tokens <= 128k - output_cost_per_token = _safe_float_cast(model_info.get("output_cost_per_token")) - completion_cost = total_completion_tokens * output_cost_per_token + + modified_usage = Usage( + prompt_tokens=usage.prompt_tokens, + completion_tokens=total_completion_tokens, + total_tokens=usage.total_tokens, + prompt_tokens_details=usage.prompt_tokens_details, + completion_tokens_details=None + ) + + prompt_cost, completion_cost = generic_cost_per_token( + model=model, + usage=modified_usage, + custom_llm_provider="xai" + ) return prompt_cost, completion_cost diff --git a/tests/test_litellm/llms/xai/test_xai_cost_calculator.py b/tests/test_litellm/llms/xai/test_xai_cost_calculator.py index 7e043e4acab5..440941735449 100644 --- a/tests/test_litellm/llms/xai/test_xai_cost_calculator.py +++ b/tests/test_litellm/llms/xai/test_xai_cost_calculator.py @@ -215,9 +215,9 @@ def test_tiered_pricing_above_128k_tokens(self): prompt_cost, completion_cost = cost_per_token(model="xai/grok-4-fast-reasoning", usage=usage) # Expected costs for grok-4-fast-reasoning with tiered pricing: - # Input: (128000 * $0.2e-6) + (22000 * $0.4e-6) = $0.0256 + $0.0088 = $0.0344 + # Input: 150000 tokens * $0.4e-6 (ALL tokens at tiered rate since input > 128k) = $0.06 # Completion: (100000 + 50000) tokens * $1e-6 (tiered rate since input > 128k) = $0.15 - expected_prompt_cost = (128000 * 0.2e-6) + (22000 * 0.4e-6) + expected_prompt_cost = 150000 * 0.4e-6 expected_completion_cost = (100000 + 50000) * 1e-6 assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) @@ -268,9 +268,9 @@ def test_tiered_pricing_grok_4_latest(self): prompt_cost, completion_cost = cost_per_token(model="xai/grok-4-latest", usage=usage) # Expected costs for grok-4-latest with tiered pricing: - # Input: (128000 * $3e-6) + (72000 * $6e-6) = $0.384 + $0.432 = $0.816 + # Input: 200000 tokens * $6e-6 (ALL tokens at tiered rate since input > 128k) = $1.2 # Completion: (100000 + 50000) tokens * $30e-6 (tiered rate since input > 128k) = $4.5 - expected_prompt_cost = (128000 * 3e-6) + (72000 * 6e-6) + expected_prompt_cost = 200000 * 6e-6 expected_completion_cost = (100000 + 50000) * 30e-6 assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) @@ -294,9 +294,9 @@ def test_tiered_pricing_output_tokens_below_128k(self): prompt_cost, completion_cost = cost_per_token(model="xai/grok-4-fast-reasoning", usage=usage) # Expected costs for grok-4-fast-reasoning: - # Input: (128000 * $0.2e-6) + (22000 * $0.4e-6) = $0.0256 + $0.0088 = $0.0344 + # Input: 150000 tokens * $0.4e-6 (ALL tokens at tiered rate since input > 128k) = $0.06 # Completion: (50000 + 10000) tokens * $1e-6 (tiered rate since input > 128k) = $0.06 - expected_prompt_cost = (128000 * 0.2e-6) + (22000 * 0.4e-6) + expected_prompt_cost = 150000 * 0.4e-6 expected_completion_cost = (50000 + 10000) * 1e-6 assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) From cb9b65d6edce1a5bd4b6890225e4a5116ffa1b83 Mon Sep 17 00:00:00 2001 From: Ishaan Jaffer Date: Sat, 11 Oct 2025 14:51:33 -0700 Subject: [PATCH 10/36] UI new build --- ...{1052-6c4e848aed27b319.js => 1052-bdb89c881be4619e.js} | 2 +- ...{1307-6127ab4e0e743a22.js => 1307-6bc3bb770f5b2b05.js} | 2 +- ...{1487-2f4bad651391939b.js => 1487-ada9ecf7dd9bca97.js} | 2 +- ...{1739-c53a0407afa8e123.js => 1739-f276f8d8fca7b189.js} | 2 +- ...{2004-c79b8e81e01004c3.js => 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litellm/proxy/_experimental/out/experimental/budgets.txt | 8 ++++---- litellm/proxy/_experimental/out/experimental/caching.html | 2 +- litellm/proxy/_experimental/out/experimental/caching.txt | 8 ++++---- .../proxy/_experimental/out/experimental/old-usage.html | 2 +- .../proxy/_experimental/out/experimental/old-usage.txt | 8 ++++---- litellm/proxy/_experimental/out/experimental/prompts.html | 2 +- litellm/proxy/_experimental/out/experimental/prompts.txt | 8 ++++---- .../_experimental/out/experimental/tag-management.html | 2 +- .../_experimental/out/experimental/tag-management.txt | 8 ++++---- .../out/{guardrails/index.html => guardrails.html} | 2 +- litellm/proxy/_experimental/out/guardrails.txt | 8 ++++---- litellm/proxy/_experimental/out/index.html | 2 +- litellm/proxy/_experimental/out/index.txt | 6 +++--- .../_experimental/out/{logs/index.html => logs.html} | 2 +- litellm/proxy/_experimental/out/logs.txt | 8 ++++---- .../out/{model-hub/index.html => model-hub.html} | 2 +- litellm/proxy/_experimental/out/model-hub.txt | 8 ++++---- litellm/proxy/_experimental/out/model_hub.txt | 6 +++--- .../{model_hub_table/index.html => model_hub_table.html} | 2 +- litellm/proxy/_experimental/out/model_hub_table.txt | 6 +++--- .../index.html => models-and-endpoints.html} | 2 +- litellm/proxy/_experimental/out/models-and-endpoints.txt | 8 ++++---- litellm/proxy/_experimental/out/onboarding.html | 1 + litellm/proxy/_experimental/out/onboarding.txt | 6 +++--- .../out/{organizations/index.html => organizations.html} | 2 +- litellm/proxy/_experimental/out/organizations.txt | 8 ++++---- .../proxy/_experimental/out/settings/admin-settings.html | 2 +- .../proxy/_experimental/out/settings/admin-settings.txt | 8 ++++---- .../_experimental/out/settings/logging-and-alerts.html | 2 +- .../_experimental/out/settings/logging-and-alerts.txt | 8 ++++---- .../proxy/_experimental/out/settings/router-settings.html | 2 +- .../proxy/_experimental/out/settings/router-settings.txt | 8 ++++---- litellm/proxy/_experimental/out/settings/ui-theme.html | 2 +- litellm/proxy/_experimental/out/settings/ui-theme.txt | 8 ++++---- .../_experimental/out/{teams/index.html => teams.html} | 2 +- litellm/proxy/_experimental/out/teams.txt | 8 ++++---- .../out/{test-key/index.html => test-key.html} | 2 +- litellm/proxy/_experimental/out/test-key.txt | 8 ++++---- litellm/proxy/_experimental/out/tools/mcp-servers.html | 2 +- litellm/proxy/_experimental/out/tools/mcp-servers.txt | 8 ++++---- litellm/proxy/_experimental/out/tools/vector-stores.html | 2 +- litellm/proxy/_experimental/out/tools/vector-stores.txt | 8 ++++---- .../_experimental/out/{usage/index.html => usage.html} | 2 +- litellm/proxy/_experimental/out/usage.txt | 8 ++++---- .../_experimental/out/{users/index.html => users.html} | 2 +- litellm/proxy/_experimental/out/users.txt | 8 ++++---- .../out/{virtual-keys/index.html => virtual-keys.html} | 2 +- litellm/proxy/_experimental/out/virtual-keys.txt | 8 ++++---- 115 files changed, 187 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n=e.graphicalItems,r=e.stackGroups,o=e.offset,a=e.updateId,u=e.dataStartIndex,c=e.dataEndIndex,l=t.barSize,s=t.layout,f=t.barGap,h=t.barCategoryGap,d=t.maxBarSize,y=eg(s),v=y.numericAxisName,m=y.cateAxisName,g=!!n&&!!n.length&&n.some(function(t){var e=(0,A.Gf)(t&&t.type);return e&&e.indexOf("Bar")>=0})&&(0,T.pt)({barSize:l,stackGroups:r}),b=[];return n.forEach(function(n,l){var y,x=el(t.data,{graphicalItems:[n],dataStartIndex:u,dataEndIndex:c}),w=n.props,j=w.dataKey,S=w.maxBarSize,E=n.props["".concat(v,"Id")],k=n.props["".concat(m,"Id")],P=p.reduce(function(t,r){var o,i=e["".concat(r.axisType,"Map")],a=n.props["".concat(r.axisType,"Id")];i&&i[a]||"zAxis"===r.axisType||(0,O.Z)(!1);var u=i[a];return ee(ee({},t),{},(en(o={},r.axisType,u),en(o,"".concat(r.axisType,"Ticks"),(0,T.uY)(u)),o))},{}),M=P[m],_=P["".concat(m,"Ticks")],C=r&&r[E]&&r[E].hasStack&&(0,T.O3)(n,r[E].stackGroups),N=(0,A.Gf)(n.type).indexOf("Bar")>=0,D=(0,T.zT)(M,_),I=[];if(N){var 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L,B,R=i()(S)?d:S,z=null!==(L=null!==(B=(0,T.zT)(M,_,!0))&&void 0!==B?B:R)&&void 0!==L?L:0;I=(0,T.qz)({barGap:f,barCategoryGap:h,bandSize:z!==D?z:D,sizeList:g[k],maxBarSize:R}),z!==D&&(I=I.map(function(t){return ee(ee({},t),{},{position:ee(ee({},t.position),{},{offset:t.position.offset-z/2})})}))}var U=n&&n.type&&n.type.getComposedData;U&&b.push({props:ee(ee({},U(ee(ee({},P),{},{displayedData:x,props:t,dataKey:j,item:n,bandSize:D,barPosition:I,offset:o,stackedData:C,layout:s,dataStartIndex:u,dataEndIndex:c}))),{},(en(y={key:n.key||"item-".concat(l)},v,P[v]),en(y,m,P[m]),en(y,"animationId",a),y)),childIndex:(0,A.$R)(n,t.children),item:n})}),b},E=function(t,e){var r=t.props,i=t.dataStartIndex,a=t.dataEndIndex,u=t.updateId;if(!(0,A.TT)({props:r}))return null;var c=r.children,l=r.layout,s=r.stackOffset,f=r.data,h=r.reverseStackOrder,y=eg(l),v=y.numericAxisName,m=y.cateAxisName,b=(0,A.NN)(c,o),x=(0,T.wh)(f,b,"".concat(v,"Id"),"".concat(m,"Id"),s,h),O=p.reduce(function(t,e){var n="".concat(e.axisType,"Map");return ee(ee({},t),{},en({},n,ey(r,ee(ee({},e),{},{graphicalItems:b,stackGroups:e.axisType===v&&x,dataStartIndex:i,dataEndIndex:a}))))},{}),w=eb(ee(ee({},O),{},{props:r,graphicalItems:b}),null==e?void 0:e.legendBBox);Object.keys(O).forEach(function(t){O[t]=d(r,O[t],w,t.replace("Map",""),n)});var j=ev(O["".concat(m,"Map")]),S=g(r,ee(ee({},O),{},{dataStartIndex:i,dataEndIndex:a,updateId:u,graphicalItems:b,stackGroups:x,offset:w}));return ee(ee({formattedGraphicalItems:S,graphicalItems:b,offset:w,stackGroups:x},j),O)};return e=function(t){(function(t,e){if("function"!=typeof e&&null!==e)throw TypeError("Super expression must either be null or a function");t.prototype=Object.create(e&&e.prototype,{constructor:{value:t,writable:!0,configurable:!0}}),Object.defineProperty(t,"prototype",{writable:!1}),e&&t6(t,e)})(l,t);var e,o,a=(e=function(){if("undefined"==typeof Reflect||!Reflect.construct||Reflect.construct.sham)return!1;if("function"==typeof 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base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n model="'.concat(w,'",\n messages=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.chat.completions.create(\n# model="').concat(w,'",\n# messages=[\n# {\n# "role": "user",\n# "content": [\n# {\n# "type": "text",\n# "text": "').concat(b,'"\n# },\n# {\n# "type": "image_url",\n# "image_url": {\n# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}\n# }\n# }\n# ]\n# }\n# ]').concat(t,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(v).length>0,t="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n extra_body=".concat(e)}let a=y.length>0?y:[{role:"user",content:h}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(w,'",\n input=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(w,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(b,'"},\n# {\n# "type": "input_image",\n# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}\n# },\n# ],\n# }\n# ]').concat(t,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:n="azure"===g?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n model=\"".concat(w,'",\n prompt="').concat(r,'",\n n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(w,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;case a.KP.IMAGE_EDITS:n="azure"===g?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(w,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(w,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. 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azure_endpoint="').concat(f,'",\n api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n api_key="'.concat(_||"YOUR_LITELLM_API_KEY",'",\n base_url="').concat(f,'"\n)');switch(u){case a.KP.CHAT:{let e=Object.keys(v).length>0,t="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n extra_body=".concat(e)}let a=y.length>0?y:[{role:"user",content:h}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n model="'.concat(w,'",\n messages=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.chat.completions.create(\n# model="').concat(w,'",\n# messages=[\n# {\n# "role": "user",\n# "content": [\n# {\n# "type": "text",\n# "text": "').concat(b,'"\n# },\n# {\n# "type": "image_url",\n# "image_url": {\n# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}\n# }\n# }\n# ]\n# }\n# ]').concat(t,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(v).length>0,t="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n extra_body=".concat(e)}let a=y.length>0?y:[{role:"user",content:h}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(w,'",\n input=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(w,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(b,'"},\n# {\n# "type": "input_image",\n# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}\n# },\n# ],\n# }\n# ]').concat(t,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:n="azure"===g?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n model=\"".concat(w,'",\n prompt="').concat(r,'",\n n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(w,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;case a.KP.IMAGE_EDITS:n="azure"===g?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(w,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. 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This could lead to incorrect routing behavior when deployments with the same model_name were dynamically removed. Changes: - Update _update_deployment_indices_after_removal to maintain model_name_to_deployment_indices mapping - Remove deleted indices and decrement indices greater than removed index - Clean up empty entries when no deployments remain for a model name - Update test to verify proper index shifting and cleanup behavior * fix(router): remove redundant index building during initialization Remove duplicate index building operations that were causing unnecessary work during router initialization: 1. Removed redundant `_build_model_id_to_deployment_index_map` call in __init__ - `set_model_list` already builds all indices from scratch 2. Removed redundant `_build_model_name_index` call at end of `set_model_list` - the index is already built incrementally via `_create_deployment` -> `_add_model_to_list_and_index_map` Both indices (model_id_to_deployment_index_map and model_name_to_deployment_indices) are properly maintained as lookup indexes through existing helper methods. This change eliminates O(N) duplicate work during initialization without any behavioral changes. The indices continue to be correctly synchronized with model_list on all operations (add/remove/upsert). --- litellm/router.py | 27 ++++++++++-- .../test_router_index_management.py | 43 ++++++++++++------- 2 files changed, 51 insertions(+), 19 deletions(-) diff --git a/litellm/router.py b/litellm/router.py index 5972b06f01a1..9bbca54c6b95 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -429,8 +429,7 @@ def __init__( # noqa: PLR0915 self.model_name_to_deployment_indices: Dict[str, List[int]] = {} if model_list is not None: - # Build model index immediately to enable O(1) lookups from the start - self._build_model_id_to_deployment_index_map(model_list) + # set_model_list will build indices automatically self.set_model_list(model_list) self.healthy_deployments: List = self.model_list # type: ignore for m in model_list: @@ -5156,8 +5155,8 @@ def set_model_list(self, model_list: list): ) self.model_names = [m["model_name"] for m in model_list] - # Build model_name index for O(1) lookups - self._build_model_name_index(self.model_list) + # Note: model_name_to_deployment_indices is already built incrementally + # by _create_deployment -> _add_model_to_list_and_index_map def _add_deployment(self, deployment: Deployment) -> Deployment: import os @@ -5380,6 +5379,26 @@ def _update_deployment_indices_after_removal( # Remove the deleted model from index if model_id in self.model_id_to_deployment_index_map: del self.model_id_to_deployment_index_map[model_id] + + # Update model_name_to_deployment_indices + for model_name, indices in list(self.model_name_to_deployment_indices.items()): + # Remove the deleted index + if removal_idx in indices: + indices.remove(removal_idx) + + # Decrement all indices greater than removal_idx + updated_indices = [] + for idx in indices: + if idx > removal_idx: + updated_indices.append(idx - 1) + else: + updated_indices.append(idx) + + # Update or remove the entry + if len(updated_indices) > 0: + self.model_name_to_deployment_indices[model_name] = updated_indices + else: + del self.model_name_to_deployment_indices[model_name] def _add_model_to_list_and_index_map( self, model: dict, model_id: Optional[str] = None diff --git a/tests/router_unit_tests/test_router_index_management.py b/tests/router_unit_tests/test_router_index_management.py index 04ea92149917..584d3995f44e 100644 --- a/tests/router_unit_tests/test_router_index_management.py +++ b/tests/router_unit_tests/test_router_index_management.py @@ -16,25 +16,38 @@ def router(self): """Create a router instance for testing""" return Router(model_list=[]) - def test_update_deployment_indices_after_removal(self, router): - """Test _update_deployment_indices_after_removal function""" - # Setup: Add models to router with proper structure + def test_deletion_updates_model_name_indices(self, router): + """Test that deleting a deployment updates model_name_to_deployment_indices correctly""" router.model_list = [ - {"model": "test1", "model_info": {"id": "model-1"}}, - {"model": "test2", "model_info": {"id": "model-2"}}, - {"model": "test3", "model_info": {"id": "model-3"}} + {"model_name": "gpt-3.5", "model_info": {"id": "model-1"}}, + {"model_name": "gpt-4", "model_info": {"id": "model-2"}}, + {"model_name": "gpt-4", "model_info": {"id": "model-3"}}, + {"model_name": "claude", "model_info": {"id": "model-4"}} ] - router.model_id_to_deployment_index_map = {"model-1": 0, "model-2": 1, "model-3": 2} - - # Test: Remove model-2 (index 1) + router.model_id_to_deployment_index_map = { + "model-1": 0, "model-2": 1, "model-3": 2, "model-4": 3 + } + router.model_name_to_deployment_indices = { + "gpt-3.5": [0], + "gpt-4": [1, 2], + "claude": [3] + } + + # Remove one of the duplicate gpt-4 deployments router._update_deployment_indices_after_removal(model_id="model-2", removal_idx=1) - # Verify: model-2 is removed from index - assert "model-2" not in router.model_id_to_deployment_index_map - # Verify: model-3 index is updated (2 -> 1) - assert router.model_id_to_deployment_index_map["model-3"] == 1 - # Verify: model-1 index remains unchanged - assert router.model_id_to_deployment_index_map["model-1"] == 0 + # Verify indices are shifted correctly + assert router.model_name_to_deployment_indices["gpt-3.5"] == [0] + assert router.model_name_to_deployment_indices["gpt-4"] == [1] # was [1,2], removed 1, shifted 2->1 + assert router.model_name_to_deployment_indices["claude"] == [2] # was [3], shifted to [2] + + # Remove the last gpt-4 deployment + router._update_deployment_indices_after_removal(model_id="model-3", removal_idx=1) + + # Verify gpt-4 is removed from dict when no deployments remain + assert "gpt-4" not in router.model_name_to_deployment_indices + assert router.model_name_to_deployment_indices["gpt-3.5"] == [0] + assert router.model_name_to_deployment_indices["claude"] == [1] def test_build_model_id_to_deployment_index_map(self, router): """Test _build_model_id_to_deployment_index_map function""" From 606053734b5cc5fe3196d44e2073f12d908333c4 Mon Sep 17 00:00:00 2001 From: Lucas <10226902+LoadingZhang@users.noreply.github.com> Date: Sun, 12 Oct 2025 08:58:08 +0800 Subject: [PATCH 12/36] fix(prometheus): Fix Prometheus metric collection in a multi-workers environment (#14929) Co-authored-by: sotazhang --- enterprise/litellm_enterprise/integrations/prometheus.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/enterprise/litellm_enterprise/integrations/prometheus.py b/enterprise/litellm_enterprise/integrations/prometheus.py index 3b37e14b8969..30e1471a1b5f 100644 --- a/enterprise/litellm_enterprise/integrations/prometheus.py +++ b/enterprise/litellm_enterprise/integrations/prometheus.py @@ -2211,7 +2211,13 @@ def _mount_metrics_endpoint(premium_user: bool): ) # Create metrics ASGI app - metrics_app = make_asgi_app() + if 'PROMETHEUS_MULTIPROC_DIR' in os.environ: + from prometheus_client import CollectorRegistry, multiprocess + registry = CollectorRegistry() + multiprocess.MultiProcessCollector(registry) + metrics_app = make_asgi_app(registry) + else: + metrics_app = make_asgi_app() # Mount the metrics app to the app app.mount("/metrics", metrics_app) From a39d2631284932bcea4bea6d30a0216e733b4a83 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Mon, 13 Oct 2025 22:04:54 +0530 Subject: [PATCH 13/36] fix conversion of thinking block --- .../adapters/transformation.py | 56 +++++++++++++++-- .../messages/transformation.py | 3 +- .../anthropic_claude3_transformation.py | 1 - litellm/types/llms/anthropic.py | 17 ++++- ...al_pass_through_adapters_transformation.py | 62 +++++++++++++++++++ 5 files changed, 130 insertions(+), 9 deletions(-) diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index 7de2a1e1c66f..931defa8a002 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -21,6 +21,8 @@ AnthropicMessagesToolChoice, AnthropicMessagesUserMessageParam, AnthropicResponseContentBlockText, + AnthropicResponseContentBlockRedactedThinking, + AnthropicResponseContentBlockThinking, AnthropicResponseContentBlockToolUse, ContentBlockDelta, ContentJsonBlockDelta, @@ -39,9 +41,11 @@ ChatCompletionAssistantToolCall, ChatCompletionImageObject, ChatCompletionImageUrlObject, + ChatCompletionRedactedThinkingBlock, ChatCompletionRequest, ChatCompletionSystemMessage, ChatCompletionTextObject, + ChatCompletionThinkingBlock, ChatCompletionToolCallFunctionChunk, ChatCompletionToolChoiceFunctionParam, ChatCompletionToolChoiceObjectParam, @@ -103,7 +107,6 @@ def translate_completion_input_params( def translate_completion_output_params( self, response: ModelResponse ) -> Optional[AnthropicMessagesResponse]: - return LiteLLMAnthropicMessagesAdapter().translate_openai_response_to_anthropic( response=response ) @@ -227,6 +230,7 @@ def translate_anthropic_messages_to_openai( # noqa: PLR0915 ## ASSISTANT MESSAGE ## assistant_message_str: Optional[str] = None tool_calls: List[ChatCompletionAssistantToolCall] = [] + thinking_blocks: List[Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]] = [] if m["role"] == "assistant": if isinstance(m.get("content"), str): assistant_message_str = str(m.get("content", "")) @@ -253,14 +257,36 @@ def translate_anthropic_messages_to_openai( # noqa: PLR0915 function=function_chunk, ) ) + elif content.get("type") == "thinking": + # Special handling for thinking blocks - + # Handle thinking blocks + thinking_block = ChatCompletionThinkingBlock( + type="thinking", + thinking=content.get("thinking", ""), + signature=content.get("signature", ""), + ) + if "cache_control" in content: + thinking_block["cache_control"] = content["cache_control"] + thinking_blocks.append(thinking_block) + elif content.get("type") == "redacted_thinking": + # Handle redacted thinking blocks + redacted_thinking_block = ChatCompletionRedactedThinkingBlock( + type="redacted_thinking", + data=content.get("data", ""), + ) + if "cache_control" in content: + redacted_thinking_block["cache_control"] = content["cache_control"] + thinking_blocks.append(redacted_thinking_block) - if assistant_message_str is not None or len(tool_calls) > 0: + if assistant_message_str is not None or len(tool_calls) > 0 or len(thinking_blocks) > 0: assistant_message = ChatCompletionAssistantMessage( role="assistant", content=assistant_message_str, ) if len(tool_calls) > 0: assistant_message["tool_calls"] = tool_calls + if len(thinking_blocks) > 0: + assistant_message["thinking_blocks"] = thinking_blocks # type: ignore new_messages.append(assistant_message) return new_messages @@ -313,6 +339,7 @@ def translate_anthropic_to_openai( """ This is used by the beta Anthropic Adapter, for translating anthropic `/v1/messages` requests to the openai format. """ + # Debug: Processing Anthropic message request new_messages: List[AllMessageValues] = [] ## CONVERT ANTHROPIC MESSAGES TO OPENAI @@ -383,14 +410,34 @@ def translate_anthropic_to_openai( def _translate_openai_content_to_anthropic( self, choices: List[Choices] ) -> List[ - Union[AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse] + Union[AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse, AnthropicResponseContentBlockThinking, AnthropicResponseContentBlockRedactedThinking] ]: new_content: List[ Union[ - AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse + AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse, AnthropicResponseContentBlockThinking, AnthropicResponseContentBlockRedactedThinking ] ] = [] for choice in choices: + # Handle thinking blocks first + if hasattr(choice.message, 'thinking_blocks') and choice.message.thinking_blocks: + for thinking_block in choice.message.thinking_blocks: + if thinking_block.get("type") == "thinking": + new_content.append( + AnthropicResponseContentBlockThinking( + type="thinking", + thinking=thinking_block.get("thinking", ""), + signature=thinking_block.get("signature", ""), + ) + ) + elif thinking_block.get("type") == "redacted_thinking": + new_content.append( + AnthropicResponseContentBlockRedactedThinking( + type="redacted_thinking", + data=thinking_block.get("data", ""), + ) + ) + + # Handle tool calls if ( choice.message.tool_calls is not None and len(choice.message.tool_calls) > 0 @@ -404,6 +451,7 @@ def _translate_openai_content_to_anthropic( input=json.loads(tool_call.function.arguments) if tool_call.function.arguments else {}, ) ) + # Handle text content elif choice.message.content is not None: new_content.append( AnthropicResponseContentBlockText( diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py index 46ba96f2605c..e04a1aef5e88 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py @@ -2,7 +2,7 @@ import httpx -from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj, verbose_logger from litellm.llms.base_llm.anthropic_messages.transformation import ( BaseAnthropicMessagesConfig, ) @@ -94,6 +94,7 @@ def transform_anthropic_messages_request( status_code=400, ) ####### get required params for all anthropic messages requests ###### + verbose_logger.info(f"🔍 TRANSFORMATION DEBUG - Messages: {messages}") anthropic_messages_request: AnthropicMessagesRequest = AnthropicMessagesRequest( messages=messages, max_tokens=max_tokens, diff --git a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py index 4fa8517a0907..be782d35766a 100644 --- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py @@ -111,7 +111,6 @@ def transform_anthropic_messages_request( litellm_params=litellm_params, headers=headers, ) - ######################################################### ############## BEDROCK Invoke SPECIFIC TRANSFORMATION ### ######################################################### diff --git a/litellm/types/llms/anthropic.py b/litellm/types/llms/anthropic.py index 02c6f2cf8cf8..a5e8ae0a1873 100644 --- a/litellm/types/llms/anthropic.py +++ b/litellm/types/llms/anthropic.py @@ -1,7 +1,7 @@ from enum import Enum -from typing import Any, Dict, Iterable, List, Optional, Union +from typing import Dict, Iterable, List, Optional, Union -from pydantic import BaseModel, validator +from pydantic import BaseModel from typing_extensions import Literal, Required, TypedDict from .openai import ChatCompletionCachedContent, ChatCompletionThinkingBlock @@ -384,6 +384,17 @@ class AnthropicResponseContentBlockToolUse(BaseModel): input: dict +class AnthropicResponseContentBlockThinking(BaseModel): + type: Literal["thinking"] + thinking: str + signature: str + + +class AnthropicResponseContentBlockRedactedThinking(BaseModel): + type: Literal["redacted_thinking"] + data: str + + class AnthropicResponseUsageBlock(BaseModel): input_tokens: int output_tokens: int @@ -403,7 +414,7 @@ class AnthropicResponse(BaseModel): """Conversational role of the generated message. This will always be "assistant".""" content: List[ - Union[AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse] + Union[AnthropicResponseContentBlockText, AnthropicResponseContentBlockToolUse, AnthropicResponseContentBlockThinking, AnthropicResponseContentBlockRedactedThinking] ] """Content generated by the model.""" diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py index e5dba275bda9..a744080ea3e0 100644 --- a/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py +++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py @@ -192,3 +192,65 @@ def test_translate_streaming_openai_chunk_to_anthropic_with_partial_json(): assert type_of_content == "input_json_delta" assert content_block_delta["type"] == "input_json_delta" assert content_block_delta["partial_json"] == ': "San ' + + +def test_translate_anthropic_messages_with_thinking_blocks_to_openai(): + """Test that thinking blocks are properly converted from Anthropic to OpenAI format""" + + # Test data: Anthropic format with thinking blocks + anthropic_message = { + "role": "assistant", + "content": [ + { + "type": "thinking", + "thinking": "I need to recall basic geography knowledge. France is a country in Western Europe, and Paris has been its capital for centuries. This is a straightforward factual question.", + "signature": "abc123def456" + }, + { + "type": "text", + "text": "The capital of France is Paris." + } + ] + } + + adapter = LiteLLMAnthropicMessagesAdapter() + anthropic_messages = [anthropic_message] + openai_messages = adapter.translate_anthropic_messages_to_openai(anthropic_messages) + + # Verify thinking blocks are preserved + assert "thinking_blocks" in openai_messages[0], "Thinking blocks should be present in OpenAI format" + assert len(openai_messages[0]["thinking_blocks"]) == 1, "Should have one thinking block" + assert openai_messages[0]["thinking_blocks"][0]["type"] == "thinking", "Thinking block type should be 'thinking'" + assert openai_messages[0]["thinking_blocks"][0]["thinking"] == "I need to recall basic geography knowledge. France is a country in Western Europe, and Paris has been its capital for centuries. This is a straightforward factual question.", "Thinking content should match" + assert openai_messages[0]["thinking_blocks"][0]["signature"] == "abc123def456", "Signature should match" + assert openai_messages[0]["content"] == "The capital of France is Paris.", "Text content should be preserved" + + +def test_translate_anthropic_messages_with_redacted_thinking_blocks_to_openai(): + """Test that redacted thinking blocks are properly converted from Anthropic to OpenAI format""" + + # Test data: Anthropic format with redacted thinking blocks + anthropic_message = { + "role": "assistant", + "content": [ + { + "type": "redacted_thinking", + "data": "EmwKAhgBEgy3va3pzix/LafPsn4aDFIT2Xlxh0L5L8rLVyIwxtE3rAFBa8cr3qpPkNRj2YfWXGmKDxH4mPnZ5sQ7vB9URj2pLmN3kF8/dW5hR7xJ0aP1oLs9yTcMnKVf2wRpEGjH9XZaBt4UvDcPrQ..." + }, + { + "type": "text", + "text": "The capital of France is Paris." + } + ] + } + + adapter = LiteLLMAnthropicMessagesAdapter() + anthropic_messages = [anthropic_message] + openai_messages = adapter.translate_anthropic_messages_to_openai(anthropic_messages) + + # Verify redacted thinking blocks are preserved + assert "thinking_blocks" in openai_messages[0], "Thinking blocks should be present in OpenAI format" + assert len(openai_messages[0]["thinking_blocks"]) == 1, "Should have one thinking block" + assert openai_messages[0]["thinking_blocks"][0]["type"] == "redacted_thinking", "Thinking block type should be 'redacted_thinking'" + assert openai_messages[0]["thinking_blocks"][0]["data"] == "EmwKAhgBEgy3va3pzix/LafPsn4aDFIT2Xlxh0L5L8rLVyIwxtE3rAFBa8cr3qpPkNRj2YfWXGmKDxH4mPnZ5sQ7vB9URj2pLmN3kF8/dW5hR7xJ0aP1oLs9yTcMnKVf2wRpEGjH9XZaBt4UvDcPrQ...", "Data should match" + assert openai_messages[0]["content"] == "The capital of France is Paris.", "Text content should be preserved" From ef0d6f020774c0a588bb117b4d0f7f9bc017576c Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Mon, 13 Oct 2025 22:44:38 +0530 Subject: [PATCH 14/36] Resolve conflicts in generated HTML files --- 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litellm/proxy/_experimental/out/api-reference.txt | 8 ++++---- .../_experimental/out/experimental/api-playground.html | 2 +- .../_experimental/out/experimental/api-playground.txt | 8 ++++---- litellm/proxy/_experimental/out/experimental/budgets.html | 2 +- litellm/proxy/_experimental/out/experimental/budgets.txt | 8 ++++---- litellm/proxy/_experimental/out/experimental/caching.html | 2 +- litellm/proxy/_experimental/out/experimental/caching.txt | 8 ++++---- .../proxy/_experimental/out/experimental/old-usage.html | 2 +- .../proxy/_experimental/out/experimental/old-usage.txt | 8 ++++---- litellm/proxy/_experimental/out/experimental/prompts.html | 2 +- litellm/proxy/_experimental/out/experimental/prompts.txt | 8 ++++---- .../_experimental/out/experimental/tag-management.html | 2 +- .../_experimental/out/experimental/tag-management.txt | 8 ++++---- .../out/{guardrails/index.html => guardrails.html} | 2 +- litellm/proxy/_experimental/out/guardrails.txt | 8 ++++---- litellm/proxy/_experimental/out/index.html | 2 +- litellm/proxy/_experimental/out/index.txt | 6 +++--- .../_experimental/out/{logs/index.html => logs.html} | 2 +- litellm/proxy/_experimental/out/logs.txt | 8 ++++---- .../out/{model-hub/index.html => model-hub.html} | 2 +- litellm/proxy/_experimental/out/model-hub.txt | 8 ++++---- litellm/proxy/_experimental/out/model_hub.txt | 6 +++--- .../{model_hub_table/index.html => model_hub_table.html} | 2 +- litellm/proxy/_experimental/out/model_hub_table.txt | 6 +++--- .../index.html => models-and-endpoints.html} | 2 +- litellm/proxy/_experimental/out/models-and-endpoints.txt | 8 ++++---- litellm/proxy/_experimental/out/onboarding.html | 1 + litellm/proxy/_experimental/out/onboarding.txt | 6 +++--- .../out/{organizations/index.html => organizations.html} | 2 +- litellm/proxy/_experimental/out/organizations.txt | 8 ++++---- .../proxy/_experimental/out/settings/admin-settings.html | 2 +- .../proxy/_experimental/out/settings/admin-settings.txt | 8 ++++---- .../_experimental/out/settings/logging-and-alerts.html | 2 +- .../_experimental/out/settings/logging-and-alerts.txt | 8 ++++---- .../proxy/_experimental/out/settings/router-settings.html | 2 +- .../proxy/_experimental/out/settings/router-settings.txt | 8 ++++---- litellm/proxy/_experimental/out/settings/ui-theme.html | 2 +- litellm/proxy/_experimental/out/settings/ui-theme.txt | 8 ++++---- .../_experimental/out/{teams/index.html => teams.html} | 2 +- litellm/proxy/_experimental/out/teams.txt | 8 ++++---- .../out/{test-key/index.html => test-key.html} | 2 +- litellm/proxy/_experimental/out/test-key.txt | 8 ++++---- litellm/proxy/_experimental/out/tools/mcp-servers.html | 2 +- litellm/proxy/_experimental/out/tools/mcp-servers.txt | 8 ++++---- litellm/proxy/_experimental/out/tools/vector-stores.html | 2 +- litellm/proxy/_experimental/out/tools/vector-stores.txt | 8 ++++---- .../_experimental/out/{usage/index.html => usage.html} | 2 +- litellm/proxy/_experimental/out/usage.txt | 8 ++++---- .../_experimental/out/{users/index.html => users.html} | 2 +- litellm/proxy/_experimental/out/users.txt | 8 ++++---- .../out/{virtual-keys/index.html => virtual-keys.html} | 2 +- litellm/proxy/_experimental/out/virtual-keys.txt | 8 ++++---- 115 files changed, 187 insertions(+), 186 deletions(-) rename litellm/proxy/_experimental/out/_next/static/chunks/{1052-6c4e848aed27b319.js => 1052-bdb89c881be4619e.js} (99%) rename litellm/proxy/_experimental/out/_next/static/chunks/{1307-6127ab4e0e743a22.js => 1307-6bc3bb770f5b2b05.js} (99%) rename litellm/proxy/_experimental/out/_next/static/chunks/{1487-2f4bad651391939b.js => 1487-ada9ecf7dd9bca97.js} (99%) rename litellm/proxy/_experimental/out/_next/static/chunks/{1739-c53a0407afa8e123.js => 1739-f276f8d8fca7b189.js} (99%) rename litellm/proxy/_experimental/out/_next/static/chunks/{2004-c79b8e81e01004c3.js => 2004-2c0ea663e63e0a2f.js} (99%) rename litellm/proxy/_experimental/out/_next/static/chunks/{2012-85549d135297168c.js => 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litellm/proxy/_experimental/out/_next/static/chunks/{603-3da54c240fd0fff4.js => 603-41b69f9ab68da547.js} (99%) rename litellm/proxy/_experimental/out/_next/static/chunks/{6202-d1a6f478990b182e.js => 6202-e6c424fe04dff54a.js} (99%) rename litellm/proxy/_experimental/out/_next/static/chunks/{6204-a34299fba4cad1d7.js => 6204-0d389019484112ee.js} (99%) rename litellm/proxy/_experimental/out/_next/static/chunks/{6836-6477b2896147bec7.js => 6836-e30124a71aafae62.js} (99%) rename litellm/proxy/_experimental/out/_next/static/chunks/{6925-5147197c0982397e.js => 6925-9e2db5c132fe9053.js} (99%) rename litellm/proxy/_experimental/out/_next/static/chunks/{7155-10ab6e628c7b1668.js => 7155-95101d73b2137e92.js} (99%) rename litellm/proxy/_experimental/out/_next/static/chunks/{7801-54da99075726cd6f.js => 7801-631ca879181868d8.js} (99%) rename litellm/proxy/_experimental/out/_next/static/chunks/{7906-8c1e7b17671f507c.js => 7906-11071e9e2e7b8318.js} (99%) rename 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litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/guardrails/{page-be36ff8871d76634.js => page-cba2ae1139d5678e.js} (97%) rename litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/{layout-82c7908c502096ef.js => layout-0374fe6c07ff896f.js} (98%) rename litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/logs/{page-a165782a8d66ce57.js => page-009011bc6f8bc171.js} (97%) rename litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/model-hub/{page-48450926ed3399af.js => page-f38983c7e128e2f2.js} (98%) rename litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/models-and-endpoints/{page-03c09a3e00c4aeaa.js => page-50ce8c6bb2821738.js} (99%) rename litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/organizations/{page-e0b45cbcb445d4cf.js => page-3f42c10932338e71.js} (99%) rename litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/settings/admin-settings/{page-faecc7e0f5174668.js 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litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/tools/vector-stores/{page-2328f69d3f2d2907.js => page-6a747c73e6811499.js} (98%) rename litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/usage/{page-31e364c8570a6bc7.js => page-0087e951a6403a40.js} (98%) rename litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/users/{page-77d811fb5a4fcf14.js => page-5350004f9323e706.js} (98%) create mode 100644 litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/virtual-keys/page-5f39dca6d04896e2.js delete mode 100644 litellm/proxy/_experimental/out/_next/static/chunks/app/(dashboard)/virtual-keys/page-842afebdceddea94.js rename litellm/proxy/_experimental/out/_next/static/chunks/app/{layout-6d8e06b275ad8577.js => layout-b4b61d636c5d2baf.js} (93%) rename litellm/proxy/_experimental/out/_next/static/chunks/app/model_hub/{page-50350ff891c0d3cd.js => page-72f15aece1cca2fe.js} (98%) rename litellm/proxy/_experimental/out/_next/static/chunks/app/model_hub_table/{page-b21fde8ae2ae718d.js => page-6f26e4d3c0a2deb0.js} (98%) rename litellm/proxy/_experimental/out/_next/static/chunks/app/onboarding/{page-d6c503dc2753c910.js => page-4aa59d8eb6dfee88.js} (96%) rename litellm/proxy/_experimental/out/_next/static/chunks/app/{page-7795710e4235e746.js => page-d7c5dbe555bb16a5.js} (99%) rename litellm/proxy/_experimental/out/_next/static/chunks/{main-app-1547e82c186a7d1e.js => main-app-77a6ca3c04ee9adf.js} (81%) rename litellm/proxy/_experimental/out/_next/static/{ZAqshlHWdpwZy_2QG1xUf => xJpMbkw-PrXOjhVmfa1qv}/_buildManifest.js (100%) rename litellm/proxy/_experimental/out/_next/static/{ZAqshlHWdpwZy_2QG1xUf => xJpMbkw-PrXOjhVmfa1qv}/_ssgManifest.js (100%) rename litellm/proxy/_experimental/out/{api-reference/index.html => api-reference.html} (93%) rename litellm/proxy/_experimental/out/{guardrails/index.html => guardrails.html} (92%) rename litellm/proxy/_experimental/out/{logs/index.html => logs.html} (91%) rename litellm/proxy/_experimental/out/{model-hub/index.html => model-hub.html} (93%) rename litellm/proxy/_experimental/out/{model_hub_table/index.html => model_hub_table.html} (93%) rename litellm/proxy/_experimental/out/{models-and-endpoints/index.html => models-and-endpoints.html} (90%) create mode 100644 litellm/proxy/_experimental/out/onboarding.html rename litellm/proxy/_experimental/out/{organizations/index.html => organizations.html} (92%) rename litellm/proxy/_experimental/out/{teams/index.html => teams.html} (92%) rename litellm/proxy/_experimental/out/{test-key/index.html => test-key.html} (92%) rename litellm/proxy/_experimental/out/{usage/index.html => usage.html} (92%) rename litellm/proxy/_experimental/out/{users/index.html => users.html} (93%) rename litellm/proxy/_experimental/out/{virtual-keys/index.html => virtual-keys.html} (93%) diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/1052-6c4e848aed27b319.js b/litellm/proxy/_experimental/out/_next/static/chunks/1052-bdb89c881be4619e.js similarity index 99% rename from litellm/proxy/_experimental/out/_next/static/chunks/1052-6c4e848aed27b319.js rename to litellm/proxy/_experimental/out/_next/static/chunks/1052-bdb89c881be4619e.js index 8ab8f6ab8aad..d760fa17b653 100644 --- a/litellm/proxy/_experimental/out/_next/static/chunks/1052-6c4e848aed27b319.js +++ b/litellm/proxy/_experimental/out/_next/static/chunks/1052-bdb89c881be4619e.js @@ -1 +1 @@ -"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[1052],{19046:function(e,t,s){s.d(t,{Dx:function(){return r.Z},Zb:function(){return a.Z},oi:function(){return l.Z},xv:function(){return n.Z},zx:function(){return o.Z}});var o=s(20831),a=s(12514),n=s(84264),l=s(49566),r=s(96761)},31052:function(e,t,s){s.d(t,{Z:function(){return eA}});var 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n=e.graphicalItems,r=e.stackGroups,o=e.offset,a=e.updateId,u=e.dataStartIndex,c=e.dataEndIndex,l=t.barSize,s=t.layout,f=t.barGap,h=t.barCategoryGap,d=t.maxBarSize,y=eg(s),v=y.numericAxisName,m=y.cateAxisName,g=!!n&&!!n.length&&n.some(function(t){var e=(0,A.Gf)(t&&t.type);return e&&e.indexOf("Bar")>=0})&&(0,T.pt)({barSize:l,stackGroups:r}),b=[];return n.forEach(function(n,l){var y,x=el(t.data,{graphicalItems:[n],dataStartIndex:u,dataEndIndex:c}),w=n.props,j=w.dataKey,S=w.maxBarSize,E=n.props["".concat(v,"Id")],k=n.props["".concat(m,"Id")],P=p.reduce(function(t,r){var o,i=e["".concat(r.axisType,"Map")],a=n.props["".concat(r.axisType,"Id")];i&&i[a]||"zAxis"===r.axisType||(0,O.Z)(!1);var u=i[a];return ee(ee({},t),{},(en(o={},r.axisType,u),en(o,"".concat(r.axisType,"Ticks"),(0,T.uY)(u)),o))},{}),M=P[m],_=P["".concat(m,"Ticks")],C=r&&r[E]&&r[E].hasStack&&(0,T.O3)(n,r[E].stackGroups),N=(0,A.Gf)(n.type).indexOf("Bar")>=0,D=(0,T.zT)(M,_),I=[];if(N){var 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L,B,R=i()(S)?d:S,z=null!==(L=null!==(B=(0,T.zT)(M,_,!0))&&void 0!==B?B:R)&&void 0!==L?L:0;I=(0,T.qz)({barGap:f,barCategoryGap:h,bandSize:z!==D?z:D,sizeList:g[k],maxBarSize:R}),z!==D&&(I=I.map(function(t){return ee(ee({},t),{},{position:ee(ee({},t.position),{},{offset:t.position.offset-z/2})})}))}var U=n&&n.type&&n.type.getComposedData;U&&b.push({props:ee(ee({},U(ee(ee({},P),{},{displayedData:x,props:t,dataKey:j,item:n,bandSize:D,barPosition:I,offset:o,stackedData:C,layout:s,dataStartIndex:u,dataEndIndex:c}))),{},(en(y={key:n.key||"item-".concat(l)},v,P[v]),en(y,m,P[m]),en(y,"animationId",a),y)),childIndex:(0,A.$R)(n,t.children),item:n})}),b},E=function(t,e){var r=t.props,i=t.dataStartIndex,a=t.dataEndIndex,u=t.updateId;if(!(0,A.TT)({props:r}))return null;var c=r.children,l=r.layout,s=r.stackOffset,f=r.data,h=r.reverseStackOrder,y=eg(l),v=y.numericAxisName,m=y.cateAxisName,b=(0,A.NN)(c,o),x=(0,T.wh)(f,b,"".concat(v,"Id"),"".concat(m,"Id"),s,h),O=p.reduce(function(t,e){var n="".concat(e.axisType,"Map");return ee(ee({},t),{},en({},n,ey(r,ee(ee({},e),{},{graphicalItems:b,stackGroups:e.axisType===v&&x,dataStartIndex:i,dataEndIndex:a}))))},{}),w=eb(ee(ee({},O),{},{props:r,graphicalItems:b}),null==e?void 0:e.legendBBox);Object.keys(O).forEach(function(t){O[t]=d(r,O[t],w,t.replace("Map",""),n)});var j=ev(O["".concat(m,"Map")]),S=g(r,ee(ee({},O),{},{dataStartIndex:i,dataEndIndex:a,updateId:u,graphicalItems:b,stackGroups:x,offset:w}));return ee(ee({formattedGraphicalItems:S,graphicalItems:b,offset:w,stackGroups:x},j),O)};return e=function(t){(function(t,e){if("function"!=typeof e&&null!==e)throw TypeError("Super expression must either be null or a function");t.prototype=Object.create(e&&e.prototype,{constructor:{value:t,writable:!0,configurable:!0}}),Object.defineProperty(t,"prototype",{writable:!1}),e&&t6(t,e)})(l,t);var e,o,a=(e=function(){if("undefined"==typeof Reflect||!Reflect.construct||Reflect.construct.sham)return!1;if("function"==typeof 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base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n model="'.concat(w,'",\n messages=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.chat.completions.create(\n# model="').concat(w,'",\n# messages=[\n# {\n# "role": "user",\n# "content": [\n# {\n# "type": "text",\n# "text": "').concat(b,'"\n# },\n# {\n# "type": "image_url",\n# "image_url": {\n# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}\n# }\n# }\n# ]\n# }\n# ]').concat(t,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(v).length>0,t="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n extra_body=".concat(e)}let a=y.length>0?y:[{role:"user",content:h}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(w,'",\n input=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(w,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(b,'"},\n# {\n# "type": "input_image",\n# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}\n# },\n# ],\n# }\n# ]').concat(t,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:n="azure"===g?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n model=\"".concat(w,'",\n prompt="').concat(r,'",\n n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(w,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;case a.KP.IMAGE_EDITS:n="azure"===g?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(w,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(w,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. 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azure_endpoint="').concat(f,'",\n api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n api_key="'.concat(_||"YOUR_LITELLM_API_KEY",'",\n base_url="').concat(f,'"\n)');switch(u){case a.KP.CHAT:{let e=Object.keys(v).length>0,t="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n extra_body=".concat(e)}let a=y.length>0?y:[{role:"user",content:h}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n model="'.concat(w,'",\n messages=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.chat.completions.create(\n# model="').concat(w,'",\n# messages=[\n# {\n# "role": "user",\n# "content": [\n# {\n# "type": "text",\n# "text": "').concat(b,'"\n# },\n# {\n# "type": "image_url",\n# "image_url": {\n# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}\n# }\n# }\n# ]\n# }\n# ]').concat(t,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(v).length>0,t="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=",\n extra_body=".concat(e)}let a=y.length>0?y:[{role:"user",content:h}];n='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(w,'",\n input=').concat(JSON.stringify(a,null,4)).concat(t,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(w,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(b,'"},\n# {\n# "type": "input_image",\n# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}\n# },\n# ],\n# }\n# ]').concat(t,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:n="azure"===g?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n model=\"".concat(w,'",\n prompt="').concat(r,'",\n n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(w,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;case a.KP.IMAGE_EDITS:n="azure"===g?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(b,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(w,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. 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This could lead to incorrect routing behavior when deployments with the same model_name were dynamically removed. Changes: - Update _update_deployment_indices_after_removal to maintain model_name_to_deployment_indices mapping - Remove deleted indices and decrement indices greater than removed index - Clean up empty entries when no deployments remain for a model name - Update test to verify proper index shifting and cleanup behavior * fix(router): remove redundant index building during initialization Remove duplicate index building operations that were causing unnecessary work during router initialization: 1. Removed redundant `_build_model_id_to_deployment_index_map` call in __init__ - `set_model_list` already builds all indices from scratch 2. Removed redundant `_build_model_name_index` call at end of `set_model_list` - the index is already built incrementally via `_create_deployment` -> `_add_model_to_list_and_index_map` Both indices (model_id_to_deployment_index_map and model_name_to_deployment_indices) are properly maintained as lookup indexes through existing helper methods. This change eliminates O(N) duplicate work during initialization without any behavioral changes. The indices continue to be correctly synchronized with model_list on all operations (add/remove/upsert). --- litellm/router.py | 27 ++++++++++-- .../test_router_index_management.py | 43 ++++++++++++------- 2 files changed, 51 insertions(+), 19 deletions(-) diff --git a/litellm/router.py b/litellm/router.py index 5972b06f01a1..9bbca54c6b95 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -429,8 +429,7 @@ def __init__( # noqa: PLR0915 self.model_name_to_deployment_indices: Dict[str, List[int]] = {} if model_list is not None: - # Build model index immediately to enable O(1) lookups from the start - self._build_model_id_to_deployment_index_map(model_list) + # set_model_list will build indices automatically self.set_model_list(model_list) self.healthy_deployments: List = self.model_list # type: ignore for m in model_list: @@ -5156,8 +5155,8 @@ def set_model_list(self, model_list: list): ) self.model_names = [m["model_name"] for m in model_list] - # Build model_name index for O(1) lookups - self._build_model_name_index(self.model_list) + # Note: model_name_to_deployment_indices is already built incrementally + # by _create_deployment -> _add_model_to_list_and_index_map def _add_deployment(self, deployment: Deployment) -> Deployment: import os @@ -5380,6 +5379,26 @@ def _update_deployment_indices_after_removal( # Remove the deleted model from index if model_id in self.model_id_to_deployment_index_map: del self.model_id_to_deployment_index_map[model_id] + + # Update model_name_to_deployment_indices + for model_name, indices in list(self.model_name_to_deployment_indices.items()): + # Remove the deleted index + if removal_idx in indices: + indices.remove(removal_idx) + + # Decrement all indices greater than removal_idx + updated_indices = [] + for idx in indices: + if idx > removal_idx: + updated_indices.append(idx - 1) + else: + updated_indices.append(idx) + + # Update or remove the entry + if len(updated_indices) > 0: + self.model_name_to_deployment_indices[model_name] = updated_indices + else: + del self.model_name_to_deployment_indices[model_name] def _add_model_to_list_and_index_map( self, model: dict, model_id: Optional[str] = None diff --git a/tests/router_unit_tests/test_router_index_management.py b/tests/router_unit_tests/test_router_index_management.py index 04ea92149917..584d3995f44e 100644 --- a/tests/router_unit_tests/test_router_index_management.py +++ b/tests/router_unit_tests/test_router_index_management.py @@ -16,25 +16,38 @@ def router(self): """Create a router instance for testing""" return Router(model_list=[]) - def test_update_deployment_indices_after_removal(self, router): - """Test _update_deployment_indices_after_removal function""" - # Setup: Add models to router with proper structure + def test_deletion_updates_model_name_indices(self, router): + """Test that deleting a deployment updates model_name_to_deployment_indices correctly""" router.model_list = [ - {"model": "test1", "model_info": {"id": "model-1"}}, - {"model": "test2", "model_info": {"id": "model-2"}}, - {"model": "test3", "model_info": {"id": "model-3"}} + {"model_name": "gpt-3.5", "model_info": {"id": "model-1"}}, + {"model_name": "gpt-4", "model_info": {"id": "model-2"}}, + {"model_name": "gpt-4", "model_info": {"id": "model-3"}}, + {"model_name": "claude", "model_info": {"id": "model-4"}} ] - router.model_id_to_deployment_index_map = {"model-1": 0, "model-2": 1, "model-3": 2} - - # Test: Remove model-2 (index 1) + router.model_id_to_deployment_index_map = { + "model-1": 0, "model-2": 1, "model-3": 2, "model-4": 3 + } + router.model_name_to_deployment_indices = { + "gpt-3.5": [0], + "gpt-4": [1, 2], + "claude": [3] + } + + # Remove one of the duplicate gpt-4 deployments router._update_deployment_indices_after_removal(model_id="model-2", removal_idx=1) - # Verify: model-2 is removed from index - assert "model-2" not in router.model_id_to_deployment_index_map - # Verify: model-3 index is updated (2 -> 1) - assert router.model_id_to_deployment_index_map["model-3"] == 1 - # Verify: model-1 index remains unchanged - assert router.model_id_to_deployment_index_map["model-1"] == 0 + # Verify indices are shifted correctly + assert router.model_name_to_deployment_indices["gpt-3.5"] == [0] + assert router.model_name_to_deployment_indices["gpt-4"] == [1] # was [1,2], removed 1, shifted 2->1 + assert router.model_name_to_deployment_indices["claude"] == [2] # was [3], shifted to [2] + + # Remove the last gpt-4 deployment + router._update_deployment_indices_after_removal(model_id="model-3", removal_idx=1) + + # Verify gpt-4 is removed from dict when no deployments remain + assert "gpt-4" not in router.model_name_to_deployment_indices + assert router.model_name_to_deployment_indices["gpt-3.5"] == [0] + assert router.model_name_to_deployment_indices["claude"] == [1] def test_build_model_id_to_deployment_index_map(self, router): """Test _build_model_id_to_deployment_index_map function""" From b49ab393f1d4111912662c91c3d779ba59742fb9 Mon Sep 17 00:00:00 2001 From: Lucas <10226902+LoadingZhang@users.noreply.github.com> Date: Sun, 12 Oct 2025 08:58:08 +0800 Subject: [PATCH 16/36] fix(prometheus): Fix Prometheus metric collection in a multi-workers environment (#14929) Co-authored-by: sotazhang --- enterprise/litellm_enterprise/integrations/prometheus.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/enterprise/litellm_enterprise/integrations/prometheus.py b/enterprise/litellm_enterprise/integrations/prometheus.py index 3b37e14b8969..30e1471a1b5f 100644 --- a/enterprise/litellm_enterprise/integrations/prometheus.py +++ b/enterprise/litellm_enterprise/integrations/prometheus.py @@ -2211,7 +2211,13 @@ def _mount_metrics_endpoint(premium_user: bool): ) # Create metrics ASGI app - metrics_app = make_asgi_app() + if 'PROMETHEUS_MULTIPROC_DIR' in os.environ: + from prometheus_client import CollectorRegistry, multiprocess + registry = CollectorRegistry() + multiprocess.MultiProcessCollector(registry) + metrics_app = make_asgi_app(registry) + else: + metrics_app = make_asgi_app() # Mount the metrics app to the app app.mount("/metrics", metrics_app) From 2afae440a864b18555b3a49d6ffcaafb20cd6ad0 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Mon, 13 Oct 2025 22:46:11 +0530 Subject: [PATCH 17/36] Remove penalty params as supported params for gemini preview model (#15503) --- .../vertex_and_google_ai_studio_gemini.py | 20 ++++++- ...test_vertex_and_google_ai_studio_gemini.py | 54 +++++++++++++++++++ 2 files changed, 72 insertions(+), 2 deletions(-) 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 cd7ebaca7904..7beeadbe6ee2 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 @@ -215,6 +215,15 @@ def __init__( @classmethod def get_config(cls): return super().get_config() + + def _supports_penalty_parameters(self, model: str) -> bool: + unsupported_models = ["gemini-2.5-pro-preview-06-05"] + + for pattern in unsupported_models: + if model in pattern: + return False + + return True def get_supported_openai_params(self, model: str) -> List[str]: supported_params = [ @@ -229,8 +238,6 @@ def get_supported_openai_params(self, model: str) -> List[str]: "response_format", "n", "stop", - "frequency_penalty", - "presence_penalty", "extra_headers", "seed", "logprobs", @@ -239,6 +246,11 @@ def get_supported_openai_params(self, model: str) -> List[str]: "parallel_tool_calls", "web_search_options", ] + + # Add penalty parameters only for non-preview models + if not self._supports_penalty_parameters(model): + supported_params.extend(["frequency_penalty", "presence_penalty"]) + if supports_reasoning(model): supported_params.append("reasoning_effort") supported_params.append("thinking") @@ -680,8 +692,12 @@ def map_openai_params( # noqa: PLR0915 ) elif param == "frequency_penalty": optional_params["frequency_penalty"] = value + if self._supports_penalty_parameters(model): + optional_params["frequency_penalty"] = value elif param == "presence_penalty": optional_params["presence_penalty"] = value + if self._supports_penalty_parameters(model): + optional_params["presence_penalty"] = value elif param == "logprobs": optional_params["responseLogprobs"] = value elif param == "top_logprobs": diff --git a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py index 5ae6cf3da283..971c71696d14 100644 --- a/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py +++ b/tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py @@ -1151,3 +1151,57 @@ def test_vertex_ai_map_google_maps_tool_with_location(): assert retrieval_config["latLng"]["latitude"] == 37.7749 assert retrieval_config["latLng"]["longitude"] == -122.4194 assert retrieval_config["languageCode"] == "en_US" + +def test_vertex_ai_penalty_parameters_validation(): + """ + Test that penalty parameters are properly validated for different Gemini models. + + This test ensures that: + 1. Models that don't support penalty parameters (like preview models) filter them out + 2. Models that support penalty parameters include them in the request + 3. Appropriate warnings are logged for unsupported models + """ + v = VertexGeminiConfig() + + # Test cases: (model_name, should_support_penalty_params) + test_cases = [ + ("gemini-2.5-pro-preview-06-05", False), # Preview model - should not support + ] + + for model, should_support in test_cases: + # Test _supports_penalty_parameters method + assert v._supports_penalty_parameters(model) == should_support, \ + f"Model {model} penalty support should be {should_support}" + + # Test get_supported_openai_params method + supported_params = v.get_supported_openai_params(model) + has_penalty_params = "frequency_penalty" in supported_params and "presence_penalty" in supported_params + assert has_penalty_params == should_support, \ + f"Model {model} should {'include' if should_support else 'exclude'} penalty params in supported list" + + # Test parameter mapping for unsupported model + model = "gemini-2.5-pro-preview-06-05" + non_default_params = { + "temperature": 0.7, + "frequency_penalty": 0.5, + "presence_penalty": 0.3, + "max_tokens": 100 + } + + optional_params = {} + result = v.map_openai_params( + non_default_params=non_default_params, + optional_params=optional_params, + model=model, + drop_params=False + ) + + # Penalty parameters should be filtered out for unsupported models + assert "frequency_penalty" not in result, "frequency_penalty should be filtered out for unsupported model" + assert "presence_penalty" not in result, "presence_penalty should be filtered out for unsupported model" + + # Other parameters should still be included + assert "temperature" in result, "temperature should still be included" + assert "max_output_tokens" in result, "max_output_tokens should still be included" + assert result["temperature"] == 0.7 + assert result["max_output_tokens"] == 100 \ No newline at end of file From 6fadfd3b0df9815230caaa8c26f97eb2981f3e7a Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Mon, 13 Oct 2025 22:58:09 +0530 Subject: [PATCH 18/36] refactor code as per comments --- litellm/llms/sagemaker/completion/handler.py | 9 +- .../sagemaker/completion/transformation.py | 139 +--------------- .../sagemaker/embedding/transformation.py | 155 ++++++++++++++++++ litellm/utils.py | 2 +- .../test_sagemaker_embedding_voyage.py | 2 +- 5 files changed, 162 insertions(+), 145 deletions(-) create mode 100644 litellm/llms/sagemaker/embedding/transformation.py diff --git a/litellm/llms/sagemaker/completion/handler.py b/litellm/llms/sagemaker/completion/handler.py index 75977100f661..2a30dc5ef381 100644 --- a/litellm/llms/sagemaker/completion/handler.py +++ b/litellm/llms/sagemaker/completion/handler.py @@ -21,10 +21,10 @@ ) from ..common_utils import AWSEventStreamDecoder, SagemakerError -from .transformation import SagemakerConfig, SagemakerEmbeddingConfig +from .transformation import SagemakerConfig +from ..embedding.transformation import SagemakerEmbeddingConfig sagemaker_config = SagemakerConfig() -sagemaker_embedding_config = SagemakerEmbeddingConfig() """ SAGEMAKER AUTH Keys/Vars @@ -627,7 +627,7 @@ def embedding( #### EMBEDDING LOGIC # Transform request based on model type - provider_config = sagemaker_embedding_config.get_model_config(model) + provider_config = SagemakerEmbeddingConfig.get_model_config(model) request_data = provider_config.transform_embedding_request(model, input, optional_params, {}) data = json.dumps(request_data).encode("utf-8") @@ -686,8 +686,7 @@ def embedding( model_response = EmbeddingResponse() - # Use the appropriate transformation config - request_data = {"input": input} if "voyage" in model.lower() else {"inputs": input} + # Use the request_data that was already transformed above return provider_config.transform_embedding_response( model=model, raw_response=mock_response, diff --git a/litellm/llms/sagemaker/completion/transformation.py b/litellm/llms/sagemaker/completion/transformation.py index c860e9b88c57..42202bbf079c 100644 --- a/litellm/llms/sagemaker/completion/transformation.py +++ b/litellm/llms/sagemaker/completion/transformation.py @@ -8,8 +8,6 @@ import time from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union -if TYPE_CHECKING: - from litellm.types.llms.openai import AllEmbeddingInputValues from httpx._models import Headers, Response @@ -20,10 +18,8 @@ prompt_factory, ) from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException -from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig from litellm.types.llms.openai import AllMessageValues -from litellm.types.utils import ModelResponse, Usage, EmbeddingResponse -from litellm.llms.voyage.embedding.transformation import VoyageEmbeddingConfig +from litellm.types.utils import ModelResponse, Usage from litellm.utils import token_counter from ..common_utils import SagemakerError @@ -284,136 +280,3 @@ def validate_environment( return headers -class SagemakerEmbeddingConfig(BaseEmbeddingConfig): - """ - SageMaker embedding configuration factory for supporting embedding parameters - """ - - def __init__(self) -> None: - pass - - @classmethod - def get_model_config(cls, model: str) -> "BaseEmbeddingConfig": - """ - Factory method to get the appropriate embedding config based on model type - - Args: - model: The model name - - Returns: - Appropriate embedding config instance - """ - if "voyage" in model.lower(): - return VoyageEmbeddingConfig() - else: - return cls() - - def get_supported_openai_params(self, model: str) -> List[str]: - # Check if this is an embedding model - if "voyage" in model.lower(): - return VoyageEmbeddingConfig().get_supported_openai_params(model) - else: - return [] - - def map_openai_params( - self, - non_default_params: dict, - optional_params: dict, - model: str, - drop_params: bool, - ) -> dict: - - return optional_params - - def get_error_class( - self, error_message: str, status_code: int, headers: Union[dict, Headers] - ) -> BaseLLMException: - return SagemakerError( - message=error_message, status_code=status_code, headers=headers - ) - - def transform_embedding_request( - self, - model: str, - input: "AllEmbeddingInputValues", - optional_params: dict, - headers: dict, - ) -> dict: - """ - Transform embedding request for Hugging Face models on SageMaker - """ - # HF models expect "inputs" field (plural) - return {"inputs": input, **optional_params} - - def transform_embedding_response( - self, - model: str, - raw_response: Response, - model_response: "EmbeddingResponse", - logging_obj: Any, - api_key: Optional[str] = None, - request_data: dict = {}, - optional_params: dict = {}, - litellm_params: dict = {}, - ) -> "EmbeddingResponse": - """ - Transform embedding response for Hugging Face models on SageMaker - """ - try: - response_data = raw_response.json() - except Exception as e: - raise SagemakerError( - message=f"Failed to parse response: {str(e)}", - status_code=raw_response.status_code - ) - - if "embedding" not in response_data: - raise SagemakerError( - status_code=500, message="HF response missing 'embedding' field" - ) - embeddings = response_data["embedding"] - - if not isinstance(embeddings, list): - raise SagemakerError( - status_code=422, - message=f"HF response not in expected format - {embeddings}", - ) - - output_data = [] - for idx, embedding in enumerate(embeddings): - output_data.append( - {"object": "embedding", "index": idx, "embedding": embedding} - ) - - model_response.object = "list" - model_response.data = output_data - model_response.model = model - - # Calculate usage from request data - input_texts = request_data.get("inputs", []) - input_tokens = 0 - for text in input_texts: - input_tokens += len(text.split()) # Simple word count fallback - - model_response.usage = Usage( - prompt_tokens=input_tokens, - completion_tokens=0, - total_tokens=input_tokens, - ) - - return model_response - - def validate_environment( - self, - headers: dict, - model: str, - messages: List[Any], - optional_params: dict, - litellm_params: dict, - api_key: Optional[str] = None, - api_base: Optional[str] = None, - ) -> dict: - """ - Validate environment for SageMaker embeddings - """ - return {"Content-Type": "application/json"} diff --git a/litellm/llms/sagemaker/embedding/transformation.py b/litellm/llms/sagemaker/embedding/transformation.py new file mode 100644 index 000000000000..cefb010ff75f --- /dev/null +++ b/litellm/llms/sagemaker/embedding/transformation.py @@ -0,0 +1,155 @@ +""" +Translate from OpenAI's `/v1/embeddings` to Sagemaker's `/invoke` + +In the Huggingface TGI format. +""" + +import json +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union + +if TYPE_CHECKING: + from litellm.types.llms.openai import AllEmbeddingInputValues + +from httpx._models import Headers, Response + +from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.types.utils import Usage, EmbeddingResponse +from litellm.llms.voyage.embedding.transformation import VoyageEmbeddingConfig + +from ..common_utils import SagemakerError + + +class SagemakerEmbeddingConfig(BaseEmbeddingConfig): + """ + SageMaker embedding configuration factory for supporting embedding parameters + """ + + def __init__(self) -> None: + pass + + @classmethod + def get_model_config(cls, model: str) -> "BaseEmbeddingConfig": + """ + Factory method to get the appropriate embedding config based on model type + + Args: + model: The model name + + Returns: + Appropriate embedding config instance + """ + if "voyage" in model.lower(): + return VoyageEmbeddingConfig() + else: + return cls() + + def get_supported_openai_params(self, model: str) -> List[str]: + # Check if this is an embedding model + if "voyage" in model.lower(): + return VoyageEmbeddingConfig().get_supported_openai_params(model) + else: + return [] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + + return optional_params + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[dict, Headers] + ) -> BaseLLMException: + return SagemakerError( + message=error_message, status_code=status_code, headers=headers + ) + + def transform_embedding_request( + self, + model: str, + input: "AllEmbeddingInputValues", + optional_params: dict, + headers: dict, + ) -> dict: + """ + Transform embedding request for Hugging Face models on SageMaker + """ + # HF models expect "inputs" field (plural) + return {"inputs": input, **optional_params} + + def transform_embedding_response( + self, + model: str, + raw_response: Response, + model_response: "EmbeddingResponse", + logging_obj: Any, + api_key: Optional[str] = None, + request_data: dict = {}, + optional_params: dict = {}, + litellm_params: dict = {}, + ) -> "EmbeddingResponse": + """ + Transform embedding response for Hugging Face models on SageMaker + """ + try: + response_data = raw_response.json() + except Exception as e: + raise SagemakerError( + message=f"Failed to parse response: {str(e)}", + status_code=raw_response.status_code + ) + + if "embedding" not in response_data: + raise SagemakerError( + status_code=500, message="HF response missing 'embedding' field" + ) + embeddings = response_data["embedding"] + + if not isinstance(embeddings, list): + raise SagemakerError( + status_code=422, + message=f"HF response not in expected format - {embeddings}", + ) + + output_data = [] + for idx, embedding in enumerate(embeddings): + output_data.append( + {"object": "embedding", "index": idx, "embedding": embedding} + ) + + model_response.object = "list" + model_response.data = output_data + model_response.model = model + + # Calculate usage from request data + input_texts = request_data.get("inputs", []) + input_tokens = 0 + for text in input_texts: + input_tokens += len(text.split()) # Simple word count fallback + + model_response.usage = Usage( + prompt_tokens=input_tokens, + completion_tokens=0, + total_tokens=input_tokens, + ) + + return model_response + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[Any], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + """ + Validate environment for SageMaker embeddings + """ + return {"Content-Type": "application/json"} diff --git a/litellm/utils.py b/litellm/utils.py index d471d463b2bb..7c7edda25b9a 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -7212,7 +7212,7 @@ def get_provider_embedding_config( elif litellm.LlmProviders.OVHCLOUD == provider: return litellm.OVHCloudEmbeddingConfig() elif litellm.LlmProviders.SAGEMAKER == provider: - from litellm.llms.sagemaker.completion.transformation import SagemakerEmbeddingConfig + from litellm.llms.sagemaker.embedding.transformation import SagemakerEmbeddingConfig return SagemakerEmbeddingConfig.get_model_config(model) return None diff --git a/tests/test_litellm/llms/sagemaker/test_sagemaker_embedding_voyage.py b/tests/test_litellm/llms/sagemaker/test_sagemaker_embedding_voyage.py index 105b2bdd9f0c..989a06f80b43 100644 --- a/tests/test_litellm/llms/sagemaker/test_sagemaker_embedding_voyage.py +++ b/tests/test_litellm/llms/sagemaker/test_sagemaker_embedding_voyage.py @@ -17,7 +17,7 @@ sys.path.insert(0, os.path.abspath("../../../../..")) from litellm import embedding -from litellm.llms.sagemaker.completion.transformation import SagemakerEmbeddingConfig +from litellm.llms.sagemaker.embedding.transformation import SagemakerEmbeddingConfig from litellm.llms.voyage.embedding.transformation import VoyageEmbeddingConfig from litellm.types.utils import EmbeddingResponse, Usage From c748321b3e6fcf3c65eb443cbaa7dd646f7cbd8f Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Mon, 13 Oct 2025 23:00:37 +0530 Subject: [PATCH 19/36] fix merge error --- .../vertex_and_google_ai_studio_gemini.py | 20 +++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) 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 cd7ebaca7904..2a17cf9c5041 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 @@ -215,6 +215,15 @@ def __init__( @classmethod def get_config(cls): return super().get_config() + + def _supports_penalty_parameters(self, model: str) -> bool: + unsupported_models = ["gemini-2.5-pro-preview-06-05"] + + for pattern in unsupported_models: + if pattern in model: + return False + + return True def get_supported_openai_params(self, model: str) -> List[str]: supported_params = [ @@ -239,6 +248,11 @@ def get_supported_openai_params(self, model: str) -> List[str]: "parallel_tool_calls", "web_search_options", ] + + # Add penalty parameters only for models that support them + if self._supports_penalty_parameters(model): + supported_params.extend(["frequency_penalty", "presence_penalty"]) + if supports_reasoning(model): supported_params.append("reasoning_effort") supported_params.append("thinking") @@ -679,9 +693,11 @@ def map_openai_params( # noqa: PLR0915 value=value, optional_params=optional_params ) elif param == "frequency_penalty": - optional_params["frequency_penalty"] = value + if self._supports_penalty_parameters(model): + optional_params["frequency_penalty"] = value elif param == "presence_penalty": - optional_params["presence_penalty"] = value + if self._supports_penalty_parameters(model): + optional_params["presence_penalty"] = value elif param == "logprobs": optional_params["responseLogprobs"] = value elif param == "top_logprobs": From 0906b9949dd9225ef3c8cf658c9087e5c84a29e5 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Mon, 13 Oct 2025 23:07:51 +0530 Subject: [PATCH 20/36] refactor code as per comments --- .../gemini/vertex_and_google_ai_studio_gemini.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) 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 2a17cf9c5041..7beeadbe6ee2 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 @@ -220,7 +220,7 @@ def _supports_penalty_parameters(self, model: str) -> bool: unsupported_models = ["gemini-2.5-pro-preview-06-05"] for pattern in unsupported_models: - if pattern in model: + if model in pattern: return False return True @@ -238,8 +238,6 @@ def get_supported_openai_params(self, model: str) -> List[str]: "response_format", "n", "stop", - "frequency_penalty", - "presence_penalty", "extra_headers", "seed", "logprobs", @@ -249,8 +247,8 @@ def get_supported_openai_params(self, model: str) -> List[str]: "web_search_options", ] - # Add penalty parameters only for models that support them - if self._supports_penalty_parameters(model): + # Add penalty parameters only for non-preview models + if not self._supports_penalty_parameters(model): supported_params.extend(["frequency_penalty", "presence_penalty"]) if supports_reasoning(model): @@ -693,9 +691,11 @@ def map_openai_params( # noqa: PLR0915 value=value, optional_params=optional_params ) elif param == "frequency_penalty": + optional_params["frequency_penalty"] = value if self._supports_penalty_parameters(model): optional_params["frequency_penalty"] = value elif param == "presence_penalty": + optional_params["presence_penalty"] = value if self._supports_penalty_parameters(model): optional_params["presence_penalty"] = value elif param == "logprobs": From a6ed454ec7479e5e4627c00e463c98d49104f1d8 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Mon, 13 Oct 2025 23:12:21 +0530 Subject: [PATCH 21/36] refactor code as per comments --- .../llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 7beeadbe6ee2..67b722e92cd1 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 @@ -248,7 +248,7 @@ def get_supported_openai_params(self, model: str) -> List[str]: ] # Add penalty parameters only for non-preview models - if not self._supports_penalty_parameters(model): + if self._supports_penalty_parameters(model): supported_params.extend(["frequency_penalty", "presence_penalty"]) if supports_reasoning(model): From 1fdd92663bef37374985d32e602887db06cd1df2 Mon Sep 17 00:00:00 2001 From: Deepanshu Lulla Date: Mon, 13 Oct 2025 20:36:08 -0400 Subject: [PATCH 22/36] add application level encryption in SQS (#15512) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * docs: fix doc * docs(index.md): bump rc * [Fix] GEMINI - CLI - add google_routes to llm_api_routes (#15500) * fix: add google_routes to llm_api_routes * test: test_virtual_key_llm_api_routes_allows_google_routes * build: bump version * bump: version 1.78.0 → 1.78.1 * add application level encryption in SQS * add application level encryption in SQS --------- Co-authored-by: Krrish Dholakia Co-authored-by: Ishaan Jaff Co-authored-by: deepanshu --- docs/my-website/docs/proxy/deploy.md | 2 +- .../release_notes/v1.78.0-stable/index.md | 4 +- litellm-proxy-extras/pyproject.toml | 4 +- litellm/integrations/sqs.py | 93 +- litellm/litellm_core_utils/app_crypto.py | 31 + litellm/proxy/_types.py | 1 + poetry.lock | 4384 +++-------------- pyproject.toml | 6 +- requirements.txt | 2 +- .../logging_callback_tests/test_sqs_logger.py | 160 +- .../proxy/auth/test_route_checks.py | 25 + 11 files changed, 897 insertions(+), 3815 deletions(-) create mode 100644 litellm/litellm_core_utils/app_crypto.py diff --git a/docs/my-website/docs/proxy/deploy.md b/docs/my-website/docs/proxy/deploy.md index 4d51aa34dc3a..7d2389383d15 100644 --- a/docs/my-website/docs/proxy/deploy.md +++ b/docs/my-website/docs/proxy/deploy.md @@ -788,7 +788,7 @@ docker run --name litellm-proxy \ ## Platform-specific Guide - ### Terraform-based ECS Deployment diff --git a/docs/my-website/release_notes/v1.78.0-stable/index.md b/docs/my-website/release_notes/v1.78.0-stable/index.md index e9a471f45b57..63d5eaca0b02 100644 --- a/docs/my-website/release_notes/v1.78.0-stable/index.md +++ b/docs/my-website/release_notes/v1.78.0-stable/index.md @@ -40,7 +40,7 @@ import TabItem from '@theme/TabItem'; docker run \ -e STORE_MODEL_IN_DB=True \ -p 4000:4000 \ -ghcr.io/berriai/litellm:v1.78.0.rc.1 +ghcr.io/berriai/litellm:v1.78.0.rc.2 ``` @@ -48,7 +48,7 @@ ghcr.io/berriai/litellm:v1.78.0.rc.1 ``` showLineNumbers title="pip install litellm" -pip install litellm==1.78.0.rc.1 +pip install litellm==1.78.0.rc.2 ``` diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml index 1da3fa405aec..8af7c212f523 100644 --- a/litellm-proxy-extras/pyproject.toml +++ b/litellm-proxy-extras/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm-proxy-extras" -version = "0.2.26" +version = "0.2.27" description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package." authors = ["BerriAI"] readme = "README.md" @@ -22,7 +22,7 @@ requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" [tool.commitizen] -version = "0.2.26" +version = "0.2.27" version_files = [ "pyproject.toml:version", "../requirements.txt:litellm-proxy-extras==", diff --git a/litellm/integrations/sqs.py b/litellm/integrations/sqs.py index 8a2ebf8d3445..c03f134b6dfc 100644 --- a/litellm/integrations/sqs.py +++ b/litellm/integrations/sqs.py @@ -7,6 +7,8 @@ from __future__ import annotations import asyncio +import base64 +import json import traceback from typing import List, Optional @@ -27,31 +29,37 @@ from litellm.types.utils import StandardLoggingPayload from .custom_batch_logger import CustomBatchLogger +from litellm.litellm_core_utils.app_crypto import AppCrypto class SQSLogger(CustomBatchLogger, BaseAWSLLM): - """Batching logger that writes logs to an AWS SQS queue.""" + """Batching logger that writes logs to an AWS SQS queue, optionally encrypting the payload.""" def __init__( - self, - sqs_queue_url: Optional[str] = None, - sqs_region_name: Optional[str] = None, - sqs_api_version: Optional[str] = None, - sqs_use_ssl: bool = True, - sqs_verify: Optional[bool] = None, - sqs_endpoint_url: Optional[str] = None, - sqs_aws_access_key_id: Optional[str] = None, - sqs_aws_secret_access_key: Optional[str] = None, - sqs_aws_session_token: Optional[str] = None, - sqs_aws_session_name: Optional[str] = None, - sqs_aws_profile_name: Optional[str] = None, - sqs_aws_role_name: Optional[str] = None, - sqs_aws_web_identity_token: Optional[str] = None, - sqs_aws_sts_endpoint: Optional[str] = None, - sqs_flush_interval: Optional[int] = DEFAULT_SQS_FLUSH_INTERVAL_SECONDS, - sqs_batch_size: Optional[int] = DEFAULT_SQS_BATCH_SIZE, - sqs_config=None, - **kwargs, + self, + # --- Standard SQS params --- + sqs_queue_url: Optional[str] = None, + sqs_region_name: Optional[str] = None, + sqs_api_version: Optional[str] = None, + sqs_use_ssl: bool = True, + sqs_verify: Optional[bool] = None, + sqs_endpoint_url: Optional[str] = None, + sqs_aws_access_key_id: Optional[str] = None, + sqs_aws_secret_access_key: Optional[str] = None, + sqs_aws_session_token: Optional[str] = None, + sqs_aws_session_name: Optional[str] = None, + sqs_aws_profile_name: Optional[str] = None, + sqs_aws_role_name: Optional[str] = None, + sqs_aws_web_identity_token: Optional[str] = None, + sqs_aws_sts_endpoint: Optional[str] = None, + sqs_flush_interval: Optional[int] = DEFAULT_SQS_FLUSH_INTERVAL_SECONDS, + sqs_batch_size: Optional[int] = DEFAULT_SQS_BATCH_SIZE, + sqs_config=None, + # --- 🔐 Application-level encryption params --- + sqs_aws_use_application_level_encryption: bool = False, + sqs_app_encryption_key_b64: Optional[str] = None, + sqs_app_encryption_aad: Optional[str] = None, + **kwargs, ) -> None: try: verbose_logger.debug( @@ -77,7 +85,11 @@ def __init__( sqs_aws_role_name=sqs_aws_role_name, sqs_aws_web_identity_token=sqs_aws_web_identity_token, sqs_aws_sts_endpoint=sqs_aws_sts_endpoint, + sqs_aws_use_application_level_encryption=sqs_aws_use_application_level_encryption, + sqs_app_encryption_key_b64=sqs_app_encryption_key_b64, + sqs_app_encryption_aad=sqs_app_encryption_aad, sqs_config=sqs_config, + **kwargs, ) asyncio.create_task(self.periodic_flush()) @@ -95,7 +107,6 @@ def __init__( ) self.log_queue: List[StandardLoggingPayload] = [] - BaseAWSLLM.__init__(self) except Exception as e: @@ -118,6 +129,9 @@ def _init_sqs_params( sqs_aws_role_name: Optional[str] = None, sqs_aws_web_identity_token: Optional[str] = None, sqs_aws_sts_endpoint: Optional[str] = None, + sqs_aws_use_application_level_encryption: bool = False, + sqs_app_encryption_key_b64: Optional[str] = None, + sqs_app_encryption_aad: Optional[str] = None, sqs_config=None, ) -> None: litellm.aws_sqs_callback_params = litellm.aws_sqs_callback_params or {} @@ -179,6 +193,27 @@ def _init_sqs_params( litellm.aws_sqs_callback_params.get("sqs_aws_sts_endpoint") or sqs_aws_sts_endpoint ) + self.sqs_aws_use_application_level_encryption = ( + litellm.aws_sqs_callback_params.get("sqs_aws_use_application_level_encryption", False) + or sqs_aws_use_application_level_encryption + ) + self.sqs_app_encryption_key_b64 = ( + litellm.aws_sqs_callback_params.get("sqs_app_encryption_key_b64") + or sqs_app_encryption_key_b64 + ) + self.sqs_app_encryption_aad = ( + litellm.aws_sqs_callback_params.get("sqs_app_encryption_aad") + or sqs_app_encryption_aad + ) + self.app_crypto: Optional[AppCrypto] = None + if self.sqs_aws_use_application_level_encryption: + if not self.sqs_app_encryption_key_b64: + raise ValueError("sqs_app_encryption_key_b64 is required when encryption is enabled.") + key = base64.b64decode(self.sqs_app_encryption_key_b64) + self.app_crypto = AppCrypto(key) + verbose_logger.debug( + "SQSLogger: Application-level encryption enabled." + ) self.sqs_config = litellm.aws_sqs_callback_params.get("sqs_config") or sqs_config async def async_log_success_event( @@ -256,11 +291,21 @@ async def async_send_message(self, payload: StandardLoggingPayload) -> None: if self.sqs_queue_url is None: raise ValueError("sqs_queue_url not set") - json_string = safe_dumps(payload) + json_data = json.loads(safe_dumps(payload)) + if self.app_crypto: + aad_bytes = ( + self.sqs_app_encryption_aad.encode("utf-8") + if self.sqs_app_encryption_aad + else None + ) + encrypted = self.app_crypto.encrypt_json(json_data, aad=aad_bytes) + json_string = json.dumps({"__encrypted__": True, "payload": encrypted}) + else: + json_string = safe_dumps(payload) body = ( - f"Action={SQS_SEND_MESSAGE_ACTION}&Version={SQS_API_VERSION}&MessageBody=" - + quote(json_string, safe="") + f"Action={SQS_SEND_MESSAGE_ACTION}&Version={SQS_API_VERSION}&MessageBody=" + + quote(json_string, safe="") ) headers = { diff --git a/litellm/litellm_core_utils/app_crypto.py b/litellm/litellm_core_utils/app_crypto.py new file mode 100644 index 000000000000..bb5043b2b7b3 --- /dev/null +++ b/litellm/litellm_core_utils/app_crypto.py @@ -0,0 +1,31 @@ +import base64 +import json +import os + +from cryptography.hazmat.primitives.ciphers.aead import AESGCM + +class AppCrypto: + def __init__(self, master_key: bytes): + if len(master_key) != 32: + raise ValueError("Master key must be 32 bytes for AES-256-GCM") + self.key = master_key + + def encrypt_json(self, data: dict, aad: bytes | None = None) -> dict: + aes = AESGCM(self.key) + nonce = os.urandom(12) + plaintext = json.dumps(data).encode("utf-8") + ct = aes.encrypt(nonce, plaintext, aad) + ciphertext, tag = ct[:-16], ct[-16:] + return { + "nonce": base64.b64encode(nonce).decode(), + "ciphertext": base64.b64encode(ciphertext).decode(), + "tag": base64.b64encode(tag).decode(), + } + + def decrypt_json(self, enc: dict, aad: bytes | None = None) -> dict: + aes = AESGCM(self.key) + nonce = base64.b64decode(enc["nonce"]) + ct = base64.b64decode(enc["ciphertext"]) + tag = base64.b64decode(enc["tag"]) + data = aes.decrypt(nonce, ct + tag, aad) + return json.loads(data.decode()) \ No newline at end of file diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index 41b36d46c755..d205d73da8eb 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -377,6 +377,7 @@ class LiteLLMRoutes(enum.Enum): llm_api_routes = ( openai_routes + anthropic_routes + + google_routes + mapped_pass_through_routes + passthrough_routes_wildcard + apply_guardrail_routes diff --git a/poetry.lock b/poetry.lock index 8b679ffaa663..1fc0b3afcada 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.2.0 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. [[package]] name = "aiohappyeyeballs" @@ -6,34 +6,17 @@ version = "2.4.4" description = "Happy Eyeballs for asyncio" optional = false python-versions = ">=3.8" -groups = ["main"] -markers = "python_full_version == \"3.8.*\" or platform_python_implementation == \"PyPy\" and python_version < \"3.10\"" files = [ {file = "aiohappyeyeballs-2.4.4-py3-none-any.whl", hash = "sha256:a980909d50efcd44795c4afeca523296716d50cd756ddca6af8c65b996e27de8"}, {file = "aiohappyeyeballs-2.4.4.tar.gz", hash = "sha256:5fdd7d87889c63183afc18ce9271f9b0a7d32c2303e394468dd45d514a757745"}, ] -[[package]] -name = "aiohappyeyeballs" -version = "2.6.1" -description = "Happy Eyeballs for asyncio" -optional = false -python-versions = ">=3.9" -groups = ["main"] -markers = "python_version >= \"3.9\" and platform_python_implementation != \"PyPy\" or python_version >= \"3.10\"" -files = [ - {file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8"}, - {file = "aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558"}, -] - [[package]] name = "aiohttp" version = "3.10.11" description = "Async http client/server framework (asyncio)" optional = false python-versions = ">=3.8" -groups = ["main"] -markers = "python_full_version == \"3.8.*\" or platform_python_implementation == \"PyPy\" and python_version < \"3.10\"" files = [ {file = "aiohttp-3.10.11-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:5077b1a5f40ffa3ba1f40d537d3bec4383988ee51fbba6b74aa8fb1bc466599e"}, {file = "aiohttp-3.10.11-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:8d6a14a4d93b5b3c2891fca94fa9d41b2322a68194422bef0dd5ec1e57d7d298"}, @@ -138,117 +121,7 @@ multidict = ">=4.5,<7.0" yarl = ">=1.12.0,<2.0" [package.extras] -speedups = ["Brotli ; platform_python_implementation == \"CPython\"", "aiodns (>=3.2.0) ; sys_platform == \"linux\" or sys_platform == \"darwin\"", "brotlicffi ; 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database migration tool for SQLAlchemy." optional = true -python-versions = ">=3.9" -groups = ["main"] -markers = "python_version >= \"3.10\" and extra == \"mlflow\"" +python-versions = ">=3.10" files = [ - {file = "alembic-1.16.5-py3-none-any.whl", hash = "sha256:e845dfe090c5ffa7b92593ae6687c5cb1a101e91fa53868497dbd79847f9dbe3"}, - {file = "alembic-1.16.5.tar.gz", hash = "sha256:a88bb7f6e513bd4301ecf4c7f2206fe93f9913f9b48dac3b78babde2d6fe765e"}, + {file = "alembic-1.17.0-py3-none-any.whl", hash = "sha256:80523bc437d41b35c5db7e525ad9d908f79de65c27d6a5a5eab6df348a352d99"}, + {file = "alembic-1.17.0.tar.gz", hash = "sha256:4652a0b3e19616b57d652b82bfa5e38bf5dbea0813eed971612671cb9e90c0fe"}, ] [package.dependencies] @@ -350,7 +174,6 @@ version = "0.7.0" description = "Reusable constraint types to use with typing.Annotated" optional = false python-versions = ">=3.8" -groups = ["main", "dev", "proxy-dev"] files = [ {file = "annotated_types-0.7.0-py3-none-any.whl", hash = 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"pytest-mypy-plugins", "pytest-xdist[psutil]"] +tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"] [[package]] name = "azure-core" @@ -474,8 +268,6 @@ version = "1.33.0" description = "Microsoft Azure Core Library for Python" optional = false python-versions = ">=3.8" -groups = ["main", "proxy-dev"] -markers = "python_full_version == \"3.8.*\" or platform_python_implementation == \"PyPy\" and python_version < \"3.10\"" files = [ {file = "azure_core-1.33.0-py3-none-any.whl", hash = "sha256:9b5b6d0223a1d38c37500e6971118c1e0f13f54951e6893968b38910bc9cda8f"}, {file = "azure_core-1.33.0.tar.gz", hash = "sha256:f367aa07b5e3005fec2c1e184b882b0b039910733907d001c20fb08ebb8c0eb9"}, @@ -490,36 +282,12 @@ typing-extensions = ">=4.6.0" aio = ["aiohttp (>=3.0)"] tracing = ["opentelemetry-api (>=1.26,<2.0)"] -[[package]] -name = "azure-core" -version = "1.35.1" -description = "Microsoft Azure Core Library for Python" -optional = false -python-versions = ">=3.9" -groups = ["main", "proxy-dev"] -markers = "python_version >= \"3.9\" and platform_python_implementation != \"PyPy\" or python_version >= \"3.10\"" -files = [ - {file = "azure_core-1.35.1-py3-none-any.whl", hash = "sha256:12da0c9e08e48e198f9158b56ddbe33b421477e1dc98c2e1c8f9e254d92c468b"}, - {file = "azure_core-1.35.1.tar.gz", hash = "sha256:435d05d6df0fff2f73fb3c15493bb4721ede14203f1ff1382aa6b6b2bdd7e562"}, -] - -[package.dependencies] -requests = ">=2.21.0" -six = ">=1.11.0" -typing-extensions = ">=4.6.0" - -[package.extras] -aio = ["aiohttp (>=3.0)"] -tracing = ["opentelemetry-api (>=1.26,<2.0)"] - [[package]] name = "azure-identity" version = "1.21.0" description = "Microsoft Azure Identity Library for Python" optional = false python-versions = ">=3.8" -groups = ["main", "proxy-dev"] -markers = "python_full_version == \"3.8.*\" or platform_python_implementation == \"PyPy\" and python_version < \"3.10\"" files = [ {file = "azure_identity-1.21.0-py3-none-any.whl", hash = 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">=1.2.0" -typing-extensions = ">=4.0.0" - [[package]] name = "azure-keyvault-secrets" version = "4.9.0" description = "Microsoft Azure Key Vault Secrets Client Library for Python" optional = true python-versions = ">=3.8" -groups = ["main"] -markers = "(python_full_version == \"3.8.*\" or platform_python_implementation == \"PyPy\") and extra == \"extra-proxy\" and python_version < \"3.10\"" files = [ {file = "azure_keyvault_secrets-4.9.0-py3-none-any.whl", hash = "sha256:33c7e2aca2cc2092cebc8c6e96eca36a5cc30c767e16ea429c5fa21270e9fba6"}, {file = "azure_keyvault_secrets-4.9.0.tar.gz", hash = "sha256:2a03bb2ffd9a0d6c8ad1c330d9d0310113985a9de06607ece378fd72a5889fe1"}, @@ -570,32 +316,12 @@ azure-core = ">=1.31.0" isodate = ">=0.6.1" typing-extensions = ">=4.0.1" -[[package]] -name = "azure-keyvault-secrets" -version = "4.10.0" -description = "Microsoft Corporation Key Vault Secrets Client Library for Python" -optional = true -python-versions = ">=3.9" -groups = ["main"] -markers = 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hash = "sha256:1c63aa6af0d0d5e37966f157a77f9396d820fba59f9e43e9415bc3dc5baff300"}, +] + +[package.dependencies] +google-auth = ">=2.14.1,<3.0.0" +googleapis-common-protos = ">=1.56.2,<2.0.0" +grpcio = {version = ">=1.49.1,<2.0.0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""} +grpcio-status = {version = ">=1.49.1,<2.0.0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""} +proto-plus = {version = ">=1.25.0,<2.0.0", markers = "python_version >= \"3.13\""} +protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<7.0.0" +requests = ">=2.18.0,<3.0.0" + +[package.extras] +async-rest = ["google-auth[aiohttp] (>=2.35.0,<3.0.0)"] +grpc = ["grpcio (>=1.33.2,<2.0.0)", "grpcio (>=1.49.1,<2.0.0)", "grpcio-status (>=1.33.2,<2.0.0)", "grpcio-status (>=1.49.1,<2.0.0)"] +grpcgcp = ["grpcio-gcp (>=0.2.2,<1.0.0)"] +grpcio-gcp = 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(platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\") and extra == \"mlflow\" and python_version < \"3.14\"" files = [ {file = "greenlet-3.2.4-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:8c68325b0d0acf8d91dde4e6f930967dd52a5302cd4062932a6b2e7c2969f47c"}, {file = "greenlet-3.2.4-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:94385f101946790ae13da500603491f04a76b6e4c059dab271b3ce2e283b2590"}, @@ -2738,8 +1814,6 @@ version = "0.14.2" description = "IAM API client library" optional = true python-versions = ">=3.7" -groups = ["main"] -markers = "extra == \"extra-proxy\"" files = [ {file = "grpc_google_iam_v1-0.14.2-py3-none-any.whl", hash = "sha256:a3171468459770907926d56a440b2bb643eec1d7ba215f48f3ecece42b4d8351"}, {file = "grpc_google_iam_v1-0.14.2.tar.gz", hash = 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"jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] type = ["pytest-mypy"] [extras] @@ -9596,6 +6418,6 @@ semantic-router = ["semantic-router"] utils = ["numpydoc"] [metadata] -lock-version = "2.1" +lock-version = "2.0" python-versions = ">=3.8.1,<4.0, !=3.9.7" -content-hash = "3ea68de15459ee89fa79e91864be3510201096d789203588770c3b522a929e11" +content-hash = "ee4b9e40ff989a3944b42791100faaf200ea24c7000889b7c0ecc02906dcc5e7" diff --git a/pyproject.toml b/pyproject.toml index 67ffe767799e..0a379ec752bb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm" -version = "1.78.0" +version = "1.78.1" description = "Library to easily interface with LLM API providers" authors = ["BerriAI"] license = "MIT" @@ -59,7 +59,7 @@ websockets = {version = "^13.1.0", optional = true} boto3 = {version = "1.36.0", optional = true} redisvl = {version = "^0.4.1", optional = true, markers = "python_version >= '3.9' and python_version < '3.14'"} mcp = {version = "^1.10.0", optional = true, python = ">=3.10"} -litellm-proxy-extras = {version = "0.2.26", optional = true} +litellm-proxy-extras = {version = "0.2.27", optional = true} rich = {version = "13.7.1", optional = true} litellm-enterprise = {version = "0.1.20", optional = true} diskcache = {version = "^5.6.1", optional = true} @@ -157,7 +157,7 @@ requires = ["poetry-core", "wheel"] build-backend = "poetry.core.masonry.api" [tool.commitizen] -version = "1.78.0" +version = "1.78.1" version_files = [ "pyproject.toml:^version" ] diff --git a/requirements.txt b/requirements.txt index d869d7f574ef..67cac4f4b01d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -43,7 +43,7 @@ sentry_sdk==2.21.0 # for sentry error handling detect-secrets==1.5.0 # Enterprise - secret detection / masking in LLM requests cryptography==44.0.1 tzdata==2025.1 # IANA time zone database -litellm-proxy-extras==0.2.26 # for proxy extras - e.g. prisma migrations +litellm-proxy-extras==0.2.27 # for proxy extras - e.g. prisma migrations ### LITELLM PACKAGE DEPENDENCIES python-dotenv==1.0.1 # for env tiktoken==0.8.0 # for calculating usage diff --git a/tests/logging_callback_tests/test_sqs_logger.py b/tests/logging_callback_tests/test_sqs_logger.py index 38d730712f3c..511d2d222732 100644 --- a/tests/logging_callback_tests/test_sqs_logger.py +++ b/tests/logging_callback_tests/test_sqs_logger.py @@ -1,6 +1,8 @@ import asyncio +import base64 import json -from unittest.mock import AsyncMock, MagicMock +import os +from unittest.mock import AsyncMock, MagicMock, patch from urllib.parse import unquote import litellm @@ -9,6 +11,8 @@ from litellm.integrations.sqs import SQSLogger from litellm.types.utils import StandardLoggingPayload +from litellm.integrations.sqs import AppCrypto + @pytest.mark.asyncio async def test_async_sqs_logger_flush(): @@ -134,3 +138,157 @@ async def test_async_sqs_logger_error_flush(): assert len(payload_data["messages"]) == 1 assert payload_data["messages"][0]["role"] == "user" assert payload_data["messages"][0]["content"] == "hello" + + + +# ============================================================================= +# 📥 Logging Queue Tests +# ============================================================================= + +@pytest.mark.asyncio +async def test_async_log_success_event_adds_to_queue(monkeypatch): + monkeypatch.setattr("litellm.aws_sqs_callback_params", {}) + logger = SQSLogger(sqs_queue_url="https://example.com", sqs_region_name="us-west-2") + + fake_payload = {"some": "data"} + await logger.async_log_success_event( + {"standard_logging_object": fake_payload}, None, None, None + ) + assert fake_payload in logger.log_queue + + +@pytest.mark.asyncio +async def test_async_log_failure_event_adds_to_queue(monkeypatch): + monkeypatch.setattr("litellm.aws_sqs_callback_params", {}) + logger = SQSLogger(sqs_queue_url="https://example.com", sqs_region_name="us-west-2") + + fake_payload = {"fail": True} + await logger.async_log_failure_event( + {"standard_logging_object": fake_payload}, None, None, None + ) + assert fake_payload in logger.log_queue + + + +# ============================================================================= +# 🧾 async_send_batch Tests +# ============================================================================= + +@pytest.mark.asyncio +async def test_async_send_batch_triggers_tasks(monkeypatch): + monkeypatch.setattr("litellm.aws_sqs_callback_params", {}) + logger = SQSLogger(sqs_queue_url="https://example.com", sqs_region_name="us-west-2") + logger.async_send_message = AsyncMock() + + logger.log_queue = [{"log": 1}, {"log": 2}] + await logger.async_send_batch() + + assert logger.async_send_message.await_count == 0 # uses create_task internally + + + +# ============================================================================= +# 🔐 AppCrypto Tests +# ============================================================================= + +def test_appcrypto_encrypt_decrypt_roundtrip(): + key = os.urandom(32) + crypto = AppCrypto(key) + data = {"event": "test", "value": 42} + aad = b"context" + enc = crypto.encrypt_json(data, aad=aad) + dec = crypto.decrypt_json(enc, aad=aad) + assert dec == data + + +def test_appcrypto_invalid_key_length(): + with pytest.raises(ValueError, match="32 bytes"): + AppCrypto(b"short") + + +# ============================================================================= +# 🪣 SQSLogger Initialization Tests +# ============================================================================= + +def test_sqs_logger_init_without_encryption(monkeypatch): + monkeypatch.setattr("litellm.aws_sqs_callback_params", {}) + # Patch asyncio.create_task to avoid RuntimeError + monkeypatch.setattr(asyncio, "create_task", MagicMock()) + logger = SQSLogger(sqs_queue_url="https://example.com", sqs_region_name="us-west-2") + assert logger.sqs_queue_url == "https://example.com" + assert logger.app_crypto is None + + +def test_sqs_logger_init_with_encryption(monkeypatch): + monkeypatch.setattr("litellm.aws_sqs_callback_params", {}) + monkeypatch.setattr(asyncio, "create_task", MagicMock()) + key_b64 = base64.b64encode(os.urandom(32)).decode() + + logger = SQSLogger( + sqs_queue_url="https://example.com", + sqs_region_name="us-west-2", + sqs_aws_use_application_level_encryption=True, + sqs_app_encryption_key_b64=key_b64, + sqs_app_encryption_aad="tenant=bill", + ) + assert logger.app_crypto is not None + assert logger.sqs_app_encryption_aad == "tenant=bill" + + +def test_sqs_logger_init_with_encryption_missing_key(monkeypatch): + monkeypatch.setattr("litellm.aws_sqs_callback_params", {}) + monkeypatch.setattr(asyncio, "create_task", MagicMock()) + with pytest.raises(ValueError, match="required when encryption is enabled"): + SQSLogger( + sqs_queue_url="https://example.com", + sqs_region_name="us-west-2", + sqs_aws_use_application_level_encryption=True, + ) + + +# ============================================================================= +# 📥 Logging Queue Tests +# ============================================================================= + +@pytest.mark.asyncio +async def test_async_log_success_event_adds_to_queue(monkeypatch): + monkeypatch.setattr("litellm.aws_sqs_callback_params", {}) + monkeypatch.setattr(asyncio, "create_task", MagicMock()) + logger = SQSLogger(sqs_queue_url="https://example.com", sqs_region_name="us-west-2") + + fake_payload = {"some": "data"} + await logger.async_log_success_event( + {"standard_logging_object": fake_payload}, None, None, None + ) + assert fake_payload in logger.log_queue + + +@pytest.mark.asyncio +async def test_async_log_failure_event_adds_to_queue(monkeypatch): + monkeypatch.setattr("litellm.aws_sqs_callback_params", {}) + monkeypatch.setattr(asyncio, "create_task", MagicMock()) + logger = SQSLogger(sqs_queue_url="https://example.com", sqs_region_name="us-west-2") + + fake_payload = {"fail": True} + await logger.async_log_failure_event( + {"standard_logging_object": fake_payload}, None, None, None + ) + assert fake_payload in logger.log_queue + + +# ============================================================================= +# 🧾 async_send_batch Tests +# ============================================================================= + +@pytest.mark.asyncio +async def test_async_send_batch_triggers_tasks(monkeypatch): + monkeypatch.setattr("litellm.aws_sqs_callback_params", {}) + monkeypatch.setattr(asyncio, "create_task", MagicMock()) + logger = SQSLogger(sqs_queue_url="https://example.com", sqs_region_name="us-west-2") + + logger.async_send_message = AsyncMock() + logger.log_queue = [{"log": 1}, {"log": 2}] + + await logger.async_send_batch() + # It uses asyncio.create_task() so direct await count = 0 is expected + asyncio.create_task.assert_called() \ No newline at end of file diff --git a/tests/test_litellm/proxy/auth/test_route_checks.py b/tests/test_litellm/proxy/auth/test_route_checks.py index 0c72c8bb1630..7d00b812a5cc 100644 --- a/tests/test_litellm/proxy/auth/test_route_checks.py +++ b/tests/test_litellm/proxy/auth/test_route_checks.py @@ -156,6 +156,31 @@ def test_virtual_key_llm_api_route_includes_passthrough_prefix(route): assert result is True +@pytest.mark.parametrize( + "route", + [ + "/v1beta/models/gemini-2.5-flash:countTokens", + "/v1beta/models/gemini-2.0-flash:generateContent", + "/v1beta/models/gemini-1.5-pro:streamGenerateContent", + "/models/gemini-2.5-flash:countTokens", + "/models/gemini-2.0-flash:generateContent", + "/models/gemini-1.5-pro:streamGenerateContent", + ], +) +def test_virtual_key_llm_api_routes_allows_google_routes(route): + """ + Test that virtual keys with llm_api_routes permission can access Google AI Studio routes. + """ + + valid_token = UserAPIKeyAuth(user_id="test_user", allowed_routes=["llm_api_routes"]) + + result = RouteChecks.is_virtual_key_allowed_to_call_route( + route=route, valid_token=valid_token + ) + + assert result is True + + def test_virtual_key_allowed_routes_with_multiple_litellm_routes_member_names(): """Test that virtual key works with multiple LiteLLMRoutes member names in allowed_routes""" From 7c47178938730f2b8357a4a435cb26c28db738af Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Mon, 13 Oct 2025 20:05:06 -0700 Subject: [PATCH 23/36] [Feat] Bedrock Knowledgebase - return search_response when using /chat/completions API with LiteLLM (#15509) * docs: fix doc * docs(index.md): bump rc * [Fix] GEMINI - CLI - add google_routes to llm_api_routes (#15500) * fix: add google_routes to llm_api_routes * test: test_virtual_key_llm_api_routes_allows_google_routes * add AnthropicCitation * fix async_post_call_success_deployment_hook * fix add vector_store_custom_logger to global callbacks * test_e2e_bedrock_knowledgebase_retrieval_with_llm_api_call * async_post_call_success_deployment_hook * add async_post_call_streaming_deployment_hook * async def test_e2e_bedrock_knowledgebase_retrieval_with_llm_api_call_streaming(setup_vector_store_registry): * fix _call_post_streaming_deployment_hook * fix async_post_call_streaming_deployment_hook * test update * docs: Accessing Search Results * docs KB * fix chatUI * fix searchResults * fix onSearchResults * fix kb --------- Co-authored-by: Krrish Dholakia --- .../docs/completion/knowledgebase.md | 213 ++++++++++++++++++ .../docs/providers/bedrock_vector_store.md | 9 +- litellm/integrations/custom_logger.py | 13 ++ .../vector_store_pre_call_hook.py | 116 +++++++++- litellm/litellm_core_utils/litellm_logging.py | 3 + .../litellm_core_utils/streaming_handler.py | 46 +++- litellm/proxy/proxy_config.yaml | 7 +- litellm/types/llms/anthropic.py | 30 +++ litellm/utils.py | 2 +- .../test_bedrock_knowledgebase_hook.py | 78 ++++++- .../src/components/chat_ui/ChatUI.tsx | 26 +++ .../chat_ui/SearchResultsDisplay.tsx | 120 ++++++++++ .../chat_ui/llm_calls/chat_completion.tsx | 8 + .../src/components/chat_ui/types.ts | 15 ++ 14 files changed, 677 insertions(+), 9 deletions(-) create mode 100644 ui/litellm-dashboard/src/components/chat_ui/SearchResultsDisplay.tsx diff --git a/docs/my-website/docs/completion/knowledgebase.md b/docs/my-website/docs/completion/knowledgebase.md index ee0e30867853..e772f4fe9556 100644 --- a/docs/my-website/docs/completion/knowledgebase.md +++ b/docs/my-website/docs/completion/knowledgebase.md @@ -412,6 +412,219 @@ This is sent to: `https://bedrock-agent-runtime.{aws_region}.amazonaws.com/knowl This process happens automatically whenever you include the `vector_store_ids` parameter in your request. +## Accessing Search Results (Citations) + +When using vector stores, LiteLLM automatically returns search results in `provider_specific_fields`. This allows you to show users citations for the AI's response. + +### Key Concept + +Search results are always in: `response.choices[0].message.provider_specific_fields["search_results"]` + +For streaming: Results appear in the **final chunk** when `finish_reason == "stop"` + +### Non-Streaming Example + + +**Non-Streaming Response with search results:** + +```json +{ + "id": "chatcmpl-abc123", + "choices": [{ + "index": 0, + "message": { + "role": "assistant", + "content": "LiteLLM is a platform...", + "provider_specific_fields": { + "search_results": [{ + "search_query": "What is litellm?", + "data": [{ + "score": 0.95, + "content": [{"text": "...", "type": "text"}], + "filename": "litellm-docs.md", + "file_id": "doc-123" + }] + }] + } + }, + "finish_reason": "stop" + }] +} +``` + + + + +```python +from openai import OpenAI + +client = OpenAI( + base_url="http://localhost:4000", + api_key="your-litellm-api-key" +) + +response = client.chat.completions.create( + model="claude-3-5-sonnet", + messages=[{"role": "user", "content": "What is litellm?"}], + tools=[{"type": "file_search", "vector_store_ids": ["T37J8R4WTM"]}] +) + +# Get AI response +print(response.choices[0].message.content) + +# Get search results (citations) +search_results = response.choices[0].message.provider_specific_fields.get("search_results", []) + +for result_page in search_results: + for idx, item in enumerate(result_page['data'], 1): + print(f"[{idx}] {item.get('filename', 'Unknown')} (score: {item['score']:.2f})") +``` + + + + + +```typescript +import OpenAI from 'openai'; + +const client = new OpenAI({ + baseURL: 'http://localhost:4000', + apiKey: process.env.LITELLM_API_KEY +}); + +const response = await client.chat.completions.create({ + model: 'claude-3-5-sonnet', + messages: [{ role: 'user', content: 'What is litellm?' }], + tools: [{ type: 'file_search', vector_store_ids: ['T37J8R4WTM'] }] +}); + +// Get AI response +console.log(response.choices[0].message.content); + +// Get search results (citations) +const message = response.choices[0].message as any; +const searchResults = message.provider_specific_fields?.search_results || []; + +searchResults.forEach((page: any) => { + page.data.forEach((item: any, idx: number) => { + console.log(`[${idx + 1}] ${item.filename || 'Unknown'} (${item.score.toFixed(2)})`); + }); +}); +``` + + + + +### Streaming Example + +**Streaming Response with search results (final chunk):** + +```json +{ + "id": "chatcmpl-abc123", + "choices": [{ + "index": 0, + "delta": { + "provider_specific_fields": { + "search_results": [{ + "search_query": "What is litellm?", + "data": [{ + "score": 0.95, + "content": [{"text": "...", "type": "text"}], + "filename": "litellm-docs.md", + "file_id": "doc-123" + }] + }] + } + }, + "finish_reason": "stop" + }] +} +``` + + + + +```python +from openai import OpenAI + +client = OpenAI( + base_url="http://localhost:4000", + api_key="your-litellm-api-key" +) + +stream = client.chat.completions.create( + model="claude-3-5-sonnet", + messages=[{"role": "user", "content": "What is litellm?"}], + tools=[{"type": "file_search", "vector_store_ids": ["T37J8R4WTM"]}], + stream=True +) + +for chunk in stream: + # Stream content + if chunk.choices[0].delta.content: + print(chunk.choices[0].delta.content, end="", flush=True) + + # Get citations in final chunk + if chunk.choices[0].finish_reason == "stop": + search_results = getattr(chunk.choices[0].delta, 'provider_specific_fields', {}).get('search_results', []) + if search_results: + print("\n\nSources:") + for page in search_results: + for idx, item in enumerate(page['data'], 1): + print(f" [{idx}] {item.get('filename', 'Unknown')} ({item['score']:.2f})") +``` + + + + + +```typescript +import OpenAI from 'openai'; + +const stream = await client.chat.completions.create({ + model: 'claude-3-5-sonnet', + messages: [{ role: 'user', content: 'What is litellm?' }], + tools: [{ type: 'file_search', vector_store_ids: ['T37J8R4WTM'] }], + stream: true +}); + +for await (const chunk of stream) { + // Stream content + if (chunk.choices[0]?.delta?.content) { + process.stdout.write(chunk.choices[0].delta.content); + } + + // Get citations in final chunk + if (chunk.choices[0]?.finish_reason === 'stop') { + const searchResults = (chunk.choices[0].delta as any).provider_specific_fields?.search_results || []; + if (searchResults.length > 0) { + console.log('\n\nSources:'); + searchResults.forEach((page: any) => { + page.data.forEach((item: any, idx: number) => { + console.log(` [${idx + 1}] ${item.filename || 'Unknown'} (${item.score.toFixed(2)})`); + }); + }); + } + } +} +``` + + + + +### Search Result Fields + +| Field | Type | Description | +|-------|------|-------------| +| `search_query` | string | The query used to search the vector store | +| `data` | array | Array of search results | +| `data[].score` | float | Relevance score (0-1, higher is more relevant) | +| `data[].content` | array | Content chunks with `text` and `type` | +| `data[].filename` | string | Name of the source file (optional) | +| `data[].file_id` | string | Identifier for the source file (optional) | +| `data[].attributes` | object | Provider-specific metadata (optional) | + ## API Reference ### LiteLLM Completion Knowledge Base Parameters diff --git a/docs/my-website/docs/providers/bedrock_vector_store.md b/docs/my-website/docs/providers/bedrock_vector_store.md index 779c4fd0417d..39e1aec5ab83 100644 --- a/docs/my-website/docs/providers/bedrock_vector_store.md +++ b/docs/my-website/docs/providers/bedrock_vector_store.md @@ -138,7 +138,14 @@ print(response.choices[0].message.content) -Futher Reading Vector Stores: +## Accessing Search Results + +See how to access vector store search results in your response: +- [Accessing Search Results (Non-Streaming & Streaming)](../completion/knowledgebase#accessing-search-results-citations) + +## Further Reading + +Vector Stores: - [Always on Vector Stores](https://docs.litellm.ai/docs/completion/knowledgebase#always-on-for-a-model) - [Listing available vector stores on litellm proxy](https://docs.litellm.ai/docs/completion/knowledgebase#listing-available-vector-stores) - [How LiteLLM Vector Stores Work](https://docs.litellm.ai/docs/completion/knowledgebase#how-it-works) \ No newline at end of file diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py index ee7e771faa60..615e64b97f99 100644 --- a/litellm/integrations/custom_logger.py +++ b/litellm/integrations/custom_logger.py @@ -204,6 +204,19 @@ async def async_post_call_success_deployment_hook( """ pass + async def async_post_call_streaming_deployment_hook( + self, + request_data: dict, + response_chunk: Any, + call_type: Optional[CallTypes], + ) -> Optional[Any]: + """ + Allow modifying streaming chunks just before they're returned to the user. + + This is called for each streaming chunk in the response. + """ + pass + #### Fallback Events - router/proxy only #### async def log_model_group_rate_limit_error( self, exception: Exception, original_model_group: Optional[str], kwargs: dict diff --git a/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py b/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py index 8ef160dd7834..b411a217575e 100644 --- a/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py +++ b/litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py @@ -5,7 +5,7 @@ It searches the vector store for relevant context and appends it to the messages. """ -from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, cast +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, cast import litellm import litellm.vector_stores @@ -88,6 +88,8 @@ async def async_get_chat_completion_prompt( return model, messages, non_default_params modified_messages: List[AllMessageValues] = messages.copy() + all_search_results: List[VectorStoreSearchResponse] = [] + for vector_store_to_run in vector_stores_to_run: # Get vector store id from the vector store config @@ -104,6 +106,8 @@ async def async_get_chat_completion_prompt( verbose_logger.debug(f"search_response: {search_response}") + # Store search results for later use in citations + all_search_results.append(search_response) # Process search results and append as context modified_messages = self._append_search_results_to_messages( @@ -115,6 +119,10 @@ async def async_get_chat_completion_prompt( num_results = 0 num_results = len(search_response.get("data", []) or []) verbose_logger.debug(f"Vector store search completed. Added context from {num_results} results") + + # Store search results as-is (already in OpenAI-compatible format) + if litellm_logging_obj and all_search_results: + litellm_logging_obj.model_call_details["search_results"] = all_search_results return model, modified_messages, non_default_params @@ -194,3 +202,109 @@ def _append_search_results_to_messages( return modified_messages return messages + + async def async_post_call_success_deployment_hook( + self, + request_data: dict, + response: Any, + call_type: Optional[Any], + ) -> Optional[Any]: + """ + Add search results to the response after successful LLM call. + + This hook adds the vector store search results (already in OpenAI-compatible format) + to the response's provider_specific_fields. + """ + try: + verbose_logger.debug("VectorStorePreCallHook.async_post_call_success_deployment_hook called") + + # Get logging object from request_data + litellm_logging_obj = request_data.get("litellm_logging_obj") + if not litellm_logging_obj: + verbose_logger.debug("No litellm_logging_obj in request_data") + return None + + verbose_logger.debug(f"model_call_details keys: {list(litellm_logging_obj.model_call_details.keys())}") + + # Get search results from model_call_details (already in OpenAI format) + search_results: Optional[List[VectorStoreSearchResponse]] = ( + litellm_logging_obj.model_call_details.get("search_results") + ) + + verbose_logger.debug(f"Search results found: {search_results is not None}") + + if not search_results: + verbose_logger.debug("No search results found") + return None + + # Add search results to response object + if hasattr(response, "choices") and response.choices: + for choice in response.choices: + if hasattr(choice, "message") and choice.message: + # Get existing provider_specific_fields or create new dict + provider_fields = getattr(choice.message, "provider_specific_fields", None) or {} + + # Add search results (already in OpenAI-compatible format) + provider_fields["search_results"] = search_results + + # Set the provider_specific_fields + setattr(choice.message, "provider_specific_fields", provider_fields) + + verbose_logger.debug(f"Added {len(search_results)} search results to response") + + # Return modified response + return response + + except Exception as e: + verbose_logger.exception(f"Error adding search results to response: {str(e)}") + # Don't fail the request if search results fail to be added + return None + + async def async_post_call_streaming_deployment_hook( + self, + request_data: dict, + response_chunk: Any, + call_type: Optional[Any], + ) -> Optional[Any]: + """ + Add search results to the final streaming chunk. + + This hook is called for the final streaming chunk, allowing us to add + search results to the stream before it's returned to the user. + """ + try: + verbose_logger.debug("VectorStorePreCallHook.async_post_call_streaming_deployment_hook called") + + # Get search results from model_call_details (already in OpenAI format) + search_results: Optional[List[VectorStoreSearchResponse]] = ( + request_data.get("search_results") + ) + + verbose_logger.debug(f"Search results found for streaming chunk: {search_results is not None}") + + if not search_results: + verbose_logger.debug("No search results found for streaming chunk") + return response_chunk + + # Add search results to streaming chunk + if hasattr(response_chunk, "choices") and response_chunk.choices: + for choice in response_chunk.choices: + if hasattr(choice, "delta") and choice.delta: + # Get existing provider_specific_fields or create new dict + provider_fields = getattr(choice.delta, "provider_specific_fields", None) or {} + + # Add search results (already in OpenAI-compatible format) + provider_fields["search_results"] = search_results + + # Set the provider_specific_fields + choice.delta.provider_specific_fields = provider_fields + + verbose_logger.debug(f"Added {len(search_results)} search results to streaming chunk") + + # Return modified chunk + return response_chunk + + except Exception as e: + verbose_logger.exception(f"Error adding search results to streaming chunk: {str(e)}") + # Don't fail the request if search results fail to be added + return response_chunk diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index eafcab885577..7369cdf92845 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -699,6 +699,9 @@ def get_custom_logger_for_prompt_management( self.model_call_details["prompt_integration"] = ( vector_store_custom_logger.__class__.__name__ ) + # Add to global callbacks so post-call hooks are invoked + if vector_store_custom_logger and vector_store_custom_logger not in litellm.callbacks: + litellm.logging_callback_manager.add_litellm_callback(vector_store_custom_logger) return vector_store_custom_logger return None diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index 1daf543cfcb4..64223a9ba4e8 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -20,7 +20,9 @@ from litellm.litellm_core_utils.thread_pool_executor import executor from litellm.types.llms.openai import ChatCompletionChunk from litellm.types.router import GenericLiteLLMParams -from litellm.types.utils import Delta +from litellm.types.utils import ( + Delta, +) from litellm.types.utils import GenericStreamingChunk as GChunk from litellm.types.utils import ( ModelResponse, @@ -1520,6 +1522,43 @@ def set_logging_event_loop(self, loop): """ self.logging_loop = loop + async def _call_post_streaming_deployment_hook(self, chunk): + """ + Call the post-call streaming deployment hook for callbacks. + + This allows callbacks to modify streaming chunks before they're returned. + """ + try: + import litellm + from litellm.integrations.custom_logger import CustomLogger + from litellm.types.utils import CallTypes + + # Get request kwargs from logging object + request_data = self.logging_obj.model_call_details + call_type_str = self.logging_obj.call_type + + try: + typed_call_type = CallTypes(call_type_str) + except ValueError: + typed_call_type = None + + # Call hooks for all callbacks + for callback in litellm.callbacks: + if isinstance(callback, CustomLogger) and hasattr(callback, "async_post_call_streaming_deployment_hook"): + result = await callback.async_post_call_streaming_deployment_hook( + request_data=request_data, + response_chunk=chunk, + call_type=typed_call_type, + ) + if result is not None: + chunk = result + + return chunk + except Exception as e: + from litellm._logging import verbose_logger + verbose_logger.exception(f"Error in post-call streaming deployment hook: {str(e)}") + return chunk + def cache_streaming_response(self, processed_chunk, cache_hit: bool): """ Caches the streaming response @@ -1825,6 +1864,11 @@ async def __anext__(self): # noqa: PLR0915 if self.sent_last_chunk is True and self.stream_options is None: usage = calculate_total_usage(chunks=self.chunks) processed_chunk._hidden_params["usage"] = usage + + # Call post-call streaming deployment hook for final chunk + if self.sent_last_chunk is True: + processed_chunk = await self._call_post_streaming_deployment_hook(processed_chunk) + return processed_chunk raise StopAsyncIteration else: # temporary patch for non-aiohttp async calls diff --git a/litellm/proxy/proxy_config.yaml b/litellm/proxy/proxy_config.yaml index 338fa98118c8..04a3e89599e3 100644 --- a/litellm/proxy/proxy_config.yaml +++ b/litellm/proxy/proxy_config.yaml @@ -1,9 +1,8 @@ model_list: - - model_name: db-openai-endpoint + - model_name: anthropic/* litellm_params: - model: openai/gm - api_key: hi - api_base: https://exampleopenaiendpoint-production.up.railway.app/ + model: anthropic/* + litellm_settings: diff --git a/litellm/types/llms/anthropic.py b/litellm/types/llms/anthropic.py index 884509a2f1c6..582d238e21c7 100644 --- a/litellm/types/llms/anthropic.py +++ b/litellm/types/llms/anthropic.py @@ -157,6 +157,36 @@ class CitationsObject(TypedDict): enabled: bool +class AnthropicCitationPageLocation(TypedDict, total=False): + """ + Anthropic citation for page-based references. + Used when citing from documents with page numbers. + """ + type: Literal["page_location"] + cited_text: str # The exact text being cited (not counted towards output tokens) + document_index: int # Index referencing the cited document + document_title: Optional[str] # Title of the cited document + start_page_number: int # 1-indexed starting page + end_page_number: int # Exclusive ending page + + +class AnthropicCitationCharLocation(TypedDict, total=False): + """ + Anthropic citation for character-based references. + Used when citing from text with character positions. + """ + type: Literal["char_location"] + cited_text: str # The exact text being cited (not counted towards output tokens) + document_index: int # Index referencing the cited document + document_title: Optional[str] # Title of the cited document + start_char_index: int # Starting character index for the citation + end_char_index: int # Ending character index for the citation + + +# Union type for all citation formats +AnthropicCitation = Union[AnthropicCitationPageLocation, AnthropicCitationCharLocation] + + class AnthropicMessagesDocumentParam(TypedDict, total=False): type: Required[Literal["document"]] source: Required[ diff --git a/litellm/utils.py b/litellm/utils.py index 7c7edda25b9a..57f1846d1ebe 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -1507,7 +1507,7 @@ async def wrapper_async(*args, **kwargs): # noqa: PLR0915 ) # Only run if call_type is a valid value in CallTypes if call_type in [ct.value for ct in CallTypes]: - await async_post_call_success_deployment_hook( + result = await async_post_call_success_deployment_hook( request_data=kwargs, response=result, call_type=CallTypes(call_type), diff --git a/tests/logging_callback_tests/test_bedrock_knowledgebase_hook.py b/tests/logging_callback_tests/test_bedrock_knowledgebase_hook.py index 60eb53ed6af5..5b8a99bff7f8 100644 --- a/tests/logging_callback_tests/test_bedrock_knowledgebase_hook.py +++ b/tests/logging_callback_tests/test_bedrock_knowledgebase_hook.py @@ -118,7 +118,7 @@ async def test_e2e_bedrock_knowledgebase_retrieval_with_completion(setup_vector_ @pytest.mark.asyncio async def test_e2e_bedrock_knowledgebase_retrieval_with_llm_api_call(setup_vector_store_registry): """ - Test that the Bedrock Knowledge Base Hook works when making a real llm api call + Test that the Bedrock Knowledge Base Hook works when making a real llm api call and returns citations. """ # Init client @@ -132,9 +132,85 @@ async def test_e2e_bedrock_knowledgebase_retrieval_with_llm_api_call(setup_vecto ], client=async_client ) + print("OPENAI RESPONSE:", json.dumps(dict(response), indent=4, default=str)) assert response is not None + + # Check that search_results are present in provider_specific_fields + assert hasattr(response.choices[0].message, "provider_specific_fields") + provider_fields = response.choices[0].message.provider_specific_fields + assert provider_fields is not None + assert "search_results" in provider_fields + search_results = provider_fields["search_results"] + assert search_results is not None + assert len(search_results) > 0 + + # Check search result structure (OpenAI-compatible format) + first_search_result = search_results[0] + assert "object" in first_search_result + assert first_search_result["object"] == "vector_store.search_results.page" + assert "data" in first_search_result + assert len(first_search_result["data"]) > 0 + + # Check individual result structure + first_result = first_search_result["data"][0] + assert "score" in first_result + assert "content" in first_result + print(f"Search results returned: {len(search_results)}") + print(f"First search result has {len(first_search_result['data'])} items") + + +@pytest.mark.asyncio +async def test_e2e_bedrock_knowledgebase_retrieval_with_llm_api_call_streaming(setup_vector_store_registry): + """ + Test that the Bedrock Knowledge Base Hook works with streaming and returns search_results in chunks. + """ + + # Init client + # litellm._turn_on_debug() + async_client = AsyncHTTPHandler() + response = await litellm.acompletion( + model="anthropic/claude-3-5-haiku-latest", + messages=[{"role": "user", "content": "what is litellm?"}], + vector_store_ids = [ + "T37J8R4WTM" + ], + stream=True, + client=async_client + ) + + # Collect chunks + chunks = [] + search_results_found = False + async for chunk in response: + chunks.append(chunk) + print(f"Chunk: {chunk}") + + # Check if this chunk has search_results in provider_specific_fields + if hasattr(chunk, "choices") and chunk.choices: + for choice in chunk.choices: + if hasattr(choice, "delta") and choice.delta: + provider_fields = getattr(choice.delta, "provider_specific_fields", None) + if provider_fields and "search_results" in provider_fields: + search_results = provider_fields["search_results"] + print(f"Found search_results in streaming chunk: {len(search_results)} results") + + # Verify structure + assert search_results is not None + assert len(search_results) > 0 + + first_search_result = search_results[0] + assert "object" in first_search_result + assert first_search_result["object"] == "vector_store.search_results.page" + assert "data" in first_search_result + assert len(first_search_result["data"]) > 0 + + search_results_found = True + + print(f"Total chunks received: {len(chunks)}") + assert len(chunks) > 0 + assert search_results_found, "search_results should be present in streaming chunks" @pytest.mark.asyncio diff --git a/ui/litellm-dashboard/src/components/chat_ui/ChatUI.tsx b/ui/litellm-dashboard/src/components/chat_ui/ChatUI.tsx index ab288c369838..680f66702bc3 100644 --- a/ui/litellm-dashboard/src/components/chat_ui/ChatUI.tsx +++ b/ui/litellm-dashboard/src/components/chat_ui/ChatUI.tsx @@ -37,6 +37,7 @@ import ChatImageRenderer from "./ChatImageRenderer"; import { createChatMultimodalMessage, createChatDisplayMessage } from "./ChatImageUtils"; import SessionManagement from "./SessionManagement"; import MCPEventsDisplay, { MCPEvent } from "./MCPEventsDisplay"; +import { SearchResultsDisplay } from "./SearchResultsDisplay"; import { ApiOutlined, KeyOutlined, @@ -436,6 +437,25 @@ const ChatUI: React.FC = ({ accessToken, token, userRole, userID, d }); }; + const updateSearchResults = (searchResults: any[]) => { + console.log("Received search results:", searchResults); + setChatHistory((prevHistory) => { + const lastMessage = prevHistory[prevHistory.length - 1]; + + if (lastMessage && lastMessage.role === "assistant") { + console.log("Updating message with search results"); + const updatedMessage = { + ...lastMessage, + searchResults, + }; + + return [...prevHistory.slice(0, prevHistory.length - 1), updatedMessage]; + } + + return prevHistory; + }); + }; + const handleResponseId = (responseId: string) => { console.log("Received response ID for session management:", responseId); if (useApiSessionManagement) { @@ -687,6 +707,7 @@ const ChatUI: React.FC = ({ accessToken, token, userRole, userID, d selectedGuardrails.length > 0 ? selectedGuardrails : undefined, selectedMCPTools, // Pass the selected tools array updateChatImageUI, // Pass the image callback + updateSearchResults, // Pass the search results callback ); } else if (endpointType === EndpointType.IMAGE) { // For image generation @@ -1101,6 +1122,11 @@ const ChatUI: React.FC = ({ accessToken, token, userRole, userID, d )} + {/* Show search results at the start of assistant messages */} + {message.role === "assistant" && message.searchResults && ( + + )} +
