huggingface: fix community dep checking (#21628)

libs/partners/huggingface/langchain_huggingface/chat_models/huggingface.py

@@ -14,10 +14,6 @@ from typing import (
     cast,
 )
 
-from langchain_community.llms.huggingface_hub import HuggingFaceHub
-from langchain_community.llms.huggingface_text_gen_inference import (
-    HuggingFaceTextGenInference,
-)
 from langchain_core.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
@@ -111,6 +107,34 @@ def _convert_TGI_message_to_LC_message(
     return AIMessage(content=content, additional_kwargs=additional_kwargs)
 
 
+def _is_huggingface_hub(llm: Any) -> bool:
+    try:
+        from langchain_community.llms.huggingface_hub import (  # type: ignore[import-not-found]
+            HuggingFaceHub,
+        )
+
+        return isinstance(llm, HuggingFaceHub)
+    except ImportError:
+        # if no langchain community, it is not a HuggingFaceHub
+        return False
+
+
+def _is_huggingface_textgen_inference(llm: Any) -> bool:
+    try:
+        from langchain_community.llms.huggingface_text_gen_inference import (  # type: ignore[import-not-found]
+            HuggingFaceTextGenInference,
+        )
+
+        return isinstance(llm, HuggingFaceTextGenInference)
+    except ImportError:
+        # if no langchain community, it is not a HuggingFaceTextGenInference
+        return False
+
+
+def _is_huggingface_endpoint(llm: Any) -> bool:
+    return isinstance(llm, HuggingFaceEndpoint)
+
+
 class ChatHuggingFace(BaseChatModel):
     """
     Wrapper for using Hugging Face LLM's as ChatModels.
@@ -147,9 +171,10 @@ class ChatHuggingFace(BaseChatModel):
 
     @root_validator()
     def validate_llm(cls, values: dict) -> dict:
-        if not isinstance(
-            values["llm"],
-            (HuggingFaceHub, HuggingFaceTextGenInference, HuggingFaceEndpoint),
+        if (
+            not _is_huggingface_hub(values["llm"])
+            and not _is_huggingface_textgen_inference(values["llm"])
+            and not _is_huggingface_endpoint(values["llm"])
         ):
             raise TypeError(
                 "Expected llm to be one of HuggingFaceTextGenInference, "
@@ -177,11 +202,11 @@ class ChatHuggingFace(BaseChatModel):
         run_manager: Optional[CallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> ChatResult:
-        if isinstance(self.llm, HuggingFaceTextGenInference):
+        if _is_huggingface_textgen_inference(self.llm):
             message_dicts = self._create_message_dicts(messages, stop)
             answer = self.llm.client.chat(messages=message_dicts, **kwargs)
             return self._create_chat_result(answer)
-        elif isinstance(self.llm, HuggingFaceEndpoint):
+        elif _is_huggingface_endpoint(self.llm):
             message_dicts = self._create_message_dicts(messages, stop)
             answer = self.llm.client.chat_completion(messages=message_dicts, **kwargs)
             return self._create_chat_result(answer)
@@ -199,7 +224,7 @@ class ChatHuggingFace(BaseChatModel):
         run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> ChatResult:
-        if isinstance(self.llm, HuggingFaceTextGenInference):
+        if _is_huggingface_textgen_inference(self.llm):
             message_dicts = self._create_message_dicts(messages, stop)
             answer = await self.llm.async_client.chat(messages=message_dicts, **kwargs)
             return self._create_chat_result(answer)
@@ -261,12 +286,12 @@ class ChatHuggingFace(BaseChatModel):
         from huggingface_hub import list_inference_endpoints  # type: ignore[import]
 
         available_endpoints = list_inference_endpoints("*")
-        if isinstance(self.llm, HuggingFaceHub) or (
+        if _is_huggingface_hub(self.llm) or (
             hasattr(self.llm, "repo_id") and self.llm.repo_id
         ):
             self.model_id = self.llm.repo_id
             return
-        elif isinstance(self.llm, HuggingFaceTextGenInference):
+        elif _is_huggingface_textgen_inference(self.llm):
             endpoint_url: Optional[str] = self.llm.inference_server_url
         else:
             endpoint_url = self.llm.endpoint_url

libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py

@@ -14,7 +14,7 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
     Example:
         .. code-block:: python
 
-            from langchain_community.embeddings import HuggingFaceEmbeddings
+            from langchain_huggingface import HuggingFaceEmbeddings
 
             model_name = "sentence-transformers/all-mpnet-base-v2"
             model_kwargs = {'device': 'cpu'}

libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py

@@ -33,7 +33,7 @@ class HuggingFacePipeline(BaseLLM):
     Example using from_model_id:
         .. code-block:: python
 
-            from langchain_community.llms import HuggingFacePipeline
+            from langchain_huggingface import HuggingFacePipeline
             hf = HuggingFacePipeline.from_model_id(
                 model_id="gpt2",
                 task="text-generation",
@@ -42,7 +42,7 @@ class HuggingFacePipeline(BaseLLM):
     Example passing pipeline in directly:
         .. code-block:: python
 
-            from langchain_community.llms import HuggingFacePipeline
+            from langchain_huggingface import HuggingFacePipeline
             from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
             model_id = "gpt2"