openai[patch]: wrap stream code in context manager blocks (#18013)

**Description:**
Use the `Stream` context managers in `ChatOpenAi` `stream` and `astream`
method.

Using the context manager returned by the OpenAI client makes it
possible to terminate the stream early since the response connection
will be closed when the context manager exists.

**Issue:** #5340
**Twitter handle:** @snopoke

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>

libs/partners/openai/langchain_openai/chat_models/base.py

@@ -457,7 +457,8 @@ class ChatOpenAI(BaseChatModel):
         params = {**params, **kwargs, "stream": True}
 
         default_chunk_class = AIMessageChunk
-        for chunk in self.client.create(messages=message_dicts, **params):
+        with self.client.create(messages=message_dicts, **params) as response:
+            for chunk in response:
                 if not isinstance(chunk, dict):
                     chunk = chunk.model_dump()
                 if len(chunk["choices"]) == 0:
@@ -479,7 +480,9 @@ class ChatOpenAI(BaseChatModel):
                     message=chunk, generation_info=generation_info or None
                 )
                 if run_manager:
-                run_manager.on_llm_new_token(chunk.text, chunk=chunk, logprobs=logprobs)
+                    run_manager.on_llm_new_token(
+                        chunk.text, chunk=chunk, logprobs=logprobs
+                    )
                 yield chunk
 
     def _generate(
@@ -553,9 +556,9 @@ class ChatOpenAI(BaseChatModel):
         params = {**params, **kwargs, "stream": True}
 
         default_chunk_class = AIMessageChunk
-        async for chunk in await self.async_client.create(
-            messages=message_dicts, **params
-        ):
+        response = await self.async_client.create(messages=message_dicts, **params)
+        async with response:
+            async for chunk in response:
                 if not isinstance(chunk, dict):
                     chunk = chunk.model_dump()
                 if len(chunk["choices"]) == 0: