community[patch]: Add streaming logic in ChatHuggingFace (#18784)

- Add functions (_stream, _astream)
- Connect to _generate and _agenerate

Thank you for contributing to LangChain!

- [x] **PR title**: "community: Add streaming logic in ChatHuggingFace"

- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Addition functions (_stream, _astream) and connection
to _generate and _agenerate
    - **Issue:** #18782
    - **Dependencies:** none
    - **Twitter handle:** @lunara_x

libs/community/langchain_community/chat_models/huggingface.py

@@ -1,19 +1,28 @@
 """Hugging Face Chat Wrapper."""
-
-from typing import Any, List, Optional
+from typing import Any, AsyncIterator, Iterator, List, Optional
 
 from langchain_core.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
 )
-from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.language_models.chat_models import (
+    BaseChatModel,
+    agenerate_from_stream,
+    generate_from_stream,
+)
 from langchain_core.messages import (
     AIMessage,
+    AIMessageChunk,
     BaseMessage,
     HumanMessage,
     SystemMessage,
 )
-from langchain_core.outputs import ChatGeneration, ChatResult, LLMResult
+from langchain_core.outputs import (
+    ChatGeneration,
+    ChatGenerationChunk,
+    ChatResult,
+    LLMResult,
+)
 from langchain_core.pydantic_v1 import root_validator
 
 from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
@@ -26,7 +35,8 @@
 
 
 class ChatHuggingFace(BaseChatModel):
-    """Hugging Face LLMs as ChatModels.
+    """
+    Wrapper for using Hugging Face LLM's as ChatModels.
 
     Works with `HuggingFaceTextGenInference`, `HuggingFaceEndpoint`,
     and `HuggingFaceHub` LLMs.
@@ -44,6 +54,7 @@ class ChatHuggingFace(BaseChatModel):
     system_message: SystemMessage = SystemMessage(content=DEFAULT_SYSTEM_PROMPT)
     tokenizer: Any = None
     model_id: Optional[str] = None
+    streaming: bool = False
 
     def __init__(self, **kwargs: Any):
         super().__init__(**kwargs)
@@ -70,13 +81,50 @@ def validate_llm(cls, values: dict) -> dict:
             )
         return values
 
+    def _stream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[ChatGenerationChunk]:
+        request = self._to_chat_prompt(messages)
+
+        for data in self.llm.stream(request, **kwargs):
+            delta = data
+            chunk = ChatGenerationChunk(message=AIMessageChunk(content=delta))
+            if run_manager:
+                run_manager.on_llm_new_token(delta, chunk=chunk)
+            yield chunk
+
+    async def _astream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[ChatGenerationChunk]:
+        request = self._to_chat_prompt(messages)
+        async for data in self.llm.astream(request, **kwargs):
+            delta = data
+            chunk = ChatGenerationChunk(message=AIMessageChunk(content=delta))
+            if run_manager:
+                await run_manager.on_llm_new_token(delta, chunk=chunk)
+            yield chunk
+
     def _generate(
         self,
         messages: List[BaseMessage],
         stop: Optional[List[str]] = None,
         run_manager: Optional[CallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> ChatResult:
+        if self.streaming:
+            stream_iter = self._stream(
+                messages, stop=stop, run_manager=run_manager, **kwargs
+            )
+            return generate_from_stream(stream_iter)
+
         llm_input = self._to_chat_prompt(messages)
         llm_result = self.llm._generate(
             prompts=[llm_input], stop=stop, run_manager=run_manager, **kwargs
@@ -90,6 +138,12 @@ async def _agenerate(
         run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
         **kwargs: Any,
     ) -> ChatResult:
+        if self.streaming:
+            stream_iter = self._astream(
+                messages, stop=stop, run_manager=run_manager, **kwargs
+            )
+            return await agenerate_from_stream(stream_iter)
+
         llm_input = self._to_chat_prompt(messages)
         llm_result = await self.llm._agenerate(
             prompts=[llm_input], stop=stop, run_manager=run_manager, **kwargs