>({}); + + if (!searchResults || searchResults.length === 0) { + return null; + } + + const toggleResult = (pageIndex: number, resultIndex: number) => { + const key = `${pageIndex}-${resultIndex}`; + setExpandedResults((prev) => ({ + ...prev, + [key]: !prev[key], + })); + }; + + const totalResults = searchResults.reduce((sum, page) => sum + page.data.length, 0); + + return ( +
+ + + {isExpanded && ( +
+
+ {searchResults.map((resultPage, pageIndex) => ( +
+
+ Query: + "{resultPage.search_query}" + • + {resultPage.data.length} result{resultPage.data.length !== 1 ? 's' : ''} +
+ +
+ {resultPage.data.map((result, resultIndex) => { + const isResultExpanded = expandedResults[`${pageIndex}-${resultIndex}`] || false; + + return ( +
+
toggleResult(pageIndex, resultIndex)} + > +
+ + + + + + {result.filename || result.file_id || `Result ${resultIndex + 1}`} + + + {result.score.toFixed(3)} + +
+
+ + {isResultExpanded && ( +
+
+ {result.content.map((content, contentIndex) => ( +
+
+ {content.text} +
+
+ ))} + + {result.attributes && Object.keys(result.attributes).length > 0 && ( +
+
Metadata:
+
+ {Object.entries(result.attributes).map(([key, value]) => ( +
+ {key}: + {String(value)} +
+ ))} +
+
+ )} +
+
+ )} +
+ ); + })} +
+
+ ))} +
+
+ )} +
+ ); +} + diff --git a/ui/litellm-dashboard/src/components/chat_ui/llm_calls/chat_completion.tsx b/ui/litellm-dashboard/src/components/chat_ui/llm_calls/chat_completion.tsx index 44aea48986bb..7baad24e48c4 100644 --- a/ui/litellm-dashboard/src/components/chat_ui/llm_calls/chat_completion.tsx +++ b/ui/litellm-dashboard/src/components/chat_ui/llm_calls/chat_completion.tsx @@ -1,6 +1,7 @@ import openai from "openai"; import { ChatCompletionMessageParam } from "openai/resources/chat/completions"; import { TokenUsage } from "../ResponseMetrics"; +import { VectorStoreSearchResponse } from "../types"; import { getProxyBaseUrl } from "@/components/networking"; export async function makeOpenAIChatCompletionRequest( @@ -18,6 +19,7 @@ export async function makeOpenAIChatCompletionRequest( guardrails?: string[], selectedMCPTools?: string[], onImageGenerated?: (imageUrl: string, model?: string) => void, + onSearchResults?: (searchResults: VectorStoreSearchResponse[]) => void, ) { // base url should be the current base_url const isLocal = process.env.NODE_ENV === "development"; @@ -127,6 +129,12 @@ export async function makeOpenAIChatCompletionRequest( fullReasoningContent += reasoningContent; } + // Check for search results in provider_specific_fields + if (delta && delta.provider_specific_fields?.search_results && onSearchResults) { + console.log("Search results found:", delta.provider_specific_fields.search_results); + onSearchResults(delta.provider_specific_fields.search_results); + } + // Check for usage data using type assertion const chunkWithUsage = chunk as any; if (chunkWithUsage.usage && onUsageData) { diff --git a/ui/litellm-dashboard/src/components/chat_ui/types.ts b/ui/litellm-dashboard/src/components/chat_ui/types.ts index 454256e56cb1..e8a98473cf40 100644 --- a/ui/litellm-dashboard/src/components/chat_ui/types.ts +++ b/ui/litellm-dashboard/src/components/chat_ui/types.ts @@ -56,6 +56,20 @@ export interface StreamingResponse { usage?: Usage; } +export interface VectorStoreSearchResult { + score: number; + content: Array<{ text: string; type: string }>; + file_id?: string; + filename?: string; + attributes?: Record; +} + +export interface VectorStoreSearchResponse { + object: string; + search_query: string; + data: VectorStoreSearchResult[]; +} + export interface MessageType { role: string; content: string | MultimodalContent[]; @@ -75,6 +89,7 @@ export interface MessageType { url: string; detail: string; }; + searchResults?: VectorStoreSearchResponse[]; } export interface MultimodalContent { From 9feafec9bc88277d78c28cf29b12394afb0af557 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Mon, 13 Oct 2025 20:14:39 -0700 Subject: [PATCH 24/36] [Feat] Add dynamic rate limits on LiteLLM Gateway (#15518) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * docs: fix doc * docs(index.md): bump rc * [Fix] GEMINI - CLI - add google_routes to llm_api_routes (#15500) * fix: add google_routes to llm_api_routes * test: test_virtual_key_llm_api_routes_allows_google_routes * build: bump version * bump: version 1.78.0 → 1.78.1 * fix: KeyRequestBase * fix rpm_limit_type * fix dynamic rate limits * fix use dynamic limits here * fix _should_enforce_rate_limit * fix _should_enforce_rate_limit * fix counter * test_dynamic_rate_limiting_v3 * use _create_rate_limit_descriptors --------- Co-authored-by: Krrish Dholakia --- litellm/constants.py | 1 + litellm/proxy/_types.py | 4 +- .../hooks/parallel_request_limiter_v3.py | 197 ++++++++++++++++-- .../key_management_endpoints.py | 12 +- litellm/proxy/proxy_config.yaml | 12 +- .../hooks/test_parallel_request_limiter_v3.py | 87 ++++++++ .../RateLimitTypeFormItem.tsx | 10 + 7 files changed, 302 insertions(+), 21 deletions(-) diff --git a/litellm/constants.py b/litellm/constants.py index 54ac3e6d6b84..4c336033241c 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -17,6 +17,7 @@ DEFAULT_NUM_WORKERS_LITELLM_PROXY = int( os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", 1) ) +DYNAMIC_RATE_LIMIT_ERROR_THRESHOLD_PER_MINUTE = int(os.getenv("DYNAMIC_RATE_LIMIT_ERROR_THRESHOLD_PER_MINUTE", 1)) DEFAULT_SQS_BATCH_SIZE = int(os.getenv("DEFAULT_SQS_BATCH_SIZE", 512)) SQS_SEND_MESSAGE_ACTION = "SendMessage" SQS_API_VERSION = "2012-11-05" diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index d205d73da8eb..15503a541c3b 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -779,10 +779,10 @@ class KeyRequestBase(GenerateRequestBase): allowed_routes: Optional[list] = [] allowed_passthrough_routes: Optional[list] = None rpm_limit_type: Optional[ - Literal["guaranteed_throughput", "best_effort_throughput"] + Literal["guaranteed_throughput", "best_effort_throughput", "dynamic"] ] = None # raise an error if 'guaranteed_throughput' is set and we're overallocating rpm tpm_limit_type: Optional[ - Literal["guaranteed_throughput", "best_effort_throughput"] + Literal["guaranteed_throughput", "best_effort_throughput", "dynamic"] ] = None # raise an error if 'guaranteed_throughput' is set and we're overallocating tpm diff --git a/litellm/proxy/hooks/parallel_request_limiter_v3.py b/litellm/proxy/hooks/parallel_request_limiter_v3.py index 2ca45b55ec71..c0459c9c6977 100644 --- a/litellm/proxy/hooks/parallel_request_limiter_v3.py +++ b/litellm/proxy/hooks/parallel_request_limiter_v3.py @@ -24,6 +24,7 @@ from litellm import DualCache from litellm._logging import verbose_proxy_logger +from litellm.constants import DYNAMIC_RATE_LIMIT_ERROR_THRESHOLD_PER_MINUTE from litellm.integrations.custom_logger import CustomLogger from litellm.proxy._types import UserAPIKeyAuth from litellm.types.llms.openai import BaseLiteLLMOpenAIResponseObject @@ -545,24 +546,90 @@ async def should_rate_limit( ) return rate_limit_response - async def async_pre_call_hook( + def _should_enforce_rate_limit( + self, + limit_type: Optional[str], + model_has_failures: bool, + ) -> bool: + """ + Determine if rate limit should be enforced based on limit type and model health. + + Args: + limit_type: Type of rate limit ("dynamic", "guaranteed_throughput", "best_effort_throughput", or None) + model_has_failures: Whether the model has recent failures + + Returns: + True if rate limit should be enforced, False otherwise + """ + if limit_type == "dynamic": + # Dynamic mode: only enforce if model has failures + return model_has_failures + # All other modes (including None): always enforce + return True + + def _get_enforced_limit( + self, + limit_value: Optional[int], + limit_type: Optional[str], + model_has_failures: bool, + ) -> Optional[int]: + """ + Get the rate limit value to enforce based on limit type and model health. + + Args: + limit_value: The configured limit value + limit_type: Type of rate limit ("dynamic", "guaranteed_throughput", "best_effort_throughput", or None) + model_has_failures: Whether the model has recent failures + + Returns: + The limit value if it should be enforced, None otherwise + """ + if limit_value is None: + return None + + if self._should_enforce_rate_limit( + limit_type=limit_type, + model_has_failures=model_has_failures, + ): + return limit_value + + return None + + def _is_dynamic_rate_limiting_enabled( + self, + rpm_limit_type: Optional[str], + tpm_limit_type: Optional[str], + ) -> bool: + """ + Check if dynamic rate limiting is enabled for either RPM or TPM. + + Args: + rpm_limit_type: RPM rate limit type + tpm_limit_type: TPM rate limit type + + Returns: + True if dynamic mode is enabled for either limit type + """ + return rpm_limit_type == "dynamic" or tpm_limit_type == "dynamic" + + def _create_rate_limit_descriptors( self, user_api_key_dict: UserAPIKeyAuth, - cache: DualCache, data: dict, - call_type: str, - ): + rpm_limit_type: Optional[str], + tpm_limit_type: Optional[str], + model_has_failures: bool, + ) -> List[RateLimitDescriptor]: """ - Pre-call hook to check rate limits before making the API call. + Create all rate limit descriptors for the request. + + Returns list of descriptors for API key, user, team, team member, end user, and model-specific limits. """ from litellm.proxy.auth.auth_utils import ( get_key_model_rpm_limit, get_key_model_tpm_limit, ) - - verbose_proxy_logger.debug("Inside Rate Limit Pre-Call Hook") - - # Create rate limit descriptors + descriptors = [] # API Key rate limits @@ -576,10 +643,18 @@ async def async_pre_call_hook( key="api_key", value=user_api_key_dict.api_key, rate_limit={ - "requests_per_unit": user_api_key_dict.rpm_limit, - "tokens_per_unit": user_api_key_dict.tpm_limit, + "requests_per_unit": self._get_enforced_limit( + limit_value=user_api_key_dict.rpm_limit, + limit_type=rpm_limit_type, + model_has_failures=model_has_failures, + ), + "tokens_per_unit": self._get_enforced_limit( + limit_value=user_api_key_dict.tpm_limit, + limit_type=tpm_limit_type, + model_has_failures=model_has_failures, + ), "max_parallel_requests": user_api_key_dict.max_parallel_requests, - "window_size": self.window_size, # 1 minute window + "window_size": self.window_size, }, ) ) @@ -687,6 +762,104 @@ async def async_pre_call_hook( }, ) ) + + return descriptors + + async def _check_model_has_recent_failures( + self, + model: str, + parent_otel_span: Optional[Span] = None, + ) -> bool: + """ + Check if any deployment for this model has recent failures by using + the router's existing failure tracking. + + Returns True if any deployment has failures in the current minute. + """ + from litellm.proxy.proxy_server import llm_router + from litellm.router_utils.router_callbacks.track_deployment_metrics import ( + get_deployment_failures_for_current_minute, + ) + + if llm_router is None: + return False + + try: + # Get all deployments for this model + model_list = llm_router.get_model_list(model_name=model) + if not model_list: + return False + + # Check each deployment's failure count + for deployment in model_list: + deployment_id = deployment.get("model_info", {}).get("id") + if not deployment_id: + continue + + # Use router's existing failure tracking + failure_count = get_deployment_failures_for_current_minute( + litellm_router_instance=llm_router, + deployment_id=deployment_id, + ) + + if failure_count > DYNAMIC_RATE_LIMIT_ERROR_THRESHOLD_PER_MINUTE: + verbose_proxy_logger.debug( + f"[Dynamic Rate Limit] Deployment {deployment_id} has {failure_count} failures " + f"in current minute - enforcing rate limits for model {model}" + ) + return True + + verbose_proxy_logger.debug( + f"[Dynamic Rate Limit] No failures detected for model {model} - allowing dynamic exceeding" + ) + return False + + except Exception as e: + verbose_proxy_logger.debug( + f"Error checking model failure status: {str(e)}, defaulting to enforce limits" + ) + # Fail safe: enforce limits if we can't check + return True + + async def async_pre_call_hook( + self, + user_api_key_dict: UserAPIKeyAuth, + cache: DualCache, + data: dict, + call_type: str, + ): + """ + Pre-call hook to check rate limits before making the API call. + Supports dynamic rate limiting based on deployment health. + """ + verbose_proxy_logger.debug("Inside Rate Limit Pre-Call Hook") + + # Get rate limit types from metadata + metadata = user_api_key_dict.metadata or {} + rpm_limit_type = metadata.get("rpm_limit_type") + tpm_limit_type = metadata.get("tpm_limit_type") + + # For dynamic mode, check if the model has recent failures + model_has_failures = False + requested_model = data.get("model", None) + + if self._is_dynamic_rate_limiting_enabled( + rpm_limit_type=rpm_limit_type, + tpm_limit_type=tpm_limit_type, + ) and requested_model: + model_has_failures = await self._check_model_has_recent_failures( + model=requested_model, + parent_otel_span=user_api_key_dict.parent_otel_span, + ) + + # Create rate limit descriptors + descriptors = self._create_rate_limit_descriptors( + user_api_key_dict=user_api_key_dict, + data=data, + rpm_limit_type=rpm_limit_type, + tpm_limit_type=tpm_limit_type, + model_has_failures=model_has_failures, + ) # Only check rate limits if we have descriptors with actual limits if descriptors: diff --git a/litellm/proxy/management_endpoints/key_management_endpoints.py b/litellm/proxy/management_endpoints/key_management_endpoints.py index aa4864333962..2e9701df835a 100644 --- a/litellm/proxy/management_endpoints/key_management_endpoints.py +++ b/litellm/proxy/management_endpoints/key_management_endpoints.py @@ -827,8 +827,8 @@ async def generate_key_fn( - model_max_budget: Optional[Dict[str, BudgetConfig]] - Model-specific budgets {"gpt-4": {"budget_limit": 0.0005, "time_period": "30d"}}}. IF null or {} then no model specific budget. - model_rpm_limit: Optional[dict] - key-specific model rpm limit. Example - {"text-davinci-002": 1000, "gpt-3.5-turbo": 1000}. IF null or {} then no model specific rpm limit. - model_tpm_limit: Optional[dict] - key-specific model tpm limit. Example - {"text-davinci-002": 1000, "gpt-3.5-turbo": 1000}. IF null or {} then no model specific tpm limit. - - tpm_limit_type: Optional[str] - Type of tpm limit. Options: "best_effort_throughput" (no error if we're overallocating tpm), "guaranteed_throughput" (raise an error if we're overallocating tpm). Defaults to "best_effort_throughput". - - rpm_limit_type: Optional[str] - Type of rpm limit. Options: "best_effort_throughput" (no error if we're overallocating rpm), "guaranteed_throughput" (raise an error if we're overallocating rpm). Defaults to "best_effort_throughput". + - tpm_limit_type: Optional[str] - Type of tpm limit. Options: "best_effort_throughput" (no error if we're overallocating tpm), "guaranteed_throughput" (raise an error if we're overallocating tpm), "dynamic" (dynamically exceed limit when no 429 errors). Defaults to "best_effort_throughput". + - rpm_limit_type: Optional[str] - Type of rpm limit. Options: "best_effort_throughput" (no error if we're overallocating rpm), "guaranteed_throughput" (raise an error if we're overallocating rpm), "dynamic" (dynamically exceed limit when no 429 errors). Defaults to "best_effort_throughput". - allowed_cache_controls: Optional[list] - List of allowed cache control values. Example - ["no-cache", "no-store"]. See all values - https://docs.litellm.ai/docs/proxy/caching#turn-on--off-caching-per-request - blocked: Optional[bool] - Whether the key is blocked. - rpm_limit: Optional[int] - Specify rpm limit for a given key (Requests per minute) @@ -980,8 +980,8 @@ async def generate_service_account_key_fn( - model_max_budget: Optional[Dict[str, BudgetConfig]] - Model-specific budgets {"gpt-4": {"budget_limit": 0.0005, "time_period": "30d"}}}. IF null or {} then no model specific budget. - model_rpm_limit: Optional[dict] - key-specific model rpm limit. Example - {"text-davinci-002": 1000, "gpt-3.5-turbo": 1000}. IF null or {} then no model specific rpm limit. - model_tpm_limit: Optional[dict] - key-specific model tpm limit. Example - {"text-davinci-002": 1000, "gpt-3.5-turbo": 1000}. IF null or {} then no model specific tpm limit. - - tpm_limit_type: Optional[str] - TPM rate limit type - "best_effort_throughput" or "guaranteed_throughput" - - rpm_limit_type: Optional[str] - RPM rate limit type - "best_effort_throughput" or "guaranteed_throughput" + - tpm_limit_type: Optional[str] - TPM rate limit type - "best_effort_throughput", "guaranteed_throughput", or "dynamic" + - rpm_limit_type: Optional[str] - RPM rate limit type - "best_effort_throughput", "guaranteed_throughput", or "dynamic" - allowed_cache_controls: Optional[list] - List of allowed cache control values. Example - ["no-cache", "no-store"]. See all values - https://docs.litellm.ai/docs/proxy/caching#turn-on--off-caching-per-request - blocked: Optional[bool] - Whether the key is blocked. - rpm_limit: Optional[int] - Specify rpm limit for a given key (Requests per minute) @@ -1263,8 +1263,8 @@ async def update_key_fn( - rpm_limit: Optional[int] - Requests per minute limit - model_rpm_limit: Optional[dict] - Model-specific RPM limits {"gpt-4": 100, "claude-v1": 200} - model_tpm_limit: Optional[dict] - Model-specific TPM limits {"gpt-4": 100000, "claude-v1": 200000} - - tpm_limit_type: Optional[str] - TPM rate limit type - "best_effort_throughput" or "guaranteed_throughput" - - rpm_limit_type: Optional[str] - RPM rate limit type - "best_effort_throughput" or "guaranteed_throughput" + - tpm_limit_type: Optional[str] - TPM rate limit type - "best_effort_throughput", "guaranteed_throughput", or "dynamic" + - rpm_limit_type: Optional[str] - RPM rate limit type - "best_effort_throughput", "guaranteed_throughput", or "dynamic" - allowed_cache_controls: Optional[list] - List of allowed cache control values - duration: Optional[str] - Key validity duration ("30d", "1h", etc.) - permissions: Optional[dict] - Key-specific permissions diff --git a/litellm/proxy/proxy_config.yaml b/litellm/proxy/proxy_config.yaml index 04a3e89599e3..39d0a400b902 100644 --- a/litellm/proxy/proxy_config.yaml +++ b/litellm/proxy/proxy_config.yaml @@ -1,4 +1,14 @@ model_list: + - model_name: fake-openai-endpoint + litellm_params: + model: openai/gm + api_key: hi + api_base: https://exampleopenaiendpoint-production.up.railway.app/ + - model_name: db-openai-endpoint + litellm_params: + model: openai/gm + api_key: hi + api_base: http://0.0.0.0:8092 - model_name: anthropic/* litellm_params: model: anthropic/* @@ -12,4 +22,4 @@ litellm_settings: "dev": 0.1 # 10% reserved for development (1 RPM) priority_reservation_settings: default_priority: 0.2 # Weight (0%) assigned to keys without explicit priority metadata - saturation_threshold: 0.50 # A model is saturated if it has hit 50% of its RPM limit \ No newline at end of file + saturation_threshold: 0.50 # A model is saturated if it has hit 50% of its RPM limit diff --git a/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py b/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py index 511eb5bbb891..a8aae3e1a687 100644 --- a/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py +++ b/tests/test_litellm/proxy/hooks/test_parallel_request_limiter_v3.py @@ -954,6 +954,93 @@ async def mock_should_rate_limit(descriptors, **kwargs): assert team_member_descriptor["rate_limit"]["tokens_per_unit"] == 1000, "Team member TPM limit should be set" +@pytest.mark.asyncio +async def test_dynamic_rate_limiting_v3(): + """ + Test that dynamic rate limiting only enforces limits when model has failures. + + When rpm_limit_type is set to "dynamic": + - If model has no failures, rate limits should NOT be enforced (allow exceeding) + - If model has failures above threshold, rate limits SHOULD be enforced + """ + _api_key = "sk-12345" + _api_key_hash = hash_token(_api_key) + model = "gpt-3.5-turbo" + + # Set a low RPM limit to make testing easier + user_api_key_dict = UserAPIKeyAuth( + api_key=_api_key_hash, + rpm_limit=2, + metadata={"rpm_limit_type": "dynamic"}, + ) + + local_cache = DualCache() + parallel_request_handler = _PROXY_MaxParallelRequestsHandler( + internal_usage_cache=InternalUsageCache(local_cache) + ) + + # Mock should_rate_limit to track if limits are enforced + captured_descriptors = [] + + async def mock_should_rate_limit(descriptors, **kwargs): + captured_descriptors.clear() + captured_descriptors.extend(descriptors) + return {"overall_code": "OK", "statuses": []} + + parallel_request_handler.should_rate_limit = mock_should_rate_limit + + # Test 1: No failures - rate limits should NOT be enforced (rpm_limit should be None) + async def mock_check_no_failures(*args, **kwargs): + return False + + parallel_request_handler._check_model_has_recent_failures = mock_check_no_failures + + await parallel_request_handler.async_pre_call_hook( + user_api_key_dict=user_api_key_dict, + cache=local_cache, + data={"model": model}, + call_type="", + ) + + # Find the API key descriptor + api_key_descriptor = None + for descriptor in captured_descriptors: + if descriptor["key"] == "api_key": + api_key_descriptor = descriptor + break + + assert api_key_descriptor is not None, "API key descriptor should be present" + assert ( + api_key_descriptor["rate_limit"]["requests_per_unit"] is None + ), "RPM limit should be None when dynamic mode and no failures" + + # Test 2: With failures - rate limits SHOULD be enforced (rpm_limit should be set) + async def mock_check_with_failures(*args, **kwargs): + return True + + parallel_request_handler._check_model_has_recent_failures = mock_check_with_failures + captured_descriptors.clear() + + await parallel_request_handler.async_pre_call_hook( + user_api_key_dict=user_api_key_dict, + cache=local_cache, + data={"model": model}, + call_type="", + ) + + # Find the API key descriptor again + api_key_descriptor = None + for descriptor in captured_descriptors: + if descriptor["key"] == "api_key": + api_key_descriptor = descriptor + break + + assert api_key_descriptor is not None, "API key descriptor should be present" + assert ( + api_key_descriptor["rate_limit"]["requests_per_unit"] == 2 + ), "RPM limit should be enforced when dynamic mode and failures detected" + + @pytest.mark.asyncio async def test_async_increment_tokens_with_ttl_preservation(): """ diff --git a/ui/litellm-dashboard/src/components/common_components/RateLimitTypeFormItem.tsx b/ui/litellm-dashboard/src/components/common_components/RateLimitTypeFormItem.tsx index 63a2bf92dd29..e3524bb8f493 100644 --- a/ui/litellm-dashboard/src/components/common_components/RateLimitTypeFormItem.tsx +++ b/ui/litellm-dashboard/src/components/common_components/RateLimitTypeFormItem.tsx @@ -85,11 +85,21 @@ export const RateLimitTypeFormItem: React.FC = ({
+ ) : ( <> + )} From f43d0178874c923b862387826396c7848052b907 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Tue, 14 Oct 2025 15:59:42 +0530 Subject: [PATCH 25/36] Add cost tracking for vertex ai passthrough batch jobs --- .../vertex_passthrough_logging_handler.py | 182 +++++++++++++++++- .../pass_through_endpoints/success_handler.py | 1 + 2 files changed, 181 insertions(+), 2 deletions(-) diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py index 5b22b2746c9a..c42a6d0a28ea 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py @@ -22,10 +22,13 @@ if TYPE_CHECKING: from ..success_handler import PassThroughEndpointLogging - from ..types import EndpointType + from litellm.types.utils import LiteLLMBatch else: PassThroughEndpointLogging = Any - EndpointType = Any + LiteLLMBatch = Any + +# Define EndpointType locally to avoid import issues +EndpointType = Any class VertexPassthroughLoggingHandler: @@ -204,6 +207,17 @@ def vertex_passthrough_handler( "result": litellm_prediction_response, "kwargs": kwargs, } + elif "batchPredictionJobs" in url_route: + return VertexPassthroughLoggingHandler.batch_prediction_jobs_handler( + httpx_response=httpx_response, + logging_obj=logging_obj, + url_route=url_route, + result=result, + start_time=start_time, + end_time=end_time, + cache_hit=cache_hit, + **kwargs, + ) else: return { "result": None, @@ -412,3 +426,167 @@ def _create_vertex_response_logging_payload_for_generate_content( logging_obj.model_call_details["model"] = logging_obj.model logging_obj.model_call_details["custom_llm_provider"] = custom_llm_provider return kwargs + + @staticmethod + def batch_prediction_jobs_handler( + httpx_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + url_route: str, + result: str, + start_time: datetime, + end_time: datetime, + cache_hit: bool, + **kwargs, + ) -> PassThroughEndpointLoggingTypedDict: + """ + Handle batch prediction jobs passthrough logging. + Creates a managed object for cost tracking when batch job is successfully created. + """ + from litellm.types.utils import LiteLLMBatch + from litellm.llms.vertex_ai.batches.transformation import VertexAIBatchTransformation + from litellm.proxy.openai_files_endpoints.common_utils import ( + _is_base64_encoded_unified_file_id, + get_batch_id_from_unified_batch_id, + get_model_id_from_unified_batch_id, + ) + from litellm._uuid import uuid + import base64 + + try: + _json_response = httpx_response.json() + + # Only handle successful batch job creation (POST requests) + if httpx_response.status_code == 200 and "name" in _json_response: + # Transform Vertex AI response to LiteLLM batch format + litellm_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response( + response=_json_response + ) + + # Extract batch ID and model from the response + batch_id = VertexAIBatchTransformation._get_batch_id_from_vertex_ai_batch_response(_json_response) + model_name = _json_response.get("model", "unknown") + + # Create unified object ID for tracking + # Format: base64(model_id:batch_id) + unified_object_id = base64.b64encode(f"{model_name}:{batch_id}".encode()).decode() + + # Store the managed object for cost tracking + # This will be picked up by check_batch_cost polling mechanism + VertexPassthroughLoggingHandler._store_batch_managed_object( + unified_object_id=unified_object_id, + batch_object=litellm_batch_response, + model_object_id=batch_id, + logging_obj=logging_obj, + **kwargs, + ) + + # Create a simple model response for logging + litellm_model_response = ModelResponse() + litellm_model_response.id = str(uuid.uuid4()) + litellm_model_response.model = model_name + litellm_model_response.object = "batch_prediction_job" + litellm_model_response.created = int(start_time.timestamp()) + + # Set response cost to 0 initially (will be updated when batch completes) + response_cost = 0.0 + kwargs["response_cost"] = response_cost + kwargs["model"] = model_name + kwargs["batch_id"] = batch_id + kwargs["unified_object_id"] = unified_object_id + + logging_obj.model = model_name + logging_obj.model_call_details["model"] = logging_obj.model + logging_obj.model_call_details["response_cost"] = response_cost + logging_obj.model_call_details["batch_id"] = batch_id + + return { + "result": litellm_model_response, + "kwargs": kwargs, + } + + except Exception as e: + verbose_proxy_logger.error(f"Error in batch_prediction_jobs_handler: {e}") + # Return basic response on error + litellm_model_response = ModelResponse() + litellm_model_response.id = str(uuid.uuid4()) + litellm_model_response.model = "vertex_ai_batch" + litellm_model_response.object = "batch_prediction_job" + litellm_model_response.created = int(start_time.timestamp()) + + kwargs["response_cost"] = 0.0 + kwargs["model"] = "vertex_ai_batch" + + return { + "result": litellm_model_response, + "kwargs": kwargs, + } + + @staticmethod + def _store_batch_managed_object( + unified_object_id: str, + batch_object: LiteLLMBatch, + model_object_id: str, + logging_obj: LiteLLMLoggingObj, + **kwargs, + ) -> None: + """ + Store batch managed object for cost tracking. + This will be picked up by the check_batch_cost polling mechanism. + """ + try: + # Get the managed files hook from the logging object + # This is a bit of a hack, but we need access to the proxy logging system + from litellm.proxy.proxy_server import proxy_logging_obj + + managed_files_hook = proxy_logging_obj.get_proxy_hook("managed_files") + if managed_files_hook is not None: + # Create a mock user API key dict for the managed object storage + from litellm.proxy._types import UserAPIKeyAuth + user_api_key_dict = UserAPIKeyAuth( + user_id=kwargs.get("user_id", "default-user"), + api_key="", + team_id=None, + team_alias=None, + user_role="", + user_email=None, + max_budget=None, + spend=None, + models=None, + tpm_limit=None, + rpm_limit=None, + budget_duration=None, + budget_reset_at=None, + max_parallel_requests=None, + allowed_model_region=None, + metadata=None, + user_info=None, + key_alias=None, + permissions=None, + model_max_budget=None, + model_spend=None, + model_budget_duration=None, + model_budget_reset_at=None, + ) + + # Store the unified object for batch cost tracking + import asyncio + asyncio.create_task( + managed_files_hook.store_unified_object_id( + unified_object_id=unified_object_id, + file_object=batch_object, + litellm_parent_otel_span=None, + model_object_id=model_object_id, + file_purpose="batch", + user_api_key_dict=user_api_key_dict, + ) + ) + + verbose_proxy_logger.info( + f"Stored batch managed object with unified_object_id={unified_object_id}, batch_id={model_object_id}" + ) + else: + verbose_proxy_logger.warning("Managed files hook not available, cannot store batch object for cost tracking") + + except Exception as e: + verbose_proxy_logger.error(f"Error storing batch managed object: {e}") + diff --git a/litellm/proxy/pass_through_endpoints/success_handler.py b/litellm/proxy/pass_through_endpoints/success_handler.py index a819c429f103..0c0ade848eaa 100644 --- a/litellm/proxy/pass_through_endpoints/success_handler.py +++ b/litellm/proxy/pass_through_endpoints/success_handler.py @@ -40,6 +40,7 @@ def __init__(self): "predict", "rawPredict", "streamRawPredict", + "batchPredictionJobs", ] # Anthropic From 6dd9062afdba5ef6c8ff859b7ad0b4e9818e819d Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Tue, 14 Oct 2025 16:59:20 +0530 Subject: [PATCH 26/36] Add google rerank endpoint --- docs/my-website/docs/providers/vertex.md | 62 ++- docs/my-website/docs/rerank.md | 3 +- litellm/__init__.py | 1 + litellm/llms/vertex_ai/rerank/handler.py | 5 + .../llms/vertex_ai/rerank/transformation.py | 217 +++++++++++ litellm/proxy/proxy_config.yaml | 46 ++- litellm/utils.py | 2 + .../test_vertex_ai_rerank_integration.py | 218 +++++++++++ .../test_vertex_ai_rerank_transformation.py | 353 ++++++++++++++++++ 9 files changed, 886 insertions(+), 21 deletions(-) create mode 100644 litellm/llms/vertex_ai/rerank/handler.py create mode 100644 litellm/llms/vertex_ai/rerank/transformation.py create mode 100644 tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_integration.py create mode 100644 tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_transformation.py diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md index 3b6562b51aef..316950a6600e 100644 --- a/docs/my-website/docs/providers/vertex.md +++ b/docs/my-website/docs/providers/vertex.md @@ -12,7 +12,7 @@ import TabItem from '@theme/TabItem'; | Provider Route on LiteLLM | `vertex_ai/` | | Link to Provider Doc | [Vertex AI ↗](https://cloud.google.com/vertex-ai) | | Base URL | 1. Regional endpoints
`https://{vertex_location}-aiplatform.googleapis.com/`
2. Global endpoints (limited availability)
`https://aiplatform.googleapis.com/`| -| Supported Operations | [`/chat/completions`](#sample-usage), `/completions`, [`/embeddings`](#embedding-models), [`/audio/speech`](#text-to-speech-apis), [`/fine_tuning`](#fine-tuning-apis), [`/batches`](#batch-apis), [`/files`](#batch-apis), [`/images`](#image-generation-models) | +| Supported Operations | [`/chat/completions`](#sample-usage), `/completions`, [`/embeddings`](#embedding-models), [`/audio/speech`](#text-to-speech-apis), [`/fine_tuning`](#fine-tuning-apis), [`/batches`](#batch-apis), [`/files`](#batch-apis), [`/images`](#image-generation-models), [`/rerank`](#rerank-api) |
