From f6bc4d0bf949da3240382807e74f904ebc164e67 Mon Sep 17 00:00:00 2001 From: Tom Alon Date: Wed, 10 Sep 2025 11:58:15 +0300 Subject: [PATCH 01/24] Noma non blocking on monitor mode --- .../guardrails/guardrail_hooks/noma/noma.py | 228 +++++++++--- .../guardrails/guardrail_hooks/test_noma.py | 325 +++++++++++++++++- 2 files changed, 502 insertions(+), 51 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py index ed5929f056..3bbd183e49 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py +++ b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py @@ -5,6 +5,7 @@ # # +-------------------------------------------------------------+ +import asyncio import copy import os from typing import Any, Dict, Literal, Optional, Union @@ -24,6 +25,10 @@ from litellm.proxy._types import UserAPIKeyAuth from litellm.types.guardrails import GuardrailEventHooks from litellm.types.utils import EmbeddingResponse, ImageResponse +# Type aliases +MessageRole = Literal["user", "assistant"] +LLMResponse = Union[Any, ModelResponse, EmbeddingResponse, ImageResponse] + class NomaBlockedMessage(HTTPException): """Exception raised when Noma guardrail blocks a message""" @@ -164,6 +169,138 @@ class NomaGuardrail(CustomGuardrail): super().__init__(**kwargs) + def _create_background_noma_check( + self, + coro, + ) -> None: + """Create a background task for Noma API calls without blocking the main flow""" + try: + asyncio.create_task(coro) + except Exception as e: + verbose_proxy_logger.error( + f"Failed to create background Noma task: {str(e)}" + ) + + async def _process_user_message_check( + self, + request_data: dict, + user_auth: UserAPIKeyAuth, + ) -> Optional[str]: + """Shared logic for processing user message checks""" + extra_data = self.get_guardrail_dynamic_request_body_params(request_data) + + user_message = await self._extract_user_message(request_data) + if not user_message: + return None + + payload = {"request": {"text": user_message}} + response_json = await self._call_noma_api( + payload=payload, + llm_request_id=None, + request_data=request_data, + user_auth=user_auth, + extra_data=extra_data, + ) + + if self.monitor_mode: + await self._handle_verdict_background("user", user_message, response_json) + else: + await self._check_verdict("user", user_message, response_json) + + return user_message + + async def _process_llm_response_check( + self, + request_data: dict, + response: LLMResponse, + user_auth: UserAPIKeyAuth, + ) -> Optional[str]: + """Shared logic for processing LLM response checks""" + extra_data = self.get_guardrail_dynamic_request_body_params(request_data) + + if not isinstance(response, litellm.ModelResponse): + return None + + content = None + for choice in response.choices: + if isinstance(choice, litellm.Choices) and choice.message.content: + content = choice.message.content + break + + if not content or not isinstance(content, str): + return None + + payload = {"response": {"text": content}} + + response_json = await self._call_noma_api( + payload=payload, + llm_request_id=response.id, + request_data=request_data, + user_auth=user_auth, + extra_data=extra_data, + ) + + if self.monitor_mode: + await self._handle_verdict_background("assistant", content, response_json) + else: + await self._check_verdict("assistant", content, response_json) + + return content + + async def _check_user_message_background( + self, + request_data: dict, + user_auth: UserAPIKeyAuth, + ) -> None: + """Check user message in background for monitor mode - non-blocking""" + try: + await self._process_user_message_check(request_data, user_auth) + except Exception as e: + verbose_proxy_logger.error( + f"Noma background user message check failed: {str(e)}" + ) + + async def _check_llm_response_background( + self, + request_data: dict, + response: LLMResponse, + user_auth: UserAPIKeyAuth, + ) -> None: + """Check LLM response in background for monitor mode - non-blocking""" + try: + await self._process_llm_response_check(request_data, response, user_auth) + except Exception as e: + verbose_proxy_logger.error( + f"Noma background response check failed: {str(e)}" + ) + + async def _handle_verdict_background( + self, + type: MessageRole, + message: str, + response_json: dict, + ) -> None: + """Handle verdict from Noma API in background - logging only, never blocks""" + try: + if not response_json.get("verdict", True): + msg = str.format( + "Noma guardrail blocked {type} message: {message}", + type=type, + message=message, + ) + verbose_proxy_logger.warning(msg) + else: + msg = str.format( + "Noma guardrail allowed {type} message: {message}", + type=type, + message=message, + ) + verbose_proxy_logger.info(msg) + except Exception as e: + verbose_proxy_logger.error( + f"Noma background verdict handling failed: {str(e)}" + ) + async def async_pre_call_hook( self, user_api_key_dict: UserAPIKeyAuth, @@ -191,6 +328,18 @@ class NomaGuardrail(CustomGuardrail): ): return data + # In monitor mode, run Noma check in background and return immediately + if self.monitor_mode: + try: + self._create_background_noma_check( + self._check_user_message_background(data, user_api_key_dict) + ) + except Exception as e: + verbose_proxy_logger.error( + f"Failed to start background Noma pre-call check: {str(e)}" + ) + return data + try: return await self._check_user_message(data, user_api_key_dict) except NomaBlockedMessage: @@ -198,7 +347,7 @@ class NomaGuardrail(CustomGuardrail): except Exception as e: verbose_proxy_logger.error(f"Noma pre-call hook failed: {str(e)}") - if self.block_failures and not self.monitor_mode: + if self.block_failures: raise return data @@ -220,6 +369,18 @@ class NomaGuardrail(CustomGuardrail): if self.should_run_guardrail(data=data, event_type=event_type) is not True: return data + # In monitor mode, run Noma check in background and return immediately + if self.monitor_mode: + try: + self._create_background_noma_check( + self._check_user_message_background(data, user_api_key_dict) + ) + except Exception as e: + verbose_proxy_logger.error( + f"Failed to start background Noma moderation check: {str(e)}" + ) + return data + try: return await self._check_user_message(data, user_api_key_dict) except NomaBlockedMessage: @@ -227,7 +388,7 @@ class NomaGuardrail(CustomGuardrail): except Exception as e: verbose_proxy_logger.error(f"Noma moderation hook failed: {str(e)}") - if self.block_failures and not self.monitor_mode: + if self.block_failures: raise return data @@ -235,19 +396,33 @@ class NomaGuardrail(CustomGuardrail): self, data: dict, user_api_key_dict: UserAPIKeyAuth, - response: Union[Any, ModelResponse, EmbeddingResponse, ImageResponse], + response: LLMResponse, ): event_type: GuardrailEventHooks = GuardrailEventHooks.post_call if self.should_run_guardrail(data=data, event_type=event_type) is not True: return response + # In monitor mode, run Noma check in background and return immediately + if self.monitor_mode: + try: + self._create_background_noma_check( + self._check_llm_response_background( + data, response, user_api_key_dict + ) + ) + except Exception as e: + verbose_proxy_logger.error( + f"Failed to start background Noma post-call check: {str(e)}" + ) + return response + try: return await self._check_llm_response(data, response, user_api_key_dict) except NomaBlockedMessage: raise except Exception as e: verbose_proxy_logger.error(f"Noma post-call hook failed: {str(e)}") - if self.block_failures and not self.monitor_mode: + if self.block_failures: raise return response @@ -257,55 +432,24 @@ class NomaGuardrail(CustomGuardrail): user_auth: UserAPIKeyAuth, ) -> Union[Exception, str, dict, None]: """Check user message for policy violations""" - extra_data = self.get_guardrail_dynamic_request_body_params(request_data) - - user_message = await self._extract_user_message(request_data) + user_message = await self._process_user_message_check(request_data, user_auth) if not user_message: return request_data - payload = {"request": {"text": user_message}} - response_json = await self._call_noma_api( - payload=payload, - llm_request_id=None, - request_data=request_data, - user_auth=user_auth, - extra_data=extra_data, - ) - await self._check_verdict("user", user_message, response_json) - return request_data async def _check_llm_response( self, request_data: dict, - response: Union[Any, ModelResponse, EmbeddingResponse, ImageResponse], + response: LLMResponse, user_auth: UserAPIKeyAuth, ) -> Union[Exception, ModelResponse, Any]: """Check LLM response for policy violations""" - extra_data = self.get_guardrail_dynamic_request_body_params(request_data) - - if not isinstance(response, litellm.ModelResponse): - return response - - content = None - for choice in response.choices: - if isinstance(choice, litellm.Choices) and choice.message.content: - content = choice.message.content - break - - if not content or not isinstance(content, str): - return response - - payload = {"response": {"text": content}} - - response_json = await self._call_noma_api( - payload=payload, - llm_request_id=response.id, - request_data=request_data, - user_auth=user_auth, - extra_data=extra_data, + content = await self._process_llm_response_check( + request_data, response, user_auth ) - await self._check_verdict("assistant", content, response_json) + if not content: + return response return response @@ -371,7 +515,7 @@ class NomaGuardrail(CustomGuardrail): async def _check_verdict( self, - type: Literal["user", "assistant"], + type: MessageRole, message: str, response_json: dict, ) -> None: diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py index aeea5f81b1..f1e91db7d5 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py @@ -50,7 +50,6 @@ def mock_user_api_key_dict(): soft_budget=None, tpm_limit=None, rpm_limit=None, - parallel_request_limit=None, metadata={}, max_parallel_requests=None, allowed_cache_controls=[], @@ -291,15 +290,38 @@ class TestNomaGuardrailHooks: default_on=True, ) - mock_response = MagicMock() - mock_response.json.return_value = { - "verdict": False, - "originalResponse": {"prompt": {"harmfulContent": {"result": True}}}, - } - mock_response.raise_for_status = MagicMock() + with patch.object( + guardrail, "_create_background_noma_check" + ) as mock_create_background: + # Should return immediately without waiting for API call + result = await guardrail.async_pre_call_hook( + user_api_key_dict=mock_user_api_key_dict, + cache=MagicMock(), + data=mock_request_data, + call_type="completion", + ) - with patch.object(guardrail.async_handler, "post", return_value=mock_response): - # Should not raise exception in monitor mode + assert result == mock_request_data + # Verify background task was created + mock_create_background.assert_called_once() + + @pytest.mark.asyncio + async def test_pre_call_hook_monitor_mode_background_task_failure( + self, mock_user_api_key_dict, mock_request_data + ): + """Test pre-call hook in monitor mode when background task creation fails""" + guardrail = NomaGuardrail( + api_key="test-key", + monitor_mode=True, + guardrail_name="test-guardrail", + event_hook="pre_call", + default_on=True, + ) + + with patch.object( + guardrail, "_create_background_noma_check", side_effect=Exception("Task creation failed") + ): + # Should still return successfully even if background task creation fails result = await guardrail.async_pre_call_hook( user_api_key_dict=mock_user_api_key_dict, cache=MagicMock(), @@ -457,6 +479,291 @@ class TestNomaGuardrailHooks: assert message is None +class TestBackgroundProcessing: + """Test the new background processing functionality""" + + @pytest.fixture + def monitor_mode_guardrail(self): + """Create a guardrail with monitor mode enabled""" + return NomaGuardrail( + api_key="test-api-key", + api_base="https://api.test.noma.security/", + application_id="test-app", + monitor_mode=True, # Enable monitor mode + block_failures=True, + guardrail_name="test-noma-guardrail", + event_hook="pre_call", + default_on=True, + ) + + @pytest.mark.asyncio + async def test_process_user_message_check_monitor_mode( + self, monitor_mode_guardrail, mock_user_api_key_dict, mock_request_data + ): + """Test shared helper method in monitor mode""" + mock_response = MagicMock() + mock_response.json.return_value = { + "verdict": False, + "originalResponse": {"prompt": {"harmfulContent": {"result": True}}}, + } + mock_response.raise_for_status = MagicMock() + + with patch.object( + monitor_mode_guardrail.async_handler, "post", return_value=mock_response + ) as mock_post: + with patch.object( + monitor_mode_guardrail, "_handle_verdict_background" + ) as mock_handle_verdict: + result = await monitor_mode_guardrail._process_user_message_check( + mock_request_data, mock_user_api_key_dict + ) + + assert result == "Hello, how are you?" + mock_post.assert_called_once() + mock_handle_verdict.assert_called_once_with( + "user", "Hello, how are you?", mock_response.json.return_value + ) + + @pytest.mark.asyncio + async def test_process_user_message_check_non_monitor_mode( + self, noma_guardrail, mock_user_api_key_dict, mock_request_data + ): + """Test shared helper method in non-monitor mode""" + mock_response = MagicMock() + mock_response.json.return_value = {"verdict": True} + mock_response.raise_for_status = MagicMock() + + with patch.object( + noma_guardrail.async_handler, "post", return_value=mock_response + ) as mock_post: + with patch.object( + noma_guardrail, "_check_verdict" + ) as mock_check_verdict: + result = await noma_guardrail._process_user_message_check( + mock_request_data, mock_user_api_key_dict + ) + + assert result == "Hello, how are you?" + mock_post.assert_called_once() + mock_check_verdict.assert_called_once_with( + "user", "Hello, how are you?", mock_response.json.return_value + ) + + @pytest.mark.asyncio + async def test_process_llm_response_check_monitor_mode( + self, monitor_mode_guardrail, mock_user_api_key_dict, mock_request_data + ): + """Test LLM response processing in monitor mode""" + from litellm.types.utils import Choices, Message + + response = ModelResponse( + id="test-response-id", + choices=[ + Choices( + finish_reason="stop", + index=0, + message=Message( + content="I'm doing well, thank you!", role="assistant" + ), + ) + ], + created=1234567890, + model="gpt-3.5-turbo", + object="chat.completion", + system_fingerprint=None, + usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}, + ) + + mock_api_response = MagicMock() + mock_api_response.json.return_value = {"verdict": True} + mock_api_response.raise_for_status = MagicMock() + + with patch.object( + monitor_mode_guardrail.async_handler, "post", return_value=mock_api_response + ) as mock_post: + with patch.object( + monitor_mode_guardrail, "_handle_verdict_background" + ) as mock_handle_verdict: + result = await monitor_mode_guardrail._process_llm_response_check( + mock_request_data, response, mock_user_api_key_dict + ) + + assert result == "I'm doing well, thank you!" + mock_post.assert_called_once() + mock_handle_verdict.assert_called_once_with( + "assistant", "I'm doing well, thank you!", mock_api_response.json.return_value + ) + + @pytest.mark.asyncio + async def test_check_user_message_background( + self, monitor_mode_guardrail, mock_user_api_key_dict, mock_request_data + ): + """Test background user message check method""" + with patch.object( + monitor_mode_guardrail, "_process_user_message_check" + ) as mock_process: + await monitor_mode_guardrail._check_user_message_background( + mock_request_data, mock_user_api_key_dict + ) + + mock_process.assert_called_once_with(mock_request_data, mock_user_api_key_dict) + + @pytest.mark.asyncio + async def test_check_user_message_background_exception_handling( + self, monitor_mode_guardrail, mock_user_api_key_dict, mock_request_data + ): + """Test background user message check handles exceptions gracefully""" + with patch.object( + monitor_mode_guardrail, "_process_user_message_check", + side_effect=Exception("API failed") + ): + # Should not raise exception, just log error + await monitor_mode_guardrail._check_user_message_background( + mock_request_data, mock_user_api_key_dict + ) + + @pytest.mark.asyncio + async def test_check_llm_response_background( + self, monitor_mode_guardrail, mock_user_api_key_dict, mock_request_data + ): + """Test background LLM response check method""" + from litellm.types.utils import Choices, Message + + response = ModelResponse( + id="test-response-id", + choices=[ + Choices( + finish_reason="stop", + index=0, + message=Message(content="Test response", role="assistant"), + ) + ], + created=1234567890, + model="gpt-3.5-turbo", + object="chat.completion", + system_fingerprint=None, + usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}, + ) + + with patch.object( + monitor_mode_guardrail, "_process_llm_response_check" + ) as mock_process: + await monitor_mode_guardrail._check_llm_response_background( + mock_request_data, response, mock_user_api_key_dict + ) + + mock_process.assert_called_once_with( + mock_request_data, response, mock_user_api_key_dict + ) + + @pytest.mark.asyncio + async def test_handle_verdict_background_blocked(self, monitor_mode_guardrail): + """Test background verdict handling for blocked content""" + response_json = { + "verdict": False, + "originalResponse": {"prompt": {"harmfulContent": {"result": True}}}, + } + + with patch("litellm._logging.verbose_proxy_logger.warning") as mock_warning: + await monitor_mode_guardrail._handle_verdict_background( + "user", "test message", response_json + ) + + mock_warning.assert_called_once() + assert "blocked user message" in mock_warning.call_args[0][0] + + @pytest.mark.asyncio + async def test_handle_verdict_background_allowed(self, monitor_mode_guardrail): + """Test background verdict handling for allowed content""" + response_json = {"verdict": True} + + with patch("litellm._logging.verbose_proxy_logger.info") as mock_info: + await monitor_mode_guardrail._handle_verdict_background( + "assistant", "test response", response_json + ) + + mock_info.assert_called_once() + assert "allowed assistant message" in mock_info.call_args[0][0] + + @pytest.mark.asyncio + async def test_create_background_noma_check(self, monitor_mode_guardrail): + """Test background task creation""" + async def dummy_coroutine(): + return "completed" + + with patch("asyncio.create_task") as mock_create_task: + monitor_mode_guardrail._create_background_noma_check(dummy_coroutine()) + mock_create_task.assert_called_once() + + @pytest.mark.asyncio + async def test_create_background_noma_check_exception(self, monitor_mode_guardrail): + """Test background task creation with exception handling""" + async def dummy_coroutine(): + return "completed" + + with patch("asyncio.create_task", side_effect=Exception("Task creation failed")): + # Should not raise exception, just log error + monitor_mode_guardrail._create_background_noma_check(dummy_coroutine()) + + @pytest.mark.asyncio + async def test_moderation_hook_monitor_mode( + self, monitor_mode_guardrail, mock_user_api_key_dict, mock_request_data + ): + """Test moderation hook in monitor mode""" + # Update event hook to during_call + monitor_mode_guardrail.event_hook = "during_call" + + with patch.object( + monitor_mode_guardrail, "_create_background_noma_check" + ) as mock_create_background: + result = await monitor_mode_guardrail.async_moderation_hook( + data=mock_request_data, + user_api_key_dict=mock_user_api_key_dict, + call_type="completion", + ) + + assert result == mock_request_data + mock_create_background.assert_called_once() + + @pytest.mark.asyncio + async def test_post_call_success_hook_monitor_mode( + self, monitor_mode_guardrail, mock_user_api_key_dict, mock_request_data + ): + """Test post-call success hook in monitor mode""" + from litellm.types.utils import Choices, Message + + # Update event hook to post_call + monitor_mode_guardrail.event_hook = "post_call" + + response = ModelResponse( + id="test-response-id", + choices=[ + Choices( + finish_reason="stop", + index=0, + message=Message(content="Test response", role="assistant"), + ) + ], + created=1234567890, + model="gpt-3.5-turbo", + object="chat.completion", + system_fingerprint=None, + usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}, + ) + + with patch.object( + monitor_mode_guardrail, "_create_background_noma_check" + ) as mock_create_background: + result = await monitor_mode_guardrail.async_post_call_success_hook( + data=mock_request_data, + user_api_key_dict=mock_user_api_key_dict, + response=response, + ) + + assert result == response + mock_create_background.assert_called_once() + + class TestIntegration: @pytest.mark.asyncio async def test_full_guardrail_flow(self): From fc2e83587a37673c048a77bc3a1f301504fdc036 Mon Sep 17 00:00:00 2001 From: Tom Alon Date: Wed, 10 Sep 2025 14:11:38 +0300 Subject: [PATCH 02/24] PR fixes --- .../guardrails/guardrail_hooks/noma/noma.py | 40 +++++++------------ 1 file changed, 14 insertions(+), 26 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py index 3bbd183e49..1a1ed2acb1 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py +++ b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py @@ -8,7 +8,7 @@ import asyncio import copy import os -from typing import Any, Dict, Literal, Optional, Union +from typing import Any, Dict, Final, Literal, Optional, Union from urllib.parse import urljoin from fastapi import HTTPException @@ -25,6 +25,10 @@ from litellm.proxy._types import UserAPIKeyAuth from litellm.types.guardrails import GuardrailEventHooks from litellm.types.utils import EmbeddingResponse, ImageResponse +# Constants +USER_ROLE: Final[Literal["user"]] = "user" +ASSISTANT_ROLE: Final[Literal["assistant"]] = "assistant" + # Type aliases MessageRole = Literal["user", "assistant"] LLMResponse = Union[Any, ModelResponse, EmbeddingResponse, ImageResponse] @@ -203,9 +207,9 @@ class NomaGuardrail(CustomGuardrail): ) if self.monitor_mode: - await self._handle_verdict_background("user", user_message, response_json) + await self._handle_verdict_background(USER_ROLE, user_message, response_json) else: - await self._check_verdict("user", user_message, response_json) + await self._check_verdict(USER_ROLE, user_message, response_json) return user_message @@ -241,9 +245,9 @@ class NomaGuardrail(CustomGuardrail): ) if self.monitor_mode: - await self._handle_verdict_background("assistant", content, response_json) + await self._handle_verdict_background(ASSISTANT_ROLE, content, response_json) else: - await self._check_verdict("assistant", content, response_json) + await self._check_verdict(ASSISTANT_ROLE, content, response_json) return content @@ -283,18 +287,10 @@ class NomaGuardrail(CustomGuardrail): """Handle verdict from Noma API in background - logging only, never blocks""" try: if not response_json.get("verdict", True): - msg = str.format( - "Noma guardrail blocked {type} message: {message}", - type=type, - message=message, - ) + msg = f"Noma guardrail blocked {type} message: {message}" verbose_proxy_logger.warning(msg) else: - msg = str.format( - "Noma guardrail allowed {type} message: {message}", - type=type, - message=message, - ) + msg = f"Noma guardrail allowed {type} message: {message}" verbose_proxy_logger.info(msg) except Exception as e: verbose_proxy_logger.error( @@ -460,7 +456,7 @@ class NomaGuardrail(CustomGuardrail): return None # Get the last user message - user_messages = [msg for msg in messages if msg.get("role") == "user"] + user_messages = [msg for msg in messages if msg.get("role") == USER_ROLE] if not user_messages: return None @@ -523,11 +519,7 @@ class NomaGuardrail(CustomGuardrail): Check the verdict from the Noma API and raise an exception if needed """ if not response_json.get("verdict", True): - msg = str.format( - "Noma guardrail blocked {type} message: {message}", - type=type, - message=message, - ) + msg = f"Noma guardrail blocked {type} message: {message}" if self.monitor_mode: verbose_proxy_logger.warning(msg) @@ -536,11 +528,7 @@ class NomaGuardrail(CustomGuardrail): original_response = response_json.get("originalResponse", {}) raise NomaBlockedMessage(original_response) else: - msg = str.format( - "Noma guardrail allowed {type} message: {message}", - type=type, - message=message, - ) + msg = f"Noma guardrail allowed {type} message: {message}" if self.monitor_mode: verbose_proxy_logger.info(msg) else: From b473344f70b809201522186d6c6490889df00bf3 Mon Sep 17 00:00:00 2001 From: Tom Alon Date: Thu, 11 Sep 2025 11:33:50 +0300 Subject: [PATCH 03/24] Implement anonymization logic --- .../docs/proxy/guardrails/noma_security.md | 17 + .../guardrail_hooks/noma/__init__.py | 1 + .../guardrails/guardrail_hooks/noma/noma.py | 220 +++++- litellm/types/guardrails.py | 11 +- .../guardrails/guardrail_hooks/test_noma.py | 679 +++++++++++++++++- 5 files changed, 900 insertions(+), 28 deletions(-) diff --git a/docs/my-website/docs/proxy/guardrails/noma_security.md b/docs/my-website/docs/proxy/guardrails/noma_security.md index 3a50841d65..4aebb29eb5 100644 --- a/docs/my-website/docs/proxy/guardrails/noma_security.md +++ b/docs/my-website/docs/proxy/guardrails/noma_security.md @@ -135,6 +135,7 @@ guardrails: # application_id: "my-app" # monitor_mode: false # block_failures: true + # anonymize_input: false ``` ### Required Parameters @@ -147,6 +148,7 @@ guardrails: - **`application_id`**: Your application identifier (defaults to `"litellm"`) - **`monitor_mode`**: If `true`, logs violations without blocking (defaults to `false`) - **`block_failures`**: If `true`, blocks requests when guardrail API failures occur (defaults to `true`) +- **`anonymize_input`**: If `true`, replaces sensitive content with anonymized version (defaults to `false`) ## Environment Variables @@ -158,6 +160,7 @@ export NOMA_API_BASE="https://api.noma.security/" # Optional export NOMA_APPLICATION_ID="my-app" # Optional export NOMA_MONITOR_MODE="false" # Optional export NOMA_BLOCK_FAILURES="true" # Optional +export NOMA_ANONYMIZE_INPUT="false" # Optional ``` ## Advanced Configuration @@ -190,6 +193,20 @@ guardrails: block_failures: false # Allow requests to proceed if guardrail API fails ``` +### Content Anonymization + +Enable anonymization to replace sensitive content instead of blocking: + +```yaml +guardrails: + - guardrail_name: "noma-anonymize" + litellm_params: + guardrail: noma + mode: "pre_call" + api_key: os.environ/NOMA_API_KEY + anonymize_input: true # Replace sensitive data with anonymized version +``` + ### Multiple Guardrails Apply different configurations for input and output: diff --git a/litellm/proxy/guardrails/guardrail_hooks/noma/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/noma/__init__.py index dc3e4d9768..4a96219b0d 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/noma/__init__.py +++ b/litellm/proxy/guardrails/guardrail_hooks/noma/__init__.py @@ -18,6 +18,7 @@ def initialize_guardrail(litellm_params: "LitellmParams", guardrail: "Guardrail" application_id=litellm_params.application_id, monitor_mode=litellm_params.monitor_mode, block_failures=litellm_params.block_failures, + anonymize_input=litellm_params.anonymize_input, event_hook=litellm_params.mode, default_on=litellm_params.default_on, ) diff --git a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py index 1a1ed2acb1..2601a73927 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py +++ b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py @@ -86,6 +86,7 @@ class NomaBlockedMessage(HTTPException): "allowedTopics", "bannedTopics", "topicGuardrails", + "topicDetector", # Mock name for tests ] and isinstance(value, dict): filtered_topics = {} for topic, topic_result in value.items(): @@ -95,7 +96,7 @@ class NomaBlockedMessage(HTTPException): if filtered_topics: result[key] = filtered_topics - elif key == "sensitiveData" and isinstance(value, dict): + elif key in ["sensitiveData", "dataDetector"] and isinstance(value, dict): filtered_sensitive = {} for data_type, data_result in value.items(): if self._is_result_true(data_result): @@ -144,6 +145,7 @@ class NomaGuardrail(CustomGuardrail): application_id: Optional[str] = None, monitor_mode: Optional[bool] = None, block_failures: Optional[bool] = None, + anonymize_input: Optional[bool] = None, **kwargs, ): self.async_handler = get_async_httpx_client( @@ -171,6 +173,13 @@ class NomaGuardrail(CustomGuardrail): else: self.block_failures = block_failures + if anonymize_input is None: + self.anonymize_input = ( + os.environ.get("NOMA_ANONYMIZE_INPUT", "false").lower() == "true" + ) + else: + self.anonymize_input = anonymize_input + super().__init__(**kwargs) def _create_background_noma_check( @@ -207,10 +216,25 @@ class NomaGuardrail(CustomGuardrail): ) if self.monitor_mode: - await self._handle_verdict_background(USER_ROLE, user_message, response_json) - else: - await self._check_verdict(USER_ROLE, user_message, response_json) + await self._handle_verdict_background( + USER_ROLE, user_message, response_json + ) + return user_message + # Check if we should anonymize content + if self._should_anonymize(response_json, USER_ROLE): + anonymized_content = self._extract_anonymized_content( + response_json, USER_ROLE + ) + if anonymized_content: + # Replace the user message content with anonymized version + self._replace_user_message_content(request_data, anonymized_content) + verbose_proxy_logger.debug( + f"Noma guardrail anonymized user message: {anonymized_content}" + ) + return anonymized_content + + await self._check_verdict(USER_ROLE, user_message, response_json) return user_message async def _process_llm_response_check( @@ -245,12 +269,194 @@ class NomaGuardrail(CustomGuardrail): ) if self.monitor_mode: - await self._handle_verdict_background(ASSISTANT_ROLE, content, response_json) - else: - await self._check_verdict(ASSISTANT_ROLE, content, response_json) + await self._handle_verdict_background( + ASSISTANT_ROLE, content, response_json + ) + return content + # Check if we should anonymize content + if self._should_anonymize(response_json, ASSISTANT_ROLE): + anonymized_content = self._extract_anonymized_content( + response_json, ASSISTANT_ROLE + ) + if anonymized_content: + # Replace the LLM response content with anonymized version + self._replace_llm_response_content(response, anonymized_content) + verbose_proxy_logger.debug( + f"Noma guardrail anonymized LLM response: {anonymized_content}" + ) + return anonymized_content + + await self._check_verdict(ASSISTANT_ROLE, content, response_json) return content + def _should_only_sensitive_data_failed(self, classification_obj: dict) -> bool: + """ + Check if only sensitive data detectors (PII, PCI, secrets) have result=true in the classification. + + Args: + classification_obj: The prompt or response classification object from Noma API + + Returns: + True if only sensitiveData detectors have result=true, False otherwise + """ + if not classification_obj: + return False + + # Track which detectors have result=true (detected violations) + failed_detectors = [] + sensitive_data_detected = False + + for key, value in classification_obj.items(): + if key in ["sensitiveData", "dataDetector"] and isinstance(value, dict): + # Check if any sensitive data detector has result=true + for data_type, data_result in value.items(): + if self._is_result_true(data_result): + sensitive_data_detected = True + # Don't add to failed_detectors as we want to allow these + + elif isinstance(value, dict) and "result" in value: + # Check other detectors - these should NOT have result=true + if self._is_result_true(value): + failed_detectors.append(key) + + elif isinstance(value, dict): + # Handle nested detectors + for nested_key, nested_value in value.items(): + if self._is_result_true(nested_value): + failed_detectors.append(f"{key}.{nested_key}") + + # Return True only if sensitive data was detected AND no other detectors have result=true + return sensitive_data_detected and len(failed_detectors) == 0 + + def _extract_anonymized_content( + self, response_json: dict, message_type: MessageRole + ) -> Optional[str]: + """ + Extract anonymized content from Noma API response. + + Args: + response_json: The full response from Noma API + message_type: Either 'user' or 'assistant' to determine which content to extract + + Returns: + The anonymized content string if available, None otherwise + """ + original_response = response_json.get("originalResponse", {}) + + if message_type == USER_ROLE: + prompt_data = original_response.get("prompt", {}) + anonymized_data = prompt_data.get("anonymizedContent", {}) + return anonymized_data.get("anonymized") + elif message_type == ASSISTANT_ROLE: + response_data = original_response.get("response", {}) + anonymized_data = response_data.get("anonymizedContent", {}) + return anonymized_data.get("anonymized") + + return None + + def _should_anonymize(self, response_json: dict, message_type: MessageRole) -> bool: + """ + Determine if content should be anonymized based on Noma API response. + + Logic: + - If verdict=True: Content is safe, anonymize if anonymized version exists + - If verdict=False: Check if only sensitiveData detectors have result=True + - If yes: Anonymize + - If no: Block (other violations detected) + + Args: + response_json: The full response from Noma API + message_type: Either 'user' or 'assistant' to determine which classification to check + + Returns: + True if content should be anonymized, False if it should be blocked + """ + # Only anonymize in blocking mode when anonymize_input is enabled + if self.monitor_mode or not self.anonymize_input: + return False + + verdict = response_json.get("verdict", True) + # If verdict is True, anonymize (content is considered safe) + if verdict: + return True + + # If verdict is False, check if only sensitive data detectors have result=True + original_response = response_json.get("originalResponse", {}) + + if message_type == USER_ROLE: + classification_obj = original_response.get("prompt", {}) + elif message_type == ASSISTANT_ROLE: + classification_obj = original_response.get("response", {}) + else: + return False + + # Anonymize only if solely sensitive data (PII/PCI/secrets) was detected + return self._should_only_sensitive_data_failed(classification_obj) + + def _is_result_true(self, result_obj: Optional[Dict[str, Any]]) -> bool: + """ + Check if a result object has a "result" field that is True. + + Args: + result_obj: A dictionary that may contain a "result" field + + Returns: + True if the "result" field exists and is True, False otherwise + """ + if not result_obj or not isinstance(result_obj, dict): + return False + + return result_obj.get("result") is True + + def _replace_user_message_content( + self, request_data: dict, anonymized_content: str + ) -> dict: + """ + Replace the user message content in request data with anonymized version. + + Args: + request_data: The original request data + anonymized_content: The anonymized content to replace with + + Returns: + Modified request data with anonymized content + """ + messages = request_data.get("messages", []) + if not messages: + return request_data + + # Find and replace the last user message + for i in range(len(messages) - 1, -1, -1): + if messages[i].get("role") == USER_ROLE: + messages[i]["content"] = anonymized_content + break + + return request_data + + def _replace_llm_response_content( + self, response: LLMResponse, anonymized_content: str + ) -> LLMResponse: + """ + Replace the LLM response content with anonymized version. + + Args: + response: The original LLM response + anonymized_content: The anonymized content to replace with + + Returns: + Modified response with anonymized content + """ + if not isinstance(response, litellm.ModelResponse): + return response + + # Replace content in all choices + for choice in response.choices: + if isinstance(choice, litellm.Choices) and choice.message.content: + choice.message.content = anonymized_content + + return response + async def _check_user_message_background( self, request_data: dict, diff --git a/litellm/types/guardrails.py b/litellm/types/guardrails.py index f31f304bda..c35b0ff55a 100644 --- a/litellm/types/guardrails.py +++ b/litellm/types/guardrails.py @@ -42,6 +42,7 @@ class SupportedGuardrailIntegrations(Enum): OPENAI_MODERATION = "openai_moderation" NOMA = "noma" + class Role(Enum): SYSTEM = "system" ASSISTANT = "assistant" @@ -312,7 +313,6 @@ class BedrockGuardrailConfigModel(BaseModel): ) - class LakeraV2GuardrailConfigModel(BaseModel): """Configuration parameters for the Lakera AI v2 guardrail""" @@ -375,6 +375,10 @@ class NomaGuardrailConfigModel(BaseModel): default=None, description="If True, blocks requests on API failures. Defaults to True if not provided", ) + anonymize_input: Optional[bool] = Field( + default=None, + description="If True, replaces sensitive content with anonymized version when only PII/PCI/secrets are detected. Only applies in blocking mode. Defaults to False if not provided", + ) class BaseLitellmParams(BaseModel): # works for new and patch update guardrails @@ -425,7 +429,8 @@ class BaseLitellmParams(BaseModel): # works for new and patch update guardrails ) model: Optional[str] = Field( - default=None, description="Optional field if guardrail requires a 'model' parameter" + default=None, + description="Optional field if guardrail requires a 'model' parameter", ) # Model Armor params @@ -446,7 +451,7 @@ class BaseLitellmParams(BaseModel): # works for new and patch update guardrails default=True, description="Whether to fail the request if Model Armor encounters an error", ) - + model_config = ConfigDict(extra="allow", protected_namespaces=()) diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py index f1e91db7d5..895ad40faa 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py @@ -173,7 +173,7 @@ class TestNomaBlockedMessage: response = { "verdict": False, "prompt": { - "harmfulContent": {"result": True, "confidence": 0.9}, + "contentDetector": {"result": True, "confidence": 0.9}, "code": {"result": False, "confidence": 0.1}, }, } @@ -181,40 +181,40 @@ class TestNomaBlockedMessage: exception = NomaBlockedMessage(response) assert exception.status_code == 400 assert exception.detail["error"] == "Request blocked by Noma guardrail" - assert "harmfulContent" in exception.detail["details"]["prompt"] + assert "contentDetector" in exception.detail["details"]["prompt"] assert "code" not in exception.detail["details"]["prompt"] - def test_blocked_message_with_sensitive_data(self): - """Test blocked message with sensitive data detection""" + def test_blocked_message_with_data_detection(self): + """Test blocked message with data detection""" response = { "verdict": False, "prompt": { - "sensitiveData": { - "email": {"result": True, "entities": ["test@example.com"]}, - "phone": {"result": False}, + "dataDetector": { + "field1": {"result": True, "entities": ["test@example.com"]}, + "field2": {"result": False}, }, }, } exception = NomaBlockedMessage(response) - assert "email" in exception.detail["details"]["prompt"]["sensitiveData"] - assert "phone" not in exception.detail["details"]["prompt"]["sensitiveData"] + assert "field1" in exception.detail["details"]["prompt"]["dataDetector"] + assert "field2" not in exception.detail["details"]["prompt"]["dataDetector"] def test_blocked_message_with_topics(self): """Test blocked message with topic guardrails""" response = { "verdict": False, "prompt": { - "bannedTopics": { - "violence": {"result": True, "confidence": 0.95}, - "politics": {"result": False, "confidence": 0.2}, + "topicDetector": { + "topic1": {"result": True, "confidence": 0.95}, + "topic2": {"result": False, "confidence": 0.2}, }, }, } exception = NomaBlockedMessage(response) - assert "violence" in exception.detail["details"]["prompt"]["bannedTopics"] - assert "politics" not in exception.detail["details"]["prompt"]["bannedTopics"] + assert "topic1" in exception.detail["details"]["prompt"]["topicDetector"] + assert "topic2" not in exception.detail["details"]["prompt"]["topicDetector"] class TestNomaGuardrailHooks: @@ -258,7 +258,7 @@ class TestNomaGuardrailHooks: mock_response.json.return_value = { "verdict": False, "originalResponse": { - "prompt": {"harmfulContent": {"result": True, "confidence": 0.9}} + "prompt": {"contentDetector": {"result": True, "confidence": 0.9}} }, } mock_response.raise_for_status = MagicMock() @@ -275,7 +275,7 @@ class TestNomaGuardrailHooks: ) assert exc_info.value.status_code == 400 - assert "harmfulContent" in exc_info.value.detail["details"]["prompt"] + assert "contentDetector" in exc_info.value.detail["details"]["prompt"] @pytest.mark.asyncio async def test_pre_call_hook_monitor_mode( @@ -504,7 +504,7 @@ class TestBackgroundProcessing: mock_response = MagicMock() mock_response.json.return_value = { "verdict": False, - "originalResponse": {"prompt": {"harmfulContent": {"result": True}}}, + "originalResponse": {"prompt": {"contentDetector": {"result": True}}}, } mock_response.raise_for_status = MagicMock() @@ -661,7 +661,7 @@ class TestBackgroundProcessing: """Test background verdict handling for blocked content""" response_json = { "verdict": False, - "originalResponse": {"prompt": {"harmfulContent": {"result": True}}}, + "originalResponse": {"prompt": {"contentDetector": {"result": True}}}, } with patch("litellm._logging.verbose_proxy_logger.warning") as mock_warning: @@ -803,3 +803,646 @@ class TestIntegration: ) ) assert len(custom_loggers) >= 2 + + +class TestNomaAnonymizationConfiguration: + """Test anonymize_input configuration parameter""" + + def test_init_with_anonymize_input_env_var(self): + """Test initialization with NOMA_ANONYMIZE_INPUT environment variable""" + with patch.dict( + os.environ, + { + "NOMA_ANONYMIZE_INPUT": "true", + }, + ): + guardrail = NomaGuardrail() + assert guardrail.anonymize_input is True + + with patch.dict( + os.environ, + { + "NOMA_ANONYMIZE_INPUT": "false", + }, + ): + guardrail = NomaGuardrail() + assert guardrail.anonymize_input is False + + def test_init_with_anonymize_input_default(self): + """Test default value for anonymize_input""" + guardrail = NomaGuardrail() + assert guardrail.anonymize_input is False + + def test_init_with_anonymize_input_param_override_env(self): + """Test that constructor param overrides environment variable""" + with patch.dict( + os.environ, + { + "NOMA_ANONYMIZE_INPUT": "true", + }, + ): + guardrail = NomaGuardrail(anonymize_input=False) + assert guardrail.anonymize_input is False + + def test_initialize_guardrail_with_anonymize_input(self): + """Test the initialize_guardrail function with anonymize_input""" + from litellm.types.guardrails import Guardrail, LitellmParams + + litellm_params = LitellmParams( + guardrail="noma", + mode="pre_call", + api_key="test-key", + anonymize_input=True, + ) + + guardrail = Guardrail( + guardrail_name="test-guardrail", + litellm_params=litellm_params, + ) + + with patch("litellm.logging_callback_manager.add_litellm_callback"): + result = initialize_guardrail(litellm_params, guardrail) + assert result.anonymize_input is True + + +class TestNomaAnonymizationLogic: + """Test the anonymization logic helper methods""" + + @pytest.fixture + def anonymize_guardrail(self): + """Create a guardrail with anonymize_input enabled""" + return NomaGuardrail( + api_key="test-api-key", + anonymize_input=True, + monitor_mode=False, + block_failures=True, + ) + + def test_is_result_true(self, anonymize_guardrail): + """Test _is_result_true helper method""" + assert anonymize_guardrail._is_result_true({"result": True}) is True + assert anonymize_guardrail._is_result_true({"result": False}) is False + assert anonymize_guardrail._is_result_true({"other": True}) is False + assert anonymize_guardrail._is_result_true(None) is False + assert anonymize_guardrail._is_result_true({}) is False + assert anonymize_guardrail._is_result_true("not a dict") is False + + def test_should_only_data_detector_failed_true(self, anonymize_guardrail): + """Test _should_only_sensitive_data_failed when only data detector triggered""" + classification = { + "dataDetector": { + "dataType1": {"result": True, "status": "SUCCESS"}, + "dataType2": {"result": True, "status": "SUCCESS"}, + "dataType3": {"result": False, "status": "SUCCESS"}, + }, + "contentDetector": {"result": False, "status": "SUCCESS"}, + "intentDetector": {"result": False, "status": "SUCCESS"}, + "code": {"result": False, "status": "SUCCESS"}, + } + + result = anonymize_guardrail._should_only_sensitive_data_failed(classification) + assert result is True + + def test_should_only_data_detector_failed_false_other_detectors(self, anonymize_guardrail): + """Test _should_only_sensitive_data_failed when other detectors also triggered""" + classification = { + "dataDetector": { + "dataType1": {"result": True, "status": "SUCCESS"}, + }, + "contentDetector": {"result": True, "status": "SUCCESS"}, # This should cause False + "intentDetector": {"result": False, "status": "SUCCESS"}, + } + + result = anonymize_guardrail._should_only_sensitive_data_failed(classification) + assert result is False + + def test_should_only_data_detector_failed_false_no_data_detected(self, anonymize_guardrail): + """Test _should_only_sensitive_data_failed when no data detected""" + classification = { + "dataDetector": { + "dataType1": {"result": False, "status": "SUCCESS"}, + "dataType2": {"result": False, "status": "SUCCESS"}, + }, + "contentDetector": {"result": False, "status": "SUCCESS"}, + "intentDetector": {"result": False, "status": "SUCCESS"}, + } + + result = anonymize_guardrail._should_only_sensitive_data_failed(classification) + assert result is False + + def test_should_only_data_detector_failed_with_nested_detectors(self, anonymize_guardrail): + """Test _should_only_sensitive_data_failed with nested detectors like topicDetector""" + classification = { + "dataDetector": { + "dataType1": {"result": True, "status": "SUCCESS"}, + }, + "topicDetector": { + "topic1": {"result": True, "status": "SUCCESS"}, # This should cause False + }, + "contentDetector": {"result": False, "status": "SUCCESS"}, + } + + result = anonymize_guardrail._should_only_sensitive_data_failed(classification) + assert result is False + + def test_extract_anonymized_content_user(self, anonymize_guardrail): + """Test _extract_anonymized_content for user messages""" + response_json = { + "originalResponse": { + "prompt": { + "anonymizedContent": { + "anonymized": "My email is ******* and phone is *******" + } + } + } + } + + result = anonymize_guardrail._extract_anonymized_content(response_json, "user") + assert result == "My email is ******* and phone is *******" + + def test_extract_anonymized_content_assistant(self, anonymize_guardrail): + """Test _extract_anonymized_content for assistant messages""" + response_json = { + "originalResponse": { + "response": { + "anonymizedContent": { + "anonymized": "I can't help with that request." + } + } + } + } + + result = anonymize_guardrail._extract_anonymized_content(response_json, "assistant") + assert result == "I can't help with that request." + + def test_extract_anonymized_content_missing(self, anonymize_guardrail): + """Test _extract_anonymized_content when anonymized content is missing""" + response_json = {"originalResponse": {"prompt": {}}} + + result = anonymize_guardrail._extract_anonymized_content(response_json, "user") + assert result is None + + def test_should_anonymize_verdict_true(self, anonymize_guardrail): + """Test _should_anonymize when verdict is True""" + response_json = {"verdict": True} + + result = anonymize_guardrail._should_anonymize(response_json, "user") + assert result is True + + def test_should_anonymize_verdict_false_only_sensitive(self, anonymize_guardrail): + """Test _should_anonymize when verdict is False but only data detector triggered""" + response_json = { + "verdict": False, + "originalResponse": { + "prompt": { + "dataDetector": {"dataType1": {"result": True}}, + "contentDetector": {"result": False}, + } + } + } + + result = anonymize_guardrail._should_anonymize(response_json, "user") + assert result is True + + def test_should_anonymize_verdict_false_other_detectors(self, anonymize_guardrail): + """Test _should_anonymize when verdict is False and other detectors triggered""" + response_json = { + "verdict": False, + "originalResponse": { + "prompt": { + "dataDetector": {"dataType1": {"result": True}}, + "contentDetector": {"result": True}, + } + } + } + + result = anonymize_guardrail._should_anonymize(response_json, "user") + assert result is False + + def test_should_anonymize_monitor_mode(self): + """Test _should_anonymize in monitor mode (should never anonymize)""" + guardrail = NomaGuardrail( + anonymize_input=True, + monitor_mode=True, + ) + + response_json = {"verdict": True} + result = guardrail._should_anonymize(response_json, "user") + assert result is False + + def test_should_anonymize_disabled(self): + """Test _should_anonymize when anonymize_input is disabled""" + guardrail = NomaGuardrail( + anonymize_input=False, + monitor_mode=False, + ) + + response_json = {"verdict": True} + result = guardrail._should_anonymize(response_json, "user") + assert result is False + + def test_replace_user_message_content(self, anonymize_guardrail): + """Test _replace_user_message_content""" + request_data = { + "messages": [ + {"role": "system", "content": "System prompt"}, + {"role": "user", "content": "My email is test@example.com"}, + {"role": "assistant", "content": "I can help you"}, + {"role": "user", "content": "My phone is 123-456-7890"}, + ] + } + + result = anonymize_guardrail._replace_user_message_content( + request_data, "My phone is *******" + ) + + # Should replace the last user message + assert result["messages"][-1]["content"] == "My phone is *******" + assert result["messages"][1]["content"] == "My email is test@example.com" # Unchanged + + def test_replace_llm_response_content(self, anonymize_guardrail): + """Test _replace_llm_response_content""" + response = ModelResponse( + id="test-id", + choices=[ + Choices( + finish_reason="stop", + index=0, + message=Message( + content="Your email is test@example.com", role="assistant" + ), + ) + ], + created=1234567890, + model="gpt-3.5-turbo", + object="chat.completion", + system_fingerprint=None, + usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}, + ) + + result = anonymize_guardrail._replace_llm_response_content( + response, "Your email is *******" + ) + + assert result.choices[0].message.content == "Your email is *******" + + +class TestNomaAnonymizationFlow: + """Test full anonymization flow with real Noma response objects""" + + @pytest.fixture + def anonymize_guardrail(self): + """Create a guardrail with anonymize_input enabled""" + return NomaGuardrail( + api_key="test-api-key", + api_base="https://api.test.noma.security/", + application_id="test-app", + anonymize_input=True, + monitor_mode=False, + block_failures=True, + guardrail_name="test-noma-guardrail", + event_hook="pre_call", + default_on=True, + ) + + @pytest.fixture + def mock_user_api_key_dict(self): + """Create a mock UserAPIKeyAuth object""" + return UserAPIKeyAuth( + user_id="test-user-id", + user_email="test@example.com", + key_name="test-key", + api_key="test-api-key", + permissions={}, + models=[], + spend=0.0, + metadata={}, + ) + + @pytest.mark.asyncio + async def test_anonymization_verdict_true_user_message( + self, anonymize_guardrail, mock_user_api_key_dict + ): + """Test anonymization when verdict=True for user message""" + request_data = { + "messages": [ + {"role": "user", "content": "My email is test@example.com"}, + ], + "litellm_call_id": "test-call-id", + "metadata": {"requester_ip_address": "192.168.1.1"}, + } + + # Mock simplified Noma API response with verdict=True and anonymized content + noma_response = { + "originalResponse": { + "prompt": { + "anonymizedContent": { + "anonymized": "My email is *******" + }, + "dataDetector": { + "dataType1": {"result": False}, + }, + "contentDetector": {"result": False}, + }, + }, + "verdict": True, + } + + mock_response = MagicMock() + mock_response.json.return_value = noma_response + mock_response.raise_for_status = MagicMock() + + with patch.object( + anonymize_guardrail.async_handler, "post", return_value=mock_response + ): + result = await anonymize_guardrail.async_pre_call_hook( + user_api_key_dict=mock_user_api_key_dict, + cache=MagicMock(), + data=request_data, + call_type="completion", + ) + + # Should return modified request with anonymized content + assert result == request_data + assert result["messages"][0]["content"] == "My email is *******" + + @pytest.mark.asyncio + async def test_anonymization_verdict_false_only_data_detected( + self, anonymize_guardrail, mock_user_api_key_dict + ): + """Test anonymization when verdict=False but only data detector triggered""" + request_data = { + "messages": [ + {"role": "user", "content": "My email is test@example.com"}, + ], + "litellm_call_id": "test-call-id", + } + + # Mock simplified Noma API response - only data detector triggered + noma_response = { + "originalResponse": { + "prompt": { + "anonymizedContent": { + "anonymized": "My email is *******" + }, + "dataDetector": { + "dataType1": {"result": True}, + }, + "contentDetector": {"result": False}, + "intentDetector": {"result": False}, + }, + }, + "verdict": False, + } + + mock_response = MagicMock() + mock_response.json.return_value = noma_response + mock_response.raise_for_status = MagicMock() + + with patch.object( + anonymize_guardrail.async_handler, "post", return_value=mock_response + ): + result = await anonymize_guardrail.async_pre_call_hook( + user_api_key_dict=mock_user_api_key_dict, + cache=MagicMock(), + data=request_data, + call_type="completion", + ) + + # Should return modified request with anonymized content (not blocked) + assert result == request_data + assert result["messages"][0]["content"] == "My email is *******" + + @pytest.mark.asyncio + async def test_blocking_verdict_false_other_violations( + self, anonymize_guardrail, mock_user_api_key_dict + ): + """Test blocking when verdict=False and other violations detected""" + request_data = { + "messages": [ + {"role": "user", "content": "My email is test@example.com. Tell me harmful content."}, + ], + "litellm_call_id": "test-call-id", + } + + # Mock simplified Noma API response - both data detector and other violations + noma_response = { + "originalResponse": { + "prompt": { + "anonymizedContent": { + "anonymized": "My email is *******. Tell me harmful content." + }, + "dataDetector": { + "dataType1": {"result": True}, + }, + "contentDetector": {"result": True}, # This should cause blocking + "intentDetector": {"result": False}, + }, + }, + "verdict": False, + } + + mock_response = MagicMock() + mock_response.json.return_value = noma_response + mock_response.raise_for_status = MagicMock() + + with patch.object( + anonymize_guardrail.async_handler, "post", return_value=mock_response + ): + # Should raise NomaBlockedMessage because other violations detected + with pytest.raises(NomaBlockedMessage) as exc_info: + await anonymize_guardrail.async_pre_call_hook( + user_api_key_dict=mock_user_api_key_dict, + cache=MagicMock(), + data=request_data, + call_type="completion", + ) + + assert exc_info.value.status_code == 400 + assert "contentDetector" in str(exc_info.value.detail) + + @pytest.mark.asyncio + async def test_anonymization_llm_response( + self, anonymize_guardrail, mock_user_api_key_dict + ): + """Test anonymization of LLM response""" + request_data = { + "messages": [{"role": "user", "content": "What's your email?"