Add inference providers support for Hugging Face (#8258) (#9738) (#9773)

* Add inference providers support for Hugging Face (#8258)

* add first version of inference providers for huggingface

* temporarily skipping tests

* Add documentation

* Fix titles

* remove max_retries from params and clean up

* add suggestions

* use llm http handler

* update doc

* add suggestions

* run formatters

* add tests

* revert

* revert

* rename file

* set maxsize for lru cache

* fix embeddings

* fix inference url

* fix tests following breaking change in main

* use ChatCompletionRequest

* fix tests and lint

* [Hugging Face] Remove outdated chat completion tests and fix embedding tests (#9749)

* remove or fix tests

* fix link in doc

* fix(config_settings.md): document hf api key

---------

Co-authored-by: célina <hanouticelina@gmail.com>

litellm/main.py

@@ -141,7 +141,7 @@ 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.groq.chat.handler import GroqChatCompletion
-from .llms.huggingface.chat.handler import Huggingface
+from .llms.huggingface.embedding.handler import HuggingFaceEmbedding
 from .llms.nlp_cloud.chat.handler import completion as nlp_cloud_chat_completion
 from .llms.ollama.completion import handler as ollama
 from .llms.oobabooga.chat import oobabooga
@@ -221,7 +221,7 @@ azure_chat_completions = AzureChatCompletion()
 azure_o1_chat_completions = AzureOpenAIO1ChatCompletion()
 azure_text_completions = AzureTextCompletion()
 azure_audio_transcriptions = AzureAudioTranscription()
-huggingface = Huggingface()
+huggingface_embed = HuggingFaceEmbedding()
 predibase_chat_completions = PredibaseChatCompletion()
 codestral_text_completions = CodestralTextCompletion()
 bedrock_converse_chat_completion = BedrockConverseLLM()
@@ -2141,7 +2141,6 @@ def completion(  # type: ignore # noqa: PLR0915
 
             response = model_response
         elif custom_llm_provider == "huggingface":
-            custom_llm_provider = "huggingface"
             huggingface_key = (
                 api_key
                 or litellm.huggingface_key
@@ -2150,40 +2149,23 @@ def completion(  # type: ignore # noqa: PLR0915
                 or litellm.api_key
             )
             hf_headers = headers or litellm.headers
-
-            custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
-            model_response = huggingface.completion(
+            response = base_llm_http_handler.completion(
                 model=model,
                 messages=messages,
-                api_base=api_base,  # type: ignore
-                headers=hf_headers or {},
+                headers=hf_headers,
                 model_response=model_response,
-                print_verbose=print_verbose,
-                optional_params=optional_params,
-                litellm_params=litellm_params,
-                logger_fn=logger_fn,
-                encoding=encoding,
                 api_key=huggingface_key,
+                api_base=api_base,
                 acompletion=acompletion,
                 logging_obj=logging,
-                custom_prompt_dict=custom_prompt_dict,
+                optional_params=optional_params,
+                litellm_params=litellm_params,
                 timeout=timeout,  # type: ignore
                 client=client,
+                custom_llm_provider=custom_llm_provider,
+                encoding=encoding,
+                stream=stream,
             )
-            if (
-                "stream" in optional_params
-                and optional_params["stream"] is True
-                and acompletion is False
-            ):
-                # don't try to access stream object,
-                response = CustomStreamWrapper(
-                    model_response,
-                    model,
-                    custom_llm_provider="huggingface",
-                    logging_obj=logging,
-                )
-                return response
-            response = model_response
         elif custom_llm_provider == "oobabooga":
             custom_llm_provider = "oobabooga"
             model_response = oobabooga.completion(
@@ -3623,7 +3605,7 @@ def embedding(  # noqa: PLR0915
                 or get_secret("HUGGINGFACE_API_KEY")
                 or litellm.api_key
             )  # type: ignore
-            response = huggingface.embedding(
+            response = huggingface_embed.embedding(
                 model=model,
                 input=input,
                 encoding=encoding,  # type: ignore