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

@@ -457,30 +457,33 @@ class ChatOpenAI(BaseChatModel):
         params = {**params, **kwargs, "stream": True}
 
         default_chunk_class = AIMessageChunk
-        for chunk in self.client.create(messages=message_dicts, **params):
-            if not isinstance(chunk, dict):
-                chunk = chunk.model_dump()
-            if len(chunk["choices"]) == 0:
-                continue
-            choice = chunk["choices"][0]
-            if choice["delta"] is None:
-                continue
-            chunk = _convert_delta_to_message_chunk(
-                choice["delta"], default_chunk_class
-            )
-            generation_info = {}
-            if finish_reason := choice.get("finish_reason"):
-                generation_info["finish_reason"] = finish_reason
-            logprobs = choice.get("logprobs")
-            if logprobs:
-                generation_info["logprobs"] = logprobs
-            default_chunk_class = chunk.__class__
-            chunk = ChatGenerationChunk(
-                message=chunk, generation_info=generation_info or None
-            )
-            if run_manager:
-                run_manager.on_llm_new_token(chunk.text, chunk=chunk, logprobs=logprobs)
-            yield chunk
+        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:
+                    continue
+                choice = chunk["choices"][0]
+                if choice["delta"] is None:
+                    continue
+                chunk = _convert_delta_to_message_chunk(
+                    choice["delta"], default_chunk_class
+                )
+                generation_info = {}
+                if finish_reason := choice.get("finish_reason"):
+                    generation_info["finish_reason"] = finish_reason
+                logprobs = choice.get("logprobs")
+                if logprobs:
+                    generation_info["logprobs"] = logprobs
+                default_chunk_class = chunk.__class__
+                chunk = ChatGenerationChunk(
+                    message=chunk, generation_info=generation_info or None
+                )
+                if run_manager:
+                    run_manager.on_llm_new_token(
+                        chunk.text, chunk=chunk, logprobs=logprobs
+                    )
+                yield chunk
 
     def _generate(
         self,
@@ -553,34 +556,34 @@ 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
-        ):
-            if not isinstance(chunk, dict):
-                chunk = chunk.model_dump()
-            if len(chunk["choices"]) == 0:
-                continue
-            choice = chunk["choices"][0]
-            if choice["delta"] is None:
-                continue
-            chunk = _convert_delta_to_message_chunk(
-                choice["delta"], default_chunk_class
-            )
-            generation_info = {}
-            if finish_reason := choice.get("finish_reason"):
-                generation_info["finish_reason"] = finish_reason
-            logprobs = choice.get("logprobs")
-            if logprobs:
-                generation_info["logprobs"] = logprobs
-            default_chunk_class = chunk.__class__
-            chunk = ChatGenerationChunk(
-                message=chunk, generation_info=generation_info or None
-            )
-            if run_manager:
-                await run_manager.on_llm_new_token(
-                    token=chunk.text, chunk=chunk, logprobs=logprobs
+        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:
+                    continue
+                choice = chunk["choices"][0]
+                if choice["delta"] is None:
+                    continue
+                chunk = _convert_delta_to_message_chunk(
+                    choice["delta"], default_chunk_class
+                )
+                generation_info = {}
+                if finish_reason := choice.get("finish_reason"):
+                    generation_info["finish_reason"] = finish_reason
+                logprobs = choice.get("logprobs")
+                if logprobs:
+                    generation_info["logprobs"] = logprobs
+                default_chunk_class = chunk.__class__
+                chunk = ChatGenerationChunk(
+                    message=chunk, generation_info=generation_info or None
                 )
-            yield chunk
+                if run_manager:
+                    await run_manager.on_llm_new_token(
+                        token=chunk.text, chunk=chunk, logprobs=logprobs
+                    )
+                yield chunk
 
     async def _agenerate(
         self,