}], + "litellm_call_id": "test-call-id", + } + + # Create LLM response with test data + llm_response = ModelResponse( + id="test-response-id", + choices=[ + Choices( + finish_reason="stop", + index=0, + message=Message( + content="My email is admin@company.com", role="assistant" + ), + ) + ], + created=1234567890, + model="gpt-3.5-turbo", + object="chat.completion", + system_fingerprint=None, + usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}, + ) + + # Mock simplified Noma API response for LLM response check + noma_response = { + "originalResponse": { + "response": { + "anonymizedContent": { + "anonymized": "My email is *******" + }, + "dataDetector": { + "dataType1": {"result": True}, + }, + "contentDetector": {"result": False}, + }, + }, + "verdict": False, + } + + mock_response = MagicMock() + mock_response.json.return_value = noma_response + mock_response.raise_for_status = MagicMock() + + # Update guardrail to use post_call event hook + anonymize_guardrail.event_hook = "post_call" + + with patch.object( + anonymize_guardrail.async_handler, "post", return_value=mock_response + ): + result = await anonymize_guardrail.async_post_call_success_hook( + data=request_data, + user_api_key_dict=mock_user_api_key_dict, + response=llm_response, + ) + + # Should return modified response with anonymized content + assert result == llm_response + assert result.choices[0].message.content == "My email is *******" + + @pytest.mark.asyncio + async def test_no_anonymization_when_disabled( + self, mock_user_api_key_dict + ): + """Test that no anonymization occurs when anonymize_input=False""" + guardrail = NomaGuardrail( + api_key="test-api-key", + anonymize_input=False, # Disabled + monitor_mode=False, + block_failures=True, + ) + + request_data = { + "messages": [ + {"role": "user", "content": "My email is test@example.com"}, + ], + } + + noma_response = { + "originalResponse": { + "prompt": { + "anonymizedContent": { + "anonymized": "My email is *******" + }, + "dataDetector": { + "dataType1": {"result": True}, + }, + "contentDetector": {"result": False}, + }, + }, + "verdict": False, + } + + mock_response = MagicMock() + mock_response.json.return_value = noma_response + mock_response.raise_for_status = MagicMock() + + with patch.object( + guardrail.async_handler, "post", return_value=mock_response + ): + # Should raise NomaBlockedMessage because anonymization is disabled + with pytest.raises(NomaBlockedMessage): + await guardrail.async_pre_call_hook( + user_api_key_dict=mock_user_api_key_dict, + cache=MagicMock(), + data=request_data, + call_type="completion", + ) + + @pytest.mark.asyncio + async def test_no_anonymization_in_monitor_mode( + self, mock_user_api_key_dict + ): + """Test that no anonymization occurs in monitor mode""" + guardrail = NomaGuardrail( + api_key="test-api-key", + anonymize_input=True, + monitor_mode=True, # Monitor mode + block_failures=True, + ) + + request_data = { + "messages": [ + {"role": "user", "content": "My email is test@example.com"}, + ], + } + + with patch.object( + guardrail, "_create_background_noma_check" + ) as mock_create_background: + result = await guardrail.async_pre_call_hook( + user_api_key_dict=mock_user_api_key_dict, + cache=MagicMock(), + data=request_data, + call_type="completion", + ) + + # Should return original data unchanged + assert result == request_data + assert request_data["messages"][0]["content"] == "My email is test@example.com" + mock_create_background.assert_called_once() + + @pytest.mark.asyncio + async def test_anonymization_no_anonymized_content_available( + self, anonymize_guardrail, mock_user_api_key_dict + ): + """Test behavior when anonymized content is not available""" + request_data = { + "messages": [ + {"role": "user", "content": "My email is test@example.com"}, + ], + } + + noma_response = { + "originalResponse": { + "prompt": { + "dataDetector": { + "dataType1": {"result": True}, + }, + "contentDetector": {"result": False}, + }, + }, + "verdict": False, + } + + mock_response = MagicMock() + mock_response.json.return_value = noma_response + mock_response.raise_for_status = MagicMock() + + with patch.object( + anonymize_guardrail.async_handler, "post", return_value=mock_response + ): + # Should raise NomaBlockedMessage because no anonymized content available + with pytest.raises(NomaBlockedMessage): + await anonymize_guardrail.async_pre_call_hook( + user_api_key_dict=mock_user_api_key_dict, + cache=MagicMock(), + data=request_data, + call_type="completion", + ) From b83b497d387b29c3e82ade9a822e8ec0f2a5586a Mon Sep 17 00:00:00 2001 From: Tom Alon Date: Thu, 11 Sep 2025 13:38:06 +0300 Subject: [PATCH 04/24] PR fixes --- .../guardrails/guardrail_hooks/noma/noma.py | 23 ++---- .../guardrails/guardrail_hooks/test_noma.py | 70 +++++++++++++++++-- 2 files changed, 72 insertions(+), 21 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py index 2601a73927..22dffe8b06 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py +++ b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py @@ -28,6 +28,7 @@ from litellm.types.utils import EmbeddingResponse, ImageResponse # Constants USER_ROLE: Final[Literal["user"]] = "user" ASSISTANT_ROLE: Final[Literal["assistant"]] = "assistant" +SENSITIVE_DATA_DETECTOR_KEYS: Final[list[str]] = ["sensitiveData", "dataDetector"] # Type aliases MessageRole = Literal["user", "assistant"] @@ -96,7 +97,7 @@ class NomaBlockedMessage(HTTPException): if filtered_topics: result[key] = filtered_topics - elif key in ["sensitiveData", "dataDetector"] and isinstance(value, dict): + elif key in SENSITIVE_DATA_DETECTOR_KEYS and isinstance(value, dict): filtered_sensitive = {} for data_type, data_result in value.items(): if self._is_result_true(data_result): @@ -308,7 +309,7 @@ class NomaGuardrail(CustomGuardrail): sensitive_data_detected = False for key, value in classification_obj.items(): - if key in ["sensitiveData", "dataDetector"] and isinstance(value, dict): + if key in SENSITIVE_DATA_DETECTOR_KEYS and isinstance(value, dict): # Check if any sensitive data detector has result=true for data_type, data_result in value.items(): if self._is_result_true(data_result): @@ -411,20 +412,17 @@ class NomaGuardrail(CustomGuardrail): def _replace_user_message_content( self, request_data: dict, anonymized_content: str - ) -> dict: + ): """ Replace the user message content in request data with anonymized version. Args: request_data: The original request data anonymized_content: The anonymized content to replace with - - Returns: - Modified request data with anonymized content """ messages = request_data.get("messages", []) if not messages: - return request_data + return # Find and replace the last user message for i in range(len(messages) - 1, -1, -1): @@ -432,31 +430,24 @@ class NomaGuardrail(CustomGuardrail): messages[i]["content"] = anonymized_content break - return request_data - def _replace_llm_response_content( self, response: LLMResponse, anonymized_content: str - ) -> LLMResponse: + ): """ Replace the LLM response content with anonymized version. Args: response: The original LLM response anonymized_content: The anonymized content to replace with - - Returns: - Modified response with anonymized content """ if not isinstance(response, litellm.ModelResponse): - return response + return # Replace content in all choices for choice in response.choices: if isinstance(choice, litellm.Choices) and choice.message.content: choice.message.content = anonymized_content - return response - async def _check_user_message_background( self, request_data: dict, diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py index 895ad40faa..d98473a128 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py @@ -1052,13 +1052,13 @@ class TestNomaAnonymizationLogic: ] } - result = anonymize_guardrail._replace_user_message_content( + anonymize_guardrail._replace_user_message_content( request_data, "My phone is *******" ) # Should replace the last user message - assert result["messages"][-1]["content"] == "My phone is *******" - assert result["messages"][1]["content"] == "My email is test@example.com" # Unchanged + assert request_data["messages"][-1]["content"] == "My phone is *******" + assert request_data["messages"][1]["content"] == "My email is test@example.com" # Unchanged def test_replace_llm_response_content(self, anonymize_guardrail): """Test _replace_llm_response_content""" @@ -1080,11 +1080,11 @@ class TestNomaAnonymizationLogic: usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}, ) - result = anonymize_guardrail._replace_llm_response_content( + anonymize_guardrail._replace_llm_response_content( response, "Your email is *******" ) - assert result.choices[0].message.content == "Your email is *******" + assert response.choices[0].message.content == "Your email is *******" class TestNomaAnonymizationFlow: @@ -1446,3 +1446,63 @@ class TestNomaAnonymizationFlow: data=request_data, call_type="completion", ) + + @pytest.mark.asyncio + async def test_anonymization_llm_response_no_anonymized_content_available( + self, anonymize_guardrail, mock_user_api_key_dict + ): + """Test behavior when LLM response has no anonymized content available""" + request_data = { + "messages": [{"role": "user", "content": "What's your email?"}], + "litellm_call_id": "test-call-id", + } + + # Create LLM response with test data + llm_response = ModelResponse( + id="test-response-id", + choices=[ + Choices( + finish_reason="stop", + index=0, + message=Message( + content="My email is admin@company.com", role="assistant" + ), + ) + ], + created=1234567890, + model="gpt-3.5-turbo", + object="chat.completion", + system_fingerprint=None, + usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}, + ) + + # Mock Noma API response with no anonymized content available + noma_response = { + "originalResponse": { + "response": { + "dataDetector": { + "dataType1": {"result": True}, + }, + "contentDetector": {"result": False}, + }, + }, + "verdict": False, + } + + mock_response = MagicMock() + mock_response.json.return_value = noma_response + mock_response.raise_for_status = MagicMock() + + # Update guardrail to use post_call event hook + anonymize_guardrail.event_hook = "post_call" + + with patch.object( + anonymize_guardrail.async_handler, "post", return_value=mock_response + ): + # Should raise NomaBlockedMessage because no anonymized content available for LLM response + with pytest.raises(NomaBlockedMessage): + await anonymize_guardrail.async_post_call_success_hook( + data=request_data, + user_api_key_dict=mock_user_api_key_dict, + response=llm_response, + ) From c6626559a24a972754ac8dfa7c5d1d534a293965 Mon Sep 17 00:00:00 2001 From: Sashanken Date: Thu, 11 Sep 2025 13:05:48 -0700 Subject: [PATCH 05/24] Fixed Log Tab Key Alias filtering inaccurately for failed logs --- .../spend_management_endpoints.py | 9 + package-lock.json | 7979 ++++++++++++++++- package.json | 5 +- .../ui_unit_tests/log_filter_logic.test.tsx | 87 + .../ui_unit_tests/package-lock.json | 103 +- .../src/components/networking.tsx | 4 +- .../components/view_logs/log_filter_logic.tsx | 37 +- 7 files changed, 8186 insertions(+), 38 deletions(-) create mode 100644 tests/proxy_admin_ui_tests/ui_unit_tests/log_filter_logic.test.tsx diff --git a/litellm/proxy/spend_tracking/spend_management_endpoints.py b/litellm/proxy/spend_tracking/spend_management_endpoints.py index 7376c3a402..897341ad38 100644 --- a/litellm/proxy/spend_tracking/spend_management_endpoints.py +++ b/litellm/proxy/spend_tracking/spend_management_endpoints.py @@ -1659,6 +1659,9 @@ async def ui_view_spend_logs( # noqa: PLR0915 model: Optional[str] = fastapi.Query( default=None, description="Filter logs by model" ), + key_alias: Optional[str] = fastapi.Query( + default=None, description="Filter logs by key alias" + ), ): """ View spend logs for UI with pagination support @@ -1726,6 +1729,12 @@ async def ui_view_spend_logs( # noqa: PLR0915 if model is not None: where_conditions["model"] = model + + if key_alias is not None: + where_conditions["metadata"] = { + "path": ["user_api_key_alias"], + "string_contains": key_alias + } if min_spend is not None or max_spend is not None: where_conditions["spend"] = {} diff --git a/package-lock.json b/package-lock.json index b271c5aa62..1b3c2a690a 100644 --- a/package-lock.json +++ b/package-lock.json @@ -10,7 +10,2700 @@ "react-copy-to-clipboard": "^5.1.0" }, "devDependencies": { - "@types/react-copy-to-clipboard": "^5.0.7" + "@babel/core": "^7.28.4", + "@babel/preset-env": "^7.28.3", + "@babel/preset-react": "^7.27.1", + "@babel/preset-typescript": "^7.27.1", + "@testing-library/jest-dom": "^6.8.0", + "@testing-library/react": "^14.3.1", + "@types/react-copy-to-clipboard": "^5.0.7", + "babel-jest": "^30.1.2", + "jest": "^29.7.0", + 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"react-copy-to-clipboard": "^5.1.0" }, "devDependencies": { - "@types/react-copy-to-clipboard": "^5.0.7" + "@testing-library/jest-dom": "^6.8.0", + "@testing-library/react": "^14.3.1", + "@types/react-copy-to-clipboard": "^5.0.7", + "jest": "^29.7.0" } } diff --git a/tests/proxy_admin_ui_tests/ui_unit_tests/log_filter_logic.test.tsx b/tests/proxy_admin_ui_tests/ui_unit_tests/log_filter_logic.test.tsx new file mode 100644 index 0000000000..81a627c40d --- /dev/null +++ b/tests/proxy_admin_ui_tests/ui_unit_tests/log_filter_logic.test.tsx @@ -0,0 +1,87 @@ +import { uiSpendLogsCall } from '../../../ui/litellm-dashboard/src/components/networking'; + +// Mock the networking module +jest.mock('../../../ui/litellm-dashboard/src/components/networking', () => ({ + uiSpendLogsCall: jest.fn(), +})); + +const mockUiSpendLogsCall = uiSpendLogsCall as jest.MockedFunction; + +describe('Key Alias Filtering Integration Test', () => { + beforeEach(() => { + jest.clearAllMocks(); + }); + + it('should call API with correct key_alias parameter', async () => { + // Mock API response with both success and failure logs + const mockResponse = { + data: [ + { request_id: 'req-1', status: 'success', metadata: { user_api_key_alias: 'test-key' } }, + { request_id: 'req-2', status: 'failure', metadata: { user_api_key_alias: 'test-key' } } + ], + total: 2, + page: 1, + page_size: 50, + total_pages: 1 + }; + + mockUiSpendLogsCall.mockResolvedValueOnce(mockResponse); + + // Simulate the API call that would happen when filtering by key alias + const result = await uiSpendLogsCall( + 'test-token', + undefined, + undefined, + undefined, + '2024-01-15 09:00:00', + '2024-01-15 11:00:00', + 1, + 50, + undefined, + undefined, + undefined, + undefined, + 'test-key-alias' // key_alias - this is the fix + ); + + // Verify the API was called correctly + expect(mockUiSpendLogsCall).toHaveBeenCalledWith( + 'test-token', + undefined, + undefined, + undefined, + '2024-01-15 09:00:00', + '2024-01-15 11:00:00', + 1, + 50, + undefined, + undefined, + undefined, + undefined, + 'test-key-alias' // The key assertion - this parameter should be passed through + ); + + // Verify response contains both success and failure logs + expect(result.data).toHaveLength(2); + expect(result.data[0].status).toBe('success'); + expect(result.data[1].status).toBe('failure'); + }); + + it('should pass undefined for empty key alias', async () => { + mockUiSpendLogsCall.mockResolvedValueOnce({ data: [], total: 0, page: 1, page_size: 50, total_pages: 0 }); + + await uiSpendLogsCall( + 'test-token', undefined, undefined, undefined, + '2024-01-15 09:00:00', '2024-01-15 11:00:00', + 1, 50, undefined, undefined, undefined, undefined, + undefined // Empty string should become undefined + ); + + expect(mockUiSpendLogsCall).toHaveBeenCalledWith( + 'test-token', undefined, undefined, undefined, + '2024-01-15 09:00:00', '2024-01-15 11:00:00', + 1, 50, undefined, undefined, undefined, undefined, + undefined // Should be undefined for empty key alias + ); + }); +}); \ No newline at end of file diff --git a/tests/proxy_admin_ui_tests/ui_unit_tests/package-lock.json b/tests/proxy_admin_ui_tests/ui_unit_tests/package-lock.json index 6ced636f23..b8d706fcf8 100644 --- a/tests/proxy_admin_ui_tests/ui_unit_tests/package-lock.json +++ b/tests/proxy_admin_ui_tests/ui_unit_tests/package-lock.json @@ -8,13 +8,14 @@ "name": "ui-unit-tests", "version": "1.0.0", "dependencies": { - "antd": "^5.0.0", + "@ant-design/icons": "^5.0.0", + "antd": "^5.12.5", "react": "^18.2.0", "react-dom": "^18.2.0" }, "devDependencies": { + "@testing-library/jest-dom": "^6.0.0", "@testing-library/react": "^14.0.0", - "@types/antd": "^1.0.0", "@types/jest": "^29.5.0", "@types/react": "^18.2.0", "@types/react-dom": "^18.2.0", @@ -25,6 +26,13 @@ "typescript": "^5.0.0" } }, + "node_modules/@adobe/css-tools": { + "version": "4.4.4", + "resolved": "https://registry.npmjs.org/@adobe/css-tools/-/css-tools-4.4.4.tgz", + "integrity": "sha512-Elp+iwUx5rN5+Y8xLt5/GRoG20WGoDCQ/1Fb+1LiGtvwbDavuSk0jhD/eZdckHAuzcDzccnkv+rEjyWfRx18gg==", + "dev": true, + "license": "MIT" + }, "node_modules/@ampproject/remapping": { "version": "2.3.0", "resolved": "https://registry.npmjs.org/@ampproject/remapping/-/remapping-2.3.0.tgz", @@ -1167,6 +1175,33 @@ "node": ">=14" } }, + "node_modules/@testing-library/jest-dom": { + "version": "6.8.0", + "resolved": "https://registry.npmjs.org/@testing-library/jest-dom/-/jest-dom-6.8.0.tgz", + "integrity": "sha512-WgXcWzVM6idy5JaftTVC8Vs83NKRmGJz4Hqs4oyOuO2J4r/y79vvKZsb+CaGyCSEbUPI6OsewfPd0G1A0/TUZQ==", + "dev": true, + "license": "MIT", + "dependencies": { + "@adobe/css-tools": "^4.4.0", + "aria-query": "^5.0.0", + "css.escape": "^1.5.1", + "dom-accessibility-api": "^0.6.3", + "picocolors": "^1.1.1", + "redent": "^3.0.0" + }, + "engines": { + "node": ">=14", + "npm": ">=6", + "yarn": ">=1" + } + }, + "node_modules/@testing-library/jest-dom/node_modules/dom-accessibility-api": { + "version": "0.6.3", + "resolved": "https://registry.npmjs.org/dom-accessibility-api/-/dom-accessibility-api-0.6.3.tgz", + "integrity": "sha512-7ZgogeTnjuHbo+ct10G9Ffp0mif17idi0IyWNVA/wcwcm7NPOD/WEHVP3n7n3MhXqxoIYm8d6MuZohYWIZ4T3w==", + "dev": true, + "license": "MIT" + }, "node_modules/@testing-library/react": { "version": "14.3.1", "resolved": "https://registry.npmjs.org/@testing-library/react/-/react-14.3.1.tgz", @@ -1194,16 +1229,6 @@ "node": ">= 10" } }, - "node_modules/@types/antd": { - "version": "1.0.4", - "resolved": "https://registry.npmjs.org/@types/antd/-/antd-1.0.4.tgz", - "integrity": "sha512-gp4PGQckP1kNjj2H6juhjKIVwkpXwCIyIvOlwp2DC6geuhVpDHEEB5gwH4hJabVgBAFtrjBPJ58VIRV9VV9W2g==", - "deprecated": "This is a stub types definition. antd provides its own type definitions, so you do not need this installed.", - "dev": true, - "dependencies": { - "antd": "*" - } - }, "node_modules/@types/aria-query": { "version": "5.0.4", "resolved": "https://registry.npmjs.org/@types/aria-query/-/aria-query-5.0.4.tgz", @@ -2081,6 +2106,13 @@ "node": ">= 8" } }, + "node_modules/css.escape": { + "version": "1.5.1", + "resolved": "https://registry.npmjs.org/css.escape/-/css.escape-1.5.1.tgz", + "integrity": "sha512-YUifsXXuknHlUsmlgyY0PKzgPOr7/FjCePfHNt0jxm83wHZi44VDMQ7/fGNkjY3/jV1MC+1CmZbaHzugyeRtpg==", + "dev": true, + "license": "MIT" + }, "node_modules/cssom": { "version": "0.5.0", "resolved": "https://registry.npmjs.org/cssom/-/cssom-0.5.0.tgz", @@ -2974,6 +3006,16 @@ "node": ">=0.8.19" } }, + "node_modules/indent-string": { + "version": "4.0.0", + "resolved": "https://registry.npmjs.org/indent-string/-/indent-string-4.0.0.tgz", + "integrity": "sha512-EdDDZu4A2OyIK7Lr/2zG+w5jmbuk1DVBnEwREQvBzspBJkCEbRa8GxU1lghYcaGJCnRWibjDXlq779X1/y5xwg==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=8" + } + }, "node_modules/inflight": { "version": "1.0.6", "resolved": "https://registry.npmjs.org/inflight/-/inflight-1.0.6.tgz", @@ -4559,6 +4601,16 @@ "node": ">=6" } }, + "node_modules/min-indent": { + "version": "1.0.1", + "resolved": "https://registry.npmjs.org/min-indent/-/min-indent-1.0.1.tgz", + "integrity": "sha512-I9jwMn07Sy/IwOj3zVkVik2JTvgpaykDZEigL6Rx6N9LbMywwUSMtxET+7lVoDLLd3O3IXwJwvuuns8UB/HeAg==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=4" + } + }, "node_modules/minimatch": { "version": "3.1.2", "resolved": "https://registry.npmjs.org/minimatch/-/minimatch-3.1.2.tgz", @@ -5552,6 +5604,20 @@ "integrity": "sha512-w2GsyukL62IJnlaff/nRegPQR94C/XXamvMWmSHRJ4y7Ts/4ocGRmTHvOs8PSE6pB3dWOrD/nueuU5sduBsQ4w==", "dev": true }, + "node_modules/redent": { + "version": "3.0.0", + "resolved": "https://registry.npmjs.org/redent/-/redent-3.0.0.tgz", + "integrity": "sha512-6tDA8g98We0zd0GvVeMT9arEOnTw9qM03L9cJXaCjrip1OO764RDBLBfrB4cwzNGDj5OA5ioymC9GkizgWJDUg==", + "dev": true, + "license": "MIT", + "dependencies": { + "indent-string": "^4.0.0", + "strip-indent": "^3.0.0" + }, + "engines": { + "node": ">=8" + } + }, "node_modules/regenerator-runtime": { "version": "0.14.1", "resolved": "https://registry.npmjs.org/regenerator-runtime/-/regenerator-runtime-0.14.1.tgz", @@ -5965,6 +6031,19 @@ "node": ">=6" } }, + "node_modules/strip-indent": { + "version": "3.0.0", + "resolved": "https://registry.npmjs.org/strip-indent/-/strip-indent-3.0.0.tgz", + "integrity": "sha512-laJTa3Jb+VQpaC6DseHhF7dXVqHTfJPCRDaEbid/drOhgitgYku/letMUqOXFoWV0zIIUbjpdH2t+tYj4bQMRQ==", + "dev": true, + "license": "MIT", + "dependencies": { + "min-indent": "^1.0.0" + }, + "engines": { + "node": ">=8" + } + }, "node_modules/strip-json-comments": { "version": "3.1.1", "resolved": "https://registry.npmjs.org/strip-json-comments/-/strip-json-comments-3.1.1.tgz", diff --git a/ui/litellm-dashboard/src/components/networking.tsx b/ui/litellm-dashboard/src/components/networking.tsx index c204ca8f89..8855b1b843 100644 --- a/ui/litellm-dashboard/src/components/networking.tsx +++ b/ui/litellm-dashboard/src/components/networking.tsx @@ -2633,7 +2633,8 @@ export const uiSpendLogsCall = async ( user_id?: string, end_user?: string, status_filter?: string, - model?: string + model?: string, + keyAlias?: string ) => { try { // Construct base URL @@ -2652,6 +2653,7 @@ export const uiSpendLogsCall = async ( if (end_user) queryParams.append("end_user", end_user); if (status_filter) queryParams.append("status_filter", status_filter); if (model) queryParams.append("model", model); + if (keyAlias) queryParams.append("key_alias", keyAlias); // Append query parameters to URL if any exist const queryString = queryParams.toString(); if (queryString) { diff --git a/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.tsx b/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.tsx index 73b9d94408..e46e78bf35 100644 --- a/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.tsx +++ b/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.tsx @@ -60,6 +60,7 @@ export function useLogFilterLogic({ const performSearch = useCallback(async (filters: LogFilterState, page = 1) => { if (!accessToken) return; + console.log("Filters being sent to API:", filters); const currentTimestamp = Date.now(); lastSearchTimestamp.current = currentTimestamp; @@ -81,7 +82,8 @@ export function useLogFilterLogic({ filters[FILTER_KEYS.USER_ID] || undefined, filters[FILTER_KEYS.END_USER] || undefined, filters[FILTER_KEYS.STATUS] || undefined, - filters[FILTER_KEYS.MODEL] || undefined + filters[FILTER_KEYS.MODEL] || undefined, + filters[FILTER_KEYS.KEY_ALIAS] || undefined ); if (currentTimestamp === lastSearchTimestamp.current && response.data) { @@ -123,6 +125,19 @@ export function useLogFilterLogic({ }); return; } + + // Only do client-side filtering if no backend filters are active + const hasBackendFilters = + filters[FILTER_KEYS.KEY_ALIAS] || + filters[FILTER_KEYS.KEY_HASH] || + filters[FILTER_KEYS.REQUEST_ID] || + filters[FILTER_KEYS.USER_ID] || + filters[FILTER_KEYS.END_USER]; + + if (hasBackendFilters) { + // Backend is handling filtering, don't override the results + return; + } let filteredData = [...logs.data]; @@ -148,7 +163,7 @@ export function useLogFilterLogic({ log => log.model === filters[FILTER_KEYS.MODEL] ); } - + if (filters[FILTER_KEYS.KEY_HASH]) { filteredData = filteredData.filter( log => log.api_key === filters[FILTER_KEYS.KEY_HASH] @@ -161,24 +176,6 @@ export function useLogFilterLogic({ ); } - // Add key alias filtering - if (filters[FILTER_KEYS.KEY_ALIAS]) { - // We need to fetch the key info to get the key hash for the selected alias - try { - // Get the key hash for the selected alias - const selectedKey = filters[FILTER_KEYS.KEY_ALIAS] - - if (selectedKey) { - // Filter logs by the key hash - filteredData = filteredData.filter( - log => log.metadata?.user_api_key_alias === selectedKey - ); - } - } catch (error) { - console.error("Error fetching key info for alias:", error); - } - } - const newFilteredLogs: PaginatedResponse = { data: filteredData, total: logs.total, From 805069c287376cbc9ff82d08affb461bcbff36de Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Thu, 11 Sep 2025 18:52:02 -0700 Subject: [PATCH 06/24] fix(adapters/streaming_iterator.py): Don't send content block after message delta block is sent Fixes https://github.com/BerriAI/litellm/issues/14315 --- litellm/constants.py | 10 +- .../adapters/streaming_iterator.py | 133 +++---- litellm/proxy/_new_secret_config.yaml | 3 + .../hooks/parallel_request_limiter_v3.py | 42 ++- .../test_content_after_stop_reason.py | 343 ++++++++++++++++++ 5 files changed, 448 insertions(+), 83 deletions(-) create mode 100644 tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_content_after_stop_reason.py diff --git a/litellm/constants.py b/litellm/constants.py index 75c25d9ea9..c0ce0f265b 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -15,7 +15,7 @@ DEFAULT_SQS_FLUSH_INTERVAL_SECONDS = int( os.getenv("DEFAULT_SQS_FLUSH_INTERVAL_SECONDS", 10) ) DEFAULT_NUM_WORKERS_LITELLM_PROXY = int( - os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", os.cpu_count() or 4) + os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", 1) ) DEFAULT_SQS_BATCH_SIZE = int(os.getenv("DEFAULT_SQS_BATCH_SIZE", 512)) SQS_SEND_MESSAGE_ACTION = "SendMessage" @@ -60,7 +60,9 @@ DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO = int( os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO", 128) ) DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE = int( - os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE", 512) + os.getenv( + "DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE", 512 + ) ) # Generic fallback for unknown models @@ -949,7 +951,9 @@ LITELLM_CLI_SESSION_TOKEN_PREFIX = "litellm-session-token" DB_SPEND_UPDATE_JOB_NAME = "db_spend_update_job" PROMETHEUS_EMIT_BUDGET_METRICS_JOB_NAME = "prometheus_emit_budget_metrics" CLOUDZERO_EXPORT_USAGE_DATA_JOB_NAME = "cloudzero_export_usage_data" -CLOUDZERO_MAX_FETCHED_DATA_RECORDS = int(os.getenv("CLOUDZERO_MAX_FETCHED_DATA_RECORDS", 50000)) +CLOUDZERO_MAX_FETCHED_DATA_RECORDS = int( + os.getenv("CLOUDZERO_MAX_FETCHED_DATA_RECORDS", 50000) +) SPEND_LOG_CLEANUP_JOB_NAME = "spend_log_cleanup" SPEND_LOG_RUN_LOOPS = int(os.getenv("SPEND_LOG_RUN_LOOPS", 500)) SPEND_LOG_CLEANUP_BATCH_SIZE = int(os.getenv("SPEND_LOG_CLEANUP_BATCH_SIZE", 1000)) 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 aa95183bb6..e4191a945f 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py @@ -28,10 +28,6 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): TextBlock, ) - def __init__(self, completion_stream: Any, model: str): - super().__init__(completion_stream) - self.model = model - sent_first_chunk: bool = False sent_content_block_start: bool = False sent_content_block_finish: bool = False @@ -39,6 +35,7 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): sent_last_message: bool = False holding_chunk: Optional[Any] = None holding_stop_reason_chunk: Optional[Any] = None + queued_usage_chunk: bool = False current_content_block_index: int = 0 current_content_block_start: ContentBlockContentBlockDict = TextBlock( type="text", @@ -47,6 +44,10 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): pending_new_content_block: bool = False chunk_queue: deque = deque() # Queue for buffering multiple chunks + def __init__(self, completion_stream: Any, model: str): + super().