openai, deepseek: make _convert_chunk_to_generation_chunk an instance method (#29731)

1. Make `_convert_chunk_to_generation_chunk` an instance method on
BaseChatOpenAI
2. Override on ChatDeepSeek to add `"reasoning_content"` to message
additional_kwargs.

Resolves https://github.com/langchain-ai/langchain/issues/29513

libs/partners/deepseek/langchain_deepseek/chat_models.py

@@ -1,9 +1,10 @@
 """DeepSeek chat models."""
 
-from typing import Dict, Optional, Union
+from typing import Dict, Optional, Type, Union
 
 import openai
-from langchain_core.outputs import ChatResult
+from langchain_core.messages import AIMessageChunk
+from langchain_core.outputs import ChatGenerationChunk, ChatResult
 from langchain_core.utils import from_env, secret_from_env
 from langchain_openai.chat_models.base import BaseChatOpenAI
 from pydantic import ConfigDict, Field, SecretStr, model_validator
@@ -218,3 +219,23 @@ class ChatDeepSeek(BaseChatOpenAI):
             )
 
         return rtn
+
+    def _convert_chunk_to_generation_chunk(
+        self,
+        chunk: dict,
+        default_chunk_class: Type,
+        base_generation_info: Optional[Dict],
+    ) -> Optional[ChatGenerationChunk]:
+        generation_chunk = super()._convert_chunk_to_generation_chunk(
+            chunk,
+            default_chunk_class,
+            base_generation_info,
+        )
+        if (choices := chunk.get("choices")) and generation_chunk:
+            top = choices[0]
+            if reasoning_content := top.get("delta", {}).get("reasoning_content"):
+                if isinstance(generation_chunk.message, AIMessageChunk):
+                    generation_chunk.message.additional_kwargs["reasoning_content"] = (
+                        reasoning_content
+                    )
+        return generation_chunk

libs/partners/deepseek/tests/integration_tests/test_chat_models.py

@@ -1,9 +1,10 @@
 """Test ChatDeepSeek chat model."""
 
-from typing import Type
+from typing import Optional, Type
 
 import pytest
 from langchain_core.language_models import BaseChatModel
+from langchain_core.messages import AIMessageChunk, BaseMessageChunk
 from langchain_core.tools import BaseTool
 from langchain_tests.integration_tests import ChatModelIntegrationTests
 
@@ -38,3 +39,13 @@ def test_reasoning_content() -> None:
     assert response.content
     assert response.additional_kwargs["reasoning_content"]
     raise ValueError()
+
+
+@pytest.mark.xfail(reason="Takes > 30s to run.")
+def test_reasoning_content_streaming() -> None:
+    chat_model = ChatDeepSeek(model="deepseek-reasoner")
+    full: Optional[BaseMessageChunk] = None
+    for chunk in chat_model.stream("What is the square root of 256256?"):
+        full = chunk if full is None else full + chunk
+    assert isinstance(full, AIMessageChunk)
+    assert full.additional_kwargs["reasoning_content"]

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

@@ -316,57 +316,6 @@ def _convert_delta_to_message_chunk(
         return default_class(content=content, id=id_)  # type: ignore
 
