LiteLLM Minor Fixes & Improvements (2024/12/18) p1

docs/my-website/docs/proxy/health.md

@@ -121,6 +121,20 @@ model_list:
       mode: audio_speech
 ```
 
+### Rerank Models 
+
+To run rerank health checks, specify the mode as "rerank" in your config for the relevant model.
+
+```yaml
+model_list:
+  - model_name: rerank-english-v3.0
+    litellm_params:
+      model: cohere/rerank-english-v3.0
+      api_key: os.environ/COHERE_API_KEY
+    model_info:
+      mode: rerank
+```
+
 ### Batch Models (Azure Only)
 
 For Azure models deployed as 'batch' models, set `mode: batch`. 

docs/my-website/docs/proxy/team_model_add.md

@@ -1,4 +1,13 @@
-# Allow Teams to Add Models
+# ✨ Allow Teams to Add Models
+
+:::info
+
+This is an Enterprise feature.
+[Enterprise Pricing](https://www.litellm.ai/#pricing)
+
+[Contact us here to get a free trial](https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat)
+
+:::
 
 Allow team to add a their own models/key for that project - so any OpenAI call they make uses their OpenAI key.
 

litellm/litellm_core_utils/prompt_templates/factory.py

@@ -3144,7 +3144,9 @@
         else:
             return gemini_text_image_pt(messages=messages)
     elif custom_llm_provider == "mistral":
-        return litellm.MistralConfig()._transform_messages(messages=messages)
+        return litellm.MistralConfig()._transform_messages(
+            messages=messages, model=model
+        )
     elif custom_llm_provider == "bedrock":
         if "amazon.titan-text" in model:
             return amazon_titan_pt(messages=messages)

litellm/llms/anthropic/completion/transformation.py

@@ -260,12 +260,6 @@ def _get_anthropic_text_prompt_from_messages(
 
         return str(prompt)
 
-    def _transform_messages(
-        self, messages: List[AllMessageValues]
-    ) -> List[AllMessageValues]:
-        "Not required"
-        raise NotImplementedError
-
     def get_model_response_iterator(
         self,
         streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],

litellm/llms/azure/chat/o1_handler.py

@@ -57,6 +57,7 @@ def completion(
         client=None,
     ):
         stream: Optional[bool] = optional_params.pop("stream", False)
+        stream_options: Optional[dict] = optional_params.pop("stream_options", None)
         response = super().completion(
             model,
             messages,
@@ -90,6 +91,7 @@ def completion(
                 model=model,
                 custom_llm_provider="openai",
                 logging_obj=logging_obj,
+                stream_options=stream_options,
             )
 
             return streaming_response

litellm/llms/azure_ai/chat/transformation.py

@@ -2,11 +2,11 @@
 
 import litellm
 from litellm._logging import verbose_logger
-from litellm.llms.openai.openai import OpenAIConfig
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     _audio_or_image_in_message_content,
     convert_content_list_to_str,
 )
+from litellm.llms.openai.openai import OpenAIConfig
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.openai import AllMessageValues
 from litellm.types.utils import ProviderField
@@ -33,6 +33,7 @@ def get_required_params(self) -> List[ProviderField]:
     def _transform_messages(
         self,
         messages: List[AllMessageValues],
+        model: str,
     ) -> List:
         """
         - Azure AI Studio doesn't support content as a list. This handles:

litellm/llms/base_llm/chat/transformation.py

@@ -82,6 +82,14 @@ def get_config(cls):
             and v is not None
         }
 
+    def should_fake_stream(
+        self, model: str, custom_llm_provider: Optional[str] = None
+    ) -> bool:
+        """
+        Returns True if the model/provider should fake stream
+        """
+        return False
+
     @abstractmethod
     def get_supported_openai_params(self, model: str) -> list:
         pass

litellm/llms/clarifai/chat/transformation.py

@@ -131,11 +131,6 @@ def validate_environment(
             headers["Authorization"] = f"Bearer {api_key}"
         return headers
 
-    def _transform_messages(
-        self, messages: List[AllMessageValues]
-    ) -> List[AllMessageValues]:
-        raise NotImplementedError
-
     def get_error_class(
         self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
     ) -> BaseLLMException:

litellm/llms/cloudflare/chat/transformation.py

@@ -158,11 +158,6 @@ def get_error_class(