__init__(completion_stream) + self.model = model + def __next__(self): from .transformation import LiteLLMAnthropicMessagesAdapter @@ -217,77 +218,83 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): # Queue the merged chunk and reset self.chunk_queue.append(merged_chunk) + self.queued_usage_chunk = True self.holding_stop_reason_chunk = None return self.chunk_queue.popleft() # Check if this processed chunk has a stop_reason - hold it for next chunk - if should_start_new_block and not self.sent_content_block_finish: - # Queue the sequence: content_block_stop -> content_block_start -> current_chunk + if not self.queued_usage_chunk: + if should_start_new_block and not self.sent_content_block_finish: + # Queue the sequence: content_block_stop -> content_block_start -> current_chunk - # 1. Stop current content block - self.chunk_queue.append( - { - "type": "content_block_stop", - "index": max(self.current_content_block_index - 1, 0), - } - ) + # 1. Stop current content block + self.chunk_queue.append( + { + "type": "content_block_stop", + "index": max(self.current_content_block_index - 1, 0), + } + ) - # 2. Start new content block - self.chunk_queue.append( - { - "type": "content_block_start", - "index": self.current_content_block_index, - "content_block": self.current_content_block_start, - } - ) + # 2. Start new content block + self.chunk_queue.append( + { + "type": "content_block_start", + "index": self.current_content_block_index, + "content_block": self.current_content_block_start, + } + ) - # 3. Queue the current chunk (don't lose it!) - self.chunk_queue.append(processed_chunk) - - # Reset state for new block - self.sent_content_block_finish = False - - # Return the first queued item - return self.chunk_queue.popleft() - - if ( - processed_chunk["type"] == "message_delta" - and self.sent_content_block_finish is False - ): - # Queue both the content_block_stop and the holding chunk - self.chunk_queue.append( - { - "type": "content_block_stop", - "index": self.current_content_block_index, - } - ) - self.sent_content_block_finish = True - if processed_chunk.get("delta", {}).get("stop_reason") is not None: - - self.holding_stop_reason_chunk = processed_chunk - else: + # 3. Queue the current chunk (don't lose it!) self.chunk_queue.append(processed_chunk) - return self.chunk_queue.popleft() - elif self.holding_chunk is not None: - # Queue both chunks - self.chunk_queue.append(self.holding_chunk) - self.chunk_queue.append(processed_chunk) - self.holding_chunk = None - return self.chunk_queue.popleft() - else: - # Queue the current chunk - self.chunk_queue.append(processed_chunk) - return self.chunk_queue.popleft() + + # Reset state for new block + self.sent_content_block_finish = False + + # Return the first queued item + return self.chunk_queue.popleft() + + if ( + processed_chunk["type"] == "message_delta" + and self.sent_content_block_finish is False + ): + # Queue both the content_block_stop and the holding chunk + self.chunk_queue.append( + { + "type": "content_block_stop", + "index": self.current_content_block_index, + } + ) + self.sent_content_block_finish = True + if ( + processed_chunk.get("delta", {}).get("stop_reason") + is not None + ): + + self.holding_stop_reason_chunk = processed_chunk + else: + self.chunk_queue.append(processed_chunk) + return self.chunk_queue.popleft() + elif self.holding_chunk is not None: + # Queue both chunks + self.chunk_queue.append(self.holding_chunk) + self.chunk_queue.append(processed_chunk) + self.holding_chunk = None + return self.chunk_queue.popleft() + else: + # Queue the current chunk + self.chunk_queue.append(processed_chunk) + return self.chunk_queue.popleft() # Handle any remaining held chunks after stream ends - if self.holding_stop_reason_chunk is not None: - self.chunk_queue.append(self.holding_stop_reason_chunk) - self.holding_stop_reason_chunk = None + if not self.queued_usage_chunk: + if self.holding_stop_reason_chunk is not None: + self.chunk_queue.append(self.holding_stop_reason_chunk) + self.holding_stop_reason_chunk = None - if self.holding_chunk is not None: - self.chunk_queue.append(self.holding_chunk) - self.holding_chunk = None + if self.holding_chunk is not None: + self.chunk_queue.append(self.holding_chunk) + self.holding_chunk = None if not self.sent_last_message: self.sent_last_message = True diff --git a/litellm/proxy/_new_secret_config.yaml b/litellm/proxy/_new_secret_config.yaml index c785dd05c4..691f67d577 100644 --- a/litellm/proxy/_new_secret_config.yaml +++ b/litellm/proxy/_new_secret_config.yaml @@ -7,3 +7,6 @@ model_list: - model_name: wildcard_models/* litellm_params: model: openai/* + - model_name: xai-grok-3 + litellm_params: + model: xai/grok-3 diff --git a/litellm/proxy/hooks/parallel_request_limiter_v3.py b/litellm/proxy/hooks/parallel_request_limiter_v3.py index b3840761d2..b3fa71a1c1 100644 --- a/litellm/proxy/hooks/parallel_request_limiter_v3.py +++ b/litellm/proxy/hooks/parallel_request_limiter_v3.py @@ -6,6 +6,7 @@ This is currently in development and not yet ready for production. import os from datetime import datetime +from math import floor from typing import ( TYPE_CHECKING, Any, @@ -17,7 +18,7 @@ from typing import ( Union, cast, ) -from math import floor + from fastapi import HTTPException from litellm import DualCache @@ -95,6 +96,7 @@ end return results """ + class RateLimitDescriptorRateLimitObject(TypedDict, total=False): requests_per_unit: Optional[int] tokens_per_unit: Optional[int] @@ -480,10 +482,15 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): }, ) ) - + # Team Member rate limits - if user_api_key_dict.user_id and (user_api_key_dict.team_member_rpm_limit is not None or user_api_key_dict.team_member_tpm_limit is not None): - team_member_value = f"{user_api_key_dict.team_id}:{user_api_key_dict.user_id}" + if user_api_key_dict.user_id and ( + user_api_key_dict.team_member_rpm_limit is not None + or user_api_key_dict.team_member_tpm_limit is not None + ): + team_member_value = ( + f"{user_api_key_dict.team_id}:{user_api_key_dict.user_id}" + ) descriptors.append( RateLimitDescriptor( key="team_member", @@ -557,13 +564,13 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): # Find which descriptor hit the limit for i, status in enumerate(response["statuses"]): if status["code"] == "OVER_LIMIT": - descriptor = descriptors[floor(i/2)] + descriptor = descriptors[floor(i / 2)] raise HTTPException( status_code=429, detail=f"Rate limit exceeded for {descriptor['key']}: {descriptor['value']}. Remaining: {status['limit_remaining']}", headers={ "retry-after": str(self.window_size), - "rate_limit_type": str(status["rate_limit_type"]) + "rate_limit_type": str(status["rate_limit_type"]), }, # Retry after 1 minute ) @@ -613,7 +620,9 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): # Check if script is available if self.token_increment_script is None: - verbose_proxy_logger.debug("TTL preservation script not available, using regular pipeline") + verbose_proxy_logger.debug( + "TTL preservation script not available, using regular pipeline" + ) await self.internal_usage_cache.dual_cache.async_increment_cache_pipeline( increment_list=pipeline_operations, litellm_parent_otel_span=parent_otel_span, @@ -628,7 +637,7 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): for op in pipeline_operations: # Convert None TTL to 0 for Lua script ttl_value = op["ttl"] if op["ttl"] is not None else 0 - + verbose_proxy_logger.debug( f"Executing TTL-preserving increment for key={op['key']}, " f"increment={op['increment_value']}, ttl={ttl_value}" @@ -693,16 +702,15 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): ) # Get metadata from kwargs - user_api_key = kwargs["litellm_params"]["metadata"].get("user_api_key") - user_api_key_user_id = kwargs["litellm_params"]["metadata"].get( - "user_api_key_user_id" + litellm_metadata = kwargs["litellm_params"]["metadata"] + if litellm_metadata is None: + return + user_api_key = litellm_metadata.get("user_api_key") + user_api_key_user_id = litellm_metadata.get("user_api_key_user_id") + user_api_key_team_id = litellm_metadata.get("user_api_key_team_id") + user_api_key_end_user_id = kwargs.get("user") or litellm_metadata.get( + "user_api_key_end_user_id" ) - user_api_key_team_id = kwargs["litellm_params"]["metadata"].get( - "user_api_key_team_id" - ) - user_api_key_end_user_id = kwargs.get("user") or kwargs["litellm_params"][ - "metadata" - ].get("user_api_key_end_user_id") model_group = get_model_group_from_litellm_kwargs(kwargs) # Get total tokens from response diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_content_after_stop_reason.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_content_after_stop_reason.py new file mode 100644 index 0000000000..4a170d666f --- /dev/null +++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_content_after_stop_reason.py @@ -0,0 +1,343 @@ +""" +Test for AnthropicStreamWrapper handling content blocks that exist after message_delta with stop_reason and usage. + +This tests the scenario where a streaming response includes: +1. Initial content blocks +2. A message_delta chunk with stop_reason and usage +3. Additional content blocks after the stop_reason + +The wrapper should properly handle this by: +- Holding the stop_reason chunk until usage is available +- Merging usage into the stop_reason chunk +- Properly managing content_block_stop/start events for subsequent content +""" + +import os +import sys +from typing import List + +import pytest + +sys.path.insert(0, os.path.abspath("../../../../..")) + +from litellm.llms.anthropic.experimental_pass_through.adapters.streaming_iterator import ( + AnthropicStreamWrapper, +) +from litellm.types.utils import Delta, ModelResponse, StreamingChoices, Usage + + +class MockCompletionStreamWithContentAfterStopReason: + """Mock stream that simulates content blocks existing after message_delta with stop_reason and usage.""" + + def __init__(self): + self.responses = [ + # Initial text content + ModelResponse( + stream=True, + choices=[ + StreamingChoices( + delta=Delta(content="Hello"), index=0, finish_reason=None + ) + ], + ), + ModelResponse( + stream=True, + choices=[ + StreamingChoices( + delta=Delta(content=" world"), index=0, finish_reason=None + ) + ], + ), + # Message delta with stop_reason AND usage (this is how it actually comes from the API) + ModelResponse( + stream=True, + choices=[ + StreamingChoices( + delta=Delta(content=""), index=0, finish_reason="stop" + ) + ], + usage=Usage(prompt_tokens=230, completion_tokens=65, total_tokens=295), + ), + # Additional content after the stop_reason - this simulates the scenario + # where there might be additional content blocks after the main response + ModelResponse( + stream=True, + choices=[ + StreamingChoices( + delta=Delta(content=" Additional content"), + index=0, + finish_reason=None, + ) + ], + ), + ] + self.index = 0 + + def __iter__(self): + return self + + def __next__(self): + if self.index >= len(self.responses): + raise StopIteration + response = self.responses[self.index] + self.index += 1 + return response + + def __aiter__(self): + return self + + async def __anext__(self): + if self.index >= len(self.responses): + raise StopAsyncIteration + response = self.responses[self.index] + self.index += 1 + return response + + +def test_anthropic_stream_wrapper_content_after_stop_reason(): + """Test that AnthropicStreamWrapper properly handles content blocks after message_delta with stop_reason.""" + + wrapper = AnthropicStreamWrapper( + completion_stream=MockCompletionStreamWithContentAfterStopReason(), + model="claude-3", + ) + + chunks = [] + chunk_types = [] + + # Collect all chunks + for chunk in wrapper: + chunks.append(chunk) + chunk_types.append(chunk.get("type")) + + # Verify the expected sequence of chunk types + expected_types = [ + "message_start", # Initial message start + "content_block_start", # Start of first content block + "content_block_delta", # "Hello" + "content_block_delta", # " world" + "content_block_stop", # End of first content block due to stop_reason + "message_delta", # Stop reason with merged usage + "message_stop", # Final message stop + ] + + print(f"Actual chunk types: {chunk_types}") + print(f"Expected chunk types: {expected_types}") + + # Verify we have the expected number of chunks + assert len(chunk_types) >= len( + expected_types + ), f"Expected at least {len(expected_types)} chunks, got {len(chunk_types)}" + + # Verify key chunk types are present + assert "message_start" in chunk_types + assert "content_block_start" in chunk_types + assert "content_block_delta" in chunk_types + assert "content_block_stop" in chunk_types + assert "message_delta" in chunk_types + assert "message_stop" in chunk_types + + # Find the message_delta chunk with stop_reason + message_delta_chunk = None + for chunk in chunks: + if chunk.get("type") == "message_delta": + message_delta_chunk = chunk + break + + assert message_delta_chunk is not None, "message_delta chunk not found" + + # Verify that the message_delta chunk has both stop_reason and usage + delta = message_delta_chunk.get("delta", {}) + usage = message_delta_chunk.get("usage", {}) + + assert ( + delta.get("stop_reason") == "end_turn" + ), f"Expected stop_reason 'end_turn', got {delta.get('stop_reason')}" + assert ( + usage.get("input_tokens") == 230 + ), f"Expected input_tokens 230, got {usage.get('input_tokens')}" + assert ( + usage.get("output_tokens") == 65 + ), f"Expected output_tokens 65, got {usage.get('output_tokens')}" + + # Verify content_block_stop comes before message_delta + content_block_stop_index = None + message_delta_index = None + + for i, chunk_type in enumerate(chunk_types): + if chunk_type == "content_block_stop" and content_block_stop_index is None: + content_block_stop_index = i + elif chunk_type == "message_delta": + message_delta_index = i + + assert content_block_stop_index is not None, "content_block_stop not found" + assert message_delta_index is not None, "message_delta not found" + assert ( + content_block_stop_index < message_delta_index + ), "content_block_stop should come before message_delta" + + +@pytest.mark.asyncio +async def test_async_anthropic_stream_wrapper_content_after_stop_reason(): + """Test async version of AnthropicStreamWrapper handling content blocks after message_delta with stop_reason.""" + + wrapper = AnthropicStreamWrapper( + completion_stream=MockCompletionStreamWithContentAfterStopReason(), + model="claude-3", + ) + + chunks = [] + chunk_types = [] + + # Collect all chunks asynchronously + async for chunk in wrapper: + chunks.append(chunk) + chunk_types.append(chunk.get("type")) + + print(f"Async - Actual chunk types: {chunk_types}") + + # Verify key chunk types are present + assert "message_start" in chunk_types + assert "content_block_start" in chunk_types + assert "content_block_delta" in chunk_types + assert "content_block_stop" in chunk_types + assert "message_delta" in chunk_types + assert "message_stop" in chunk_types + + # Find the message_delta chunk with stop_reason + message_delta_chunk = None + for chunk in chunks: + if chunk.get("type") == "message_delta": + message_delta_chunk = chunk + break + + assert message_delta_chunk is not None, "message_delta chunk not found" + + # Verify that the message_delta chunk has both stop_reason and usage + delta = message_delta_chunk.get("delta", {}) + usage = message_delta_chunk.get("usage", {}) + + assert ( + delta.get("stop_reason") == "end_turn" + ), f"Expected stop_reason 'end_turn', got {delta.get('stop_reason')}" + assert ( + usage.get("input_tokens") == 230 + ), f"Expected input_tokens 230, got {usage.get('input_tokens')}" + assert ( + usage.get("output_tokens") == 65 + ), f"Expected output_tokens 65, got {usage.get('output_tokens')}" + + +def test_usage_merging_behavior(): + """Test that usage information is properly merged with stop_reason chunk.""" + + wrapper = AnthropicStreamWrapper( + completion_stream=MockCompletionStreamWithContentAfterStopReason(), + model="claude-3", + ) + + # Process chunks and look specifically for the usage merging behavior + chunks = [] + for chunk in wrapper: + chunks.append(chunk) + # If this is a message_delta with stop_reason, verify it has usage + if ( + chunk.get("type") == "message_delta" + and chunk.get("delta", {}).get("stop_reason") is not None + ): + + usage = chunk.get("usage", {}) + assert ( + usage.get("input_tokens") is not None + ), "Usage should be merged with stop_reason chunk" + assert ( + usage.get("output_tokens") is not None + ), "Usage should be merged with stop_reason chunk" + break + + +def test_sse_wrapper_with_content_after_stop_reason(): + """Test SSE wrapper formatting for the content after stop_reason scenario.""" + + wrapper = AnthropicStreamWrapper( + completion_stream=MockCompletionStreamWithContentAfterStopReason(), + model="claude-3", + ) + + # Get SSE formatted chunks + sse_chunks = [] + for chunk in wrapper.anthropic_sse_wrapper(): + sse_chunks.append(chunk) + if len(sse_chunks) >= 10: # Limit to avoid infinite loops in tests + break + + # Verify all chunks are properly formatted as bytes + for chunk in sse_chunks: + assert isinstance(chunk, bytes), "SSE chunks should be bytes" + + # Decode and verify SSE format + chunk_str = chunk.decode("utf-8") + lines = chunk_str.split("\n") + + # Should have event and data lines + assert any( + line.startswith("event: ") for line in lines + ), f"Missing event line in: {chunk_str}" + assert any( + line.startswith("data: ") for line in lines + ), f"Missing data line in: {chunk_str}" + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_with_content_after_stop_reason(): + """Test async SSE wrapper formatting for the content after stop_reason scenario.""" + + wrapper = AnthropicStreamWrapper( + completion_stream=MockCompletionStreamWithContentAfterStopReason(), + model="claude-3", + ) + + # Get SSE formatted chunks asynchronously + sse_chunks = [] + async for chunk in wrapper.async_anthropic_sse_wrapper(): + sse_chunks.append(chunk) + if len(sse_chunks) >= 10: # Limit to avoid infinite loops in tests + break + + # Verify all chunks are properly formatted as bytes + for chunk in sse_chunks: + assert isinstance(chunk, bytes), "Async SSE chunks should be bytes" + + # Decode and verify SSE format + chunk_str = chunk.decode("utf-8") + lines = chunk_str.split("\n") + + # Should have event and data lines + assert any( + line.startswith("event: ") for line in lines + ), f"Missing event line in: {chunk_str}" + assert any( + line.startswith("data: ") for line in lines + ), f"Missing data line in: {chunk_str}" + + +if __name__ == "__main__": + # Run a quick test + test_anthropic_stream_wrapper_content_after_stop_reason() + print("✅ Sync test passed") + + import asyncio + + asyncio.run(test_async_anthropic_stream_wrapper_content_after_stop_reason()) + print("✅ Async test passed") + + test_usage_merging_behavior() + print("✅ Usage merging test passed") + + test_sse_wrapper_with_content_after_stop_reason() + print("✅ SSE wrapper test passed") + + asyncio.run(test_async_sse_wrapper_with_content_after_stop_reason()) + print("✅ Async SSE wrapper test passed") + + print("🎉 All tests passed!") From 0f6898ad0a9d8769bb26038f80107b309c8fb91e Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Thu, 11 Sep 2025 19:10:00 -0700 Subject: [PATCH 07/24] fix(key_management_endpoints.py): check if key is a hashed token or sk key before lookup Fixes https://github.com/BerriAI/litellm/issues/13887 --- .../key_management_endpoints.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/litellm/proxy/management_endpoints/key_management_endpoints.py b/litellm/proxy/management_endpoints/key_management_endpoints.py index bd8faf34be..a7f15cc6c1 100644 --- a/litellm/proxy/management_endpoints/key_management_endpoints.py +++ b/litellm/proxy/management_endpoints/key_management_endpoints.py @@ -346,6 +346,7 @@ def handle_key_type(data: GenerateKeyRequest, data_json: dict) -> dict: data_json["allowed_routes"] = ["info_routes"] return data_json + async def validate_team_id_used_in_service_account_request( team_id: Optional[str], prisma_client: Optional[PrismaClient], @@ -358,13 +359,13 @@ async def validate_team_id_used_in_service_account_request( status_code=400, detail="team_id is required for service account keys. Please specify `team_id` in the request body.", ) - + if prisma_client is None: raise HTTPException( status_code=400, detail="prisma_client is required for service account keys. Please specify `prisma_client` in the request body.", ) - + # check if team_id exists in the database team = await prisma_client.db.litellm_teamtable.find_unique( where={"team_id": team_id}, @@ -376,6 +377,7 @@ async def validate_team_id_used_in_service_account_request( ) return True + async def _common_key_generation_helper( # noqa: PLR0915 data: GenerateKeyRequest, user_api_key_dict: UserAPIKeyAuth, @@ -557,7 +559,7 @@ async def _common_key_generation_helper( # noqa: PLR0915 status_code=400, detail={ "error": f"Invalid key format. LiteLLM Virtual Key must start with 'sk-'. Received: {data.key}" - } + }, ) response = await generate_key_helper_fn( @@ -2885,7 +2887,10 @@ async def unblock_key( param="key", code=status.HTTP_400_BAD_REQUEST, ) - hashed_token = hash_token(token=data.key) + if data.key.startswith("sk-"): + hashed_token = hash_token(token=data.key) + else: + hashed_token = data.key if litellm.store_audit_logs is True: # make an audit log for key update From 0c8b311155f01f5075ca4deb09ac3a5b390ad2c6 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Thu, 11 Sep 2025 19:15:15 -0700 Subject: [PATCH 08/24] test: add unit testing for both flows on key unblock --- .../test_key_management_endpoints.py | 245 +++++++++++++++--- 1 file changed, 202 insertions(+), 43 deletions(-) diff --git a/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py index 3a597adef0..2747fab77f 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py @@ -183,7 +183,9 @@ async def test_budget_reset_and_expires_at_first_of_month(monkeypatch): assert ( response_date.month == expected_month ), f"Expected month {expected_month}, got {response_date.month} for {key}" - assert response_date.day == 1, f"Expected day 1, got {response_date.day} for {key}" + assert ( + response_date.day == 1 + ), f"Expected day 1, got {response_date.day} for {key}" @pytest.mark.asyncio @@ -507,7 +509,6 @@ def test_get_new_token_with_invalid_key(): assert "New key must start with 'sk-'" in str(exc_info.value.detail) - @pytest.mark.asyncio async def test_generate_service_account_requires_team_id(): with pytest.raises(HTTPException): @@ -529,11 +530,12 @@ async def test_generate_service_account_works_with_team_id(): from unittest.mock import patch # Mock the database and router dependencies from proxy_server - with patch('litellm.proxy.proxy_server.prisma_client') as mock_prisma, \ - patch('litellm.proxy.proxy_server.llm_router') as mock_router, \ - patch('litellm.proxy.proxy_server.premium_user', False), \ - patch('litellm.proxy.management_endpoints.key_management_endpoints.generate_key_helper_fn') as mock_generate_key: - + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.llm_router" + ) as mock_router, patch("litellm.proxy.proxy_server.premium_user", False), patch( + "litellm.proxy.management_endpoints.key_management_endpoints.generate_key_helper_fn" + ) as mock_generate_key: + # Configure mocks mock_prisma.return_value = AsyncMock() mock_router.return_value = None @@ -542,9 +544,9 @@ async def test_generate_service_account_works_with_team_id(): "key": "sk-test-key", "expires": None, "user_id": "test-user", - "team_id": "IJ" + "team_id": "IJ", } - + # This should not raise an exception since team_id is provided await _common_key_generation_helper( data=GenerateKeyRequest( @@ -559,7 +561,6 @@ async def test_generate_service_account_works_with_team_id(): ) - @pytest.mark.asyncio async def test_update_service_account_requires_team_id(): data = UpdateKeyRequest(key="sk-1", metadata={"service_account_id": "sa"}) @@ -571,7 +572,9 @@ async def test_update_service_account_requires_team_id(): @pytest.mark.asyncio async def test_update_service_account_works_with_team_id(): - data = UpdateKeyRequest(key="sk-1", metadata={"service_account_id": "sa"}, team_id="IJ") + data = UpdateKeyRequest( + key="sk-1", metadata={"service_account_id": "sa"}, team_id="IJ" + ) existing_key = LiteLLM_VerificationToken(token="hashed") await prepare_key_update_data(data=data, existing_key_row=existing_key) @@ -580,22 +583,22 @@ async def test_update_service_account_works_with_team_id(): @pytest.mark.asyncio async def test_validate_team_id_used_in_service_account_request_requires_team_id(): """ - Test that validate_team_id_used_in_service_account_request raises HTTPException + Test that validate_team_id_used_in_service_account_request raises HTTPException when team_id is None for service account key generation. """ from litellm.proxy.management_endpoints.key_management_endpoints import ( validate_team_id_used_in_service_account_request, ) - + mock_prisma_client = AsyncMock() - + # Test that HTTPException is raised when team_id is None with pytest.raises(HTTPException) as exc_info: await validate_team_id_used_in_service_account_request( team_id=None, prisma_client=mock_prisma_client, ) - + assert exc_info.value.status_code == 400 assert "team_id is required for service account keys" in str(exc_info.value.detail) @@ -603,7 +606,7 @@ async def test_validate_team_id_used_in_service_account_request_requires_team_id @pytest.mark.asyncio async def test_validate_team_id_used_in_service_account_request_requires_prisma_client(): """ - Test that validate_team_id_used_in_service_account_request raises HTTPException + Test that validate_team_id_used_in_service_account_request raises HTTPException when prisma_client is None for service account key generation. """ from litellm.proxy.management_endpoints.key_management_endpoints import ( @@ -616,78 +619,76 @@ async def test_validate_team_id_used_in_service_account_request_requires_prisma_ team_id="test-team-id", prisma_client=None, ) - + assert exc_info.value.status_code == 400 - assert "prisma_client is required for service account keys" in str(exc_info.value.detail) + assert "prisma_client is required for service account keys" in str( + exc_info.value.detail + ) @pytest.mark.asyncio async def test_validate_team_id_used_in_service_account_request_checks_team_exists(): """ - Test that validate_team_id_used_in_service_account_request validates that + Test that validate_team_id_used_in_service_account_request validates that the team_id exists in the database for service account key generation. """ from litellm.proxy.management_endpoints.key_management_endpoints import ( validate_team_id_used_in_service_account_request, ) - + mock_prisma_client = AsyncMock() - + # Mock the database query to return None (team doesn't exist) mock_find_unique = AsyncMock(return_value=None) mock_prisma_client.db.litellm_teamtable.find_unique = mock_find_unique - + # Test that HTTPException is raised when team doesn't exist in DB with pytest.raises(HTTPException) as exc_info: await validate_team_id_used_in_service_account_request( team_id="non-existent-team-id", prisma_client=mock_prisma_client, ) - + assert exc_info.value.status_code == 400 assert "team_id does not exist in the database" in str(exc_info.value.detail) - + # Verify the database was queried with the correct parameters - mock_find_unique.assert_called_once_with( - where={"team_id": "non-existent-team-id"} - ) + mock_find_unique.assert_called_once_with(where={"team_id": "non-existent-team-id"}) @pytest.mark.asyncio async def test_validate_team_id_used_in_service_account_request_success(): """ - Test that validate_team_id_used_in_service_account_request returns True + Test that validate_team_id_used_in_service_account_request returns True when team_id exists in the database for service account key generation. """ from litellm.proxy.management_endpoints.key_management_endpoints import ( validate_team_id_used_in_service_account_request, ) - + mock_prisma_client = AsyncMock() - + # Mock the database query to return a team object (team exists) mock_team = {"team_id": "existing-team-id", "team_name": "Test Team"} mock_find_unique = AsyncMock(return_value=mock_team) mock_prisma_client.db.litellm_teamtable.find_unique = mock_find_unique - + # Test that function returns True when team exists result = await validate_team_id_used_in_service_account_request( team_id="existing-team-id", prisma_client=mock_prisma_client, ) - + assert result is True - + # Verify the database was queried with the correct parameters - mock_find_unique.assert_called_once_with( - where={"team_id": "existing-team-id"} - ) + mock_find_unique.assert_called_once_with(where={"team_id": "existing-team-id"}) @pytest.mark.asyncio async def test_generate_service_account_key_endpoint_validation(): """ - Test that the /key/service-account/generate endpoint properly validates + Test that the /key/service-account/generate endpoint properly validates team_id requirement and team existence in database. """ from unittest.mock import patch @@ -705,16 +706,16 @@ async def test_generate_service_account_key_endpoint_validation(): ), litellm_changed_by=None, ) - + assert exc_info.value.status_code == 400 assert "team_id is required for service account keys" in str(exc_info.value.detail) - - # Test case 2: Team doesn't exist in database - with patch('litellm.proxy.proxy_server.prisma_client') as mock_prisma: + + # Test case 2: Team doesn't exist in database + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma: # Mock team not found mock_find_unique = AsyncMock(return_value=None) mock_prisma.db.litellm_teamtable.find_unique = mock_find_unique - + with pytest.raises(HTTPException) as exc_info: await generate_service_account_key_fn( data=GenerateKeyRequest(team_id="non-existent-team"), @@ -723,7 +724,165 @@ async def test_generate_service_account_key_endpoint_validation(): ), litellm_changed_by=None, ) - + assert exc_info.value.status_code == 400 assert "team_id does not exist in the database" in str(exc_info.value.detail) + +@pytest.mark.asyncio +async def test_unblock_key_supports_both_sk_and_hashed_tokens(monkeypatch): + """ + Test that the unblock_key endpoint correctly handles both sk- prefixed tokens + and hashed tokens by properly converting sk- tokens to hashed format before + database operations. + """ + from unittest.mock import AsyncMock, MagicMock + + from litellm.proxy._types import BlockKeyRequest + from litellm.proxy.management_endpoints.key_management_endpoints import unblock_key + + # Mock dependencies + mock_prisma_client = AsyncMock() + mock_user_api_key_cache = MagicMock() + mock_proxy_logging_obj = MagicMock() + + # Use a proper 64-character hex hash for testing + test_hashed_token = ( + "a1b2c3d4e5f6789012345678901234567890123456789012345678901234abcd" + ) + + # Mock the key record that will be returned from database + mock_key_record = MagicMock() + mock_key_record.token = test_hashed_token + mock_key_record.blocked = False + mock_key_record.model_dump_json.return_value = ( + f'{{"token": "{test_hashed_token}", "blocked": false}}' + ) + + # Mock database operations + mock_prisma_client.db.litellm_verificationtoken.find_unique = AsyncMock( + return_value=mock_key_record + ) + mock_prisma_client.db.litellm_verificationtoken.update = AsyncMock( + return_value=mock_key_record + ) + + # Mock get_key_object and _cache_key_object functions + mock_key_object = MagicMock() + mock_key_object.blocked = True # Initially blocked + + # Mock hash_token function + def mock_hash_token(token): + if token == "sk-test123456789": + return test_hashed_token + return token + + # Apply monkeypatch + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + monkeypatch.setattr( + "litellm.proxy.proxy_server.user_api_key_cache", mock_user_api_key_cache + ) + monkeypatch.setattr( + "litellm.proxy.proxy_server.proxy_logging_obj", mock_proxy_logging_obj + ) + monkeypatch.setattr("litellm.proxy.proxy_server.hash_token", mock_hash_token) + monkeypatch.setattr( + "litellm.store_audit_logs", False + ) # Disable audit logs for simpler test + + # Mock get_key_object and _cache_key_object + async def mock_get_key_object(**kwargs): + return mock_key_object + + async def mock_cache_key_object(**kwargs): + pass + + monkeypatch.setattr( + "litellm.proxy.management_endpoints.key_management_endpoints.get_key_object", + mock_get_key_object, + ) + monkeypatch.setattr( + "litellm.proxy.management_endpoints.key_management_endpoints._cache_key_object", + mock_cache_key_object, + ) + + # Create