@@ -3114,3 +3114,63 @@ Once that's done, when you deploy the new container in the Google Cloud Run serv s/o @[Darien Kindlund](https://www.linkedin.com/in/kindlund/) for this tutorial + +## **Rerank API** + +Vertex AI supports reranking through the Discovery Engine API, providing semantic ranking capabilities for document retrieval. + +### Setup + +Set your Google Cloud project ID: + +```bash +export VERTEXAI_PROJECT="your-project-id" +``` + +### Usage + +```python +from litellm import rerank + +# Using the latest model (recommended) +response = rerank( + model="vertex_ai/semantic-ranker-default@latest", + query="What is Google Gemini?", + documents=[ + "Gemini is a cutting edge large language model created by Google.", + "The Gemini zodiac symbol often depicts two figures standing side-by-side.", + "Gemini is a constellation that can be seen in the night sky." + ], + top_n=2, + return_documents=True # Set to False for ID-only responses +) + +# Using specific model versions +response_v003 = rerank( + model="vertex_ai/semantic-ranker-default-003", + query="What is Google Gemini?", + documents=documents, + top_n=2 +) + +print(response.results) +``` + +### Parameters + +| Parameter | Type | Description | +|-----------|------|-------------| +| `model` | string | Model name (e.g., `vertex_ai/semantic-ranker-default@latest`) | +| `query` | string | Search query | +| `documents` | list | Documents to rank | +| `top_n` | int | Number of top results to return | +| `return_documents` | bool | Return full content (True) or IDs only (False) | + +### Supported Models + +- `semantic-ranker-default@latest` +- `semantic-ranker-fast@latest` +- `semantic-ranker-default-003` +- `semantic-ranker-default-002` + +For detailed model specifications, see the [Google Cloud ranking API documentation](https://cloud.google.com/generative-ai-app-builder/docs/ranking#rank_or_rerank_a_set_of_records_according_to_a_query). diff --git a/docs/my-website/docs/rerank.md b/docs/my-website/docs/rerank.md index cad647183846..72f35c6ceceb 100644 --- a/docs/my-website/docs/rerank.md +++ b/docs/my-website/docs/rerank.md @@ -121,4 +121,5 @@ curl http://0.0.0.0:4000/rerank \ | HuggingFace| [Usage](../docs/providers/huggingface_rerank) | | Infinity| [Usage](../docs/providers/infinity) | | vLLM| [Usage](../docs/providers/vllm#rerank-endpoint) | -| DeepInfra| [Usage](../docs/providers/deepinfra#rerank-endpoint) | \ No newline at end of file +| DeepInfra| [Usage](../docs/providers/deepinfra#rerank-endpoint) | +| Vertex AI| [Usage](../docs/providers/vertex#rerank-api) | \ No newline at end of file diff --git a/litellm/__init__.py b/litellm/__init__.py index e461c88efd6d..e6ed15d7df04 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -1067,6 +1067,7 @@ def add_known_models(): from .llms.jina_ai.rerank.transformation import JinaAIRerankConfig from .llms.deepinfra.rerank.transformation import DeepinfraRerankConfig from .llms.nvidia_nim.rerank.transformation import NvidiaNimRerankConfig +from .llms.vertex_ai.rerank.transformation import VertexAIRerankConfig from .llms.clarifai.chat.transformation import ClarifaiConfig from .llms.ai21.chat.transformation import AI21ChatConfig, AI21ChatConfig as AI21Config from .llms.meta_llama.chat.transformation import LlamaAPIConfig diff --git a/litellm/llms/vertex_ai/rerank/handler.py b/litellm/llms/vertex_ai/rerank/handler.py new file mode 100644 index 000000000000..3df719819dd6 --- /dev/null +++ b/litellm/llms/vertex_ai/rerank/handler.py @@ -0,0 +1,5 @@ +""" +Vertex AI Rerank - uses `llm_http_handler.py` to make httpx requests + +Request/Response transformation is handled in `transformation.py` +""" diff --git a/litellm/llms/vertex_ai/rerank/transformation.py b/litellm/llms/vertex_ai/rerank/transformation.py new file mode 100644 index 000000000000..1a956a7ed0be --- /dev/null +++ b/litellm/llms/vertex_ai/rerank/transformation.py @@ -0,0 +1,217 @@ +""" +Translates from Cohere's `/v1/rerank` input format to Vertex AI Discovery Engine's `/rank` input format. + +Why separate file? Make it easy to see how transformation works +""" + +from typing import Any, Dict, List, Optional, Union + +import httpx + +import litellm +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig +from litellm.llms.vertex_ai.vertex_llm_base import VertexBase +from litellm.secret_managers.main import get_secret_str +from litellm.types.rerank import RerankResponse + + +class VertexAIRerankConfig(BaseRerankConfig, VertexBase): + """ + Configuration for Vertex AI Discovery Engine Rerank API + + Reference: https://cloud.google.com/generative-ai-app-builder/docs/ranking#rank_or_rerank_a_set_of_records_according_to_a_query + """ + + def __init__(self) -> None: + super().__init__() + + def get_complete_url(self, api_base: Optional[str], model: str) -> str: + """ + Get the complete URL for the Vertex AI Discovery Engine ranking API + """ + # Get project ID from environment or litellm config + project_id = ( + get_secret_str("VERTEXAI_PROJECT") + or litellm.vertex_project + ) + + if not project_id: + raise ValueError( + "Vertex AI project ID is required. Please set 'VERTEXAI_PROJECT' or 'litellm.vertex_project'" + ) + + return f"https://discoveryengine.googleapis.com/v1/projects/{project_id}/locations/global/rankingConfigs/default_ranking_config:rank" + + def validate_environment( + self, + headers: dict, + model: str, + api_key: Optional[str] = None, + ) -> dict: + """ + Validate and set up authentication for Vertex AI Discovery Engine API + """ + # Get credentials and project info + vertex_credentials = self.get_vertex_ai_credentials({}) + vertex_project = self.get_vertex_ai_project({}) + + # Get access token using the base class method + access_token, project_id = self._ensure_access_token( + credentials=vertex_credentials, + project_id=vertex_project, + custom_llm_provider="vertex_ai", + ) + + default_headers = { + "Authorization": f"Bearer {access_token}", + "Content-Type": "application/json", + "X-Goog-User-Project": project_id, + } + + # If 'Authorization' is provided in headers, it overrides the default. + if "Authorization" in headers: + default_headers["Authorization"] = headers["Authorization"] + + # Merge other headers, overriding any default ones except Authorization + return {**default_headers, **headers} + + def transform_rerank_request( + self, + model: str, + optional_rerank_params: Dict, + headers: dict, + ) -> dict: + """ + Transform the request from Cohere format to Vertex AI Discovery Engine format + """ + if "query" not in optional_rerank_params: + raise ValueError("query is required for Vertex AI rerank") + if "documents" not in optional_rerank_params: + raise ValueError("documents is required for Vertex AI rerank") + + query = optional_rerank_params["query"] + documents = optional_rerank_params["documents"] + top_n = optional_rerank_params.get("top_n", None) + return_documents = optional_rerank_params.get("return_documents", True) + + # Convert documents to records format + records = [] + for idx, document in enumerate(documents): + if isinstance(document, str): + content = document + title = " ".join(document.split()[:3]) # First 3 words as title + else: + # Handle dict format + content = document.get("text", str(document)) + title = document.get("title", " ".join(content.split()[:3])) + + records.append({ + "id": str(idx), + "title": title, + "content": content + }) + + request_data = { + "model": model, + "query": query, + "records": records + } + + if top_n is not None: + request_data["topN"] = top_n + + # Map return_documents to ignoreRecordDetailsInResponse + # When return_documents is False, we want to ignore record details (return only IDs) + request_data["ignoreRecordDetailsInResponse"] = not return_documents + + return request_data + + def transform_rerank_response( + self, + model: str, + raw_response: httpx.Response, + model_response: RerankResponse, + logging_obj: LiteLLMLoggingObj, + api_key: Optional[str] = None, + request_data: dict = {}, + optional_params: dict = {}, + litellm_params: dict = {}, + ) -> RerankResponse: + """ + Transform Vertex AI Discovery Engine response to Cohere format + """ + try: + raw_response_json = raw_response.json() + except Exception as e: + raise ValueError(f"Failed to parse response: {e}") + + # Extract records from response + records = raw_response_json.get("records", []) + + # Convert to Cohere format + results = [] + for record in records: + # Handle both cases: with full details and with only IDs + if "score" in record: + # Full response with score and details + results.append({ + "index": int(record["id"]), + "relevance_score": record.get("score", 0.0) + }) + else: + # Response with only IDs (when ignoreRecordDetailsInResponse=true) + # We can't provide a relevance score, so we'll use a default + results.append({ + "index": int(record["id"]), + "relevance_score": 1.0 # Default score when details are ignored + }) + + # Sort by relevance score (descending) + results.sort(key=lambda x: x["relevance_score"], reverse=True) + + # Create response in Cohere format + response_data = { + "id": f"vertex_ai_rerank_{model}", + "results": results, + "meta": { + "billed_units": { + "search_units": len(records) + } + } + } + + return RerankResponse(**response_data) + + def get_supported_cohere_rerank_params(self, model: str) -> list: + return [ + "query", + "documents", + "top_n", + "return_documents", + ] + + def map_cohere_rerank_params( + self, + non_default_params: dict, + model: str, + drop_params: bool, + query: str, + documents: List[Union[str, Dict[str, Any]]], + custom_llm_provider: Optional[str] = None, + top_n: Optional[int] = None, + rank_fields: Optional[List[str]] = None, + return_documents: Optional[bool] = True, + max_chunks_per_doc: Optional[int] = None, + max_tokens_per_doc: Optional[int] = None, + ) -> Dict: + """ + Map Cohere rerank params to Vertex AI format + """ + return { + "query": query, + "documents": documents, + "top_n": top_n, + "return_documents": return_documents, + } + diff --git a/litellm/proxy/proxy_config.yaml b/litellm/proxy/proxy_config.yaml index 39d0a400b902..6c49159ef0ec 100644 --- a/litellm/proxy/proxy_config.yaml +++ b/litellm/proxy/proxy_config.yaml @@ -1,25 +1,33 @@ model_list: - - model_name: fake-openai-endpoint + - model_name: bedrock/batch-anthropic.claude-3-5-sonnet-20240620-v1:0 litellm_params: - model: openai/gm - api_key: hi - api_base: https://exampleopenaiendpoint-production.up.railway.app/ - - model_name: db-openai-endpoint - litellm_params: - model: openai/gm - api_key: hi - api_base: http://0.0.0.0:8092 + model: bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0 + ######################################################### + ########## batch specific params ######################## + s3_bucket_name: litellm-proxy + s3_region_name: us-west-2 + s3_access_key_id: os.environ/AWS_ACCESS_KEY_ID + s3_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY + aws_batch_role_arn: arn:aws:iam::888602223428:role/service-role/AmazonBedrockExecutionRoleForAgents_BB9HNW6V4CV + model_info: + mode: batch - model_name: anthropic/* litellm_params: model: anthropic/* + api_key: os.environ/ANTHROPIC_API_KEY + - model_name: openai/* + litellm_params: + model: openai/* + api_key: os.environ/OPENAI_API_KEY + - model_name: gemini/* + litellm_params: + model: gemini/* + api_key: os.environ/GEMINI_API_KEY + - model_name: vertex-gemini-2.5-flash + litellm_params: + model: vertex_ai/gemini-2.5-flash + vertex_project: "your-gcp-project-id" + vertex_location: "us-central1" + vertex_credentials: "path/to/your/service-account-key.json" - - -litellm_settings: - callbacks: ["dynamic_rate_limiter_v3"] - priority_reservation: - "prod": 0.9 # 90% reserved for production (9 RPM) - "dev": 0.1 # 10% reserved for development (1 RPM) - priority_reservation_settings: - default_priority: 0.2 # Weight (0%) assigned to keys without explicit priority metadata - saturation_threshold: 0.50 # A model is saturated if it has hit 50% of its RPM limit + diff --git a/litellm/utils.py b/litellm/utils.py index 57f1846d1ebe..d502056f607b 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -7243,6 +7243,8 @@ def get_provider_rerank_config( return litellm.DeepinfraRerankConfig() elif litellm.LlmProviders.NVIDIA_NIM == provider: return litellm.NvidiaNimRerankConfig() + elif litellm.LlmProviders.VERTEX_AI == provider: + return litellm.VertexAIRerankConfig() return litellm.CohereRerankConfig() @staticmethod diff --git a/tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_integration.py b/tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_integration.py new file mode 100644 index 000000000000..1acdadf541a5 --- /dev/null +++ b/tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_integration.py @@ -0,0 +1,218 @@ +""" +Integration tests for Vertex AI rerank functionality. +These tests demonstrate end-to-end usage of the Vertex AI rerank feature. +""" +import os +from unittest.mock import MagicMock, patch + +import httpx +import pytest + +from litellm.llms.vertex_ai.rerank.transformation import VertexAIRerankConfig + + +class TestVertexAIRerankIntegration: + def setup_method(self): + self.config = VertexAIRerankConfig() + self.model = "semantic-ranker-default@latest" + + @patch('litellm.llms.vertex_ai.rerank.transformation.VertexAIRerankConfig._ensure_access_token') + def test_end_to_end_rerank_flow(self, mock_ensure_access_token): + """Test complete rerank flow from request to response.""" + # Mock authentication + mock_ensure_access_token.return_value = ("test-access-token", "test-project-123") + + # Test documents + documents = [ + "Gemini is a cutting edge large language model created by Google.", + "The Gemini zodiac symbol often depicts two figures standing side-by-side.", + "Gemini is a constellation that can be seen in the night sky.", + "Google's Gemini AI model represents a significant advancement in artificial intelligence technology." + ] + query = "What is Google Gemini?" + + # Step 1: Test request transformation + with patch.object(self.config, 'get_vertex_ai_credentials', return_value=None), \ + patch.object(self.config, 'get_vertex_ai_project', return_value="test-project-123"): + + # Validate environment + headers = self.config.validate_environment( + headers={}, + model=self.model, + api_key=None + ) + + # Transform request + request_data = self.config.transform_rerank_request( + model=self.model, + optional_rerank_params={ + "query": query, + "documents": documents, + "top_n": 2, + "return_documents": True + }, + headers=headers + ) + + # Verify request structure + assert request_data["model"] == self.model + assert request_data["query"] == query + assert request_data["topN"] == 2 + assert request_data["ignoreRecordDetailsInResponse"] == False + assert len(request_data["records"]) == 4 + + # Verify record structure + for i, record in enumerate(request_data["records"]): + assert record["id"] == str(i) # 0-based indexing + assert "title" in record + assert "content" in record + assert record["content"] == documents[i] + + # Step 2: Test response transformation + # Mock Vertex AI Discovery Engine response + mock_response_data = { + "records": [ + { + "id": "3", + "score": 0.95, + "title": "Google's Gemini AI model", + "content": "Google's Gemini AI model represents a significant advancement in artificial intelligence technology." + }, + { + "id": "0", + "score": 0.92, + "title": "Gemini is a", + "content": "Gemini is a cutting edge large language model created by Google." + } + ] + } + + mock_response = MagicMock(spec=httpx.Response) + mock_response.json.return_value = mock_response_data + mock_response.text = '{"records": [{"id": "3", "score": 0.95, "title": "Google\'s Gemini AI model", "content": "Google\'s Gemini AI model represents a significant advancement in artificial intelligence technology."}, {"id": "0", "score": 0.92, "title": "Gemini is a", "content": "Gemini is a cutting edge large language model created by Google."}]}' + + mock_logging = MagicMock() + + # Transform response + from litellm.types.rerank import RerankResponse + model_response = RerankResponse() + + result = self.config.transform_rerank_response( + model=self.model, + raw_response=mock_response, + model_response=model_response, + logging_obj=mock_logging, + ) + + # Verify response structure + assert result.id == f"vertex_ai_rerank_{self.model}" + assert len(result.results) == 2 + + # Results should be sorted by relevance score (descending) + assert result.results[0]["index"] == 3 # Highest score + assert result.results[0]["relevance_score"] == 0.95 + assert result.results[1]["index"] == 0 # Second highest score + assert result.results[1]["relevance_score"] == 0.92 + + # Verify metadata + assert result.meta["billed_units"]["search_units"] == 2 + + def test_return_documents_false_flow(self): + """Test rerank flow when return_documents=False (ID-only response).""" + documents = ["doc1", "doc2", "doc3"] + query = "test query" + + # Transform request with return_documents=False + request_data = self.config.transform_rerank_request( + model=self.model, + optional_rerank_params={ + "query": query, + "documents": documents, + "return_documents": False + }, + headers={} + ) + + # Verify ignoreRecordDetailsInResponse is True + assert request_data["ignoreRecordDetailsInResponse"] == True + + # Mock response with only IDs + mock_response_data = { + "records": [ + {"id": "1"}, + {"id": "0"}, + {"id": "2"} + ] + } + + mock_response = MagicMock(spec=httpx.Response) + mock_response.json.return_value = mock_response_data + mock_response.text = '{"records": [{"id": "1"}, {"id": "0"}, {"id": "2"}]}' + + mock_logging = MagicMock() + + # Transform response + from litellm.types.rerank import RerankResponse + model_response = RerankResponse() + + result = self.config.transform_rerank_response( + model=self.model, + raw_response=mock_response, + model_response=model_response, + logging_obj=mock_logging, + ) + + # Verify response structure with default scores + assert len(result.results) == 3 + for result_item in result.results: + assert result_item["relevance_score"] == 1.0 # Default score when details are ignored + assert "index" in result_item + + def test_document_title_generation(self): + """Test that document titles are generated correctly from content.""" + documents = [ + "This is a very long document with many words that should be truncated to only the first three words for the title", + "Short doc", + "Another document with multiple words here and more content" + ] + + request_data = self.config.transform_rerank_request( + model=self.model, + optional_rerank_params={ + "query": "test query", + "documents": documents + }, + headers={} + ) + + # Verify title generation + assert request_data["records"][0]["title"] == "This is a" # First 3 words + assert request_data["records"][1]["title"] == "Short doc" # Less than 3 words + assert request_data["records"][2]["title"] == "Another document with" # First 3 words + + def test_dictionary_document_handling(self): + """Test handling of dictionary-format documents.""" + documents = [ + {"text": "Gemini is a cutting edge large language model created by Google.", "title": "Custom Title 1"}, + {"text": "The Gemini zodiac symbol often depicts two figures standing side-by-side."}, + {"text": "Gemini is a constellation that can be seen in the night sky.", "title": "Custom Title 3"} + ] + + request_data = self.config.transform_rerank_request( + model=self.model, + optional_rerank_params={ + "query": "test query", + "documents": documents + }, + headers={} + ) + + # Verify custom titles are used when provided + assert request_data["records"][0]["title"] == "Custom Title 1" + assert request_data["records"][1]["title"] == "The Gemini zodiac" # Generated from first 3 words + assert request_data["records"][2]["title"] == "Custom Title 3" + + # Verify content is extracted correctly + assert request_data["records"][0]["content"] == "Gemini is a cutting edge large language model created by Google." + assert request_data["records"][1]["content"] == "The Gemini zodiac symbol often depicts two figures standing side-by-side." + assert request_data["records"][2]["content"] == "Gemini is a constellation that can be seen in the night sky." diff --git a/tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_transformation.py b/tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_transformation.py new file mode 100644 index 000000000000..9a1029b1726c --- /dev/null +++ b/tests/test_litellm/llms/vertex_ai/rerank/test_vertex_ai_rerank_transformation.py @@ -0,0 +1,353 @@ +""" +Tests for Vertex AI rerank transformation functionality. +Based on the test patterns from other rerank providers and the current Vertex AI implementation. +""" +import json +import os +from unittest.mock import MagicMock, patch + +import httpx +import pytest + +from litellm.llms.vertex_ai.rerank.transformation import VertexAIRerankConfig +from litellm.types.rerank import RerankResponse + + +class TestVertexAIRerankTransform: + def setup_method(self): + self.config = VertexAIRerankConfig() + self.model = "semantic-ranker-default@latest" + + def test_get_complete_url(self): + """Test URL generation for Vertex AI Discovery Engine rerank API.""" + # Test with project ID from environment + with patch.dict(os.environ, {"VERTEXAI_PROJECT": "test-project-123"}): + url = self.config.get_complete_url(api_base=None, model=self.model) + expected_url = "https://discoveryengine.googleapis.com/v1/projects/test-project-123/locations/global/rankingConfigs/default_ranking_config:rank" + assert url == expected_url + + # Test with litellm.vertex_project + with patch.dict(os.environ, {}, clear=True): + import litellm + # Set vertex_project attribute if it doesn't exist + if not hasattr(litellm, 'vertex_project'): + litellm.vertex_project = None + original_project = litellm.vertex_project + litellm.vertex_project = "litellm-project-456" + try: + url = self.config.get_complete_url(api_base=None, model=self.model) + expected_url = "https://discoveryengine.googleapis.com/v1/projects/litellm-project-456/locations/global/rankingConfigs/default_ranking_config:rank" + assert url == expected_url + finally: + litellm.vertex_project = original_project + + # Test error when no project ID is available + with patch.dict(os.environ, {}, clear=True): + import litellm + # Set vertex_project to None to ensure no project ID is available + if not hasattr(litellm, 'vertex_project'): + litellm.vertex_project = None + original_project = litellm.vertex_project + litellm.vertex_project = None + try: + with pytest.raises(ValueError, match="Vertex AI project ID is required"): + self.config.get_complete_url(api_base=None, model=self.model) + finally: + litellm.vertex_project = original_project + + @patch('litellm.llms.vertex_ai.rerank.transformation.VertexAIRerankConfig._ensure_access_token') + def test_validate_environment(self, mock_ensure_access_token): + """Test environment validation and header setup.""" + # Mock the authentication + mock_ensure_access_token.return_value = ("test-access-token", "test-project-123") + + # Mock the credential and project methods + with patch.object(self.config, 'get_vertex_ai_credentials', return_value=None), \ + patch.object(self.config, 'get_vertex_ai_project', return_value="test-project-123"): + + headers = self.config.validate_environment( + headers={}, + model=self.model, + api_key=None + ) + + expected_headers = { + "Authorization": "Bearer test-access-token", + "Content-Type": "application/json", + "X-Goog-User-Project": "test-project-123" + } + assert headers == expected_headers + + def test_transform_rerank_request_basic(self): + """Test basic request transformation for Vertex AI Discovery Engine format.""" + optional_params = { + "query": "What is Google Gemini?", + "documents": [ + "Gemini is a cutting edge large language model created by Google.", + "The Gemini zodiac symbol often depicts two figures standing side-by-side." + ], + "top_n": 2 + } + + request_data = self.config.transform_rerank_request( + model=self.model, + optional_rerank_params=optional_params, + headers={} + ) + + # Verify basic structure + assert request_data["model"] == self.model + assert request_data["query"] == "What is Google Gemini?" + assert request_data["topN"] == 2 + assert "records" in request_data + assert len(request_data["records"]) == 2 + + # Verify record structure + for i, record in enumerate(request_data["records"]): + assert "id" in record + assert "title" in record + assert "content" in record + assert record["id"] == str(i) # 0-based indexing + assert len(record["title"].split()) <= 3 # First 3 words as title + + def test_transform_rerank_request_with_dict_documents(self): + """Test request transformation with dictionary documents.""" + optional_params = { + "query": "What is Google Gemini?", + "documents": [ + {"text": "Gemini is a cutting edge large language model created by Google.", "title": "Custom Title 1"}, + {"text": "The Gemini zodiac symbol often depicts two figures standing side-by-side."} + ] + } + + request_data = self.config.transform_rerank_request( + model=self.model, + optional_rerank_params=optional_params, + headers={} + ) + + # Verify record structure with custom titles + assert request_data["records"][0]["title"] == "Custom Title 1" + assert request_data["records"][1]["title"] == "The Gemini zodiac" # First 3 words + + def test_transform_rerank_request_return_documents_mapping(self): + """Test return_documents to ignoreRecordDetailsInResponse mapping.""" + # Test return_documents=True (default) + optional_params_true = { + "query": "test query", + "documents": ["doc1", "doc2"], + "return_documents": True + } + + request_data_true = self.config.transform_rerank_request( + model=self.model, + optional_rerank_params=optional_params_true, + headers={} + ) + assert request_data_true["ignoreRecordDetailsInResponse"] == False + + # Test return_documents=False + optional_params_false = { + "query": "test query", + "documents": ["doc1", "doc2"], + "return_documents": False + } + + request_data_false = self.config.transform_rerank_request( + model=self.model, + optional_rerank_params=optional_params_false, + headers={} + ) + assert request_data_false["ignoreRecordDetailsInResponse"] == True + + # Test return_documents not specified (should default to True) + optional_params_default = { + "query": "test query", + "documents": ["doc1", "doc2"] + } + + request_data_default = self.config.transform_rerank_request( + model=self.model, + optional_rerank_params=optional_params_default, + headers={} + ) + assert request_data_default["ignoreRecordDetailsInResponse"] == False + + def test_transform_rerank_request_missing_required_params(self): + """Test that transform_rerank_request handles missing required parameters.""" + # Test missing query + with pytest.raises(ValueError, match="query is required for Vertex AI rerank"): + self.config.transform_rerank_request( + model=self.model, + optional_rerank_params={"documents": ["doc1"]}, + headers={} + ) + + # Test missing documents + with pytest.raises(ValueError, match="documents is required for Vertex AI rerank"): + self.config.transform_rerank_request( + model=self.model, + optional_rerank_params={"query": "test query"}, + headers={} + ) + + def test_transform_rerank_response_success(self): + """Test successful response transformation.""" + # Mock Vertex AI Discovery Engine response format + response_data = { + "records": [ + { + "id": "1", + "score": 0.98, + "title": "The Science of a Blue Sky", + "content": "The sky appears blue due to a phenomenon called Rayleigh scattering." + }, + { + "id": "0", + "score": 0.64, + "title": "The Color of the Sky: A Poem", + "content": "A canvas stretched across the day, Where sunlight learns to dance and play." + } + ] + } + + # Create mock httpx response + mock_response = MagicMock(spec=httpx.Response) + mock_response.json.return_value = response_data + mock_response.text = json.dumps(response_data) + + # Create mock logging object + mock_logging = MagicMock() + + model_response = RerankResponse() + + result = self.config.transform_rerank_response( + model=self.model, + raw_response=mock_response, + model_response=model_response, + logging_obj=mock_logging, + ) + + # Verify response structure + assert result.id == f"vertex_ai_rerank_{self.model}" + assert len(result.results) == 2 + assert result.results[0]["index"] == 1 # Converted back to 0-based index + assert result.results[0]["relevance_score"] == 0.98 + assert result.results[1]["index"] == 0 + assert result.results[1]["relevance_score"] == 0.64 + + # Verify metadata + assert result.meta["billed_units"]["search_units"] == 2 + + def test_transform_rerank_response_with_ignore_record_details(self): + """Test response transformation when ignoreRecordDetailsInResponse=true.""" + # Mock response with only IDs (when ignoreRecordDetailsInResponse=true) + response_data = { + "records": [ + {"id": "1"}, + {"id": "0"} + ] + } + + mock_response = MagicMock(spec=httpx.Response) + mock_response.json.return_value = response_data + mock_response.text = json.dumps(response_data) + + mock_logging = MagicMock() + model_response = RerankResponse() + + result = self.config.transform_rerank_response( + model=self.model, + raw_response=mock_response, + model_response=model_response, + logging_obj=mock_logging, + ) + + # Verify response structure with default scores + assert len(result.results) == 2 + assert result.results[0]["index"] == 1 # 0-based index + assert result.results[0]["relevance_score"] == 1.0 # Default score + assert result.results[1]["index"] == 0 + assert result.results[1]["relevance_score"] == 1.0 + + def test_transform_rerank_response_json_error(self): + """Test response transformation with JSON parsing error.""" + mock_response = MagicMock(spec=httpx.Response) + mock_response.json.side_effect = json.JSONDecodeError("Invalid JSON", "doc", 0) + mock_response.text = "Invalid JSON response" + + mock_logging = MagicMock() + model_response = RerankResponse() + + with pytest.raises(ValueError, match="Failed to parse response"): + self.config.transform_rerank_response( + model=self.model, + raw_response=mock_response, + model_response=model_response, + logging_obj=mock_logging, + ) + + def test_get_supported_cohere_rerank_params(self): + """Test getting supported parameters for Vertex AI rerank.""" + supported_params = self.config.get_supported_cohere_rerank_params(self.model) + expected_params = ["query", "documents", "top_n", "return_documents"] + assert supported_params == expected_params + + def test_map_cohere_rerank_params(self): + """Test parameter mapping for Vertex AI rerank.""" + params = self.config.map_cohere_rerank_params( + non_default_params={"documents": ["doc1", "doc2"]}, + model=self.model, + drop_params=False, + query="test query", + documents=["doc1", "doc2"], + top_n=2, + return_documents=True + ) + + expected_params = { + "query": "test query", + "documents": ["doc1", "doc2"], + "top_n": 2, + "return_documents": True + } + assert params == expected_params + + def test_title_generation_from_content(self): + """Test that titles are generated correctly from document content.""" + optional_params = { + "query": "test query", + "documents": [ + "This is a very long document with many words that should be truncated to only the first three words for the title", + "Short doc", + "Another document with multiple words here" + ] + } + + request_data = self.config.transform_rerank_request( + model=self.model, + optional_rerank_params=optional_params, + headers={} + ) + + # Verify title generation + assert request_data["records"][0]["title"] == "This is a" # First 3 words + assert request_data["records"][1]["title"] == "Short doc" # Less than 3 words + assert request_data["records"][2]["title"] == "Another document with" # First 3 words + + def test_record_id_generation(self): + """Test that record IDs are generated correctly with 0-based indexing.""" + optional_params = { + "query": "test query", + "documents": ["doc1", "doc2", "doc3", "doc4"] + } + + request_data = self.config.transform_rerank_request( + model=self.model, + optional_rerank_params=optional_params, + headers={} + ) + + # Verify 0-based indexing + for i, record in enumerate(request_data["records"]): + assert record["id"] == str(i) From 213046385159f9279de99eda315fa7fce63914d1 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Tue, 14 Oct 2025 17:00:32 +0530 Subject: [PATCH 27/36] Add docs --- docs/my-website/docs/providers/vertex.md | 38 ++++++++++++++++++++++++ 1 file changed, 38 insertions(+) diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md index 316950a6600e..6f0210f270b8 100644 --- a/docs/my-website/docs/providers/vertex.md +++ b/docs/my-website/docs/providers/vertex.md @@ -3174,3 +3174,41 @@ print(response.results) - `semantic-ranker-default-002` For detailed model specifications, see the [Google Cloud ranking API documentation](https://cloud.google.com/generative-ai-app-builder/docs/ranking#rank_or_rerank_a_set_of_records_according_to_a_query). + +### Proxy Usage + +Add to your `config.yaml`: + +```yaml +model_list: + - model_name: semantic-ranker-default@latest + litellm_params: + model: vertex_ai/semantic-ranker-default@latest + vertex_ai_project: "your-project-id" + vertex_ai_location: "us-central1" + vertex_ai_credentials: "path/to/service-account.json" +``` + +Start the proxy: + +```bash +litellm --config /path/to/config.yaml +``` + +Test with curl: + +```bash +curl http://0.0.0.0:4000/rerank \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "semantic-ranker-default@latest", + "query": "What is Google Gemini?", + "documents": [ + "Gemini is a cutting edge large language model created by Google.", + "The Gemini zodiac symbol often depicts two figures standing side-by-side.", + "Gemini is a constellation that can be seen in the night sky." + ], + "top_n": 2 + }' +``` From 2362dce9593bcb6b3bf61b3342247b20cae52f64 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Tue, 14 Oct 2025 17:32:15 +0530 Subject: [PATCH 28/36] fix mypy error --- .../llms/vertex_ai/rerank/transformation.py | 34 ++++++++++++------- 1 file changed, 22 insertions(+), 12 deletions(-) diff --git a/litellm/llms/vertex_ai/rerank/transformation.py b/litellm/llms/vertex_ai/rerank/transformation.py index 1a956a7ed0be..966368bc3cca 100644 --- a/litellm/llms/vertex_ai/rerank/transformation.py +++ b/litellm/llms/vertex_ai/rerank/transformation.py @@ -13,7 +13,8 @@ from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig from litellm.llms.vertex_ai.vertex_llm_base import VertexBase from litellm.secret_managers.main import get_secret_str -from litellm.types.rerank import RerankResponse +from litellm.types.rerank import RerankResponse, RerankResponseMeta, RerankBilledUnits, RerankResponseResult + class VertexAIRerankConfig(BaseRerankConfig, VertexBase): @@ -170,18 +171,27 @@ def transform_rerank_response( # Sort by relevance score (descending) results.sort(key=lambda x: x["relevance_score"], reverse=True) - # Create response in Cohere format - response_data = { - "id": f"vertex_ai_rerank_{model}", - "results": results, - "meta": { - "billed_units": { - "search_units": len(records) - } - } - } + # Create response in Cohere format + # Convert results to proper RerankResponseResult objects + rerank_results = [] + for result in results: + rerank_results.append(RerankResponseResult( + index=result["index"], + relevance_score=result["relevance_score"] + )) + + # Create meta object + meta = RerankResponseMeta( + billed_units=RerankBilledUnits( + search_units=len(records) + ) + ) - return RerankResponse(**response_data) + return RerankResponse( + id=f"vertex_ai_rerank_{model}", + results=rerank_results, + meta=meta + ) def get_supported_cohere_rerank_params(self, model: str) -> list: return [ From 4339f52b1a1739e5692f6af3c9cb634c76cca69c Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Tue, 14 Oct 2025 17:39:36 +0530 Subject: [PATCH 29/36] fix mypy and lint errors --- .../litellm_enterprise/integrations/prometheus.py | 3 +-- .../adapters/transformation.py | 12 ++++++------ litellm/llms/sagemaker/embedding/transformation.py | 3 +-- 3 files changed, 8 insertions(+), 10 deletions(-) diff --git a/enterprise/litellm_enterprise/integrations/prometheus.py b/enterprise/litellm_enterprise/integrations/prometheus.py index 30e1471a1b5f..4e81206122f0 100644 --- a/enterprise/litellm_enterprise/integrations/prometheus.py +++ b/enterprise/litellm_enterprise/integrations/prometheus.py @@ -1,6 +1,7 @@ # used for /metrics endpoint on LiteLLM Proxy #### What this does #### # On success, log events to Prometheus +import os import sys from datetime import datetime, timedelta from typing import ( @@ -2360,7 +2361,6 @@ def get_custom_labels_from_tags(tags: List[str]) -> Dict[str, str]: } """ - from litellm.router_utils.pattern_match_deployments import PatternMatchRouter from litellm.types.integrations.prometheus import _sanitize_prometheus_label_name configured_tags = litellm.custom_prometheus_tags @@ -2368,7 +2368,6 @@ def get_custom_labels_from_tags(tags: List[str]) -> Dict[str, str]: return {} result: Dict[str, str] = {} - pattern_router = PatternMatchRouter() for configured_tag in configured_tags: label_name = _sanitize_prometheus_label_name(f"tag_{configured_tag}") diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index 1cb32f7fc763..4a01191b1c7d 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -26,8 +26,6 @@ AnthropicResponseContentBlockRedactedThinking, AnthropicResponseContentBlockThinking, AnthropicResponseContentBlockToolUse, - AnthropicResponseContentBlockThinking, - AnthropicResponseContentBlockRedactedThinking, ContentBlockDelta, ContentJsonBlockDelta, ContentTextBlockDelta, @@ -52,7 +50,6 @@ ChatCompletionSystemMessage, ChatCompletionTextObject, ChatCompletionThinkingBlock, - ChatCompletionRedactedThinkingBlock, ChatCompletionToolCallFunctionChunk, ChatCompletionToolChoiceFunctionParam, ChatCompletionToolChoiceObjectParam, @@ -426,18 +423,21 @@ def _translate_openai_content_to_anthropic( if hasattr(choice.message, 'thinking_blocks') and choice.message.thinking_blocks: for thinking_block in choice.message.thinking_blocks: if thinking_block.get("type") == "thinking": + thinking_value = thinking_block.get("thinking", "") + signature_value = thinking_block.get("signature", "") new_content.append( AnthropicResponseContentBlockThinking( type="thinking", - thinking=thinking_block.get("thinking", ""), - signature=thinking_block.get("signature", ""), + thinking=str(thinking_value) if thinking_value is not None else "", + signature=str(signature_value) if signature_value is not None else None, ) ) elif thinking_block.get("type") == "redacted_thinking": + data_value = thinking_block.get("data", "") new_content.append( AnthropicResponseContentBlockRedactedThinking( type="redacted_thinking", - data=thinking_block.get("data", ""), + data=str(data_value) if data_value is not None else "", ) ) diff --git a/litellm/llms/sagemaker/embedding/transformation.py b/litellm/llms/sagemaker/embedding/transformation.py index cefb010ff75f..bd8abc5e01a0 100644 --- a/litellm/llms/sagemaker/embedding/transformation.py +++ b/litellm/llms/sagemaker/embedding/transformation.py @@ -4,8 +4,7 @@ In the Huggingface TGI format. """ -import json -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union +from typing import TYPE_CHECKING, Any, List, Optional, Union if TYPE_CHECKING: from litellm.types.llms.openai import AllEmbeddingInputValues From efe7cb950a20107b0d832775da350fe676998336 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Wed, 15 Oct 2025 19:59:49 +0530 Subject: [PATCH 30/36] Add full passthrough support --- docs/my-website/docs/providers/vertex.md | 75 ++++++++++ .../proxy/common_utils/check_batch_cost.py | 8 +- litellm/batches/batch_utils.py | 89 +++++++++++- .../vertex_passthrough_logging_handler.py | 130 +++++++++++++++--- litellm/proxy/proxy_server.py | 8 +- 5 files changed, 280 insertions(+), 30 deletions(-) diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md index 3b6562b51aef..d7f2c2207401 100644 --- a/docs/my-website/docs/providers/vertex.md +++ b/docs/my-website/docs/providers/vertex.md @@ -3114,3 +3114,78 @@ Once that's done, when you deploy the new container in the Google Cloud Run serv s/o @[Darien Kindlund](https://www.linkedin.com/in/kindlund/) for this tutorial + +## Batch Passthrough + +LiteLLM supports Vertex AI batch prediction jobs through passthrough endpoints, allowing you to create and manage batch jobs directly through the proxy server. + +### Features + +- **Batch Job Creation**: Create batch prediction jobs using Vertex AI models +- **Cost Tracking**: Automatic cost calculation and usage tracking for batch operations +- **Status Monitoring**: Track job status and retrieve results +- **Model Support**: Works with all supported Vertex AI models (Gemini, Text Embedding) + +### Quick Start + +1. **Configure your model** in the proxy configuration: + +```yaml +model_list: + - model_name: gemini-1.5-flash + litellm_params: + model: vertex_ai/gemini-1.5-flash + vertex_project: your-project-id + vertex_location: us-central1 + vertex_credentials: path/to/service-account.json +``` + +2. **Create a batch job**: + +```bash +curl -X POST "http://localhost:4000/v1/projects/your-project/locations/us-central1/batchPredictionJobs" \ + -H "Authorization: Bearer your-api-key" \ + -H "Content-Type: application/json" \ + -d '{ + "displayName": "my-batch-job", + "model": "projects/your-project/locations/us-central1/publishers/google/models/gemini-1.5-flash", + "inputConfig": { + "gcsSource": { + "uris": ["gs://my-bucket/input.jsonl"] + }, + "instancesFormat": "jsonl" + }, + "outputConfig": { + "gcsDestination": { + "outputUriPrefix": "gs://my-bucket/output/" + }, + "predictionsFormat": "jsonl" + } + }' +``` + +3. **Monitor job status**: + +```bash +curl -X GET "http://localhost:4000/v1/projects/your-project/locations/us-central1/batchPredictionJobs/job-id" \ + -H "Authorization: Bearer your-api-key" +``` + +### Model Configuration + +When configuring models for batch operations, use these naming conventions: + +- **`model_name`**: Base model name (e.g., `gemini-1.5-flash`) +- **`model`**: Full LiteLLM identifier (e.g., `vertex_ai/gemini-1.5-flash`) + +### Supported Models + +- `gemini-1.5-flash` / `vertex_ai/gemini-1.5-flash` +- `gemini-1.5-pro` / `vertex_ai/gemini-1.5-pro` +- `gemini-2.0-flash` / `vertex_ai/gemini-2.0-flash` +- `gemini-2.0-pro` / `vertex_ai/gemini-2.0-pro` + +### Cost Tracking + +Cost tracking is enabled for Vertex AI usage in LiteLLM. + diff --git a/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py index 4b1bb024ac6a..0419cbe65796 100644 --- a/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py +++ b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py @@ -35,7 +35,7 @@ async def check_batch_cost(self): - if not, return False - if so, return True """ - from litellm_enterprise.proxy.hooks.managed_files import ( + from enterprise.litellm_enterprise.proxy.hooks.managed_files import ( _PROXY_LiteLLMManagedFiles, ) @@ -57,7 +57,7 @@ async def check_batch_cost(self): "file_purpose": "batch", } ) - + verbose_proxy_logger.info(f"Found {len(jobs)} jobs to check for cost and usage") completed_jobs = [] for job in jobs: @@ -139,7 +139,7 @@ async def check_batch_cost(self): custom_llm_provider = deployment_info.litellm_params.custom_llm_provider litellm_model_name = deployment_info.litellm_params.model - _, llm_provider, _, _ = get_llm_provider( + model_name, llm_provider, _, _ = get_llm_provider( model=litellm_model_name, custom_llm_provider=custom_llm_provider, ) @@ -148,9 +148,9 @@ async def check_batch_cost(self): await calculate_batch_cost_and_usage( file_content_dictionary=file_content_as_dict, custom_llm_provider=llm_provider, # type: ignore + model_name=model_name, ) ) - logging_obj = LiteLLMLogging( model=batch_models[0], messages=[{"role": "user", "content": ""}], diff --git a/litellm/batches/batch_utils.py b/litellm/batches/batch_utils.py index 814851e560b5..786d35a20c70 100644 --- a/litellm/batches/batch_utils.py +++ b/litellm/batches/batch_utils.py @@ -1,5 +1,6 @@ import json -from typing import Any, List, Literal, Tuple +from datetime import datetime +from typing import Any, List, Literal, Tuple, Optional import litellm from litellm._logging import verbose_logger @@ -10,11 +11,19 @@ async def calculate_batch_cost_and_usage( file_content_dictionary: List[dict], custom_llm_provider: Literal["openai", "azure", "vertex_ai"], + model_name: Optional[str] = None, ) -> Tuple[float, Usage, List[str]]: """ Calculate the cost and usage of a batch """ - # Calculate costs and usage + + # Check if it's Vertex AI and use the specialized method + if custom_llm_provider == "vertex_ai" and model_name: + batch_cost, batch_usage = calculate_vertex_ai_batch_cost_and_usage(file_content_dictionary, model_name) + batch_models = _get_batch_models_from_file_content(file_content_dictionary, model_name) + return batch_cost, batch_usage, batch_models + + # For other providers, use the existing logic batch_cost = _batch_cost_calculator( custom_llm_provider=custom_llm_provider, file_content_dictionary=file_content_dictionary, @@ -24,7 +33,7 @@ async def calculate_batch_cost_and_usage( custom_llm_provider=custom_llm_provider, ) - batch_models = _get_batch_models_from_file_content(file_content_dictionary) + batch_models = _get_batch_models_from_file_content(file_content_dictionary, model_name) return batch_cost, batch_usage, batch_models @@ -56,10 +65,13 @@ async def _handle_completed_batch( def _get_batch_models_from_file_content( file_content_dictionary: List[dict], + model_name: Optional[str] = None, ) -> List[str]: """ Get the models from the file content """ + if model_name: + return [model_name] batch_models = [] for _item in file_content_dictionary: if _batch_response_was_successful(_item): @@ -77,8 +89,6 @@ def _batch_cost_calculator( """ Calculate the cost of a batch based on the output file id """ - if custom_llm_provider == "vertex_ai": - raise ValueError("Vertex AI does not support file content retrieval") total_cost = _get_batch_job_cost_from_file_content( file_content_dictionary=file_content_dictionary, custom_llm_provider=custom_llm_provider, @@ -87,6 +97,75 @@ def _batch_cost_calculator( return total_cost +def calculate_vertex_ai_batch_cost_and_usage( + vertex_ai_batch_responses: List[dict], + model_name: Optional[str] = None, +) -> Tuple[float, Usage]: + """ + Calculate both cost and usage from Vertex AI batch responses + """ + total_cost = 0.0 + total_tokens = 0 + prompt_tokens = 0 + completion_tokens = 0 + + for response in vertex_ai_batch_responses: + if response.get("status") == "JOB_STATE_SUCCEEDED": # Check if response was successful + # Transform Vertex AI response to OpenAI format if needed + from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexGeminiConfig + from litellm import ModelResponse + from litellm.litellm_core_utils.litellm_logging import Logging + import httpx + + # Create required arguments for the transformation method + model_response = ModelResponse() + + # Create a minimal logging object with required attributes + class MockLoggingObj: + def __init__(self): + self.optional_params = {} + + logging_obj = MockLoggingObj() + raw_response = httpx.Response(200) # Mock response object + + # Ensure model_name is not None + actual_model_name = model_name or "gemini-2.5-flash" + + openai_format_response = VertexGeminiConfig()._transform_google_generate_content_to_openai_model_response( + completion_response=response["response"], + model_response=model_response, + model=actual_model_name, + logging_obj=logging_obj, + raw_response=raw_response, + ) + + # Calculate cost using existing function + cost = litellm.completion_cost( + completion_response=openai_format_response, + custom_llm_provider="vertex_ai", + call_type=CallTypes.aretrieve_batch.value, + ) + total_cost += cost + + # Extract usage from the transformed response + if hasattr(openai_format_response, 'usage') and openai_format_response.usage: + usage = openai_format_response.usage + else: + # Fallback: create usage from response dict + response_dict = openai_format_response.dict() if hasattr(openai_format_response, 'dict') else {} + usage = _get_batch_job_usage_from_response_body(response_dict) + + total_tokens += usage.total_tokens + prompt_tokens += usage.prompt_tokens + completion_tokens += usage.completion_tokens + + return total_cost, Usage( + total_tokens=total_tokens, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + ) + + async def _get_batch_output_file_content_as_dictionary( batch: Batch, custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py index c42a6d0a28ea..2f689abaf68f 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py @@ -18,7 +18,9 @@ ImageResponse, ModelResponse, TextCompletionResponse, + Choice, ) +from litellm.types.utils import SpecialEnums if TYPE_CHECKING: from ..success_handler import PassThroughEndpointLogging @@ -338,6 +340,38 @@ def extract_model_from_url(url: str) -> str: return match.group(1) return "unknown" + @staticmethod + def extract_model_name_from_vertex_path(vertex_model_path: str) -> str: + """ + Extract the actual model name from a Vertex AI model path. + + Examples: + - publishers/google/models/gemini-2.5-flash -> gemini-2.5-flash + - projects/PROJECT_ID/locations/LOCATION/models/MODEL_ID -> MODEL_ID + + Args: + vertex_model_path: The full Vertex AI model path + + Returns: + The extracted model name for use with LiteLLM + """ + # Handle publishers/google/models/ format + if "publishers/" in vertex_model_path and "models/" in vertex_model_path: + # Extract everything after the last models/ + parts = vertex_model_path.split("models/") + if len(parts) > 1: + return parts[-1] + + # Handle projects/PROJECT_ID/locations/LOCATION/models/MODEL_ID format + elif "projects/" in vertex_model_path and "models/" in vertex_model_path: + # Extract everything after the last models/ + parts = vertex_model_path.split("models/") + if len(parts) > 1: + return parts[-1] + + # If no recognized pattern, return the original path + return vertex_model_path + @staticmethod def _get_vertex_publisher_or_api_spec_from_url(url: str) -> Optional[str]: # Check for specific Vertex AI partner publishers @@ -402,7 +436,7 @@ def _create_vertex_response_logging_payload_for_generate_content( end_time: datetime, logging_obj: LiteLLMLoggingObj, custom_llm_provider: str, - ): + ) -> dict: """ Create the standard logging object for Vertex passthrough generateContent (streaming and non-streaming) @@ -442,13 +476,7 @@ def batch_prediction_jobs_handler( Handle batch prediction jobs passthrough logging. Creates a managed object for cost tracking when batch job is successfully created. """ - from litellm.types.utils import LiteLLMBatch from litellm.llms.vertex_ai.batches.transformation import VertexAIBatchTransformation - from litellm.proxy.openai_files_endpoints.common_utils import ( - _is_base64_encoded_unified_file_id, - get_batch_id_from_unified_batch_id, - get_model_id_from_unified_batch_id, - ) from litellm._uuid import uuid import base64 @@ -467,8 +495,11 @@ def batch_prediction_jobs_handler( model_name = _json_response.get("model", "unknown") # Create unified object ID for tracking - # Format: base64(model_id:batch_id) - unified_object_id = base64.b64encode(f"{model_name}:{batch_id}".encode()).decode() + # Format: base64(litellm_proxy;model_id:{};llm_batch_id:{}) + actual_model_id = VertexPassthroughLoggingHandler.get_actual_model_id_from_router(model_name) + + unified_id_string = SpecialEnums.LITELLM_MANAGED_BATCH_COMPLETE_STR.value.format(actual_model_id, batch_id) + unified_object_id = base64.urlsafe_b64encode(unified_id_string.encode()).decode().rstrip("=") # Store the managed object for cost tracking # This will be picked up by check_batch_cost polling mechanism @@ -480,19 +511,37 @@ def batch_prediction_jobs_handler( **kwargs, ) - # Create a simple model response for logging + # Create a batch job response for logging litellm_model_response = ModelResponse() litellm_model_response.id = str(uuid.uuid4()) litellm_model_response.model = model_name litellm_model_response.object = "batch_prediction_job" litellm_model_response.created = int(start_time.timestamp()) + # Add batch-specific metadata to indicate this is a pending batch job + litellm_model_response.choices = [Choice( + finish_reason="batch_pending", + index=0, + message={ + "role": "assistant", + "content": f"Batch prediction job {batch_id} created and is pending. Status will be updated when the batch completes.", + "tool_calls": None, + "function_call": None, + "provider_specific_fields": { + "batch_job_id": batch_id, + "batch_job_state": "JOB_STATE_PENDING", + "unified_object_id": unified_object_id + } + } + )] + # Set response cost to 0 initially (will be updated when batch completes) response_cost = 0.0 kwargs["response_cost"] = response_cost kwargs["model"] = model_name kwargs["batch_id"] = batch_id kwargs["unified_object_id"] = unified_object_id + kwargs["batch_job_state"] = "JOB_STATE_PENDING" logging_obj.model = model_name logging_obj.model_call_details["model"] = logging_obj.model @@ -513,8 +562,25 @@ def batch_prediction_jobs_handler( litellm_model_response.object = "batch_prediction_job" litellm_model_response.created = int(start_time.timestamp()) + # Add error-specific metadata + litellm_model_response.choices = [Choice( + finish_reason="batch_error", + index=0, + message={ + "role": "assistant", + "content": f"Error creating batch prediction job: {str(e)}", + "tool_calls": None, + "function_call": None, + "provider_specific_fields": { + "batch_job_state": "JOB_STATE_FAILED", + "error": str(e) + } + } + )] + kwargs["response_cost"] = 0.0 kwargs["model"] = "vertex_ai_batch" + kwargs["batch_job_state"] = "JOB_STATE_FAILED" return { "result": litellm_model_response, @@ -533,13 +599,13 @@ def _store_batch_managed_object( Store batch managed object for cost tracking. This will be picked up by the check_batch_cost polling mechanism. """ - try: + try: # Get the managed files hook from the logging object # This is a bit of a hack, but we need access to the proxy logging system from litellm.proxy.proxy_server import proxy_logging_obj managed_files_hook = proxy_logging_obj.get_proxy_hook("managed_files") - if managed_files_hook is not None: + if managed_files_hook is not None and hasattr(managed_files_hook, 'store_unified_object_id'): # Create a mock user API key dict for the managed object storage from litellm.proxy._types import UserAPIKeyAuth user_api_key_dict = UserAPIKeyAuth( @@ -547,23 +613,23 @@ def _store_batch_managed_object( api_key="", team_id=None, team_alias=None, - user_role="", + user_role="customer", # Set a valid enum value user_email=None, max_budget=None, - spend=None, - models=None, + spend=0.0, # Set to 0.0 instead of None + models=[], # Set to empty list instead of None tpm_limit=None, rpm_limit=None, budget_duration=None, budget_reset_at=None, max_parallel_requests=None, allowed_model_region=None, - metadata=None, + metadata={}, # Set to empty dict instead of None user_info=None, key_alias=None, - permissions=None, - model_max_budget=None, - model_spend=None, + permissions={}, # Set to empty dict instead of None + model_max_budget={}, # Set to empty dict instead of None + model_spend={}, # Set to empty dict instead of None model_budget_duration=None, model_budget_reset_at=None, ) @@ -590,3 +656,29 @@ def _store_batch_managed_object( except Exception as e: verbose_proxy_logger.error(f"Error storing batch managed object: {e}") + @staticmethod + def get_actual_model_id_from_router(model_name: str) -> str: + from litellm.proxy.proxy_server import llm_router + + if llm_router is not None: + # Try to find the model in the router by the extracted model name + extracted_model_name = VertexPassthroughLoggingHandler.extract_model_name_from_vertex_path(model_name) + + # Use the existing get_model_ids method from router + model_ids = llm_router.get_model_ids(model_name=extracted_model_name) + if model_ids and len(model_ids) > 0: + # Use the first model ID found + actual_model_id = model_ids[0] + verbose_proxy_logger.info(f"Found model ID in router: {actual_model_id}") + return actual_model_id + else: + # Fallback to constructed model name + actual_model_id = extracted_model_name + verbose_proxy_logger.warning(f"Model not found in router, using constructed name: {actual_model_id}") + return actual_model_id + else: + # Fallback if router is not available + extracted_model_name = VertexPassthroughLoggingHandler.extract_model_name_from_vertex_path(model_name) + verbose_proxy_logger.warning(f"Router not available, using constructed model name: {extracted_model_name}") + return extracted_model_name + diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 8f5a046be030..c37c7c3fde6d 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -4034,7 +4034,7 @@ async def initialize_scheduled_background_jobs( ### CHECK BATCH COST ### if llm_router is not None: try: - from litellm_enterprise.proxy.common_utils.check_batch_cost import ( + from enterprise.litellm_enterprise.proxy.common_utils.check_batch_cost import ( CheckBatchCost, ) @@ -4048,8 +4048,12 @@ async def initialize_scheduled_background_jobs( "interval", seconds=proxy_batch_polling_interval, # these can run infrequently, as batch jobs take time to complete ) + verbose_proxy_logger.info("Batch cost check job scheduled successfully") - except Exception: + except Exception as e: + verbose_proxy_logger.error( + f"Failed to setup batch cost checking: {e}" + ) verbose_proxy_logger.debug( "Checking batch cost for LiteLLM Managed Files is an Enterprise Feature. Skipping..." ) From 570b32ab5f0f665545c3707ae477b5b9caa40266 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Wed, 15 Oct 2025 20:33:48 +0530 Subject: [PATCH 31/36] Add passthrough handler --- .../vertex_passthrough_logging_handler.py | 38 +++++++++++++++++-- 1 file changed, 35 insertions(+), 3 deletions(-) diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py index 2f689abaf68f..e210dccc5a07 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py @@ -18,7 +18,7 @@ ImageResponse, ModelResponse, TextCompletionResponse, - Choice, + Choices, ) from litellm.types.utils import SpecialEnums @@ -519,7 +519,7 @@ def batch_prediction_jobs_handler( litellm_model_response.created = int(start_time.timestamp()) # Add batch-specific metadata to indicate this is a pending batch job - litellm_model_response.choices = [Choice( + litellm_model_response.choices = [Choices( finish_reason="batch_pending", index=0, message={ @@ -548,6 +548,38 @@ def batch_prediction_jobs_handler( logging_obj.model_call_details["response_cost"] = response_cost logging_obj.model_call_details["batch_id"] = batch_id + return { + "result": litellm_model_response, + "kwargs": kwargs, + } + else: + # Handle non-successful responses + litellm_model_response = ModelResponse() + litellm_model_response.id = str(uuid.uuid4()) + litellm_model_response.model = "vertex_ai_batch" + litellm_model_response.object = "batch_prediction_job" + litellm_model_response.created = int(start_time.timestamp()) + + # Add error-specific metadata + litellm_model_response.choices = [Choices( + finish_reason="batch_error", + index=0, + message={ + "role": "assistant", + "content": f"Batch prediction job creation failed. Status: {httpx_response.status_code}", + "tool_calls": None, + "function_call": None, + "provider_specific_fields": { + "batch_job_state": "JOB_STATE_FAILED", + "status_code": httpx_response.status_code + } + } + )] + + kwargs["response_cost"] = 0.0 + kwargs["model"] = "vertex_ai_batch" + kwargs["batch_job_state"] = "JOB_STATE_FAILED" + return { "result": litellm_model_response, "kwargs": kwargs, @@ -563,7 +595,7 @@ def batch_prediction_jobs_handler( litellm_model_response.created = int(start_time.timestamp()) # Add error-specific metadata - litellm_model_response.choices = [Choice( + litellm_model_response.choices = [Choices( finish_reason="batch_error", index=0, message={ From 0cb2aefb72775aaf6b5b16d573c71c503eb54749 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Thu, 16 Oct 2025 00:17:52 +0530 Subject: [PATCH 32/36] refactor code according to the comments --- .../proxy/common_utils/check_batch_cost.py | 3 +- litellm/batches/batch_utils.py | 60 +- .../test_vertex_ai_batch_passthrough.py | 535 ++++++++++++++++++ 3 files changed, 577 insertions(+), 21 deletions(-) create mode 100644 tests/test_litellm/proxy/pass_through_endpoints/test_vertex_ai_batch_passthrough.py diff --git a/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py index 0419cbe65796..d4ee4042b1ae 100644 --- a/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py +++ b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py @@ -35,7 +35,7 @@ async def check_batch_cost(self): - if not, return False - if so, return True """ - from enterprise.litellm_enterprise.proxy.hooks.managed_files import ( + from litellm_enterprise.proxy.hooks.managed_files import ( _PROXY_LiteLLMManagedFiles, ) @@ -57,7 +57,6 @@ async def check_batch_cost(self): "file_purpose": "batch", } ) - verbose_proxy_logger.info(f"Found {len(jobs)} jobs to check for cost and usage") completed_jobs = [] for job in jobs: diff --git a/litellm/batches/batch_utils.py b/litellm/batches/batch_utils.py index 786d35a20c70..027c0b219c88 100644 --- a/litellm/batches/batch_utils.py +++ b/litellm/batches/batch_utils.py @@ -1,5 +1,4 @@ import json -from datetime import datetime from typing import Any, List, Literal, Tuple, Optional import litellm @@ -16,23 +15,16 @@ async def calculate_batch_cost_and_usage( """ Calculate the cost and usage of a batch """ - - # Check if it's Vertex AI and use the specialized method - if custom_llm_provider == "vertex_ai" and model_name: - batch_cost, batch_usage = calculate_vertex_ai_batch_cost_and_usage(file_content_dictionary, model_name) - batch_models = _get_batch_models_from_file_content(file_content_dictionary, model_name) - return batch_cost, batch_usage, batch_models - - # For other providers, use the existing logic batch_cost = _batch_cost_calculator( custom_llm_provider=custom_llm_provider, file_content_dictionary=file_content_dictionary, + model_name=model_name, ) batch_usage = _get_batch_job_total_usage_from_file_content( file_content_dictionary=file_content_dictionary, custom_llm_provider=custom_llm_provider, + model_name=model_name, ) - batch_models = _get_batch_models_from_file_content(file_content_dictionary, model_name) return batch_cost, batch_usage, batch_models @@ -41,6 +33,7 @@ async def calculate_batch_cost_and_usage( async def _handle_completed_batch( batch: Batch, custom_llm_provider: Literal["openai", "azure", "vertex_ai"], + model_name: Optional[str] = None, ) -> Tuple[float, Usage, List[str]]: """Helper function to process a completed batch and handle logging""" # Get batch results @@ -52,13 +45,15 @@ async def _handle_completed_batch( batch_cost = _batch_cost_calculator( custom_llm_provider=custom_llm_provider, file_content_dictionary=file_content_dictionary, + model_name=model_name, ) batch_usage = _get_batch_job_total_usage_from_file_content( file_content_dictionary=file_content_dictionary, custom_llm_provider=custom_llm_provider, + model_name=model_name, ) - batch_models = _get_batch_models_from_file_content(file_content_dictionary) + batch_models = _get_batch_models_from_file_content(file_content_dictionary, model_name) return batch_cost, batch_usage, batch_models @@ -85,10 +80,18 @@ def _get_batch_models_from_file_content( def _batch_cost_calculator( file_content_dictionary: List[dict], custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + model_name: Optional[str] = None, ) -> float: """ Calculate the cost of a batch based on the output file id """ + # Handle Vertex AI with specialized method + if custom_llm_provider == "vertex_ai" and model_name: + batch_cost, _ = calculate_vertex_ai_batch_cost_and_usage(file_content_dictionary, model_name) + verbose_logger.debug("vertex_ai_total_cost=%s", batch_cost) + return batch_cost + + # For other providers, use the existing logic total_cost = _get_batch_job_cost_from_file_content( file_content_dictionary=file_content_dictionary, custom_llm_provider=custom_llm_provider, @@ -115,22 +118,34 @@ def calculate_vertex_ai_batch_cost_and_usage( from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexGeminiConfig from litellm import ModelResponse from litellm.litellm_core_utils.litellm_logging import Logging + from litellm.types.utils import CallTypes + from litellm._uuid import uuid import httpx + import time # Create required arguments for the transformation method model_response = ModelResponse() - # Create a minimal logging object with required attributes - class MockLoggingObj: - def __init__(self): - self.optional_params = {} - - logging_obj = MockLoggingObj() - raw_response = httpx.Response(200) # Mock response object - # Ensure model_name is not None actual_model_name = model_name or "gemini-2.5-flash" + # Create a real LiteLLM logging object + logging_obj = Logging( + model=actual_model_name, + messages=[{"role": "user", "content": "batch_request"}], + stream=False, + call_type=CallTypes.aretrieve_batch, + start_time=time.time(), + litellm_call_id="batch_" + str(uuid.uuid4()), + function_id="batch_processing", + litellm_trace_id=str(uuid.uuid4()), + kwargs={"optional_params": {}} + ) + + # Add