             message=error_message,
         )
 
-    def _transform_messages(
-        self, messages: List[AllMessageValues]
-    ) -> List[AllMessageValues]:
-        raise NotImplementedError
-
     def get_model_response_iterator(
         self,
         streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],

litellm/llms/cohere/chat/transformation.py

@@ -365,8 +365,3 @@ def get_error_class(
         self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
     ) -> BaseLLMException:
         return CohereError(status_code=status_code, message=error_message)
-
-    def _transform_messages(
-        self, messages: List[AllMessageValues]
-    ) -> List[AllMessageValues]:
-        raise NotImplementedError

litellm/llms/cohere/completion/transformation.py

@@ -121,12 +121,6 @@ def validate_environment(
             api_key=api_key,
         )
 
-    def _transform_messages(
-        self,
-        messages: List[AllMessageValues],
-    ) -> List[AllMessageValues]:
-        raise NotImplementedError
-
     def get_error_class(
         self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
     ) -> BaseLLMException:

litellm/llms/databricks/chat/handler.py

@@ -2,11 +2,12 @@
 Handles the chat completion request for Databricks
 """
 
-from typing import Any, Callable, Literal, Optional, Tuple, Union
+from typing import Any, Callable, List, Literal, Optional, Tuple, Union, cast
 
 from httpx._config import Timeout
 
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.types.llms.openai import AllMessageValues
 from litellm.types.utils import CustomStreamingDecoder
 from litellm.utils import ModelResponse
 
@@ -44,7 +45,9 @@ def completion(
         streaming_decoder: Optional[CustomStreamingDecoder] = None,
         fake_stream: bool = False,
     ):
-        messages = DatabricksConfig()._transform_messages(messages)  # type: ignore
+        messages = DatabricksConfig()._transform_messages(
+            messages=cast(List[AllMessageValues], messages), model=model
+        )
         api_base, headers = self.databricks_validate_environment(
             api_base=api_base,
             api_key=api_key,

litellm/llms/databricks/chat/transformation.py

@@ -7,14 +7,14 @@
 
 from pydantic import BaseModel
 
-from litellm.types.llms.openai import AllMessageValues
-from litellm.types.utils import ProviderField
-
-from ...openai_like.chat.transformation import OpenAILikeChatConfig
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     handle_messages_with_content_list_to_str_conversion,
     strip_name_from_messages,
 )
+from litellm.types.llms.openai import AllMessageValues
+from litellm.types.utils import ProviderField
+
+from ...openai_like.chat.transformation import OpenAILikeChatConfig
 
 
 class DatabricksConfig(OpenAILikeChatConfig):
@@ -86,7 +86,7 @@ def _should_fake_stream(self, optional_params: dict) -> bool:
         return False
 
     def _transform_messages(
-        self, messages: List[AllMessageValues]
+        self, messages: List[AllMessageValues], model: str
     ) -> List[AllMessageValues]:
         """
         Databricks does not support:
@@ -102,4 +102,4 @@ def _transform_messages(
             new_messages.append(_message)
         new_messages = handle_messages_with_content_list_to_str_conversion(new_messages)
         new_messages = strip_name_from_messages(new_messages)
-        return super()._transform_messages(new_messages)
+        return super()._transform_messages(messages=new_messages, model=model)

litellm/llms/deepseek/chat/transformation.py

@@ -8,26 +8,26 @@
 from pydantic import BaseModel
 
 import litellm
+from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    handle_messages_with_content_list_to_str_conversion,
+)
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.openai import AllMessageValues, ChatCompletionAssistantMessage
 
 from ....utils import _remove_additional_properties, _remove_strict_from_schema
 from ...openai.chat.gpt_transformation import OpenAIGPTConfig
-from litellm.litellm_core_utils.prompt_templates.common_utils import (
-    handle_messages_with_content_list_to_str_conversion,
-)
 
 
 class DeepSeekChatConfig(OpenAIGPTConfig):
 
     def _transform_messages(
-        self, messages: List[AllMessageValues]
+        self, messages: List[AllMessageValues], model: str
     ) -> List[AllMessageValues]:
         """
         DeepSeek does not support content in list format.