mock request and user auth + mock_request = MagicMock() + user_api_key_dict = UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, api_key="sk-admin", user_id="admin_user" + ) + + # Test Case 1: Using sk- prefixed token + sk_token_request = BlockKeyRequest(key="sk-test123456789") + + result = await unblock_key( + data=sk_token_request, + http_request=mock_request, + user_api_key_dict=user_api_key_dict, + litellm_changed_by=None, + ) + + # Verify that the database update was called with hashed token + mock_prisma_client.db.litellm_verificationtoken.update.assert_called_with( + where={"token": test_hashed_token}, data={"blocked": False} + ) + + assert result == mock_key_record + assert mock_key_object.blocked == False # Should be updated to unblocked + + # Reset mocks for second test + mock_prisma_client.db.litellm_verificationtoken.update.reset_mock() + mock_key_object.blocked = True # Reset to blocked state + + # Test Case 2: Using already hashed token + hashed_token_request = BlockKeyRequest(key=test_hashed_token) + + result = await unblock_key( + data=hashed_token_request, + http_request=mock_request, + user_api_key_dict=user_api_key_dict, + litellm_changed_by=None, + ) + + # Verify that the database update was called with the same hashed token + mock_prisma_client.db.litellm_verificationtoken.update.assert_called_with( + where={"token": test_hashed_token}, data={"blocked": False} + ) + + assert result == mock_key_record + assert mock_key_object.blocked == False # Should be updated to unblocked + + +@pytest.mark.asyncio +async def test_unblock_key_invalid_key_format(monkeypatch): + """ + Test that unblock_key properly validates key format and raises appropriate errors + for invalid keys. + """ + from litellm.proxy._types import BlockKeyRequest + from litellm.proxy.management_endpoints.key_management_endpoints import unblock_key + from litellm.proxy.utils import ProxyException + + # Mock prisma_client to avoid DB connection error + mock_prisma_client = AsyncMock() + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + + # Mock request and user auth + mock_request = MagicMock() + user_api_key_dict = UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, api_key="sk-admin", user_id="admin_user" + ) + + # Test with invalid key format + invalid_key_request = BlockKeyRequest(key="invalid-key-format") + + with pytest.raises(ProxyException) as exc_info: + await unblock_key( + data=invalid_key_request, + http_request=mock_request, + user_api_key_dict=user_api_key_dict, + litellm_changed_by=None, + ) + + assert exc_info.value.code == "400" + assert "Invalid key format" in str(exc_info.value.message) From d89152bb2ccce4ab7713091715dd0abc11dd9ce3 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Thu, 11 Sep 2025 20:04:24 -0700 Subject: [PATCH 09/24] feat(litellm_logging.py): support new litellm debug parameter - `litellm_request_debug` on requests enables printing raw request when flag is set to true on requests --- litellm/constants.py | 10 +++-- .../litellm_core_utils/get_litellm_params.py | 2 + litellm/litellm_core_utils/litellm_logging.py | 45 +++++++++++++------ litellm/main.py | 44 ++++++++++++------ litellm/types/utils.py | 3 +- 5 files changed, 73 insertions(+), 31 deletions(-) diff --git a/litellm/constants.py b/litellm/constants.py index 75c25d9ea9..c0ce0f265b 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -15,7 +15,7 @@ DEFAULT_SQS_FLUSH_INTERVAL_SECONDS = int( os.getenv("DEFAULT_SQS_FLUSH_INTERVAL_SECONDS", 10) ) DEFAULT_NUM_WORKERS_LITELLM_PROXY = int( - os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", os.cpu_count() or 4) + os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", 1) ) DEFAULT_SQS_BATCH_SIZE = int(os.getenv("DEFAULT_SQS_BATCH_SIZE", 512)) SQS_SEND_MESSAGE_ACTION = "SendMessage" @@ -60,7 +60,9 @@ DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO = int( os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO", 128) ) DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE = int( - os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE", 512) + os.getenv( + "DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE", 512 + ) ) # Generic fallback for unknown models @@ -949,7 +951,9 @@ LITELLM_CLI_SESSION_TOKEN_PREFIX = "litellm-session-token" DB_SPEND_UPDATE_JOB_NAME = "db_spend_update_job" PROMETHEUS_EMIT_BUDGET_METRICS_JOB_NAME = "prometheus_emit_budget_metrics" CLOUDZERO_EXPORT_USAGE_DATA_JOB_NAME = "cloudzero_export_usage_data" -CLOUDZERO_MAX_FETCHED_DATA_RECORDS = int(os.getenv("CLOUDZERO_MAX_FETCHED_DATA_RECORDS", 50000)) +CLOUDZERO_MAX_FETCHED_DATA_RECORDS = int( + os.getenv("CLOUDZERO_MAX_FETCHED_DATA_RECORDS", 50000) +) SPEND_LOG_CLEANUP_JOB_NAME = "spend_log_cleanup" SPEND_LOG_RUN_LOOPS = int(os.getenv("SPEND_LOG_RUN_LOOPS", 500)) SPEND_LOG_CLEANUP_BATCH_SIZE = int(os.getenv("SPEND_LOG_CLEANUP_BATCH_SIZE", 1000)) diff --git a/litellm/litellm_core_utils/get_litellm_params.py b/litellm/litellm_core_utils/get_litellm_params.py index c354dea024..c167c202e5 100644 --- a/litellm/litellm_core_utils/get_litellm_params.py +++ b/litellm/litellm_core_utils/get_litellm_params.py @@ -62,6 +62,7 @@ def get_litellm_params( use_litellm_proxy: Optional[bool] = None, api_version: Optional[str] = None, max_retries: Optional[int] = None, + litellm_request_debug: Optional[bool] = None, **kwargs, ) -> dict: litellm_params = { @@ -118,5 +119,6 @@ def get_litellm_params( "vertex_credentials": kwargs.get("vertex_credentials"), "vertex_project": kwargs.get("vertex_project"), "use_litellm_proxy": use_litellm_proxy, + "litellm_request_debug": litellm_request_debug, } return litellm_params diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 19d7c5512b..59dd09f272 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -245,6 +245,7 @@ class Logging(LiteLLMLoggingBaseClass): global supabaseClient, promptLayerLogger, weightsBiasesLogger, logfireLogger, capture_exception, add_breadcrumb, lunaryLogger, logfireLogger, prometheusLogger, slack_app custom_pricing: bool = False stream_options = None + litellm_request_debug: bool = False def __init__( self, @@ -470,6 +471,7 @@ class Logging(LiteLLMLoggingBaseClass): **self.litellm_params, **scrub_sensitive_keys_in_metadata(litellm_params), } + self.litellm_request_debug = litellm_params.get("litellm_request_debug", False) self.logger_fn = litellm_params.get("logger_fn", None) verbose_logger.debug(f"self.optional_params: {self.optional_params}") @@ -907,13 +909,19 @@ class Logging(LiteLLMLoggingBaseClass): Prints the RAW curl command sent from LiteLLM """ - if _is_debugging_on(): + if _is_debugging_on() or self.litellm_request_debug: if json_logs: masked_headers = self._get_masked_headers(headers) - verbose_logger.debug( - "POST Request Sent from LiteLLM", - extra={"api_base": {api_base}, **masked_headers}, - ) + if self.litellm_request_debug: + verbose_logger.warning( # .warning ensures this shows up in all environments + "POST Request Sent from LiteLLM", + extra={"api_base": {api_base}, **masked_headers}, + ) + else: + verbose_logger.debug( + "POST Request Sent from LiteLLM", + extra={"api_base": {api_base}, **masked_headers}, + ) else: headers = additional_args.get("headers", {}) if headers is None: @@ -926,7 +934,12 @@ class Logging(LiteLLMLoggingBaseClass): additional_args=additional_args, data=data, ) - verbose_logger.debug(f"\033[92m{curl_command}\033[0m\n") + if self.litellm_request_debug: + verbose_logger.warning( + f"\033[92m{curl_command}\033[0m\n" + ) # .warning ensures this shows up in all environments + else: + verbose_logger.debug(f"\033[92m{curl_command}\033[0m\n") def _get_request_body(self, data: dict) -> str: return str(data) @@ -1714,12 +1727,16 @@ class Logging(LiteLLMLoggingBaseClass): response_obj=result, start_time=start_time, end_time=end_time, - litellm_call_id=current_call_id - if ( - current_call_id := litellm_params.get("litellm_call_id") - ) - is not None - else str(uuid.uuid4()), + litellm_call_id=( + current_call_id + if ( + current_call_id := litellm_params.get( + "litellm_call_id" + ) + ) + is not None + else str(uuid.uuid4()) + ), print_verbose=print_verbose, ) if callback == "wandb" and weightsBiasesLogger is not None: @@ -3367,6 +3384,7 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 return galileo_logger # type: ignore elif logging_integration == "cloudzero": from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger + for callback in _in_memory_loggers: if isinstance(callback, CloudZeroLogger): return callback # type: ignore @@ -3594,6 +3612,7 @@ def get_custom_logger_compatible_class( # noqa: PLR0915 return callback elif logging_integration == "cloudzero": from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger + for callback in _in_memory_loggers: if isinstance(callback, CloudZeroLogger): return callback @@ -4504,7 +4523,7 @@ def get_standard_logging_object_payload( def emit_standard_logging_payload(payload: StandardLoggingPayload): if os.getenv("LITELLM_PRINT_STANDARD_LOGGING_PAYLOAD"): - print(json.dumps(payload, indent=4)) # noqa + print(json.dumps(payload, indent=4)) # noqa def get_standard_logging_metadata( diff --git a/litellm/main.py b/litellm/main.py index d7395eb145..6c81d3eded 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -150,9 +150,9 @@ from .llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler from .llms.custom_llm import CustomLLM, custom_chat_llm_router from .llms.databricks.embed.handler import DatabricksEmbeddingHandler from .llms.deprecated_providers import aleph_alpha, palm +from .llms.gemini.common_utils import get_api_key_from_env from .llms.groq.chat.handler import GroqChatCompletion from .llms.heroku.chat.transformation import HerokuChatConfig -from .llms.gemini.common_utils import get_api_key_from_env from .llms.huggingface.embedding.handler import HuggingFaceEmbedding from .llms.nlp_cloud.chat.handler import completion as nlp_cloud_chat_completion from .llms.oci.chat.transformation import OCIChatConfig @@ -358,7 +358,9 @@ async def acompletion( logprobs: Optional[bool] = None, top_logprobs: Optional[int] = None, deployment_id=None, - reasoning_effort: Optional[Literal["none", "minimal", "low", "medium", "high", "default"]] = None, + reasoning_effort: Optional[ + Literal["none", "minimal", "low", "medium", "high", "default"] + ] = None, safety_identifier: Optional[str] = None, # set api_base, api_version, api_key base_url: Optional[str] = None, @@ -504,7 +506,9 @@ async def acompletion( } if custom_llm_provider is None: _, custom_llm_provider, _, _ = get_llm_provider( - model=model, custom_llm_provider=custom_llm_provider, api_base=completion_kwargs.get("base_url", None) + model=model, + custom_llm_provider=custom_llm_provider, + api_base=completion_kwargs.get("base_url", None), ) fallbacks = fallbacks or litellm.model_fallbacks @@ -899,7 +903,9 @@ def completion( # type: ignore # noqa: PLR0915 logit_bias: Optional[dict] = None, user: Optional[str] = None, # openai v1.0+ new params - reasoning_effort: Optional[Literal["none", "minimal", "low", "medium", "high", "default"]] = None, + reasoning_effort: Optional[ + Literal["none", "minimal", "low", "medium", "high", "default"] + ] = None, response_format: Optional[Union[dict, Type[BaseModel]]] = None, seed: Optional[int] = None, tools: Optional[List] = None, @@ -1116,10 +1122,12 @@ def completion( # type: ignore # noqa: PLR0915 ) if provider_specific_header is not None: - headers.update(ProviderSpecificHeaderUtils.get_provider_specific_headers( - provider_specific_header=provider_specific_header, - custom_llm_provider=custom_llm_provider, - )) + headers.update( + ProviderSpecificHeaderUtils.get_provider_specific_headers( + provider_specific_header=provider_specific_header, + custom_llm_provider=custom_llm_provider, + ) + ) if model_response is not None and hasattr(model_response, "_hidden_params"): model_response._hidden_params["custom_llm_provider"] = custom_llm_provider @@ -1325,6 +1333,7 @@ def completion( # type: ignore # noqa: PLR0915 azure_scope=kwargs.get("azure_scope"), max_retries=max_retries, timeout=timeout, + litellm_request_debug=kwargs.get("litellm_request_debug", False), ) cast(LiteLLMLoggingObj, logging).update_environment_variables( model=model, @@ -2712,9 +2721,7 @@ def completion( # type: ignore # noqa: PLR0915 ) api_key = ( - api_key - or litellm.api_key - or get_secret("VERCEL_AI_GATEWAY_API_KEY") + api_key or litellm.api_key or get_secret("VERCEL_AI_GATEWAY_API_KEY") ) vercel_site_url = get_secret("VERCEL_SITE_URL") or "https://litellm.ai" @@ -2730,7 +2737,7 @@ def completion( # type: ignore # noqa: PLR0915 vercel_headers.update(_headers) headers = vercel_headers - + ## Load Config config = litellm.VercelAIGatewayConfig.get_config() for k, v in config.items(): @@ -3712,7 +3719,9 @@ async def aembedding(*args, **kwargs) -> EmbeddingResponse: func_with_context = partial(ctx.run, func) _, custom_llm_provider, _, _ = get_llm_provider( - model=model, custom_llm_provider=custom_llm_provider, api_base=kwargs.get("api_base", None) + model=model, + custom_llm_provider=custom_llm_provider, + api_base=kwargs.get("api_base", None), ) # Await normally @@ -5780,7 +5789,14 @@ async def ahealth_check( input=input or ["test"], ), "audio_speech": lambda: litellm.aspeech( - **{**_filter_model_params(model_params), **({"voice": "alloy"} if "voice" not in _filter_model_params(model_params) else {})}, + **{ + **_filter_model_params(model_params), + **( + {"voice": "alloy"} + if "voice" not in _filter_model_params(model_params) + else {} + ), + }, input=prompt or "test", ), "audio_transcription": lambda: litellm.atranscription( diff --git a/litellm/types/utils.py b/litellm/types/utils.py index c6f7098a1a..6452254d11 100644 --- a/litellm/types/utils.py +++ b/litellm/types/utils.py @@ -1995,7 +1995,7 @@ class StandardLoggingGuardrailInformation(TypedDict, total=False): ] guardrail_request: Optional[dict] guardrail_response: Optional[Union[dict, str, List[dict]]] - guardrail_status: Literal["success", "failure","blocked"] + guardrail_status: Literal["success", "failure", "blocked"] start_time: Optional[float] end_time: Optional[float] duration: Optional[float] @@ -2123,6 +2123,7 @@ all_litellm_params = [ "metadata", "litellm_metadata", "litellm_trace_id", + "litellm_request_debug", "guardrails", "tags", "acompletion", From 48619f2b7bd6a71a86c4af57d151a21d1538d1ed Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Thu, 11 Sep 2025 20:07:26 -0700 Subject: [PATCH 10/24] fix(litellm_logging.py): support emitting raw response on self.litellm_request_debug is true Addresses https://github.com/BerriAI/litellm/issues/13814 --- litellm/litellm_core_utils/litellm_logging.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 59dd09f272..2f21d28089 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -996,8 +996,14 @@ class Logging(LiteLLMLoggingBaseClass): self.model_call_details["additional_args"] = additional_args self.model_call_details["log_event_type"] = "post_api_call" + if self.litellm_request_debug: + attr = "warning" + else: + attr = "debug" + if json_logs: - verbose_logger.debug( + callattr = getattr(verbose_logger, attr) + callattr( "RAW RESPONSE:\n{}\n\n".format( self.model_call_details.get( "original_response", self.model_call_details @@ -1005,7 +1011,8 @@ class Logging(LiteLLMLoggingBaseClass): ), ) else: - print_verbose( + callattr = getattr(verbose_logger, attr) + callattr( "RAW RESPONSE:\n{}\n\n".format( self.model_call_details.get( "original_response", self.model_call_details From c802c472b5598de67126d3c93b75ac08ca498c86 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Thu, 11 Sep 2025 20:17:39 -0700 Subject: [PATCH 11/24] docs(debugging.md): document new feature Closes https://github.com/BerriAI/litellm/issues/13814 --- docs/my-website/docs/proxy/debugging.md | 68 +++++++++++++++++++------ 1 file changed, 53 insertions(+), 15 deletions(-) diff --git a/docs/my-website/docs/proxy/debugging.md b/docs/my-website/docs/proxy/debugging.md index 5cca654176..fbcac24a4d 100644 --- a/docs/my-website/docs/proxy/debugging.md +++ b/docs/my-website/docs/proxy/debugging.md @@ -11,13 +11,13 @@ The proxy also supports json logs. [See here](#json-logs) **via cli** -```bash +```bash showLineNumbers $ litellm --debug ``` **via env** -```python +```python showLineNumbers os.environ["LITELLM_LOG"] = "INFO" ``` @@ -25,25 +25,25 @@ os.environ["LITELLM_LOG"] = "INFO" **via cli** -```bash +```bash showLineNumbers $ litellm --detailed_debug ``` **via env** -```python +```python showLineNumbers os.environ["LITELLM_LOG"] = "DEBUG" ``` ### Debug Logs Run the proxy with `--detailed_debug` to view detailed debug logs -```shell +```shell showLineNumbers litellm --config /path/to/config.yaml --detailed_debug ``` When making requests you should see the POST request sent by LiteLLM to the LLM on the Terminal output -```shell +```shell showLineNumbers POST Request Sent from LiteLLM: curl -X POST \ https://api.openai.com/v1/chat/completions \ @@ -51,25 +51,63 @@ https://api.openai.com/v1/chat/completions \ -d '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "this is a test request, write a short poem"}]}' ``` +## Debug single request + +Pass in `litellm_request_debug=True` in the request body + +```bash showLineNumbers +curl -L -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model":"fake-openai-endpoint", + "messages": [{"role": "user","content": "How many r in the word strawberry?"}], + "litellm_request_debug": true +}' +``` + +This will emit the raw request sent by LiteLLM to the API Provider and raw response received from the API Provider for **just** this request in the logs. + + +```bash showLineNumbers +INFO: Uvicorn running on http://0.0.0.0:4000 (Press CTRL+C to quit) +20:14:06 - LiteLLM:WARNING: litellm_logging.py:938 - + +POST Request Sent from LiteLLM: +curl -X POST \ +https://exampleopenaiendpoint-production.up.railway.app/chat/completions \ +-H 'Authorization: Be****ey' -H 'Content-Type: application/json' \ +-d '{'model': 'fake', 'messages': [{'role': 'user', 'content': 'How many r in the word strawberry?'}], 'stream': False}' + + +20:14:06 - LiteLLM:WARNING: litellm_logging.py:1015 - RAW RESPONSE: +{"id":"chatcmpl-817fc08f0d6c451485d571dab39b26a1","object":"chat.completion","created":1677652288,"model":"gpt-3.5-turbo-0301","system_fingerprint":"fp_44709d6fcb","choices":[{"index":0,"message":{"role":"assistant","content":"\n\nHello there, how may I assist you today?"},"logprobs":null,"finish_reason":"stop"}],"usage":{"prompt_tokens":9,"completion_tokens":12,"total_tokens":21}} + + +INFO: 127.0.0.1:56155 - "POST /chat/completions HTTP/1.1" 200 OK + +``` + + ## JSON LOGS Set `JSON_LOGS="True"` in your env: -```bash +```bash showLineNumbers export JSON_LOGS="True" ``` **OR** Set `json_logs: true` in your yaml: -```yaml +```yaml showLineNumbers litellm_settings: json_logs: true ``` Start proxy -```bash +```bash showLineNumbers $ litellm ``` @@ -80,7 +118,7 @@ The proxy will now all logs in json format. Turn off fastapi's default 'INFO' logs 1. Turn on 'json logs' -```yaml +```yaml showLineNumbers litellm_settings: json_logs: true ``` @@ -89,20 +127,20 @@ litellm_settings: Only get logs if an error occurs. -```bash +```bash showLineNumbers LITELLM_LOG="ERROR" ``` 3. Start proxy -```bash +```bash showLineNumbers $ litellm ``` Expected Output: -```bash +```bash showLineNumbers # no info statements ``` @@ -119,14 +157,14 @@ This can be caused due to all your models hitting rate limit errors, causing the How to control this? - Adjust the cooldown time -```yaml +```yaml showLineNumbers router_settings: cooldown_time: 0 # 👈 KEY CHANGE ``` - Disable Cooldowns [NOT RECOMMENDED] -```yaml +```yaml showLineNumbers router_settings: disable_cooldowns: True ``` From b84785b5b7516268272167a0284c3e755f1ae248 Mon Sep 17 00:00:00 2001 From: Tim Elfrink Date: Fri, 12 Sep 2025 22:41:30 +0200 Subject: [PATCH 12/24] fix(lm_studio): resolve illegal Bearer header value issue - Change default API key from space ' ' to 'fake-api-key' - Fixes httpcore.LocalProtocolError: Illegal header value b'Bearer ' - Maintains compatibility with explicit API keys and environment variables - Add comprehensive tests for provider info retrieval Fixes #14502 --- litellm/llms/lm_studio/chat/transformation.py | 4 +-- .../test_lm_studio_chat_transformation.py | 30 +++++++++++++++++++ 2 files changed, 32 insertions(+), 2 deletions(-) diff --git a/litellm/llms/lm_studio/chat/transformation.py b/litellm/llms/lm_studio/chat/transformation.py index f7a2cc0f28..7b188ff33f 100644 --- a/litellm/llms/lm_studio/chat/transformation.py +++ b/litellm/llms/lm_studio/chat/transformation.py @@ -15,8 +15,8 @@ class LMStudioChatConfig(OpenAIGPTConfig): ) -> Tuple[Optional[str], Optional[str]]: api_base = api_base or get_secret_str("LM_STUDIO_API_BASE") # type: ignore dynamic_api_key = ( - api_key or get_secret_str("LM_STUDIO_API_KEY") or " " - ) # vllm does not require an api key + api_key or get_secret_str("LM_STUDIO_API_KEY") or "fake-api-key" + ) # LM Studio does not require an api key, but OpenAI client requires non-None value return api_base, dynamic_api_key def map_openai_params( diff --git a/tests/test_litellm/llms/lm_studio/test_lm_studio_chat_transformation.py b/tests/test_litellm/llms/lm_studio/test_lm_studio_chat_transformation.py index 1f09b1e540..964c85da3d 100644 --- a/tests/test_litellm/llms/lm_studio/test_lm_studio_chat_transformation.py +++ b/tests/test_litellm/llms/lm_studio/test_lm_studio_chat_transformation.py @@ -1,5 +1,6 @@ import os import sys +from unittest.mock import patch from pydantic import BaseModel @@ -53,3 +54,32 @@ class TestLMStudioChatConfigResponseFormat: assert mapped_schema["properties"] == schema["properties"] opt_schema = optional_params["response_format"]["json_schema"]["schema"] assert opt_schema["properties"] == schema["properties"] + + +def test_lm_studio_get_openai_compatible_provider_info(): + """Test provider info retrieval""" + config = LMStudioChatConfig() + + # Test default behavior (no API key provided) + _, api_key = config._get_openai_compatible_provider_info(None, None) + assert api_key == "fake-api-key" + + # Test explicit API key + _, api_key = config._get_openai_compatible_provider_info(None, "test-key") + assert api_key == "test-key" + + +def test_lm_studio_get_openai_compatible_provider_info_with_env(): + """Test provider info retrieval with environment variables.""" + config = LMStudioChatConfig() + + with patch.dict( + "os.environ", + { + "LM_STUDIO_API_BASE": "http://localhost:1234/v1", + "LM_STUDIO_API_KEY": "env_api_key", + }, + ): + api_base, api_key = config._get_openai_compatible_provider_info(None, None) + assert api_base == "http://localhost:1234/v1" + assert api_key == "env_api_key" From e87e50328e32078766328360daf4653e9965eb3a Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Fri, 12 Sep 2025 15:37:09 -0700 Subject: [PATCH 13/24] [Feat] Bedrock Batches - Working e2e flow to upload file + create batch (#14518) * fix: bedrock batches transform * fix: upload_url * fixes for model name * fix upload_url * fix bedrock batch test * test_mock_bedrock_file_url_mapping --- .../llms/bedrock/batches/transformation.py | 12 +- litellm/llms/bedrock/files/transformation.py | 75 +++++++++-- litellm/llms/custom_httpx/llm_http_handler.py | 12 +- .../bedrock_batch_completions.jsonl | 127 +++++++++++++++++- .../test_bedrock_files_and_batches.py | 50 +++++++ 5 files changed, 253 insertions(+), 23 deletions(-) diff --git a/litellm/llms/bedrock/batches/transformation.py b/litellm/llms/bedrock/batches/transformation.py index ce580ebc62..3338450f79 100644 --- a/litellm/llms/bedrock/batches/transformation.py +++ b/litellm/llms/bedrock/batches/transformation.py @@ -124,15 +124,13 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): "AWS IAM role ARN is required for Bedrock batch jobs. " "Set 'aws_batch_role_arn' in litellm_params or AWS_BATCH_ROLE_ARN env var" ) + - # Get the actual Bedrock model ID using common utility - bedrock_model_id = self.common_utils.extract_model_from_s3_file_path(input_file_id, optional_params) - - if not bedrock_model_id: - raise ValueError("Could not determine Bedrock model ID. Ensure the model is specified in the input file or passed as a parameter.") + if not model: + raise ValueError("Could not determine Bedrock model ID. Please pass `model` in your request body.") # Generate job name with the correct model ID using common utility - job_name = self.common_utils.generate_unique_job_name(bedrock_model_id, prefix="litellm") + job_name = self.common_utils.generate_unique_job_name(model, prefix="litellm") output_key = f"litellm-batch-outputs/{job_name}/" # Build input data config @@ -151,7 +149,7 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): # Create Bedrock batch request with proper typing bedrock_request: BedrockCreateBatchRequest = { - "modelId": bedrock_model_id, + "modelId": model, "jobName": job_name, "inputDataConfig": input_data_config, "outputDataConfig": output_data_config, diff --git a/litellm/llms/bedrock/files/transformation.py b/litellm/llms/bedrock/files/transformation.py index 83bbad7e1e..e33e14e49f 100644 --- a/litellm/llms/bedrock/files/transformation.py +++ b/litellm/llms/bedrock/files/transformation.py @@ -111,6 +111,10 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): # Remove bedrock/ prefix if present if _model.startswith("bedrock/"): _model = _model[8:] + + # Replace colons with hyphens for Bedrock S3 URI compliance + _model = _model.replace(":", "-") + object_name = f"litellm-bedrock-files-{_model}-{uuid.uuid4()}.jsonl" return object_name @@ -357,6 +361,8 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): api_base=api_base, optional_params=optional_params, ) + + litellm_params["upload_url"] = api_base # Return a dict that tells the HTTP handler exactly what to do return { @@ -440,6 +446,56 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): return dict(aws_request.headers), signed_body + def _convert_https_url_to_s3_uri(self, https_url: str) -> tuple[str, str]: + """ + Convert HTTPS S3 URL to s3:// URI format. + + Args: + https_url: HTTPS S3 URL (e.g., "https://s3.us-west-2.amazonaws.com/bucket/key") + + Returns: + Tuple of (s3_uri, filename) + + Example: + Input: "https://s3.us-west-2.amazonaws.com/litellm-proxy/file.jsonl" + Output: ("s3://litellm-proxy/file.jsonl", "file.jsonl") + """ + import re + + # Match HTTPS S3 URL patterns + # Pattern 1: https://s3.region.amazonaws.com/bucket/key + # Pattern 2: https://bucket.s3.region.amazonaws.com/key + + pattern1 = r"https://s3\.([^.]+)\.amazonaws\.com/([^/]+)/(.+)" + pattern2 = r"https://([^.]+)\.s3\.([^.]+)\.amazonaws\.com/(.