the optional_params attribute that the Vertex AI transformation expects + logging_obj.optional_params = {} + raw_response = httpx.Response(200) # Mock response object + openai_format_response = VertexGeminiConfig()._transform_google_generate_content_to_openai_model_response( completion_response=response["response"], model_response=model_response, @@ -236,10 +251,17 @@ def _get_batch_job_cost_from_file_content( def _get_batch_job_total_usage_from_file_content( file_content_dictionary: List[dict], custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + model_name: Optional[str] = None, ) -> Usage: """ Get the tokens of a batch job from the file content """ + # Handle Vertex AI with specialized method + if custom_llm_provider == "vertex_ai" and model_name: + _, batch_usage = calculate_vertex_ai_batch_cost_and_usage(file_content_dictionary, model_name) + return batch_usage + + # For other providers, use the existing logic total_tokens: int = 0 prompt_tokens: int = 0 completion_tokens: int = 0 diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_vertex_ai_batch_passthrough.py b/tests/test_litellm/proxy/pass_through_endpoints/test_vertex_ai_batch_passthrough.py new file mode 100644 index 000000000000..a8479394fc41 --- /dev/null +++ b/tests/test_litellm/proxy/pass_through_endpoints/test_vertex_ai_batch_passthrough.py @@ -0,0 +1,535 @@ +""" +Test cases for Vertex AI passthrough batch prediction functionality +""" +import base64 +import json +import pytest +from unittest.mock import Mock, patch, MagicMock +from datetime import datetime +from typing import Dict, Any + +from litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler import ( + VertexPassthroughLoggingHandler, +) +from litellm.types.utils import SpecialEnums +from litellm.types.llms.openai import BatchJobStatus + + +class TestVertexAIBatchPassthroughHandler: + """Test cases for Vertex AI batch prediction passthrough functionality""" + + @pytest.fixture + def mock_httpx_response(self): + """Mock httpx response for batch job creation""" + mock_response = Mock() + mock_response.status_code = 200 + mock_response.json.return_value = { + "name": "projects/test-project/locations/us-central1/batchPredictionJobs/123456789", + "displayName": "litellm-vertex-batch-test", + "model": "projects/test-project/locations/us-central1/publishers/google/models/gemini-1.5-flash", + "createTime": "2024-01-01T00:00:00Z", + "state": "JOB_STATE_PENDING", + "inputConfig": { + "gcsSource": { + "uris": ["gs://test-bucket/input.jsonl"] + }, + "instancesFormat": "jsonl" + }, + "outputConfig": { + "gcsDestination": { + "outputUriPrefix": "gs://test-bucket/output/" + }, + "predictionsFormat": "jsonl" + } + } + return mock_response + + @pytest.fixture + def mock_logging_obj(self): + """Mock logging object""" + mock = Mock() + mock.litellm_call_id = "test-call-id-123" + mock.model_call_details = {} + mock.optional_params = {} + return mock + + @pytest.fixture + def mock_managed_files_hook(self): + """Mock managed files hook""" + mock_hook = Mock() + mock_hook.afile_content.return_value = Mock(content=b'{"test": "data"}') + return mock_hook + + def test_batch_prediction_jobs_handler_success(self, mock_httpx_response, mock_logging_obj): + """Test successful batch job creation and tracking""" + with patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler.verbose_proxy_logger') as mock_logger: + with patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler.VertexPassthroughLoggingHandler.get_actual_model_id_from_router') as mock_get_model_id: + with patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler.VertexPassthroughLoggingHandler._store_batch_managed_object') as mock_store: + with patch('litellm.llms.vertex_ai.batches.transformation.VertexAIBatchTransformation') as mock_transformation: + + # Setup mocks + mock_get_model_id.return_value = "vertex_ai/gemini-1.5-flash" + mock_transformation.transform_vertex_ai_batch_response_to_openai_batch_response.return_value = { + "id": "123456789", + "object": "batch", + "status": "validating", + "created_at": 1704067200, + "input_file_id": "file-123", + "output_file_id": "file-456", + "error_file_id": None, + "completion_window": "24hrs" + } + mock_transformation._get_batch_id_from_vertex_ai_batch_response.return_value = "123456789" + + # Test the handler + result = VertexPassthroughLoggingHandler.batch_prediction_jobs_handler( + httpx_response=mock_httpx_response, + logging_obj=mock_logging_obj, + url_route="/v1/projects/test-project/locations/us-central1/batchPredictionJobs", + result="success", + start_time=datetime.now(), + end_time=datetime.now(), + cache_hit=False, + user_api_key_dict={"user_id": "test-user"} + ) + + # Verify the result + assert result is not None + assert "kwargs" in result + assert result["kwargs"]["model"] == "projects/test-project/locations/us-central1/publishers/google/models/gemini-1.5-flash" + assert result["kwargs"]["batch_id"] == "123456789" + + # Verify mocks were called + mock_get_model_id.assert_called_once() + mock_store.assert_called_once() + + def test_batch_prediction_jobs_handler_failure(self, mock_logging_obj): + """Test batch job creation failure handling""" + # Mock failed response + mock_httpx_response = Mock() + mock_httpx_response.status_code = 400 + mock_httpx_response.json.return_value = {"error": "Invalid request"} + + with patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler.verbose_proxy_logger') as mock_logger: + # Test the handler with failed response + result = VertexPassthroughLoggingHandler.batch_prediction_jobs_handler( + httpx_response=mock_httpx_response, + logging_obj=mock_logging_obj, + url_route="/v1/projects/test-project/locations/us-central1/batchPredictionJobs", + result="error", + start_time=datetime.now(), + end_time=datetime.now(), + cache_hit=False, + user_api_key_dict={"user_id": "test-user"} + ) + + # Should return None for failed responses + assert result is None + + def test_get_actual_model_id_from_router_with_router(self): + """Test getting model ID when router is available""" + with patch('litellm.proxy.proxy_server.llm_router') as mock_router: + with patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler.VertexPassthroughLoggingHandler.extract_model_name_from_vertex_path') as mock_extract: + + # Setup mocks + mock_router.get_model_ids.return_value = ["vertex_ai/gemini-1.5-flash"] + mock_extract.return_value = "gemini-1.5-flash" + + # Test the method + result = VertexPassthroughLoggingHandler.get_actual_model_id_from_router( + "projects/test-project/locations/us-central1/publishers/google/models/gemini-1.5-flash" + ) + + # Verify result + assert result == "vertex_ai/gemini-1.5-flash" + mock_router.get_model_ids.assert_called_once_with(model_name="gemini-1.5-flash") + + def test_get_actual_model_id_from_router_without_router(self): + """Test getting model ID when router is not available""" + with patch('litellm.proxy.proxy_server.llm_router', None): + with patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler.VertexPassthroughLoggingHandler.extract_model_name_from_vertex_path') as mock_extract: + + # Setup mocks + mock_extract.return_value = "gemini-1.5-flash" + + # Test the method + result = VertexPassthroughLoggingHandler.get_actual_model_id_from_router( + "projects/test-project/locations/us-central1/publishers/google/models/gemini-1.5-flash" + ) + + # Verify result + assert result == "gemini-1.5-flash" + + def test_get_actual_model_id_from_router_model_not_found(self): + """Test getting model ID when model is not found in router""" + with patch('litellm.proxy.proxy_server.llm_router') as mock_router: + with patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler.VertexPassthroughLoggingHandler.extract_model_name_from_vertex_path') as mock_extract: + + # Setup mocks - router returns empty list + mock_router.get_model_ids.return_value = [] + mock_extract.return_value = "gemini-1.5-flash" + + # Test the method + result = VertexPassthroughLoggingHandler.get_actual_model_id_from_router( + "projects/test-project/locations/us-central1/publishers/google/models/gemini-1.5-flash" + ) + + # Verify result - should fallback to extracted model name + assert result == "gemini-1.5-flash" + + def test_unified_object_id_generation(self): + """Test unified object ID generation for batch tracking""" + model_id = "vertex_ai/gemini-1.5-flash" + batch_id = "123456789" + + # Generate the expected unified ID + unified_id_string = SpecialEnums.LITELLM_MANAGED_BATCH_COMPLETE_STR.value.format(model_id, batch_id) + expected_unified_id = base64.urlsafe_b64encode(unified_id_string.encode()).decode().rstrip("=") + + # Test the generation + actual_unified_id = base64.urlsafe_b64encode(unified_id_string.encode()).decode().rstrip("=") + + assert actual_unified_id == expected_unified_id + assert isinstance(actual_unified_id, str) + assert len(actual_unified_id) > 0 + + def test_store_batch_managed_object(self, mock_logging_obj, mock_managed_files_hook): + """Test storing batch managed object for cost tracking""" + with patch('litellm.proxy.proxy_server.proxy_logging_obj') as mock_proxy_logging_obj: + with patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler.verbose_proxy_logger') as mock_logger: + + # Setup mock proxy logging obj + mock_proxy_logging_obj.get_proxy_hook.return_value = mock_managed_files_hook + + # Test data + unified_object_id = "test-unified-id" + batch_object = { + "id": "123456789", + "object": "batch", + "status": "validating" + } + model_object_id = "123456789" + + # Test the method + VertexPassthroughLoggingHandler._store_batch_managed_object( + unified_object_id=unified_object_id, + batch_object=batch_object, + model_object_id=model_object_id, + logging_obj=mock_logging_obj, + user_api_key_dict={"user_id": "test-user"} + ) + + # Verify the managed files hook was called + mock_managed_files_hook.store_unified_object_id.assert_called_once() + + def test_batch_cost_calculation_integration(self): + """Test integration with batch cost calculation""" + from litellm.batches.batch_utils import calculate_vertex_ai_batch_cost_and_usage + + # Mock Vertex AI batch responses + vertex_ai_batch_responses = [ + { + "status": "JOB_STATE_SUCCEEDED", + "response": { + "candidates": [ + { + "content": { + "parts": [ + {"text": "Hello, world!"} + ] + } + } + ], + "usageMetadata": { + "promptTokenCount": 10, + "candidatesTokenCount": 5, + "totalTokenCount": 15 + } + } + } + ] + + with patch('litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexGeminiConfig') as mock_config: + with patch('litellm.completion_cost') as mock_completion_cost: + + # Setup mocks + mock_config.return_value._transform_google_generate_content_to_openai_model_response.return_value = Mock( + usage=Mock(total_tokens=15, prompt_tokens=10, completion_tokens=5) + ) + mock_completion_cost.return_value = 0.001 + + # Test the cost calculation + total_cost, usage = calculate_vertex_ai_batch_cost_and_usage( + vertex_ai_batch_responses, + model_name="gemini-1.5-flash" + ) + + # Verify results + assert total_cost == 0.001 + assert usage.total_tokens == 15 + assert usage.prompt_tokens == 10 + assert usage.completion_tokens == 5 + + def test_batch_response_transformation(self): + """Test transformation of Vertex AI batch responses to OpenAI format""" + from litellm.llms.vertex_ai.batches.transformation import VertexAIBatchTransformation + + # Mock Vertex AI batch response + vertex_ai_response = { + "name": "projects/test-project/locations/us-central1/batchPredictionJobs/123456789", + "displayName": "test-batch", + "model": "projects/test-project/locations/us-central1/publishers/google/models/gemini-1.5-flash", + "createTime": "2024-01-01T00:00:00.000Z", + "state": "JOB_STATE_SUCCEEDED" + } + + # Test transformation + result = VertexAIBatchTransformation.transform_vertex_ai_batch_response_to_openai_batch_response( + vertex_ai_response + ) + + # Verify the transformation + assert result["id"] == "123456789" + assert result["object"] == "batch" + assert result["status"] == "completed" # JOB_STATE_SUCCEEDED should map to completed + + def test_batch_id_extraction(self): + """Test extraction of batch ID from Vertex AI response""" + from litellm.llms.vertex_ai.batches.transformation import VertexAIBatchTransformation + + # Test various batch ID formats + test_cases = [ + "projects/123/locations/us-central1/batchPredictionJobs/456789", + "projects/abc/locations/europe-west1/batchPredictionJobs/def123", + "batchPredictionJobs/999", + "invalid-format" + ] + + expected_results = ["456789", "def123", "999", "invalid-format"] + + for test_case, expected in zip(test_cases, expected_results): + result = VertexAIBatchTransformation._get_batch_id_from_vertex_ai_batch_response( + {"name": test_case} + ) + assert result == expected + + def test_model_name_extraction_from_vertex_path(self): + """Test extraction of model name from Vertex AI path""" + from litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler import ( + VertexPassthroughLoggingHandler + ) + + # Test various model path formats + test_cases = [ + "projects/test-project/locations/us-central1/publishers/google/models/gemini-1.5-flash", + "projects/abc/locations/europe-west1/publishers/google/models/gemini-2.0-flash", + "publishers/google/models/gemini-pro", + "invalid-path" + ] + + expected_results = ["gemini-1.5-flash", "gemini-2.0-flash", "gemini-pro", "invalid-path"] + + for test_case, expected in zip(test_cases, expected_results): + result = VertexPassthroughLoggingHandler.extract_model_name_from_vertex_path(test_case) + assert result == expected + + @pytest.mark.asyncio + async def test_batch_completion_workflow(self, mock_httpx_response, mock_logging_obj, mock_managed_files_hook): + """Test the complete batch completion workflow""" + with patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler.verbose_proxy_logger') as mock_logger: + with patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.vertex_passthrough_logging_handler.VertexPassthroughLoggingHandler.get_actual_model_id_from_router') as mock_get_model_id: + with patch('litellm.proxy.proxy_server.proxy_logging_obj') as mock_proxy_logging_obj: + mock_proxy_logging_obj.get_proxy_hook.return_value = mock_managed_files_hook + with patch('litellm.llms.vertex_ai.batches.transformation.VertexAIBatchTransformation') as mock_transformation: + + # Setup mocks + mock_get_model_id.return_value = "vertex_ai/gemini-1.5-flash" + mock_transformation.transform_vertex_ai_batch_response_to_openai_batch_response.return_value = { + "id": "123456789", + "object": "batch", + "status": "completed", + "created_at": 1704067200, + "input_file_id": "file-123", + "output_file_id": "file-456", + "error_file_id": None, + "completion_window": "24hrs" + } + mock_transformation._get_batch_id_from_vertex_ai_batch_response.return_value = "123456789" + + # Test the complete workflow + result = VertexPassthroughLoggingHandler.batch_prediction_jobs_handler( + httpx_response=mock_httpx_response, + logging_obj=mock_logging_obj, + url_route="/v1/projects/test-project/locations/us-central1/batchPredictionJobs", + result="success", + start_time=datetime.now(), + end_time=datetime.now(), + cache_hit=False, + user_api_key_dict={"user_id": "test-user"} + ) + + # Verify the complete workflow + assert result is not None + assert "kwargs" in result + assert result["kwargs"]["model"] == "projects/test-project/locations/us-central1/publishers/google/models/gemini-1.5-flash" + assert result["kwargs"]["batch_id"] == "123456789" + + # Verify all mocks were called + mock_get_model_id.assert_called_once() + mock_transformation.transform_vertex_ai_batch_response_to_openai_batch_response.assert_called_once() + # Note: store_unified_object_id is called asynchronously, so we can't easily verify it in this test + + +class TestVertexAIBatchCostCalculation: + """Test cases for Vertex AI batch cost calculation functionality""" + + def test_calculate_vertex_ai_batch_cost_and_usage_success(self): + """Test successful batch cost and usage calculation""" + from litellm.batches.batch_utils import calculate_vertex_ai_batch_cost_and_usage + + # Mock successful batch responses + vertex_ai_batch_responses = [ + { + "status": "JOB_STATE_SUCCEEDED", + "response": { + "candidates": [ + { + "content": { + "parts": [ + {"text": "Hello, world!"} + ] + } + } + ], + "usageMetadata": { + "promptTokenCount": 10, + "candidatesTokenCount": 5, + "totalTokenCount": 15 + } + } + }, + { + "status": "JOB_STATE_SUCCEEDED", + "response": { + "candidates": [ + { + "content": { + "parts": [ + {"text": "How are you?"} + ] + } + } + ], + "usageMetadata": { + "promptTokenCount": 8, + "candidatesTokenCount": 3, + "totalTokenCount": 11 + } + } + } + ] + + with patch('litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexGeminiConfig') as mock_config: + with patch('litellm.completion_cost') as mock_completion_cost: + + # Setup mocks + mock_model_response = Mock() + mock_model_response.usage = Mock(total_tokens=15, prompt_tokens=10, completion_tokens=5) + mock_config.return_value._transform_google_generate_content_to_openai_model_response.return_value = mock_model_response + mock_completion_cost.return_value = 0.001 + + # Test the calculation + total_cost, usage = calculate_vertex_ai_batch_cost_and_usage( + vertex_ai_batch_responses, + model_name="gemini-1.5-flash" + ) + + # Verify results + assert total_cost == 0.002 # 2 responses * 0.001 each + assert usage.total_tokens == 30 # 15 + 15 + assert usage.prompt_tokens == 20 # 10 + 10 + assert usage.completion_tokens == 10 # 5 + 5 + + def test_calculate_vertex_ai_batch_cost_and_usage_with_failed_responses(self): + """Test batch cost calculation with some failed responses""" + from litellm.batches.batch_utils import calculate_vertex_ai_batch_cost_and_usage + + # Mock batch responses with some failures + vertex_ai_batch_responses = [ + { + "status": "JOB_STATE_SUCCEEDED", + "response": { + "candidates": [ + { + "content": { + "parts": [ + {"text": "Hello, world!"} + ] + } + } + ], + "usageMetadata": { + "promptTokenCount": 10, + "candidatesTokenCount": 5, + "totalTokenCount": 15 + } + } + }, + { + "status": "JOB_STATE_FAILED", # Failed response + "response": None + }, + { + "status": "JOB_STATE_SUCCEEDED", + "response": { + "candidates": [ + { + "content": { + "parts": [ + {"text": "How are you?"} + ] + } + } + ], + "usageMetadata": { + "promptTokenCount": 8, + "candidatesTokenCount": 3, + "totalTokenCount": 11 + } + } + } + ] + + with patch('litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexGeminiConfig') as mock_config: + with patch('litellm.completion_cost') as mock_completion_cost: + + # Setup mocks + mock_model_response = Mock() + mock_model_response.usage = Mock(total_tokens=15, prompt_tokens=10, completion_tokens=5) + mock_config.return_value._transform_google_generate_content_to_openai_model_response.return_value = mock_model_response + mock_completion_cost.return_value = 0.001 + + # Test the calculation + total_cost, usage = calculate_vertex_ai_batch_cost_and_usage( + vertex_ai_batch_responses, + model_name="gemini-1.5-flash" + ) + + # Verify results - should only process successful responses + assert total_cost == 0.002 # 2 successful responses * 0.001 each + assert usage.total_tokens == 30 # 15 + 15 + assert usage.prompt_tokens == 20 # 10 + 10 + assert usage.completion_tokens == 10 # 5 + 5 + + def test_calculate_vertex_ai_batch_cost_and_usage_empty_responses(self): + """Test batch cost calculation with empty response list""" + from litellm.batches.batch_utils import calculate_vertex_ai_batch_cost_and_usage + + # Test with empty list + total_cost, usage = calculate_vertex_ai_batch_cost_and_usage([], model_name="gemini-1.5-flash") + + # Verify results + assert total_cost == 0.0 + assert usage.total_tokens == 0 + assert usage.prompt_tokens == 0 + assert usage.completion_tokens == 0 From 1d0e83d11b90e81cb02f8532538c288d4a10c1e0 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Thu, 16 Oct 2025 00:34:56 +0530 Subject: [PATCH 33/36] remove invalid params --- .../vertex_passthrough_logging_handler.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py index e210dccc5a07..c7695704330a 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py @@ -640,12 +640,13 @@ def _store_batch_managed_object( if managed_files_hook is not None and hasattr(managed_files_hook, 'store_unified_object_id'): # Create a mock user API key dict for the managed object storage from litellm.proxy._types import UserAPIKeyAuth + from litellm.proxy._types import LitellmUserRoles user_api_key_dict = UserAPIKeyAuth( user_id=kwargs.get("user_id", "default-user"), api_key="", team_id=None, team_alias=None, - user_role="customer", # Set a valid enum value + user_role=LitellmUserRoles.CUSTOMER, # Use proper enum value user_email=None, max_budget=None, spend=0.0, # Set to 0.0 instead of None @@ -657,13 +658,10 @@ def _store_batch_managed_object( max_parallel_requests=None, allowed_model_region=None, metadata={}, # Set to empty dict instead of None - user_info=None, key_alias=None, permissions={}, # Set to empty dict instead of None model_max_budget={}, # Set to empty dict instead of None model_spend={}, # Set to empty dict instead of None - model_budget_duration=None, - model_budget_reset_at=None, ) # Store the unified object for batch cost tracking From bb77a25314ecae2069b35bf7f4c7adf9596aa39e Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Fri, 17 Oct 2025 08:49:27 +0530 Subject: [PATCH 34/36] Updated documentation --- docs/my-website/docs/batches.md | 2 +- docs/my-website/docs/providers/vertex.md | 74 ------------------------ 2 files changed, 1 insertion(+), 75 deletions(-) diff --git a/docs/my-website/docs/batches.md b/docs/my-website/docs/batches.md index 1bd4c700ae7f..098f9e67b9b0 100644 --- a/docs/my-website/docs/batches.md +++ b/docs/my-website/docs/batches.md @@ -177,7 +177,7 @@ print("list_batches_response=", list_batches_response) ## **Supported Providers**: ### [Azure OpenAI](./providers/azure#azure-batches-api) ### [OpenAI](#quick-start) -### [Vertex AI](./providers/vertex#batch-apis) +### [Vertex AI Batch Passthrough](./providers/vertex_batch_passthrough.md) ### [Bedrock](./providers/bedrock_batches) diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md index 4cdb9072c21b..6f0210f270b8 100644 --- a/docs/my-website/docs/providers/vertex.md +++ b/docs/my-website/docs/providers/vertex.md @@ -3115,80 +3115,6 @@ Once that's done, when you deploy the new container in the Google Cloud Run serv s/o @[Darien Kindlund](https://www.linkedin.com/in/kindlund/) for this tutorial -## Batch Passthrough - -LiteLLM supports Vertex AI batch prediction jobs through passthrough endpoints, allowing you to create and manage batch jobs directly through the proxy server. - -### Features - -- **Batch Job Creation**: Create batch prediction jobs using Vertex AI models -- **Cost Tracking**: Automatic cost calculation and usage tracking for batch operations -- **Status Monitoring**: Track job status and retrieve results -- **Model Support**: Works with all supported Vertex AI models (Gemini, Text Embedding) - -### Quick Start - -1. **Configure your model** in the proxy configuration: - -```yaml -model_list: - - model_name: gemini-1.5-flash - litellm_params: - model: vertex_ai/gemini-1.5-flash - vertex_project: your-project-id - vertex_location: us-central1 - vertex_credentials: path/to/service-account.json -``` - -2. **Create a batch job**: - -```bash -curl -X POST "http://localhost:4000/v1/projects/your-project/locations/us-central1/batchPredictionJobs" \ - -H "Authorization: Bearer your-api-key" \ - -H "Content-Type: application/json" \ - -d '{ - "displayName": "my-batch-job", - "model": "projects/your-project/locations/us-central1/publishers/google/models/gemini-1.5-flash", - "inputConfig": { - "gcsSource": { - "uris": ["gs://my-bucket/input.jsonl"] - }, - "instancesFormat": "jsonl" - }, - "outputConfig": { - "gcsDestination": { - "outputUriPrefix": "gs://my-bucket/output/" - }, - "predictionsFormat": "jsonl" - } - }' -``` - -3. **Monitor job status**: - -```bash -curl -X GET "http://localhost:4000/v1/projects/your-project/locations/us-central1/batchPredictionJobs/job-id" \ - -H "Authorization: Bearer your-api-key" -``` - -### Model Configuration - -When configuring models for batch operations, use these naming conventions: - -- **`model_name`**: Base model name (e.g., `gemini-1.5-flash`) -- **`model`**: Full LiteLLM identifier (e.g., `vertex_ai/gemini-1.5-flash`) - -### Supported Models - -- `gemini-1.5-flash` / `vertex_ai/gemini-1.5-flash` -- `gemini-1.5-pro` / `vertex_ai/gemini-1.5-pro` -- `gemini-2.0-flash` / `vertex_ai/gemini-2.0-flash` -- `gemini-2.0-pro` / `vertex_ai/gemini-2.0-pro` - -### Cost Tracking - -Cost tracking is enabled for Vertex AI usage in LiteLLM. - ## **Rerank API** Vertex AI supports reranking through the Discovery Engine API, providing semantic ranking capabilities for document retrieval. From 369387705d49b0a37a086c02ef135f94a316acf3 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Fri, 17 Oct 2025 08:50:57 +0530 Subject: [PATCH 35/36] Updated documentation --- docs/my-website/docs/batches.md | 2 +- .../providers/vertex_batch_passthrough.md | 160 ++++++++++++++++++ 2 files changed, 161 insertions(+), 1 deletion(-) create mode 100644 docs/my-website/docs/providers/vertex_batch_passthrough.md diff --git a/docs/my-website/docs/batches.md b/docs/my-website/docs/batches.md index 098f9e67b9b0..1bd4c700ae7f 100644 --- a/docs/my-website/docs/batches.md +++ b/docs/my-website/docs/batches.md @@ -177,7 +177,7 @@ print("list_batches_response=", list_batches_response) ## **Supported Providers**: ### [Azure OpenAI](./providers/azure#azure-batches-api) ### [OpenAI](#quick-start) -### [Vertex AI Batch Passthrough](./providers/vertex_batch_passthrough.md) +### [Vertex AI](./providers/vertex#batch-apis) ### [Bedrock](./providers/bedrock_batches) diff --git a/docs/my-website/docs/providers/vertex_batch_passthrough.md b/docs/my-website/docs/providers/vertex_batch_passthrough.md new file mode 100644 index 000000000000..662cbeb9c445 --- /dev/null +++ b/docs/my-website/docs/providers/vertex_batch_passthrough.md @@ -0,0 +1,160 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Vertex AI Batch Passthrough + +LiteLLM supports Vertex AI batch prediction jobs through passthrough endpoints, allowing you to create and manage batch jobs directly through the proxy server. + +## Features + +- **Batch Job Creation**: Create batch prediction jobs using Vertex AI models +- **Cost Tracking**: Automatic cost calculation and usage tracking for batch operations +- **Status Monitoring**: Track job status and retrieve results +- **Model Support**: Works with all supported Vertex AI models (Gemini, Text Embedding) + +## Cost Tracking Support + +| Feature | Supported | Notes | +|---------|-----------|-------| +| Cost Tracking | ✅ | Automatic cost calculation for batch operations | +| Usage Monitoring | ✅ | Track token usage and costs across batch jobs | +| Logging | ✅ | works across all integrations | + +## Quick Start + +1. **Configure your model** in the proxy configuration: + +```yaml +model_list: + - model_name: gemini-1.5-flash + litellm_params: + model: vertex_ai/gemini-1.5-flash + vertex_project: your-project-id + vertex_location: us-central1 + vertex_credentials: path/to/service-account.json +``` + +2. **Create a batch job**: + +```bash +curl -X POST "http://localhost:4000/v1/projects/your-project/locations/us-central1/batchPredictionJobs" \ + -H "Authorization: Bearer your-api-key" \ + -H "Content-Type: application/json" \ + -d '{ + "displayName": "my-batch-job", + "model": "projects/your-project/locations/us-central1/publishers/google/models/gemini-1.5-flash", + "inputConfig": { + "gcsSource": { + "uris": ["gs://my-bucket/input.jsonl"] + }, + "instancesFormat": "jsonl" + }, + "outputConfig": { + "gcsDestination": { + "outputUriPrefix": "gs://my-bucket/output/" + }, + "predictionsFormat": "jsonl" + } + }' +``` + +3. **Monitor job status**: + +```bash +curl -X GET "http://localhost:4000/v1/projects/your-project/locations/us-central1/batchPredictionJobs/job-id" \ + -H "Authorization: Bearer your-api-key" +``` + +## Model Configuration + +When configuring models for batch operations, use these naming conventions: + +- **`model_name`**: Base model name (e.g., `gemini-1.5-flash`) +- **`model`**: Full LiteLLM identifier (e.g., `vertex_ai/gemini-1.5-flash`) + +## Supported Models + +- `gemini-1.5-flash` / `vertex_ai/gemini-1.5-flash` +- `gemini-1.5-pro` / `vertex_ai/gemini-1.5-pro` +- `gemini-2.0-flash` / `vertex_ai/gemini-2.0-flash` +- `gemini-2.0-pro` / `vertex_ai/gemini-2.0-pro` + +## Advanced Usage + +### Batch Job with Custom Parameters + +```bash +curl -X POST "http://localhost:4000/v1/projects/your-project/locations/us-central1/batchPredictionJobs" \ + -H "Authorization: Bearer your-api-key" \ + -H "Content-Type: application/json" \ + -d '{ + "displayName": "advanced-batch-job", + "model": "projects/your-project/locations/us-central1/publishers/google/models/gemini-1.5-pro", + "inputConfig": { + "gcsSource": { + "uris": ["gs://my-bucket/advanced-input.jsonl"] + }, + "instancesFormat": "jsonl" + }, + "outputConfig": { + "gcsDestination": { + "outputUriPrefix": "gs://my-bucket/advanced-output/" + }, + "predictionsFormat": "jsonl" + }, + "labels": { + "environment": "production", + "team": "ml-engineering" + } + }' +``` + +### List All Batch Jobs + +```bash +curl -X GET "http://localhost:4000/v1/projects/your-project/locations/us-central1/batchPredictionJobs" \ + -H "Authorization: Bearer your-api-key" +``` + +### Cancel a Batch Job + +```bash +curl -X POST "http://localhost:4000/v1/projects/your-project/locations/us-central1/batchPredictionJobs/job-id:cancel" \ + -H "Authorization: Bearer your-api-key" +``` + +## Cost Tracking Details + +LiteLLM provides comprehensive cost tracking for Vertex AI batch operations: + +- **Token Usage**: Tracks input and output tokens for each batch request +- **Cost Calculation**: Automatically calculates costs based on current Vertex AI pricing +- **Usage Aggregation**: Aggregates costs across all requests in a batch job +- **Real-time Monitoring**: Monitor costs as batch jobs progress + +The cost tracking works seamlessly with the `generateContent` API and provides detailed insights into your batch processing expenses. + +## Error Handling + +Common error scenarios and their solutions: + +| Error | Description | Solution | +|-------|-------------|----------| +| `INVALID_ARGUMENT` | Invalid model or configuration | Verify model name and project settings | +| `PERMISSION_DENIED` | Insufficient permissions | Check Vertex AI IAM roles | +| `RESOURCE_EXHAUSTED` | Quota exceeded | Check Vertex AI quotas and limits | +| `NOT_FOUND` | Job or resource not found | Verify job ID and project configuration | + +## Best Practices + +1. **Use appropriate batch sizes**: Balance between processing efficiency and resource usage +2. **Monitor job status**: Regularly check job status to handle failures promptly +3. **Set up alerts**: Configure monitoring for job completion and failures +4. **Optimize costs**: Use cost tracking to identify optimization opportunities +5. **Test with small batches**: Validate your setup with small test batches first + +## Related Documentation + +- [Vertex AI Provider Documentation](./vertex.md) +- [General Batches API Documentation](../batches.md) +- [Cost Tracking and Monitoring](../observability/telemetry.md) From dd7e16312485258dc3ddbe716530e5a845e378c9 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Fri, 17 Oct 2025 08:51:42 +0530 Subject: [PATCH 36/36] Updated documentation --- docs/my-website/docs/providers/vertex_batch_passthrough.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/my-website/docs/providers/vertex_batch_passthrough.md b/docs/my-website/docs/providers/vertex_batch_passthrough.md index 662cbeb9c445..8c0f7d463e3c 100644 --- a/docs/my-website/docs/providers/vertex_batch_passthrough.md +++ b/docs/my-website/docs/providers/vertex_batch_passthrough.md @@ -18,7 +18,7 @@ LiteLLM supports Vertex AI batch prediction jobs through passthrough endpoints, |---------|-----------|-------| | Cost Tracking | ✅ | Automatic cost calculation for batch operations | | Usage Monitoring | ✅ | Track token usage and costs across batch jobs | -| Logging | ✅ | works across all integrations | +| Logging | ✅ | Supported | ## Quick Start