         """
         messages = handle_messages_with_content_list_to_str_conversion(messages)
-        return super()._transform_messages(messages)
+        return super()._transform_messages(messages=messages, model=model)
 
     def _get_openai_compatible_provider_info(
         self, api_base: Optional[str], api_key: Optional[str]

litellm/llms/groq/chat/handler.py

@@ -2,11 +2,12 @@
 Handles the chat completion request for groq
 """
 
-from typing import Any, Callable, Optional, Union
+from typing import Any, Callable, List, Optional, Union, cast
 
 from httpx._config import Timeout
 
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.types.llms.openai import AllMessageValues
 from litellm.types.utils import CustomStreamingDecoder
 from litellm.utils import ModelResponse
 
@@ -42,7 +43,9 @@ def completion(
         streaming_decoder: Optional[CustomStreamingDecoder] = None,
         fake_stream: bool = False,
     ):
-        messages = GroqChatConfig()._transform_messages(messages)  # type: ignore
+        messages = GroqChatConfig()._transform_messages(
+            messages=cast(List[AllMessageValues], messages), model=model
+        )
 
         if optional_params.get("stream") is True:
             fake_stream = GroqChatConfig()._should_fake_stream(optional_params)

litellm/llms/groq/chat/transformation.py

@@ -61,7 +61,7 @@ def __init__(
     def get_config(cls):
         return super().get_config()
 
-    def _transform_messages(self, messages: List[AllMessageValues]) -> List:
+    def _transform_messages(self, messages: List[AllMessageValues], model: str) -> List:
         for idx, message in enumerate(messages):
             """
             1. Don't pass 'null' function_call assistant message to groq - https://github.com/BerriAI/litellm/issues/5839

litellm/llms/huggingface/chat/transformation.py

@@ -369,12 +369,6 @@ def validate_environment(
         headers = {**headers, **default_headers}
         return headers
 
-    def _transform_messages(
-        self,
-        messages: List[AllMessageValues],
-    ) -> List[AllMessageValues]:
-        return messages
-
     def get_error_class(
         self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
     ) -> BaseLLMException:

litellm/llms/mistral/mistral_chat_transformation.py

@@ -9,11 +9,11 @@
 import types
 from typing import List, Literal, Optional, Tuple, Union
 
-from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     handle_messages_with_content_list_to_str_conversion,
     strip_none_values_from_message,
 )
+from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.openai import AllMessageValues
 
@@ -148,7 +148,7 @@ def _get_openai_compatible_provider_info(
         return api_base, dynamic_api_key
 
     def _transform_messages(
-        self, messages: List[AllMessageValues]
+        self, messages: List[AllMessageValues], model: str
     ) -> List[AllMessageValues]:
         """
         - handles scenario where content is list and not string

litellm/llms/ollama/completion/transformation.py

@@ -23,6 +23,7 @@
 from litellm.types.utils import (
     GenericStreamingChunk,
     ModelInfo,
+    ModelInfoBase,
     ModelResponse,
     ProviderField,
     StreamingChoices,
@@ -198,7 +199,7 @@ def _get_max_tokens(self, ollama_model_info: dict) -> Optional[int]:
                 return v
         return None
 
-    def get_model_info(self, model: str) -> ModelInfo:
+    def get_model_info(self, model: str) -> ModelInfoBase:
         """
         curl http://localhost:11434/api/show -d '{
           "name": "mistral"
@@ -222,11 +223,10 @@ def get_model_info(self, model: str) -> ModelInfo:
 
         _max_tokens: Optional[int] = self._get_max_tokens(model_info)
 
-        return ModelInfo(
+        return ModelInfoBase(
             key=model,
             litellm_provider="ollama",
             mode="chat",
-            supported_openai_params=self.get_supported_openai_params(model=model),
             supports_function_calling=self._supports_function_calling(model_info),
             input_cost_per_token=0.0,
             output_cost_per_token=0.0,
@@ -235,11 +235,6 @@ def get_model_info(self, model: str) -> ModelInfo:
             max_output_tokens=_max_tokens,
         )
 
-    def _transform_messages(
-        self, messages: List[AllMessageValues]
-    ) -> List[AllMessageValues]:
-        return messages
-
     def get_error_class(
         self, error_message: str, status_code: int, headers: Union[dict, Headers]
     ) -> BaseLLMException:

litellm/llms/oobabooga/chat/transformation.py

@@ -23,11 +23,6 @@
 
 
 class OobaboogaConfig(OpenAIGPTConfig):
-    def _transform_messages(
-        self, messages: List[AllMessageValues]
-    ) -> List[AllMessageValues]:
-        return messages
-
     def get_error_class(
         self,
         error_message: str,

litellm/llms/openai/chat/gpt_transformation.py

@@ -164,7 +164,7 @@ def map_openai_params(
         )
 
     def _transform_messages(
-        self, messages: List[AllMessageValues]
+        self, messages: List[AllMessageValues], model: str
     ) -> List[AllMessageValues]:
         return messages