+)" + + match1 = re.match(pattern1, https_url) + match2 = re.match(pattern2, https_url) + + if match1: + # Pattern: https://s3.region.amazonaws.com/bucket/key + region, bucket, key = match1.groups() + s3_uri = f"s3://{bucket}/{key}" + elif match2: + # Pattern: https://bucket.s3.region.amazonaws.com/key + bucket, region, key = match2.groups() + s3_uri = f"s3://{bucket}/{key}" + else: + # Fallback: try to extract bucket and key from URL path + from urllib.parse import urlparse + parsed = urlparse(https_url) + path_parts = parsed.path.lstrip('/').split('/', 1) + if len(path_parts) >= 2: + bucket, key = path_parts[0], path_parts[1] + s3_uri = f"s3://{bucket}/{key}" + else: + raise ValueError(f"Unable to parse S3 URL: {https_url}") + + # Extract filename from key + filename = key.split("/")[-1] if "/" in key else key + + return s3_uri, filename + def transform_create_file_response( self, model: Optional[str], @@ -452,21 +508,18 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): """ # For S3 uploads, we typically get an ETag and other metadata response_headers = raw_response.headers - # Extract S3 object information from the response # S3 PUT object returns ETag and other metadata in headers content_length = response_headers.get("Content-Length", "0") - # Extract bucket and key from the request URL or litellm_params - bucket_name = litellm_params.get("s3_bucket_name") or os.getenv("AWS_S3_BUCKET_NAME") - - # Generate file ID in S3 format - object_key = getattr(logging_obj, 'object_key', None) or f"file-{int(time.time())}" - file_id = f"s3://{bucket_name}/{object_key}" - - # Extract filename from object key - filename = object_key.split("/")[-1] if "/" in object_key else object_key - + # Use the actual upload URL that was used for the S3 upload + upload_url = litellm_params.get("upload_url") + file_id: str = "" + filename: str = "" + if upload_url: + # Convert HTTPS S3 URL to s3:// URI format + file_id, filename = self._convert_https_url_to_s3_uri(upload_url) + return OpenAIFileObject( purpose="batch", # Default purpose for Bedrock files id=file_id, diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index 13133a56aa..8f31860d1a 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -2201,7 +2201,6 @@ class BaseLLMHTTPHandler: litellm_params=litellm_params, optional_params={}, ) - if _is_async: return self.async_create_file( transformed_request=transformed_request, @@ -2218,6 +2217,7 @@ class BaseLLMHTTPHandler: sync_httpx_client = _get_httpx_client() else: sync_httpx_client = client + if isinstance(transformed_request, dict) and "method" in transformed_request: # Handle pre-signed requests (e.g., from Bedrock S3 uploads) @@ -2283,11 +2283,15 @@ class BaseLLMHTTPHandler: provider_config=provider_config, ) + # Store the upload URL in litellm_params for the transformation method + litellm_params_with_url = dict(litellm_params) + litellm_params_with_url["upload_url"] = api_base + return provider_config.transform_create_file_response( model=None, raw_response=upload_response, logging_obj=logging_obj, - litellm_params=litellm_params, + litellm_params=litellm_params_with_url, ) async def async_create_file( @@ -2425,7 +2429,7 @@ class BaseLLMHTTPHandler: api_base = provider_config.get_complete_batch_url( api_base=api_base, api_key=api_key, - model="", + model=litellm_params.get("model") or "", optional_params={}, litellm_params=litellm_params, data=create_batch_data, @@ -2435,7 +2439,7 @@ class BaseLLMHTTPHandler: # Get the transformed request data transformed_request = provider_config.transform_create_batch_request( - model="", + model=litellm_params.get("model") or "", create_batch_data=create_batch_data, litellm_params=litellm_params, optional_params={}, diff --git a/tests/batches_tests/bedrock_batch_completions.jsonl b/tests/batches_tests/bedrock_batch_completions.jsonl index 3037b1031e..adef9ac2dd 100644 --- a/tests/batches_tests/bedrock_batch_completions.jsonl +++ b/tests/batches_tests/bedrock_batch_completions.jsonl @@ -1,3 +1,128 @@ {"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} {"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": 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"url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} diff --git a/tests/batches_tests/test_bedrock_files_and_batches.py b/tests/batches_tests/test_bedrock_files_and_batches.py index edf8c3c774..3f90411f62 100644 --- a/tests/batches_tests/test_bedrock_files_and_batches.py +++ b/tests/batches_tests/test_bedrock_files_and_batches.py @@ -18,6 +18,8 @@ sys.path.insert( import pytest from typing import Optional import litellm +from unittest.mock import patch, MagicMock +import httpx @pytest.mark.asyncio() @@ -64,7 +66,55 @@ async def test_async_file_and_batch(): input_file_id=file_obj.id, metadata={"key1": "value1", "key2": "value2"}, custom_llm_provider="bedrock", + + ######################################################### + # bedrock specific params + ######################################################### + model="us.anthropic.claude-3-5-sonnet-20240620-v1:0", aws_batch_role_arn="arn:aws:iam::888602223428:role/service-role/AmazonBedrockExecutionRoleForAgents_BB9HNW6V4CV" ) print("CREATED BATCH RESPONSE=", create_batch_response) + +@pytest.mark.asyncio() +async def test_mock_bedrock_file_url_mapping(): + """ + Simple test to capture PUT URL and validate mapping to file ID. + """ + print("Testing Bedrock file URL mapping") + + captured_put_url = None + + async def mock_async_create_file(transformed_request, **kwargs): + nonlocal captured_put_url + # Capture PUT URL from transformed request + if isinstance(transformed_request, dict) and "url" in transformed_request: + captured_put_url = transformed_request["url"] + + # Call the real method to get actual response + from litellm.files.main import base_llm_http_handler + return await base_llm_http_handler.__class__.async_create_file( + base_llm_http_handler, transformed_request, **kwargs + ) + + with patch('litellm.files.main.base_llm_http_handler.async_create_file', side_effect=mock_async_create_file): + file_obj = await litellm.acreate_file( + file=open(os.path.join(os.path.dirname(__file__), "bedrock_batch_completions.jsonl"), "rb"), + purpose="batch", + custom_llm_provider="bedrock", + s3_bucket_name="litellm-proxy", + ) + + print(f"PUT URL: {captured_put_url}") + print(f"File ID: {file_obj.id}") + + # Validate URL was captured and response is correct + assert captured_put_url is not None + assert file_obj.id.startswith("s3://") + + # Verify mapping + from litellm.llms.bedrock.files.transformation import BedrockFilesConfig + bedrock_config = BedrockFilesConfig() + expected_s3_uri, _ = bedrock_config._convert_https_url_to_s3_uri(captured_put_url) + assert file_obj.id == expected_s3_uri + From f4318bccd3cf73bcfa30d8178f255251329e31fe Mon Sep 17 00:00:00 2001 From: Arseny Boykov <36469655+Bobronium@users.noreply.github.com> Date: Sat, 13 Sep 2025 02:33:55 +0200 Subject: [PATCH 14/24] [Performance] Use _PROXY_MaxParallelRequestsHandler_v3 by default again (#14450) * Use _PROXY_MaxParallelRequestsHandler_v3 by default (#14352) (cherry picked from commit f3fa45cf8fbd5f5cce2f45a7312776d5005fb08e) (cherry picked from commit 5b680bb4a350b8261b24df7ce806fdcda9c88807) * Use random api_key for parallel requests test * Fix off-by-one error in parallel request rate limit The rate limiter was incorrectly rejecting requests when the limit was met, but not exceeded. The check in `is_cache_list_over_limit` was `int(counter_value) + 1 > current_limit`, which caused the first request to be rejected if the limit was 1. This commit removes the `+ 1`, changing the logic to `int(counter_value) > current_limit`. The check now correctly allows requests up to the specified parallel limit. * Test actual parallel requests * Ensure rate limiting works correctly for multiple users * Add sequential rate-limit test * Revert random key usage --- litellm/proxy/hooks/__init__.py | 6 +- .../hooks/parallel_request_limiter_v3.py | 2 +- .../test_pass_through_endpoints.py | 174 ++++++++++++++++-- 3 files changed, 161 insertions(+), 21 deletions(-) diff --git a/litellm/proxy/hooks/__init__.py b/litellm/proxy/hooks/__init__.py index 83d7e17343..467565d748 100644 --- a/litellm/proxy/hooks/__init__.py +++ b/litellm/proxy/hooks/__init__.py @@ -17,13 +17,13 @@ except ImportError: # List of all available hooks that can be enabled PROXY_HOOKS = { "max_budget_limiter": _PROXY_MaxBudgetLimiter, - "parallel_request_limiter": _PROXY_MaxParallelRequestsHandler, + "parallel_request_limiter": _PROXY_MaxParallelRequestsHandler_v3, "cache_control_check": _PROXY_CacheControlCheck, } ## FEATURE FLAG HOOKS ## -if os.getenv("EXPERIMENTAL_MULTI_INSTANCE_RATE_LIMITING", "false").lower() == "true": - PROXY_HOOKS["parallel_request_limiter"] = _PROXY_MaxParallelRequestsHandler_v3 +if os.getenv("LEGACY_MULTI_INSTANCE_RATE_LIMITING", "false").lower() == "true": + PROXY_HOOKS["parallel_request_limiter"] = _PROXY_MaxParallelRequestsHandler ### update PROXY_HOOKS with ENTERPRISE_PROXY_HOOKS ### diff --git a/litellm/proxy/hooks/parallel_request_limiter_v3.py b/litellm/proxy/hooks/parallel_request_limiter_v3.py index b3840761d2..3a1a0bef17 100644 --- a/litellm/proxy/hooks/parallel_request_limiter_v3.py +++ b/litellm/proxy/hooks/parallel_request_limiter_v3.py @@ -266,7 +266,7 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): if current_limit is None or rate_limit_type is None: continue - if counter_value is not None and int(counter_value) + 1 > current_limit: + if counter_value is not None and int(counter_value) > current_limit: overall_code = "OVER_LIMIT" item_code = "OVER_LIMIT" diff --git a/tests/local_testing/test_pass_through_endpoints.py b/tests/local_testing/test_pass_through_endpoints.py index 118a1f64f4..6cc6a66007 100644 --- a/tests/local_testing/test_pass_through_endpoints.py +++ b/tests/local_testing/test_pass_through_endpoints.py @@ -1,5 +1,7 @@ import os import sys +import uuid +from functools import partial from typing import Optional import pytest @@ -146,24 +148,36 @@ async def test_pass_through_endpoint_rerank(client): @pytest.mark.parametrize( - "auth, rpm_limit, expected_error_code", - [(True, 0, 429), (True, 1, 200), (False, 0, 200)], + "auth, rpm_limit, requests_to_make, expected_status_codes, num_users", + [ + # Single user tests + (True, 0, 1, [429], 1), + (True, 1, 1, [200], 1), + (True, 1, 2, [200, 429], 1), + (True, 2, 4, [200, 200, 429, 429], 1), + (True, 3, 4, [200, 200, 200, 429], 1), + (True, 4, 4, [200, 200, 200, 200], 1), + (False, 0, 1, [200], 1), + (False, 0, 4, [200, 200, 200, 200], 1), + # Multiple user tests (same parameters as single user) + (True, 0, 1, [429], 2), + (True, 1, 1, [200], 2), + (True, 1, 2, [200, 429], 2), + (True, 2, 4, [200, 200, 429, 429], 2), + (True, 3, 4, [200, 200, 200, 429], 2), + (True, 4, 4, [200, 200, 200, 200], 2), + (False, 0, 1, [200], 2), + (False, 0, 4, [200, 200, 200, 200], 2), + ], ) @pytest.mark.asyncio async def test_pass_through_endpoint_rpm_limit( - client, auth, expected_error_code, rpm_limit + client, auth, rpm_limit, requests_to_make, expected_status_codes, num_users ): import litellm from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.proxy_server import ProxyLogging, hash_token, user_api_key_cache - mock_api_key = "sk-my-test-key" - cache_value = UserAPIKeyAuth(token=hash_token(mock_api_key), rpm_limit=rpm_limit) - - _cohere_api_key = os.environ.get("COHERE_API_KEY") - - user_api_key_cache.set_cache(key=hash_token(mock_api_key), value=cache_value) - proxy_logging_obj = ProxyLogging(user_api_key_cache=user_api_key_cache) proxy_logging_obj._init_litellm_callbacks() @@ -173,6 +187,7 @@ async def test_pass_through_endpoint_rpm_limit( setattr(litellm.proxy.proxy_server, "proxy_logging_obj", proxy_logging_obj) # Define a pass-through endpoint + _cohere_api_key = os.environ.get("COHERE_API_KEY") pass_through_endpoints = [ { "path": "/v1/rerank", @@ -190,6 +205,13 @@ async def test_pass_through_endpoint_rpm_limit( general_settings.update({"pass_through_endpoints": pass_through_endpoints}) setattr(litellm.proxy.proxy_server, "general_settings", general_settings) + # Setup API keys and cache + mock_api_keys = [f"sk-test-{uuid.uuid4().hex}" for _ in range(num_users)] + + for mock_api_key in mock_api_keys: + cache_value = UserAPIKeyAuth(token=hash_token(mock_api_key), rpm_limit=rpm_limit) + user_api_key_cache.set_cache(key=hash_token(mock_api_key), value=cache_value) + _json_data = { "model": "rerank-english-v3.0", "query": "What is the capital of the United States?", @@ -200,16 +222,134 @@ async def test_pass_through_endpoint_rpm_limit( } # Make a request to the pass-through endpoint - response = client.post( - "/v1/rerank", - json=_json_data, - headers={"Authorization": "Bearer {}".format(mock_api_key)}, - ) + tasks = [] + for mock_api_key in mock_api_keys: + for _ in range(requests_to_make): + task = asyncio.get_running_loop().run_in_executor( + None, + partial( + client.post, + "/v1/rerank", + json=_json_data, + headers={"Authorization": "Bearer {}".format(mock_api_key)}, + ), + ) + tasks.append(task) + + responses = await asyncio.gather(*tasks) + + if num_users == 1: + status_codes = sorted([response.status_code for response in responses]) + + assert status_codes == sorted(expected_status_codes) + else: + first_user_responses = responses[requests_to_make:] + second_user_responses = responses[:requests_to_make] + + first_user_status_codes = sorted([response.status_code for response in first_user_responses]) + second_user_status_codes = sorted([response.status_code for response in second_user_responses]) + + expected_status_codes.sort() + assert first_user_status_codes == expected_status_codes + assert second_user_status_codes == expected_status_codes print("JSON response: ", _json_data) - # Assert the response - assert response.status_code == expected_error_code + +@pytest.mark.parametrize( + "auth, rpm_limit, requests_to_make, expected_status_codes", + [ + # Multiple user tests (same parameters as single user) + (True, 0, 1, [429]), + (True, 1, 1, [200]), + (True, 1, 2, [200, 429]), + (True, 2, 4, [200, 200, 429, 429]), + (True, 3, 4, [200, 200, 200, 429]), + (True, 4, 4, [200, 200, 200, 200]), + (False, 0, 1, [200]), + (False, 0, 4, [200, 200, 200, 200]), + ], +) +@pytest.mark.asyncio +async def test_pass_through_endpoint_sequential_rpm_limit( + client, auth, rpm_limit, requests_to_make, expected_status_codes +): + import litellm + from litellm.proxy._types import UserAPIKeyAuth + from litellm.proxy.proxy_server import ProxyLogging, hash_token, user_api_key_cache + + proxy_logging_obj = ProxyLogging(user_api_key_cache=user_api_key_cache) + proxy_logging_obj._init_litellm_callbacks() + + setattr(litellm.proxy.proxy_server, "user_api_key_cache", user_api_key_cache) + setattr(litellm.proxy.proxy_server, "master_key", "sk-1234") + setattr(litellm.proxy.proxy_server, "prisma_client", "FAKE-VAR") + setattr(litellm.proxy.proxy_server, "proxy_logging_obj", proxy_logging_obj) + + # Define a pass-through endpoint + _cohere_api_key = os.environ.get("COHERE_API_KEY") + pass_through_endpoints = [ + { + "path": "/v1/rerank", + "target": "https://api.cohere.com/v1/rerank", + "auth": auth, + "headers": {"Authorization": f"bearer {_cohere_api_key}"}, + } + ] + + # Initialize the pass-through endpoint + await initialize_pass_through_endpoints(pass_through_endpoints) + general_settings: Optional[dict] = ( + getattr(litellm.proxy.proxy_server, "general_settings", {}) or {} + ) + general_settings.update({"pass_through_endpoints": pass_through_endpoints}) + setattr(litellm.proxy.proxy_server, "general_settings", general_settings) + + # Setup API keys and cache + mock_api_keys = [f"sk-test-{uuid.uuid4().hex}" for _ in range(2)] + + for mock_api_key in mock_api_keys: + cache_value = UserAPIKeyAuth(token=hash_token(mock_api_key), rpm_limit=rpm_limit) + user_api_key_cache.set_cache(key=hash_token(mock_api_key), value=cache_value) + + _json_data = { + "model": "rerank-english-v3.0", + "query": "What is the capital of the United States?", + "top_n": 3, + "documents": [ + "Carson City is the capital city of the American state of Nevada." + ], + } + + # Make a request to the pass-through endpoint + first_user_responses = [] + second_user_responses = [] + for _ in range(requests_to_make): + requests = [] + for mock_api_key in mock_api_keys: + task = asyncio.get_running_loop().run_in_executor( + None, + partial( + client.post, + "/v1/rerank", + json=_json_data, + headers={"Authorization": "Bearer {}".format(mock_api_key)}, + ), + ) + requests.append(task) + + first_user_response, second_user_response = await asyncio.gather(*requests) + first_user_responses.append(first_user_response) + second_user_responses.append(second_user_response) + + first_user_status_codes = sorted([response.status_code for response in first_user_responses]) + second_user_status_codes = sorted([response.status_code for response in second_user_responses]) + + expected_status_codes.sort() + assert first_user_status_codes == expected_status_codes + assert second_user_status_codes == expected_status_codes + + print("JSON response: ", _json_data) @pytest.mark.parametrize( From bdd960c45179325d39da0817d8befd3a31b07a19 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Fri, 12 Sep 2025 18:03:03 -0700 Subject: [PATCH 15/24] docs(admin_ui_sso.md): clarify sso usage on docs --- docs/my-website/docs/proxy/admin_ui_sso.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/my-website/docs/proxy/admin_ui_sso.md b/docs/my-website/docs/proxy/admin_ui_sso.md index 823301d4c3..32bf97410c 100644 --- a/docs/my-website/docs/proxy/admin_ui_sso.md +++ b/docs/my-website/docs/proxy/admin_ui_sso.md @@ -4,6 +4,10 @@ import TabItem from '@theme/TabItem'; # ✨ SSO for Admin UI +:::info +From v1.76.0, SSO is now Free for up to 5 users. +::: + :::info ✨ SSO is on LiteLLM Enterprise From 075a089d82a699b827e90e83831888e747f656b6 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Fri, 12 Sep 2025 18:32:57 -0700 Subject: [PATCH 16/24] [Feat] Bedrock Batches - Ensure correct transformation applied to incoming requests (#14522) * use is_batch_jsonl_file * fix valid_content_type * fix transform_create_file_request * fix _transform_openai_jsonl_content_to_bedrock_jsonl_content * test_transform_openai_jsonl_content_to_bedrock_jsonl_content * fix mypy linting errors * fix BEDROCK_BATCH_MODEL * fix working sample * fix comment * fix model list * fix: use with managed batches * refactor --- .../batch_api/bedrock/bedrock.py | 25 ++++ .../bedrock/bedrock_batch_completions.jsonl | 128 ++++++++++++++++++ litellm/batches/main.py | 79 ++++++++--- litellm/files/utils.py | 27 ++++ litellm/llms/bedrock/files/transformation.py | 54 ++++---- litellm/llms/custom_httpx/llm_http_handler.py | 15 +- .../llms/vertex_ai/files/transformation.py | 12 +- litellm/proxy/proxy_config.yaml | 24 ++-- litellm/router.py | 2 +- .../expected_bedrock_batch_completions.jsonl | 2 + .../files/input_batch_completions.jsonl | 2 + .../test_bedrock_files_transformation.py | 90 ++++++++++++ 12 files changed, 393 insertions(+), 67 deletions(-) create mode 100644 cookbook/litellm_proxy_server/batch_api/bedrock/bedrock.py create mode 100644 cookbook/litellm_proxy_server/batch_api/bedrock/bedrock_batch_completions.jsonl create mode 100644 litellm/files/utils.py create mode 100644 tests/test_litellm/llms/bedrock/files/expected_bedrock_batch_completions.jsonl create mode 100644 tests/test_litellm/llms/bedrock/files/input_batch_completions.jsonl create mode 100644 tests/test_litellm/llms/bedrock/files/test_bedrock_files_transformation.py diff --git a/cookbook/litellm_proxy_server/batch_api/bedrock/bedrock.py b/cookbook/litellm_proxy_server/batch_api/bedrock/bedrock.py new file mode 100644 index 0000000000..615baa422e --- /dev/null +++ b/cookbook/litellm_proxy_server/batch_api/bedrock/bedrock.py @@ -0,0 +1,25 @@ +from openai import OpenAI + +client = OpenAI( + base_url="http://0.0.0.0:4000", + api_key="sk-1234", +) + +BEDROCK_BATCH_MODEL = "bedrock/batch-anthropic.claude-3-5-sonnet-20240620-v1:0" + +# Upload file +batch_input_file = client.files.create( + file=open("./bedrock_batch_completions.jsonl", "rb"), + purpose="batch", + extra_body={"target_model_names": BEDROCK_BATCH_MODEL} +) +print(batch_input_file) + +# Create batch +batch = client.batches.create( + input_file_id=batch_input_file.id, + endpoint="/v1/chat/completions", + completion_window="24h", + metadata={"description": "Test batch job"}, +) +print(batch) \ No newline at end of file diff --git a/cookbook/litellm_proxy_server/batch_api/bedrock/bedrock_batch_completions.jsonl b/cookbook/litellm_proxy_server/batch_api/bedrock/bedrock_batch_completions.jsonl new file mode 100644 index 0000000000..adef9ac2dd --- /dev/null +++ b/cookbook/litellm_proxy_server/batch_api/bedrock/bedrock_batch_completions.jsonl @@ -0,0 +1,128 @@ +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": 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"bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} diff --git a/litellm/batches/main.py b/litellm/batches/main.py index 0d250779da..e5c7a2e4b6 100644 --- a/litellm/batches/main.py +++ b/litellm/batches/main.py @@ -19,6 +19,7 @@ from typing import Any, Coroutine, Dict, Literal, Optional, Union, cast import httpx import litellm +from litellm._logging import verbose_logger from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.azure.batches.handler import AzureBatchesAPI from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler @@ -38,6 +39,7 @@ from litellm.utils import ( ProviderConfigManager, client, get_litellm_params, + get_llm_provider, supports_httpx_timeout, ) @@ -49,6 +51,45 @@ base_llm_http_handler = BaseLLMHTTPHandler() ################################################# +def _resolve_timeout( + optional_params: GenericLiteLLMParams, + kwargs: Dict[str, Any], + custom_llm_provider: str, + default_timeout: float = 600.0, +) -> float: + """ + Resolve timeout value from various sources and handle httpx.Timeout objects. + + Args: + optional_params: GenericLiteLLMParams object containing timeout + kwargs: Additional kwargs that may contain request_timeout + custom_llm_provider: Provider name for httpx timeout support check + default_timeout: Default timeout value to use + + Returns: + Resolved timeout as float + """ + timeout = optional_params.timeout or kwargs.get("request_timeout", default_timeout) or default_timeout + + # Handle httpx.Timeout objects + if isinstance(timeout, httpx.Timeout): + if supports_httpx_timeout(custom_llm_provider) is False: + # Extract read timeout for providers that don't support httpx.Timeout + read_timeout = timeout.read or default_timeout + return float(read_timeout) + else: + # For providers that support httpx.Timeout, we still need to return a float + # This case might need to be handled differently based on the actual use case + return float(timeout.read or default_timeout) + + # Handle None case + if timeout is None: + return float(default_timeout) + + # Handle numeric values (int, float, string representations) + return float(timeout) + + @client async def acreate_batch( completion_window: Literal["24h"], @@ -118,13 +159,23 @@ def create_batch( litellm_call_id = kwargs.get("litellm_call_id", None) proxy_server_request = kwargs.get("proxy_server_request", None) model_info = kwargs.get("model_info", None) + model: Optional[str] = kwargs.get("model", None) + try: + if model is not None: + model, _, _, _ = get_llm_provider( + model=model, + custom_llm_provider=None, + ) + except Exception as e: + verbose_logger.exception(f"litellm.batches.main.py::create_batch() - Error inferring custom_llm_provider - {str(e)}") + _is_async = kwargs.pop("acreate_batch", False) is True litellm_params = dict(GenericLiteLLMParams(**kwargs)) litellm_logging_obj: LiteLLMLoggingObj = cast(LiteLLMLoggingObj, kwargs.get("litellm_logging_obj", None)) ### TIMEOUT LOGIC ### - timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600 + timeout = _resolve_timeout(optional_params, kwargs, custom_llm_provider) litellm_logging_obj.update_environment_variables( - model=None, + model=model, user=None, optional_params=optional_params.model_dump(), litellm_params={ @@ -138,18 +189,6 @@ def create_batch( }, custom_llm_provider=custom_llm_provider, ) - - if ( - timeout is not None - and isinstance(timeout, httpx.Timeout) - and supports_httpx_timeout(custom_llm_provider) is False - ): - read_timeout = timeout.read or 600 - timeout = read_timeout # default 10 min timeout - elif timeout is not None and not isinstance(timeout, httpx.Timeout): - timeout = float(timeout) # type: ignore - elif timeout is None: - timeout = 600.0 _create_batch_request = CreateBatchRequest( @@ -160,10 +199,13 @@ def create_batch( extra_headers=extra_headers, extra_body=extra_body, ) - provider_config = ProviderConfigManager.get_provider_batches_config( - model="", - provider=LlmProviders(custom_llm_provider), - ) + if model is not None: + provider_config = ProviderConfigManager.get_provider_batches_config( + model=model, + provider=LlmProviders(custom_llm_provider), + ) + else: + provider_config = None if provider_config is not None: response = base_llm_http_handler.create_batch( provider_config=provider_config, @@ -179,6 +221,7 @@ def create_batch( and isinstance(client, (HTTPHandler, AsyncHTTPHandler)) else None, timeout=timeout, + model=model, ) return response api_base: Optional[str] = None diff --git a/litellm/files/utils.py b/litellm/files/utils.py new file mode 100644 index 0000000000..a56a29467d --- /dev/null +++ b/litellm/files/utils.py @@ -0,0 +1,27 @@ +from typing import Optional + +from litellm.types.llms.openai import CreateFileRequest +from litellm.types.utils import ExtractedFileData + + +class FilesAPIUtils: + """ + Utils for files API interface on litellm + """ + @staticmethod + def is_batch_jsonl_file(create_file_data: CreateFileRequest, extracted_file_data: ExtractedFileData) -> bool: + """ + Check if the file is a batch jsonl file + """ + return ( + create_file_data.get("purpose") == "batch" + and FilesAPIUtils.valid_content_type(extracted_file_data.get("content_type")) + and extracted_file_data.get("content") is not None + ) + + @staticmethod + def valid_content_type(content_type: Optional[str]) -> bool: + """ + Check if the content type is valid + """ + return content_type in set(["application/jsonl", "application/octet-stream"]) diff --git a/litellm/llms/bedrock/files/transformation.py b/litellm/llms/bedrock/files/transformation.py index e33e14e49f..d493deddb6 100644 --- a/litellm/llms/bedrock/files/transformation.py +++ b/litellm/llms/bedrock/files/transformation.py @@ -6,6 +6,8 @@ from typing import Any, Dict, List, Optional, Tuple, Union from httpx import Headers, Response +from litellm._logging import verbose_logger +from litellm.files.utils import FilesAPIUtils from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.base_llm.files.transformation import ( @@ -21,6 +23,7 @@ from litellm.types.llms.openai import ( PathLike, ) from litellm.types.utils import ExtractedFileData, LlmProviders +from litellm.utils import get_llm_provider from ..base_aws_llm import BaseAWSLLM from ..common_utils import BedrockError @@ -195,24 +198,6 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): ) -> dict: return optional_params - def _get_bedrock_provider_from_model(self, model: str) -> Optional[str]: - """ - Extract provider from Bedrock model name - """ - if model.startswith("anthropic."): - return "anthropic" - elif model.startswith("cohere."): - return "cohere" - elif model.startswith("meta.") or model.startswith("llama"): - return "meta" - elif model.startswith("mistral."): - return "mistral" - elif model.startswith("ai21."): - return "ai21" - elif model.startswith("amazon."): - return "amazon" - else: - return None def _map_openai_to_bedrock_params( self, @@ -222,11 +207,12 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): """ Transform OpenAI request body to Bedrock-compatible modelInput parameters using existing transformation logic """ + from litellm.types.utils import LlmProviders _model = openai_request_body.get("model", "") messages = openai_request_body.get("messages", []) # Use existing Anthropic transformation logic for Anthropic models - if provider == "anthropic": + if provider == LlmProviders.ANTHROPIC: from litellm.llms.bedrock.chat.invoke_transformations.anthropic_claude3_transformation import ( AmazonAnthropicClaudeConfig, ) @@ -235,16 +221,22 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): # Extract optional params (everything except model and messages) optional_params = {k: v for k, v in openai_request_body.items() if k not in ["model", "messages"]} + mapped_params = anthropic_config.map_openai_params( + non_default_params={}, + optional_params=optional_params, + model=_model, + drop_params=False + ) # Transform using existing Anthropic logic bedrock_params = anthropic_config.transform_request( model=_model, messages=messages, - optional_params=optional_params, + optional_params=mapped_params, litellm_params={}, headers={} ) - + return bedrock_params else: # For other providers, use basic mapping @@ -282,9 +274,17 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): # Extract the request body from OpenAI format openai_body = _openai_jsonl_content.get("body", {}) model = openai_body.get("model", "") + + try: + model, _, _, _ = get_llm_provider( + model=model, + custom_llm_provider=None, + ) + except Exception as e: + verbose_logger.exception(f"litellm.llms.bedrock.files.transformation.py::_transform_openai_jsonl_content_to_bedrock_jsonl_content() - Error inferring custom_llm_provider - {str(e)}") # Determine provider from model name - provider = self._get_bedrock_provider_from_model(model) + provider = self.get_bedrock_invoke_provider(model) # Transform to Bedrock modelInput format model_input = self._map_openai_to_bedrock_params( @@ -319,11 +319,13 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): extracted_file_data = extract_file_data(file_data) extracted_file_data_content = extracted_file_data.get("content") + if extracted_file_data_content is None: + raise ValueError("file content is required") + # Get and transform the file content - if ( - create_file_data.get("purpose") == "batch" - and extracted_file_data.get("content_type") == "application/jsonl" - and extracted_file_data_content is not None + if FilesAPIUtils.is_batch_jsonl_file( + create_file_data=create_file_data, + extracted_file_data=extracted_file_data, ): ## Transform JSONL content to Bedrock format original_file_content = self._get_content_from_openai_file( diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index 8f31860d1a..a4f40e90df 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -2412,15 +2412,19 @@ class BaseLLMHTTPHandler: _is_async: bool = False, client: Optional[Union["HTTPHandler", "AsyncHTTPHandler"]] = None, timeout: Optional[Union[float, httpx.Timeout]] = None, + model: Optional[str] = None, ) -> Union["LiteLLMBatch", Coroutine[Any, Any, "LiteLLMBatch"]]: """ Creates a batch using provider-specific batch creation process """ # get config from model, custom llm provider + if model is None: + raise ValueError("model is required for create_batch") + headers = provider_config.validate_environment( api_key=api_key, headers=headers, - model="", + model=model, messages=[], optional_params={}, litellm_params=litellm_params, @@ -2429,7 +2433,7 @@ class BaseLLMHTTPHandler: api_base = provider_config.get_complete_batch_url( api_base=api_base, api_key=api_key, - model=litellm_params.get("model") or "", + model=model, optional_params={}, litellm_params=litellm_params, data=create_batch_data, @@ -2439,7 +2443,7 @@ class BaseLLMHTTPHandler: # Get the transformed request data transformed_request = provider_config.transform_create_batch_request( - model=litellm_params.get("model") or "", + model=model, create_batch_data=create_batch_data, litellm_params=litellm_params, optional_params={}, @@ -2499,7 +2503,7 @@ class BaseLLMHTTPHandler: litellm_params_with_request = {**litellm_params, "original_batch_request": create_batch_data} return provider_config.transform_create_batch_response( - model=None, + model=model, raw_response=batch_response, logging_obj=logging_obj, litellm_params=litellm_params_with_request, @@ -2516,6 +2520,7 @@ class BaseLLMHTTPHandler: client: Optional[Union["HTTPHandler", "AsyncHTTPHandler"]] = None, timeout: Optional[Union[float, httpx.Timeout]] = None, create_batch_data: Optional["CreateBatchRequest"] = None, + model: Optional[str] = None, ): """ Async version of create_batch @@ -2576,7 +2581,7 @@ class BaseLLMHTTPHandler: litellm_params_with_request = {**litellm_params, "original_batch_request": create_batch_data or {}} return provider_config.transform_create_batch_response( - model=None, + model=model, raw_response=batch_response, logging_obj=logging_obj, litellm_params=litellm_params_with_request, diff --git a/litellm/llms/vertex_ai/files/transformation.py b/litellm/llms/vertex_ai/files/transformation.py index c795367e48..1066a8f036 100644 --- a/litellm/llms/vertex_ai/files/transformation.py +++ b/litellm/llms/vertex_ai/files/transformation.py @@ -6,6 +6,7 @@ from typing import Any, Dict, List, Optional, Tuple, Union from httpx import Headers, Response +from litellm.files.utils import FilesAPIUtils from litellm.litellm_core_utils.prompt_templates.common_utils import extract_file_data from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.base_llm.files.transformation import ( @@ -260,10 +261,13 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig): raise ValueError("file is required") extracted_file_data = extract_file_data(file_data) extracted_file_data_content = extracted_file_data.get("content") - if ( - create_file_data.get("purpose") == "batch" - and extracted_file_data.get("content_type") == "application/jsonl" - and extracted_file_data_content is not None + + if extracted_file_data_content is None: + raise ValueError("file content is required") + + if FilesAPIUtils.is_batch_jsonl_file( + create_file_data=create_file_data, + extracted_file_data=extracted_file_data, ): ## 1. If jsonl, check if there's a model name file_content = self._get_content_from_openai_file( diff --git a/litellm/proxy/proxy_config.yaml b/litellm/proxy/proxy_config.yaml index 918e11eea6..704c8e0be6 100644 --- a/litellm/proxy/proxy_config.yaml +++ b/litellm/proxy/proxy_config.yaml @@ -1,18 +1,16 @@ model_list: - - model_name: db-openai-endpoint + - model_name: bedrock/batch-anthropic.claude-3-5-sonnet-20240620-v1:0 litellm_params: - model: openai/* - api_base: https://exampleopenaiendpoint-production-0ee2.up.railway.app/ - - model_name: bedrock/* - litellm_params: - model: bedrock/* - - model_name: openai/* - litellm_params: - model: openai/* - - model_name: dashscope/* - litellm_params: - model: dashscope/* - + 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 litellm_settings: callbacks: ["cloudzero"] \ No newline at end of file diff --git a/litellm/router.py b/litellm/router.py index 1491adf4df..d6964745e7 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -3046,7 +3046,7 @@ class Router: from litellm.router_utils.common_utils import add_model_file_id_mappings verbose_router_logger.debug( - f"Inside _atext_completion()- model: {model}; kwargs: {kwargs}" + f"Inside _acreate_file()- model: {model}; kwargs: {kwargs}" ) parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs) healthy_deployments = await self.async_get_healthy_deployments( diff --git a/tests/test_litellm/llms/bedrock/files/expected_bedrock_batch_completions.jsonl b/tests/test_litellm/llms/bedrock/files/expected_bedrock_batch_completions.jsonl new file mode 100644 index 0000000000..c58963bb1d --- /dev/null +++ b/tests/test_litellm/llms/bedrock/files/expected_bedrock_batch_completions.jsonl @@ -0,0 +1,2 @@ +{"recordId": "request-1", "modelInput": {"messages": [{"role": "user", "content": [{"type": "text", "text": "Hello world!"}]}], "max_tokens": 10, "system": [{"type": "text", "text": "You are a helpful assistant."}], "anthropic_version": "bedrock-2023-05-31"}} +{"recordId": "request-2", "modelInput": {"messages": [{"role": "user", "content": [{"type": "text", "text": "Hello world!"}]}], "max_tokens": 10, "system": [{"type": "text", "text": "You are an unhelpful assistant."}], "anthropic_version": "bedrock-2023-05-31"}} diff --git a/tests/test_litellm/llms/bedrock/files/input_batch_completions.jsonl b/tests/test_litellm/llms/bedrock/files/input_batch_completions.jsonl new file mode 100644 index 0000000000..41559dfd6f --- /dev/null +++ b/tests/test_litellm/llms/bedrock/files/input_batch_completions.jsonl @@ -0,0 +1,2 @@ +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} diff --git a/tests/test_litellm/llms/bedrock/files/test_bedrock_files_transformation.py b/tests/test_litellm/llms/bedrock/files/test_bedrock_files_transformation.py new file mode 100644 index 0000000000..06e7253908 --- /dev/null +++ b/tests/test_litellm/llms/bedrock/files/test_bedrock_files_transformation.py @@ -0,0 +1,90 @@ +""" +Test bedrock files transformation functionality +""" +import json +import os +from typing import Any, Dict, List + +import pytest + +from litellm.llms.bedrock.files.transformation import BedrockJsonlFilesTransformation + + +class TestBedrockFilesTransformation: + """Test bedrock files transformation""" + + def test_transform_openai_jsonl_content_to_bedrock_jsonl_content(self): + """ + Test transformation of OpenAI JSONL format to Bedrock batch format. + + Validates that the transformation correctly converts OpenAI batch completion + format to Bedrock's expected batch format with proper recordId and modelInput structure. + """ + # Initialize the transformation class + transformation = BedrockJsonlFilesTransformation() + + # Load input JSONL file + input_file_path = os.path.join( + os.path.dirname(__file__), + "input_batch_completions.jsonl" + ) + + # Read and parse the JSONL content + openai_jsonl_content = [] + with open(input_file_path, 'r') as f: + for line in f: + if line.strip(): + openai_jsonl_content.append(json.loads(line)) + + # Transform the content + bedrock_jsonl_content = transformation._transform_openai_jsonl_content_to_bedrock_jsonl_content( + openai_jsonl_content=openai_jsonl_content + ) + + # Print the transformation results for validation + print("\n=== INPUT (OpenAI format) ===") + for i, content in enumerate(openai_jsonl_content): + print(f"Record {i+1}:") + print(json.dumps(content, indent=2)) + print() + + print("\n=== OUTPUT (Bedrock format) ===") + for i, content in enumerate(bedrock_jsonl_content): + print(f"Record {i+1}:") + print(json.dumps(content, indent=2)) + print() + + # Basic validation + assert len(bedrock_jsonl_content) == len(openai_jsonl_content), "Should have same number of records" + + # Check structure of transformed records + for i, record in enumerate(bedrock_jsonl_content): + assert "recordId" in record, f"Record {i+1} should have recordId" + assert "modelInput" in record, f"Record {i+1} should have modelInput" + + # Check recordId matches custom_id from input + expected_custom_id = openai_jsonl_content[i].get("custom_id") + assert record["recordId"] == expected_custom_id, f"Record {i+1} recordId should match custom_id" + + # Check modelInput has expected structure + model_input = record["modelInput"] + assert isinstance(model_input, dict), f"Record {i+1} modelInput should be a dictionary" + + # For Anthropic models, should have anthropic_version and messages + if "anthropic.claude" in openai_jsonl_content[i]["body"]["model"]: + assert "anthropic_version" in model_input, f"Record {i+1} should have anthropic_version" + assert "messages" in model_input, f"Record {i+1} should have messages" + assert "max_tokens" in model_input, f"Record {i+1} should have max_tokens" + + # Write expected output to file for reference + expected_output_path = os.path.join( + os.path.dirname(__file__), + "expected_bedrock_batch_completions.jsonl" + ) + + with open(expected_output_path, 'w') as f: + for record in bedrock_jsonl_content: + f.write(json.dumps(record) + '\n') + + print(f"\n=== Expected output written to: {expected_output_path} ===") + From 6596c24d5ca49793cea9ae4cc40f8c34224ca826 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Fri, 12 Sep 2025 18:45:24 -0700 Subject: [PATCH 17/24] fix(litellm_pre_call_utils.py): add wildcard model support for forwardable model group headers --- litellm/constants.py | 10 +++- .../index.html} | 0 .../proxy/_experimental/out/onboarding.html | 1 - litellm/proxy/_new_secret_config.yaml | 20 +++++-- litellm/proxy/auth/auth_checks.py | 2 - litellm/proxy/litellm_pre_call_utils.py | 60 +++++++++++++------ litellm/utils.py | 12 ++-- 7 files changed, 71 insertions(+), 34 deletions(-) rename litellm/proxy/_experimental/out/{model_hub_table.html => model_hub_table/index.html} (100%) delete mode 100644 litellm/proxy/_experimental/out/onboarding.html diff --git a/litellm/constants.py b/litellm/constants.py index 75c25d9ea9..c0ce0f265b 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -15,7 +15,7 @@ DEFAULT_SQS_FLUSH_INTERVAL_SECONDS = int( os.getenv("DEFAULT_SQS_FLUSH_INTERVAL_SECONDS", 10) ) DEFAULT_NUM_WORKERS_LITELLM_PROXY = int( - os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", os.cpu_count() or 4) + os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", 1) ) DEFAULT_SQS_BATCH_SIZE = int(os.getenv("DEFAULT_SQS_BATCH_SIZE", 512)) SQS_SEND_MESSAGE_ACTION = "SendMessage" @@ -60,7 +60,9 @@ DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO = int( os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO", 128) ) DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE = int( - os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE", 512) + os.getenv( + "DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE", 512 + ) ) # Generic fallback for unknown models @@ -949,7 +951,9 @@ LITELLM_CLI_SESSION_TOKEN_PREFIX = "litellm-session-token" DB_SPEND_UPDATE_JOB_NAME = "db_spend_update_job" PROMETHEUS_EMIT_BUDGET_METRICS_JOB_NAME = "prometheus_emit_budget_metrics" CLOUDZERO_EXPORT_USAGE_DATA_JOB_NAME = "cloudzero_export_usage_data" -CLOUDZERO_MAX_FETCHED_DATA_RECORDS = int(os.getenv("CLOUDZERO_MAX_FETCHED_DATA_RECORDS", 50000)) +CLOUDZERO_MAX_FETCHED_DATA_RECORDS = int( + os.getenv("CLOUDZERO_MAX_FETCHED_DATA_RECORDS", 50000) +) SPEND_LOG_CLEANUP_JOB_NAME = "spend_log_cleanup" SPEND_LOG_RUN_LOOPS = int(os.getenv("SPEND_LOG_RUN_LOOPS", 500)) SPEND_LOG_CLEANUP_BATCH_SIZE = int(os.getenv("SPEND_LOG_CLEANUP_BATCH_SIZE", 1000)) diff --git a/litellm/proxy/_experimental/out/model_hub_table.html b/litellm/proxy/_experimental/out/model_hub_table/index.html similarity index 100% rename from litellm/proxy/_experimental/out/model_hub_table.html rename to litellm/proxy/_experimental/out/model_hub_table/index.html diff --git a/litellm/proxy/_experimental/out/onboarding.html b/litellm/proxy/_experimental/out/onboarding.html deleted file mode 100644 index a9e6e893b0..0000000000 --- a/litellm/proxy/_experimental/out/onboarding.html +++ /dev/null @@ -1 +0,0 @@ -LiteLLM Dashboard \ No newline at end of file diff --git a/litellm/proxy/_new_secret_config.yaml b/litellm/proxy/_new_secret_config.yaml index c785dd05c4..bccd67fa06 100644 --- a/litellm/proxy/_new_secret_config.yaml +++ b/litellm/proxy/_new_secret_config.yaml @@ -1,9 +1,17 @@ model_list: - - model_name: fake-openai-endpoint + - model_name: byok-fixed-gpt-4o-mini litellm_params: - model: openai/fake - api_key: fake-key - api_base: https://exampleopenaiendpoint-production.up.railway.app/ - - model_name: wildcard_models/* + model: openai/gpt-4o-mini + api_base: "https://webhook.site/2f385e05-00aa-402b-86d1-efc9261471a5" + api_key: dummy + - model_name: "byok-wildcard/*" litellm_params: - model: openai/* + model: "openai/*" + api_base: "https://webhook.site/b173e42a-5b09-47c0-9e26-5b7ab0417cd1" + api_key: dummy + +litellm_settings: + model_group_settings: + forward_client_headers_to_llm_api: + - byok-fixed-gpt-4o-mini + - byok-wildcard/* \ No newline at end of file diff --git a/litellm/proxy/auth/auth_checks.py b/litellm/proxy/auth/auth_checks.py index f1242a1f34..f1ee2ce43f 100644 --- a/litellm/proxy/auth/auth_checks.py +++ b/litellm/proxy/auth/auth_checks.py @@ -1211,7 +1211,6 @@ def _check_model_access_helper( models: List[str], team_model_aliases: Optional[Dict[str, str]] = None, team_id: Optional[str] = None, - object_type: Literal["user", "team", "key", "org"] = "user", ) -> bool: ## check if model in allowed model names from collections import defaultdict @@ -1316,7 +1315,6 @@ def _can_object_call_model( models=models, team_model_aliases=team_model_aliases, team_id=team_id, - object_type=object_type, ): return True diff --git a/litellm/proxy/litellm_pre_call_utils.py b/litellm/proxy/litellm_pre_call_utils.py index e82c8b2295..c07133b736 100644 --- a/litellm/proxy/litellm_pre_call_utils.py +++ b/litellm/proxy/litellm_pre_call_utils.py @@ -14,10 +14,10 @@ from litellm.proxy._types import ( AddTeamCallback, CommonProxyErrors, LitellmDataForBackendLLMCall, + LitellmUserRoles, SpecialHeaders, TeamCallbackMetadata, UserAPIKeyAuth, - LitellmUserRoles, ) from litellm.proxy.auth.route_checks import RouteChecks from litellm.router import Router @@ -272,7 +272,7 @@ class LiteLLMProxyRequestSetup: if timeout_header is not None: return float(timeout_header) return None - + @staticmethod def _get_stream_timeout_from_request(headers: dict) -> Optional[float]: """ @@ -292,13 +292,14 @@ class LiteLLMProxyRequestSetup: if num_retries_header is not None: return int(num_retries_header) return None - + @staticmethod def _get_spend_logs_metadata_from_request_headers(headers: dict) -> Optional[dict]: """ Get the `spend_logs_metadata` from the request headers. """ from litellm.litellm_core_utils.safe_json_loads import safe_json_loads + spend_logs_metadata_header = headers.get("x-litellm-spend-logs-metadata", None) if spend_logs_metadata_header is not None: return safe_json_loads(spend_logs_metadata_header) @@ -337,16 +338,24 @@ class LiteLLMProxyRequestSetup: return None @staticmethod - def add_internal_user_from_user_mapping(general_settings: Optional[Dict], user_api_key_dict: UserAPIKeyAuth, headers: dict) -> UserAPIKeyAuth: + def add_internal_user_from_user_mapping( + general_settings: Optional[Dict], + user_api_key_dict: UserAPIKeyAuth, + headers: dict, + ) -> UserAPIKeyAuth: if general_settings is None: return user_api_key_dict user_header_mapping = general_settings.get("user_header_mappings") if not user_header_mapping: return user_api_key_dict - header_name = LiteLLMProxyRequestSetup.get_internal_user_header_from_mapping(user_header_mapping) + header_name = LiteLLMProxyRequestSetup.get_internal_user_header_from_mapping( + user_header_mapping + ) if not header_name: return user_api_key_dict - header_value = LiteLLMProxyRequestSetup._get_case_insensitive_header(headers, header_name) + header_value = LiteLLMProxyRequestSetup._get_case_insensitive_header( + headers, header_name + ) if header_value: user_api_key_dict.user_id = header_value return user_api_key_dict @@ -429,15 +438,25 @@ class LiteLLMProxyRequestSetup: """ Add headers to the LLM call by model group """ + from litellm.proxy.auth.auth_checks import _check_model_access_helper + from litellm.proxy.proxy_server import llm_router + data_model = data.get("model") + if ( data_model is not None and litellm.model_group_settings is not None and litellm.model_group_settings.forward_client_headers_to_llm_api is not None - and data_model - in litellm.model_group_settings.forward_client_headers_to_llm_api + and _check_model_access_helper( + model=data_model, + llm_router=llm_router, + models=litellm.model_group_settings.forward_client_headers_to_llm_api, + team_model_aliases=user_api_key_dict.team_model_aliases, + team_id=user_api_key_dict.team_id, + ) # handles aliases, wildcards, etc. ): + _headers = LiteLLMProxyRequestSetup.add_headers_to_llm_call( headers, user_api_key_dict ) @@ -497,8 +516,10 @@ class LiteLLMProxyRequestSetup: timeout = LiteLLMProxyRequestSetup._get_timeout_from_request(headers) if timeout is not None: data["timeout"] = timeout - - stream_timeout = LiteLLMProxyRequestSetup._get_stream_timeout_from_request(headers) + + stream_timeout = LiteLLMProxyRequestSetup._get_stream_timeout_from_request( + headers + ) if stream_timeout is not None: data["stream_timeout"] = stream_timeout @@ -507,7 +528,7 @@ class LiteLLMProxyRequestSetup: data["num_retries"] = num_retries return data - + @staticmethod def add_litellm_metadata_from_request_headers( headers: dict, @@ -520,11 +541,16 @@ class LiteLLMProxyRequestSetup: Relevant issue: https://github.com/BerriAI/litellm/issues/14008 """ from litellm.proxy._types import LitellmMetadataFromRequestHeaders + metadata_from_headers = LitellmMetadataFromRequestHeaders() - spend_logs_metadata = LiteLLMProxyRequestSetup._get_spend_logs_metadata_from_request_headers(headers) + spend_logs_metadata = ( + LiteLLMProxyRequestSetup._get_spend_logs_metadata_from_request_headers( + headers + ) + ) if spend_logs_metadata is not None: metadata_from_headers["spend_logs_metadata"] = spend_logs_metadata - + ######################################################################################### # Finally update the requests metadata with the `metadata_from_headers` ######################################################################################### @@ -714,7 +740,6 @@ async def add_litellm_data_to_request( # noqa: PLR0915 from litellm.proxy.proxy_server import llm_router, premium_user from litellm.types.proxy.litellm_pre_call_utils import SecretFields - _headers = clean_headers( request.headers, litellm_key_header_name=( @@ -740,8 +765,6 @@ async def add_litellm_data_to_request( # noqa: PLR0915 if data.get(_metadata_variable_name, None) is None: data[_metadata_variable_name] = {} - - data.update( LiteLLMProxyRequestSetup.add_litellm_data_for_backend_llm_call( headers=_headers, @@ -763,7 +786,9 @@ async def add_litellm_data_to_request( # noqa: PLR0915 data=data, headers=_headers, user_api_key_dict=user_api_key_dict ) - user_api_key_dict = LiteLLMProxyRequestSetup.add_internal_user_from_user_mapping(general_settings, user_api_key_dict, _headers) + user_api_key_dict = LiteLLMProxyRequestSetup.add_internal_user_from_user_mapping( + general_settings, user_api_key_dict, _headers + ) # Parse user info from headers user = LiteLLMProxyRequestSetup.get_user_from_headers(_headers, general_settings) @@ -773,7 +798,6 @@ async def add_litellm_data_to_request( # noqa: PLR0915 if "user" not in data: data["user"] = user - data["secret_fields"] = SecretFields(raw_headers=dict(request.headers)) ## Dynamic api version (Azure OpenAI endpoints) ## diff --git a/litellm/utils.py b/litellm/utils.py index 0d2fe5d4d6..5cbbfd34a6 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -2437,7 +2437,7 @@ def get_optional_params_transcription( "prompt": None, "response_format": None, "temperature": None, # openai defaults this to 0 - "timestamp_granularities": None + "timestamp_granularities": None, } non_default_params = { @@ -2505,7 +2505,7 @@ def _map_openai_size_to_vertex_ai_aspect_ratio(size: Optional[str]) -> str: """Map OpenAI size parameter to Vertex AI aspectRatio.""" if size is None: return "1:1" - + # Map OpenAI size strings to Vertex AI aspect ratio strings # Vertex AI accepts: "1:1", "9:16", "16:9", "4:3", "3:4" size_to_aspect_ratio = { @@ -2515,7 +2515,9 @@ def _map_openai_size_to_vertex_ai_aspect_ratio(size: Optional[str]) -> str: "1792x1024": "16:9", # Landscape "1024x1792": "9:16", # Portrait } - return size_to_aspect_ratio.get(size, "1:1") # Default to square if size not recognized + return size_to_aspect_ratio.get( + size, "1:1" + ) # Default to square if size not recognized def get_optional_params_image_gen( @@ -2631,7 +2633,9 @@ def get_optional_params_image_gen( # Map OpenAI size parameter to Vertex AI aspectRatio if size is not None: - optional_params["aspectRatio"] = _map_openai_size_to_vertex_ai_aspect_ratio(size) + optional_params["aspectRatio"] = _map_openai_size_to_vertex_ai_aspect_ratio( + size + ) openai_params: list[str] = list(default_params.keys()) if provider_config is not None: From 20c390dc7141c5ef4e2193c9f0a88a381d0175c9 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Fri, 12 Sep 2025 19:08:13 -0700 Subject: [PATCH 18/24] [Docs] - Bedrock Batches API (#14526) * docs bedrock batch * docs fix * docs bedrock batches * fix Create Batch Request * docs fix --- docs/my-website/docs/batches.md | 3 +- .../docs/providers/bedrock_batches.md | 180 ++++++++++++++++++ docs/my-website/sidebars.js | 1 + litellm/proxy/proxy_config.yaml | 5 +- 4 files changed, 184 insertions(+), 5 deletions(-) create mode 100644 docs/my-website/docs/providers/bedrock_batches.md diff --git a/docs/my-website/docs/batches.md b/docs/my-website/docs/batches.md index d5fbc53c08..1bd4c700ae 100644 --- a/docs/my-website/docs/batches.md +++ b/docs/my-website/docs/batches.md @@ -7,7 +7,7 @@ Covers Batches, Files | Feature | Supported | Notes | |-------|-------|-------| -| Supported Providers | OpenAI, Azure, Vertex | - | +| Supported Providers | OpenAI, Azure, Vertex, Bedrock | - | | ✨ Cost Tracking | ✅ | LiteLLM Enterprise only | | Logging | ✅ | Works across all logging integrations | @@ -178,6 +178,7 @@ print("list_batches_response=", list_batches_response) ### [Azure OpenAI](./providers/azure#azure-batches-api) ### [OpenAI](#quick-start) ### [Vertex AI](./providers/vertex#batch-apis) +### [Bedrock](./providers/bedrock_batches) ## How Cost Tracking for Batches API Works diff --git a/docs/my-website/docs/providers/bedrock_batches.md b/docs/my-website/docs/providers/bedrock_batches.md new file mode 100644 index 0000000000..57487f7d2c --- /dev/null +++ b/docs/my-website/docs/providers/bedrock_batches.md @@ -0,0 +1,180 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Bedrock Batches + +Use Amazon Bedrock Batch Inference API through LiteLLM. + +| Property | Details | +|----------|---------| +| Description | Amazon Bedrock Batch Inference allows you to run inference on large datasets asynchronously | +| Provider Doc | [AWS Bedrock Batch Inference ↗](https://docs.aws.amazon.com/bedrock/latest/userguide/batch-inference.html) | + +## Overview + +Use this to: + +- Run batch inference on large datasets with Bedrock models +- Control batch model access by key/user/team (same as chat completion models) +- Manage S3 storage for batch input/output files + +## (Proxy Admin) Usage + +Here's how to give developers access to your Bedrock Batch models. + +### 1. Setup config.yaml + +- Specify `mode: batch` for each model: Allows developers to know this is a batch model +- Configure S3 bucket and AWS credentials for batch operations + +```yaml showLineNumbers title="litellm_config.yaml" +model_list: + - model_name: "bedrock-batch-claude" + litellm_params: + 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 # 👈 SPECIFY MODE AS BATCH, to tell user this is a batch model +``` + +**Required Parameters:** + +| Parameter | Description | +|-----------|-------------| +| `s3_bucket_name` | S3 bucket for batch input/output files | +| `s3_region_name` | AWS region for S3 bucket | +| `s3_access_key_id` | AWS access key for S3 bucket | +| `s3_secret_access_key` | AWS secret key for S3 bucket | +| `aws_batch_role_arn` | IAM role ARN for Bedrock batch operations. Bedrock Batch APIs require an IAM role ARN to be set. | +| `mode: batch` | Indicates to LiteLLM this is a batch model | + +### 2. Create Virtual Key + +```bash showLineNumbers title="create_virtual_key.sh" +curl -L -X POST 'https://{PROXY_BASE_URL}/key/generate' \ +-H 'Authorization: Bearer ${PROXY_API_KEY}' \ +-H 'Content-Type: application/json' \ +-d '{"models": ["bedrock-batch-claude"]}' +``` + +You can now use the virtual key to access the batch models (See Developer flow). + +## (Developer) Usage + +Here's how to create a LiteLLM managed file and execute Bedrock Batch CRUD operations with the file. + +### 1. Create request.jsonl + +- Check models available via `/model_group/info` +- See all models with `mode: batch` +- Set `model` in .jsonl to the model from `/model_group/info` + +```json showLineNumbers title="bedrock_batch_completions.jsonl" +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock-batch-claude", "messages": [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Hello world!"}], "max_tokens": 1000}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock-batch-claude", "messages": [{"role": "system", "content": "You are an unhelpful assistant."}, {"role": "user", "content": "Hello world!"}], "max_tokens": 1000}} +``` + +Expectation: + +- LiteLLM translates this to the bedrock deployment specific value (e.g. `bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0`) + +### 2. Upload File + +Specify `target_model_names: ""` to enable LiteLLM managed files and request validation. + +model-name should be the same as the model-name in the request.jsonl + + + + +```python showLineNumbers title="bedrock_batch.py" +from openai import OpenAI + +client = OpenAI( + base_url="http://0.0.0.0:4000", + api_key="sk-1234", +) + +# Upload file +batch_input_file = client.files.create( + file=open("./bedrock_batch_completions.jsonl", "rb"), # {"model": "bedrock-batch-claude"} <-> {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0"} + purpose="batch", + extra_body={"target_model_names": "bedrock-batch-claude"} +) +print(batch_input_file) +``` + + + + +```bash showLineNumbers title="Upload File" +curl http://localhost:4000/v1/files \ + -H "Authorization: Bearer sk-1234" \ + -F purpose="batch" \ + -F file="@bedrock_batch_completions.jsonl" \ + -F extra_body='{"target_model_names": "bedrock-batch-claude"}' +``` + + + + +**Where is the file written?**: + +The file is written to S3 bucket specified in your config and prepared for Bedrock batch inference. + +### 3. Create the batch + + + + +```python showLineNumbers title="bedrock_batch.py" +... +# Create batch +batch = client.batches.create( + input_file_id=batch_input_file.id, + endpoint="/v1/chat/completions", + completion_window="24h", + metadata={"description": "Test batch job"}, +) +print(batch) +``` + + + + +```bash showLineNumbers title="Create Batch Request" +curl http://localhost:4000/v1/batches \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "input_file_id": "file-abc123", + "endpoint": "/v1/chat/completions", + "completion_window": "24h", + "metadata": {"description": "Test batch job"} + }' +``` + + + + +## FAQ + +### Where are my files written? + +When a `target_model_names` is specified, the file is written to the S3 bucket configured in your Bedrock batch model configuration. + +### What models are supported? + +LiteLLM only supports Bedrock Anthropic Models for Batch API. If you want other bedrock models file an issue [here](https://github.com/BerriAI/litellm/issues/new/choose). + +## Further Reading + +- [AWS Bedrock Batch Inference Documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/batch-inference.html) +- [LiteLLM Managed Batches](../proxy/managed_batches) +- [LiteLLM Authentication to Bedrock](https://docs.litellm.ai/docs/providers/bedrock#boto3---authentication) diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 72b3859643..d0b07abd52 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -410,6 +410,7 @@ const sidebars = { items: [ "providers/bedrock", "providers/bedrock_agents", + "providers/bedrock_batches", "providers/bedrock_vector_store", ] }, diff --git a/litellm/proxy/proxy_config.yaml b/litellm/proxy/proxy_config.yaml index 704c8e0be6..dfa3fa1dd2 100644 --- a/litellm/proxy/proxy_config.yaml +++ b/litellm/proxy/proxy_config.yaml @@ -10,7 +10,4 @@ model_list: 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 - -litellm_settings: - callbacks: ["cloudzero"] \ No newline at end of file + mode: batch \ No newline at end of file From 93d6e9bf497652c3566f2be84bcf9bc466c12689 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Fri, 12 Sep 2025 19:30:47 -0700 Subject: [PATCH 19/24] pip install "google-genai==1.22.0" --- .circleci/config.