 
-def _convert_chunk_to_generation_chunk(
-    chunk: dict, default_chunk_class: Type, base_generation_info: Optional[Dict]
-) -> Optional[ChatGenerationChunk]:
-    if chunk.get("type") == "content.delta":  # from beta.chat.completions.stream
-        return None
-    token_usage = chunk.get("usage")
-    choices = (
-        chunk.get("choices", [])
-        # from beta.chat.completions.stream
-        or chunk.get("chunk", {}).get("choices", [])
-    )
-
-    usage_metadata: Optional[UsageMetadata] = (
-        _create_usage_metadata(token_usage) if token_usage else None
-    )
-    if len(choices) == 0:
-        # logprobs is implicitly None
-        generation_chunk = ChatGenerationChunk(
-            message=default_chunk_class(content="", usage_metadata=usage_metadata)
-        )
-        return generation_chunk
-
-    choice = choices[0]
-    if choice["delta"] is None:
-        return None
-
-    message_chunk = _convert_delta_to_message_chunk(
-        choice["delta"], default_chunk_class
-    )
-    generation_info = {**base_generation_info} if base_generation_info else {}
-
-    if finish_reason := choice.get("finish_reason"):
-        generation_info["finish_reason"] = finish_reason
-        if model_name := chunk.get("model"):
-            generation_info["model_name"] = model_name
-        if system_fingerprint := chunk.get("system_fingerprint"):
-            generation_info["system_fingerprint"] = system_fingerprint
-
-    logprobs = choice.get("logprobs")
-    if logprobs:
-        generation_info["logprobs"] = logprobs
-
-    if usage_metadata and isinstance(message_chunk, AIMessageChunk):
-        message_chunk.usage_metadata = usage_metadata
-
-    generation_chunk = ChatGenerationChunk(
-        message=message_chunk, generation_info=generation_info or None
-    )
-    return generation_chunk
-
-
 def _update_token_usage(
     overall_token_usage: Union[int, dict], new_usage: Union[int, dict]
 ) -> Union[int, dict]:
@@ -692,6 +641,59 @@ class BaseChatOpenAI(BaseChatModel):
             combined["system_fingerprint"] = system_fingerprint
         return combined
 
+    def _convert_chunk_to_generation_chunk(
+        self,
+        chunk: dict,
+        default_chunk_class: Type,
+        base_generation_info: Optional[Dict],
+    ) -> Optional[ChatGenerationChunk]:
+        if chunk.get("type") == "content.delta":  # from beta.chat.completions.stream
+            return None
+        token_usage = chunk.get("usage")
+        choices = (
+            chunk.get("choices", [])
+            # from beta.chat.completions.stream
+            or chunk.get("chunk", {}).get("choices", [])
+        )
+
+        usage_metadata: Optional[UsageMetadata] = (
+            _create_usage_metadata(token_usage) if token_usage else None
+        )
+        if len(choices) == 0:
+            # logprobs is implicitly None
+            generation_chunk = ChatGenerationChunk(
+                message=default_chunk_class(content="", usage_metadata=usage_metadata)
+            )
+            return generation_chunk
+
+        choice = choices[0]
+        if choice["delta"] is None:
+            return None
+
+        message_chunk = _convert_delta_to_message_chunk(
+            choice["delta"], default_chunk_class
+        )
+        generation_info = {**base_generation_info} if base_generation_info else {}
+
+        if finish_reason := choice.get("finish_reason"):
+            generation_info["finish_reason"] = finish_reason
+            if model_name := chunk.get("model"):
+                generation_info["model_name"] = model_name
+            if system_fingerprint := chunk.get("system_fingerprint"):
+                generation_info["system_fingerprint"] = system_fingerprint
+
+        logprobs = choice.get("logprobs")
+        if logprobs:
+            generation_info["logprobs"] = logprobs
+
+        if usage_metadata and isinstance(message_chunk, AIMessageChunk):
+            message_chunk.usage_metadata = usage_metadata
+
+        generation_chunk = ChatGenerationChunk(
+            message=message_chunk, generation_info=generation_info or None
+        )
+        return generation_chunk
+
     def _stream(
         self,
         messages: List[BaseMessage],
@@ -727,7 +729,7 @@ class BaseChatOpenAI(BaseChatModel):
                 for chunk in response:
                     if not isinstance(chunk, dict):
                         chunk = chunk.model_dump()
-                    generation_chunk = _convert_chunk_to_generation_chunk(
+                    generation_chunk = self._convert_chunk_to_generation_chunk(
                         chunk,
                         default_chunk_class,
                         base_generation_info if is_first_chunk else {},
@@ -895,7 +897,7 @@ class BaseChatOpenAI(BaseChatModel):
                 async for chunk in response:
                     if not isinstance(chunk, dict):
                         chunk = chunk.model_dump()
-                    generation_chunk = _convert_chunk_to_generation_chunk(
+                    generation_chunk = self._convert_chunk_to_generation_chunk(
                         chunk,
                         default_chunk_class,
                         base_generation_info if is_first_chunk else {},