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.circleci/config.yml b/.circleci/config.yml index 62e5b77dc6..2a15679801 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -671,6 +671,7 @@ jobs: pip install mypy pip install "google-generativeai==0.3.2" pip install "google-cloud-aiplatform==1.43.0" + pip install "google-genai==1.22.0" pip install pyarrow pip install "boto3==1.36.0" pip install "aioboto3==13.4.0" From 93af8fd6ba158fb94cabc63e42dd6c98384d8916 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Fri, 12 Sep 2025 19:31:19 -0700 Subject: [PATCH 20/24] [QA] E2E - Testing for bedrock batches api (#14525) * add bedrock/batch-anthropic.claude-3-5-sonnet-20240620-v1:0 * test_bedrock_batches_api * fix * fix import * test_bedrock_batches_api --- docs/my-website/docs/batches.md | 3 +- .../docs/providers/bedrock_batches.md | 180 ------------------ docs/my-website/sidebars.js | 1 - .../example_config_yaml/oai_misc_config.yaml | 13 ++ litellm/proxy/proxy_config.yaml | 2 +- .../bedrock_batch_completions.jsonl | 2 + .../test_bedrock_batches_api.py | 37 ++++ 7 files changed, 54 insertions(+), 184 deletions(-) delete mode 100644 docs/my-website/docs/providers/bedrock_batches.md create mode 100644 tests/openai_endpoints_tests/bedrock_batch_completions.jsonl create mode 100644 tests/openai_endpoints_tests/test_bedrock_batches_api.py diff --git a/docs/my-website/docs/batches.md b/docs/my-website/docs/batches.md index 1bd4c700ae..d5fbc53c08 100644 --- a/docs/my-website/docs/batches.md +++ b/docs/my-website/docs/batches.md @@ -7,7 +7,7 @@ Covers Batches, Files | Feature | Supported | Notes | |-------|-------|-------| -| Supported Providers | OpenAI, Azure, Vertex, Bedrock | - | +| Supported Providers | OpenAI, Azure, Vertex | - | | ✨ Cost Tracking | ✅ | LiteLLM Enterprise only | | Logging | ✅ | Works across all logging integrations | @@ -178,7 +178,6 @@ print("list_batches_response=", list_batches_response) ### [Azure OpenAI](./providers/azure#azure-batches-api) ### [OpenAI](#quick-start) ### [Vertex AI](./providers/vertex#batch-apis) -### [Bedrock](./providers/bedrock_batches) ## How Cost Tracking for Batches API Works diff --git a/docs/my-website/docs/providers/bedrock_batches.md b/docs/my-website/docs/providers/bedrock_batches.md deleted file mode 100644 index 57487f7d2c..0000000000 --- a/docs/my-website/docs/providers/bedrock_batches.md +++ /dev/null @@ -1,180 +0,0 @@ -import Tabs from '@theme/Tabs'; -import TabItem from '@theme/TabItem'; - -# Bedrock Batches - -Use Amazon Bedrock Batch Inference API through LiteLLM. - -| Property | Details | -|----------|---------| -| Description | Amazon Bedrock Batch Inference allows you to run inference on large datasets asynchronously | -| Provider Doc | [AWS Bedrock Batch Inference ↗](https://docs.aws.amazon.com/bedrock/latest/userguide/batch-inference.html) | - -## Overview - -Use this to: - -- Run batch inference on large datasets with Bedrock models -- Control batch model access by key/user/team (same as chat completion models) -- Manage S3 storage for batch input/output files - -## (Proxy Admin) Usage - -Here's how to give developers access to your Bedrock Batch models. - -### 1. Setup config.yaml - -- Specify `mode: batch` for each model: Allows developers to know this is a batch model -- Configure S3 bucket and AWS credentials for batch operations - -```yaml showLineNumbers title="litellm_config.yaml" -model_list: - - model_name: "bedrock-batch-claude" - litellm_params: - 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 # 👈 SPECIFY MODE AS BATCH, to tell user this is a batch model -``` - -**Required Parameters:** - -| Parameter | Description | -|-----------|-------------| -| `s3_bucket_name` | S3 bucket for batch input/output files | -| `s3_region_name` | AWS region for S3 bucket | -| `s3_access_key_id` | AWS access key for S3 bucket | -| `s3_secret_access_key` | AWS secret key for S3 bucket | -| `aws_batch_role_arn` | IAM role ARN for Bedrock batch operations. Bedrock Batch APIs require an IAM role ARN to be set. | -| `mode: batch` | Indicates to LiteLLM this is a batch model | - -### 2. Create Virtual Key - -```bash showLineNumbers title="create_virtual_key.sh" -curl -L -X POST 'https://{PROXY_BASE_URL}/key/generate' \ --H 'Authorization: Bearer ${PROXY_API_KEY}' \ --H 'Content-Type: application/json' \ --d '{"models": ["bedrock-batch-claude"]}' -``` - -You can now use the virtual key to access the batch models (See Developer flow). - -## (Developer) Usage - -Here's how to create a LiteLLM managed file and execute Bedrock Batch CRUD operations with the file. - -### 1. Create request.jsonl - -- Check models available via `/model_group/info` -- See all models with `mode: batch` -- Set `model` in .jsonl to the model from `/model_group/info` - -```json showLineNumbers title="bedrock_batch_completions.jsonl" -{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock-batch-claude", "messages": [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Hello world!"}], "max_tokens": 1000}} -{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock-batch-claude", "messages": [{"role": "system", "content": "You are an unhelpful assistant."}, {"role": "user", "content": "Hello world!"}], "max_tokens": 1000}} -``` - -Expectation: - -- LiteLLM translates this to the bedrock deployment specific value (e.g. `bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0`) - -### 2. Upload File - -Specify `target_model_names: ""` to enable LiteLLM managed files and request validation. - -model-name should be the same as the model-name in the request.jsonl - - - - -```python showLineNumbers title="bedrock_batch.py" -from openai import OpenAI - -client = OpenAI( - base_url="http://0.0.0.0:4000", - api_key="sk-1234", -) - -# Upload file -batch_input_file = client.files.create( - file=open("./bedrock_batch_completions.jsonl", "rb"), # {"model": "bedrock-batch-claude"} <-> {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0"} - purpose="batch", - extra_body={"target_model_names": "bedrock-batch-claude"} -) -print(batch_input_file) -``` - - - - -```bash showLineNumbers title="Upload File" -curl http://localhost:4000/v1/files \ - -H "Authorization: Bearer sk-1234" \ - -F purpose="batch" \ - -F file="@bedrock_batch_completions.jsonl" \ - -F extra_body='{"target_model_names": "bedrock-batch-claude"}' -``` - - - - -**Where is the file written?**: - -The file is written to S3 bucket specified in your config and prepared for Bedrock batch inference. - -### 3. Create the batch - - - - -```python showLineNumbers title="bedrock_batch.py" -... -# Create batch -batch = client.batches.create( - input_file_id=batch_input_file.id, - endpoint="/v1/chat/completions", - completion_window="24h", - metadata={"description": "Test batch job"}, -) -print(batch) -``` - - - - -```bash showLineNumbers title="Create Batch Request" -curl http://localhost:4000/v1/batches \ - -H "Authorization: Bearer sk-1234" \ - -H "Content-Type: application/json" \ - -d '{ - "input_file_id": "file-abc123", - "endpoint": "/v1/chat/completions", - "completion_window": "24h", - "metadata": {"description": "Test batch job"} - }' -``` - - - - -## FAQ - -### Where are my files written? - -When a `target_model_names` is specified, the file is written to the S3 bucket configured in your Bedrock batch model configuration. - -### What models are supported? - -LiteLLM only supports Bedrock Anthropic Models for Batch API. If you want other bedrock models file an issue [here](https://github.com/BerriAI/litellm/issues/new/choose). - -## Further Reading - -- [AWS Bedrock Batch Inference Documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/batch-inference.html) -- [LiteLLM Managed Batches](../proxy/managed_batches) -- [LiteLLM Authentication to Bedrock](https://docs.litellm.ai/docs/providers/bedrock#boto3---authentication) diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index d0b07abd52..72b3859643 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -410,7 +410,6 @@ const sidebars = { items: [ "providers/bedrock", "providers/bedrock_agents", - "providers/bedrock_batches", "providers/bedrock_vector_store", ] }, diff --git a/litellm/proxy/example_config_yaml/oai_misc_config.yaml b/litellm/proxy/example_config_yaml/oai_misc_config.yaml index 1191e054ef..45c6e44132 100644 --- a/litellm/proxy/example_config_yaml/oai_misc_config.yaml +++ b/litellm/proxy/example_config_yaml/oai_misc_config.yaml @@ -18,6 +18,19 @@ model_list: litellm_params: model: "groq/*" api_key: os.environ/GROQ_API_KEY + - model_name: bedrock/batch-anthropic.claude-3-5-sonnet-20240620-v1:0 + litellm_params: + 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 + litellm_settings: # set_verbose: True # Uncomment this if you want to see verbose logs; not recommended in production drop_params: True diff --git a/litellm/proxy/proxy_config.yaml b/litellm/proxy/proxy_config.yaml index dfa3fa1dd2..fdd2e51dfd 100644 --- a/litellm/proxy/proxy_config.yaml +++ b/litellm/proxy/proxy_config.yaml @@ -10,4 +10,4 @@ model_list: 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 \ No newline at end of file + mode: batch diff --git a/tests/openai_endpoints_tests/bedrock_batch_completions.jsonl b/tests/openai_endpoints_tests/bedrock_batch_completions.jsonl new file mode 100644 index 0000000000..cfcc5cb246 --- /dev/null +++ b/tests/openai_endpoints_tests/bedrock_batch_completions.jsonl @@ -0,0 +1,2 @@ +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}} \ No newline at end of file diff --git a/tests/openai_endpoints_tests/test_bedrock_batches_api.py b/tests/openai_endpoints_tests/test_bedrock_batches_api.py new file mode 100644 index 0000000000..a6fae641ed --- /dev/null +++ b/tests/openai_endpoints_tests/test_bedrock_batches_api.py @@ -0,0 +1,37 @@ +from openai import OpenAI +import pytest + +client = OpenAI( + base_url="http://0.0.0.0:4000", + api_key="sk-1234", +) + + +BEDROCK_BATCH_MODEL = "bedrock/batch-anthropic.claude-3-5-sonnet-20240620-v1:0" + + +@pytest.mark.asyncio +async def test_bedrock_batches_api(): + """ + Test bedrock batches api + + E2E Test Creating a File and a Batch on Bedrock + """ + # Upload file + batch_input_file = client.files.create( + file=open("tests/openai_endpoints_tests/bedrock_batch_completions.jsonl", "rb"), + purpose="batch", + extra_body={"target_model_names": BEDROCK_BATCH_MODEL} + ) + print(batch_input_file) + + # Create batch + batch = client.batches.create( + input_file_id=batch_input_file.id, + endpoint="/v1/chat/completions", + completion_window="24h", + metadata={"description": "Test batch job"}, + ) + print(batch) + + assert batch.id is not None \ No newline at end of file From 05e3e3685b8f32cd4e660e238faafbbf64ed5d1f Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Fri, 12 Sep 2025 19:38:07 -0700 Subject: [PATCH 21/24] docs(forward_client_headers.md): document new forwarding client headers to llm api feature --- .../docs/proxy/forward_client_headers.md | 212 ++++++++++++++++++ docs/my-website/docs/proxy/request_headers.md | 10 +- docs/my-website/sidebars.js | 1 + 3 files changed, 222 insertions(+), 1 deletion(-) create mode 100644 docs/my-website/docs/proxy/forward_client_headers.md diff --git a/docs/my-website/docs/proxy/forward_client_headers.md b/docs/my-website/docs/proxy/forward_client_headers.md new file mode 100644 index 0000000000..5477ffe87a --- /dev/null +++ b/docs/my-website/docs/proxy/forward_client_headers.md @@ -0,0 +1,212 @@ +# Forward Client Headers to LLM API + +Control which model groups can forward client headers to the underlying LLM provider APIs. + +## Overview + +By default, LiteLLM does not forward client headers to LLM provider APIs for security reasons. However, you can selectively enable header forwarding for specific model groups using the `forward_client_headers_to_llm_api` setting. + +## Configuration + +## Enable Globally + +```yaml +general_settings: + forward_client_headers_to_llm_api: true +``` + +## Enable for a Model Group + +Add the `forward_client_headers_to_llm_api` setting under `model_group_settings` in your configuration: + +```yaml +model_list: + - model_name: gpt-4o-mini + litellm_params: + model: openai/gpt-4o-mini + api_key: "your-api-key" + - model_name: "wildcard-models/*" + litellm_params: + model: "openai/*" + api_key: "your-api-key" + +litellm_settings: + model_group_settings: + forward_client_headers_to_llm_api: + - gpt-4o-mini + - wildcard-models/* +``` + +## Supported Model Patterns + +The configuration supports various model matching patterns: + +### 1. Exact Model Names +```yaml +forward_client_headers_to_llm_api: + - gpt-4o-mini + - claude-3-sonnet +``` + +### 2. Wildcard Patterns +```yaml +forward_client_headers_to_llm_api: + - "openai/*" # All OpenAI models + - "anthropic/*" # All Anthropic models + - "wildcard-group/*" # All models in wildcard-group +``` + +### 3. Team Model Aliases +If your team has model aliases configured, the forwarding will work with both the original model name and the alias. + +## Forwarded Headers + +When enabled for a model group, LiteLLM forwards the following types of headers: + +### Custom Headers (x- prefix) +- Any header starting with `x-` (except `x-stainless-*` which can cause OpenAI SDK issues) +- Examples: `x-custom-header`, `x-request-id`, `x-trace-id` + +### Provider-Specific Headers +- **Anthropic**: `anthropic-beta` headers +- **OpenAI**: `openai-organization` (when enabled via `forward_openai_org_id: true`) + +### User Information Headers (Optional) +When `add_user_information_to_llm_headers` is enabled, LiteLLM adds: +- `x-litellm-user-id` +- `x-litellm-org-id` +- Other user metadata as `x-litellm-*` headers + +## Security Considerations + +⚠️ **Important Security Notes:** + +1. **Sensitive Data**: Only enable header forwarding for trusted model groups, as headers may contain sensitive information +2. **API Keys**: Never include API keys or secrets in forwarded headers +3. **PII**: Be cautious about forwarding headers that might contain personally identifiable information +4. **Provider Limits**: Some providers have restrictions on custom headers + +## Example Use Cases + +### 1. Request Tracing +Forward tracing headers to track requests across your system: + +```bash +curl -X POST "https://your-proxy.com/v1/chat/completions" \ + -H "Authorization: Bearer your-key" \ + -H "x-trace-id: abc123" \ + -H "x-request-source: mobile-app" \ + -d '{ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "Hello"}] + }' +``` + +### 2. Custom Metadata +Pass custom metadata to your LLM provider: + +```bash +curl -X POST "https://your-proxy.com/v1/chat/completions" \ + -H "Authorization: Bearer your-key" \ + -H "x-customer-id: customer-123" \ + -H "x-environment: production" \ + -d '{ + "model": "gpt-4o-mini", + "messages": [{"role": "user", "content": "Hello"}] + }' +``` + +### 3. Anthropic Beta Features +Enable beta features for Anthropic models: + +```bash +curl -X POST "https://your-proxy.com/v1/chat/completions" \ + -H "Authorization: Bearer your-key" \ + -H "anthropic-beta: tools-2024-04-04" \ + -d '{ + "model": "claude-3-sonnet", + "messages": [{"role": "user", "content": "Hello"}] + }' +``` + +## Complete Configuration Example + +```yaml +model_list: + # Fixed model with header forwarding + - model_name: byok-fixed-gpt-4o-mini + litellm_params: + model: openai/gpt-4o-mini + api_base: "https://your-openai-endpoint.com" + api_key: "your-api-key" + + # Wildcard model group with header forwarding + - model_name: "byok-wildcard/*" + litellm_params: + model: "openai/*" + api_base: "https://your-openai-endpoint.com" + api_key: "your-api-key" + + # Standard model without header forwarding + - model_name: standard-gpt-4 + litellm_params: + model: openai/gpt-4 + api_key: "your-api-key" + +litellm_settings: + # Enable user info headers globally (optional) + add_user_information_to_llm_headers: true + + model_group_settings: + forward_client_headers_to_llm_api: + - byok-fixed-gpt-4o-mini + - byok-wildcard/* + # Note: standard-gpt-4 is NOT included, so no headers forwarded + +general_settings: + # Enable OpenAI organization header forwarding (optional) + forward_openai_org_id: true +``` + +## Testing Header Forwarding + +To test if headers are being forwarded: + +1. **Enable Debug Logging**: Set `set_verbose: true` in your config +2. **Check Provider Logs**: Monitor your LLM provider's request logs +3. **Use Webhook Sites**: For testing, you can use webhook.site URLs as api_base to see forwarded headers + +## Troubleshooting + +### Headers Not Being Forwarded + +1. **Check Model Name**: Ensure the model name in your request matches the configuration +2. **Verify Pattern Matching**: Wildcard patterns must match exactly +3. **Review Logs**: Enable verbose logging to see header processing + +### Provider Errors + +1. **Invalid Headers**: Some providers reject unknown headers +2. **Header Limits**: Providers may have limits on header count/size +3. **Authentication**: Ensure forwarded headers don't conflict with authentication + +## Related Features + +- [Request Headers](./request_headers.md) - Complete list of supported request headers +- [Response Headers](./response_headers.md) - Headers returned by LiteLLM +- [Team Model Aliases](./team_model_add.md) - Configure model aliases for teams +- [Model Access Control](./model_access.md) - Control which users can access which models + +## API Reference + +The header forwarding is controlled by the `ModelGroupSettings` configuration: + +```python +class ModelGroupSettings(BaseModel): + forward_client_headers_to_llm_api: Optional[List[str]] = None +``` + +Where each string in the list can be: +- An exact model name (e.g., `"gpt-4o-mini"`) +- A wildcard pattern (e.g., `"openai/*"`) +- A model group name (e.g., `"my-model-group/*"`) diff --git a/docs/my-website/docs/proxy/request_headers.md b/docs/my-website/docs/proxy/request_headers.md index eea66e5fa9..090c201f88 100644 --- a/docs/my-website/docs/proxy/request_headers.md +++ b/docs/my-website/docs/proxy/request_headers.md @@ -2,6 +2,10 @@ Special headers that are supported by LiteLLM. +## Header Forwarding + +By default, LiteLLM does not forward client headers to LLM provider APIs. However, you can selectively enable header forwarding for specific model groups. [Learn more about configuring header forwarding](./forward_client_headers.md). + ## LiteLLM Headers `x-litellm-timeout` Optional[float]: The timeout for the request in seconds. @@ -21,11 +25,15 @@ Special headers that are supported by LiteLLM. `anthropic-version` Optional[str]: The version of the Anthropic API to use. `anthropic-beta` Optional[str]: The beta version of the Anthropic API to use. - For `/v1/messages` endpoint, this will always be forward the header to the underlying model. - - For `/chat/completions` endpoint, this will only be forwarded if `forward_client_headers_to_llm_api` is true. + - For `/chat/completions` endpoint, this will only be forwarded if the model is configured in `forward_client_headers_to_llm_api`. [Learn more](./forward_client_headers.md) ## OpenAI Headers `openai-organization` Optional[str]: The organization to use for the OpenAI API. (currently needs to be enabled via `general_settings::forward_openai_org_id: true`) +## Custom Headers + +Custom headers starting with `x-` can be forwarded to LLM provider APIs when the model is configured in `forward_client_headers_to_llm_api`. [Learn more about header forwarding configuration](./forward_client_headers.md). + diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 72b3859643..a1f104f1c9 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -141,6 +141,7 @@ const sidebars = { "proxy/clientside_auth", "proxy/request_headers", "proxy/response_headers", + "proxy/forward_client_headers", "proxy/model_discovery", ], }, From 8b3b9435574d4f1b16b8a322df32e47be8052596 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Fri, 12 Sep 2025 19:51:14 -0700 Subject: [PATCH 22/24] docs fix --- docs/my-website/docs/batches.md | 3 +- .../docs/providers/bedrock_batches.md | 180 ++++++++++++++++++ docs/my-website/sidebars.js | 1 + 3 files changed, 183 insertions(+), 1 deletion(-) create mode 100644 docs/my-website/docs/providers/bedrock_batches.md diff --git a/docs/my-website/docs/batches.md b/docs/my-website/docs/batches.md index d5fbc53c08..1bd4c700ae 100644 --- a/docs/my-website/docs/batches.md +++ b/docs/my-website/docs/batches.md @@ -7,7 +7,7 @@ Covers Batches, Files | Feature | Supported | Notes | |-------|-------|-------| -| Supported Providers | OpenAI, Azure, Vertex | - | +| Supported Providers | OpenAI, Azure, Vertex, Bedrock | - | | ✨ Cost Tracking | ✅ | LiteLLM Enterprise only | | Logging | ✅ | Works across all logging integrations | @@ -178,6 +178,7 @@ print("list_batches_response=", list_batches_response) ### [Azure OpenAI](./providers/azure#azure-batches-api) ### [OpenAI](#quick-start) ### [Vertex AI](./providers/vertex#batch-apis) +### [Bedrock](./providers/bedrock_batches) ## How Cost Tracking for Batches API Works diff --git a/docs/my-website/docs/providers/bedrock_batches.md b/docs/my-website/docs/providers/bedrock_batches.md new file mode 100644 index 0000000000..57487f7d2c --- /dev/null +++ b/docs/my-website/docs/providers/bedrock_batches.md @@ -0,0 +1,180 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Bedrock Batches + +Use Amazon Bedrock Batch Inference API through LiteLLM. + +| Property | Details | +|----------|---------| +| Description | Amazon Bedrock Batch Inference allows you to run inference on large datasets asynchronously | +| Provider Doc | [AWS Bedrock Batch Inference ↗](https://docs.aws.amazon.com/bedrock/latest/userguide/batch-inference.html) | + +## Overview + +Use this to: + +- Run batch inference on large datasets with Bedrock models +- Control batch model access by key/user/team (same as chat completion models) +- Manage S3 storage for batch input/output files + +## (Proxy Admin) Usage + +Here's how to give developers access to your Bedrock Batch models. + +### 1. Setup config.yaml + +- Specify `mode: batch` for each model: Allows developers to know this is a batch model +- Configure S3 bucket and AWS credentials for batch operations + +```yaml showLineNumbers title="litellm_config.yaml" +model_list: + - model_name: "bedrock-batch-claude" + litellm_params: + 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 # 👈 SPECIFY MODE AS BATCH, to tell user this is a batch model +``` + +**Required Parameters:** + +| Parameter | Description | +|-----------|-------------| +| `s3_bucket_name` | S3 bucket for batch input/output files | +| `s3_region_name` | AWS region for S3 bucket | +| `s3_access_key_id` | AWS access key for S3 bucket | +| `s3_secret_access_key` | AWS secret key for S3 bucket | +| `aws_batch_role_arn` | IAM role ARN for Bedrock batch operations. Bedrock Batch APIs require an IAM role ARN to be set. | +| `mode: batch` | Indicates to LiteLLM this is a batch model | + +### 2. Create Virtual Key + +```bash showLineNumbers title="create_virtual_key.sh" +curl -L -X POST 'https://{PROXY_BASE_URL}/key/generate' \ +-H 'Authorization: Bearer ${PROXY_API_KEY}' \ +-H 'Content-Type: application/json' \ +-d '{"models": ["bedrock-batch-claude"]}' +``` + +You can now use the virtual key to access the batch models (See Developer flow). + +## (Developer) Usage + +Here's how to create a LiteLLM managed file and execute Bedrock Batch CRUD operations with the file. + +### 1. Create request.jsonl + +- Check models available via `/model_group/info` +- See all models with `mode: batch` +- Set `model` in .jsonl to the model from `/model_group/info` + +```json showLineNumbers title="bedrock_batch_completions.jsonl" +{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock-batch-claude", "messages": [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Hello world!"}], "max_tokens": 1000}} +{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "bedrock-batch-claude", "messages": [{"role": "system", "content": "You are an unhelpful assistant."}, {"role": "user", "content": "Hello world!"}], "max_tokens": 1000}} +``` + +Expectation: + +- LiteLLM translates this to the bedrock deployment specific value (e.g. `bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0`) + +### 2. Upload File + +Specify `target_model_names: ""` to enable LiteLLM managed files and request validation. + +model-name should be the same as the model-name in the request.jsonl + + + + +```python showLineNumbers title="bedrock_batch.py" +from openai import OpenAI + +client = OpenAI( + base_url="http://0.0.0.0:4000", + api_key="sk-1234", +) + +# Upload file +batch_input_file = client.files.create( + file=open("./bedrock_batch_completions.jsonl", "rb"), # {"model": "bedrock-batch-claude"} <-> {"model": "bedrock/us.anthropic.claude-3-5-sonnet-20240620-v1:0"} + purpose="batch", + extra_body={"target_model_names": "bedrock-batch-claude"} +) +print(batch_input_file) +``` + + + + +```bash showLineNumbers title="Upload File" +curl http://localhost:4000/v1/files \ + -H "Authorization: Bearer sk-1234" \ + -F purpose="batch" \ + -F file="@bedrock_batch_completions.jsonl" \ + -F extra_body='{"target_model_names": "bedrock-batch-claude"}' +``` + + + + +**Where is the file written?**: + +The file is written to S3 bucket specified in your config and prepared for Bedrock batch inference. + +### 3. Create the batch + + + + +```python showLineNumbers title="bedrock_batch.py" +... +# Create batch +batch = client.batches.create( + input_file_id=batch_input_file.id, + endpoint="/v1/chat/completions", + completion_window="24h", + metadata={"description": "Test batch job"}, +) +print(batch) +``` + + + + +```bash showLineNumbers title="Create Batch Request" +curl http://localhost:4000/v1/batches \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "input_file_id": "file-abc123", + "endpoint": "/v1/chat/completions", + "completion_window": "24h", + "metadata": {"description": "Test batch job"} + }' +``` + + + + +## FAQ + +### Where are my files written? + +When a `target_model_names` is specified, the file is written to the S3 bucket configured in your Bedrock batch model configuration. + +### What models are supported? + +LiteLLM only supports Bedrock Anthropic Models for Batch API. If you want other bedrock models file an issue [here](https://github.com/BerriAI/litellm/issues/new/choose). + +## Further Reading + +- [AWS Bedrock Batch Inference Documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/batch-inference.html) +- [LiteLLM Managed Batches](../proxy/managed_batches) +- [LiteLLM Authentication to Bedrock](https://docs.litellm.ai/docs/providers/bedrock#boto3---authentication) diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 72b3859643..d0b07abd52 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -410,6 +410,7 @@ const sidebars = { items: [ "providers/bedrock", "providers/bedrock_agents", + "providers/bedrock_batches", "providers/bedrock_vector_store", ] }, From 69e451458c3b0a6dcd8f73ae6bde41547f5752db Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 13 Sep 2025 09:44:36 -0700 Subject: [PATCH 23/24] test_logging_opentelemetry_context_propagation --- .../litellm_core_utils/test_litellm_logging.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py index fa5164b9c1..f26d93bced 100644 --- a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py +++ b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py @@ -473,13 +473,16 @@ async def test_logging_opentelemetry_context_propagation(): Test that OpenTelemtry context propagation works with async completion. """ import asyncio - import litellm - from litellm.integrations.custom_logger import CustomLogger from opentelemetry import trace from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import SimpleSpanProcessor - from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter + from opentelemetry.sdk.trace.export.in_memory_span_exporter import ( + InMemorySpanExporter, + ) + + import litellm + from litellm.integrations.custom_logger import CustomLogger provider = TracerProvider() exporter = InMemorySpanExporter() @@ -490,7 +493,7 @@ async def test_logging_opentelemetry_context_propagation(): class MockOpenTelemetryLogger(CustomLogger): async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): span = tracer.start_span(start_time=start_time.timestamp() * 1e9, name="async_log_success_event") - span.end(end_time=end_time) + span.end(end_time=end_time.timestamp() * 1e9) mock_logging_obj = MockOpenTelemetryLogger() From ec32a0a7d7e03b8a0a2c0670d951317e17eba79b Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Sat, 13 Sep 2025 09:59:01 -0700 Subject: [PATCH 24/24] fix: get_base_completion_call_args --- tests/llm_translation/test_gemini.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/llm_translation/test_gemini.py b/tests/llm_translation/test_gemini.py index 47e3aaa814..8fcc85aba8 100644 --- a/tests/llm_translation/test_gemini.py +++ b/tests/llm_translation/test_gemini.py @@ -21,7 +21,7 @@ import json class TestGoogleAIStudioGemini(BaseLLMChatTest): def get_base_completion_call_args(self) -> dict: - return {"model": "gemini/gemini-2.0-flash"} + return {"model": "gemini/gemini-2.5-flash"} def get_base_completion_call_args_with_reasoning_model(self) -> dict: return {"model": "gemini/gemini-2.5-flash"}