openai[patch]: add Responses API attributes to BaseChatOpenAI (#30329)

`reasoning`, `include`, `store`, `truncation`.

Previously these had to be added through `model_kwargs`.

Author: ccurme

libs/langchain/tests/unit_tests/chat_models/test_base.py

@@ -116,8 +116,10 @@ def test_configurable() -> None:
             "request_timeout": None,
             "max_retries": None,
             "presence_penalty": None,
+            "reasoning": None,
             "reasoning_effort": None,
             "frequency_penalty": None,
+            "include": None,
             "seed": None,
             "service_tier": None,
             "logprobs": None,
@@ -126,11 +128,13 @@ def test_configurable() -> None:
             "streaming": False,
             "n": None,
             "top_p": None,
+            "truncation": None,
             "max_tokens": None,
             "tiktoken_model_name": None,
             "default_headers": None,
             "default_query": None,
             "stop": None,
+            "store": None,
             "extra_body": None,
             "include_response_headers": False,
             "stream_usage": False,

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

@@ -502,15 +502,31 @@ class BaseChatOpenAI(BaseChatModel):
     max_tokens: Optional[int] = Field(default=None)
     """Maximum number of tokens to generate."""
     reasoning_effort: Optional[str] = None
-    """Constrains effort on reasoning for reasoning models. 
-    
-    Reasoning models only, like OpenAI o1 and o3-mini.
+    """Constrains effort on reasoning for reasoning models. For use with the Chat
+    Completions API.
+
+    Reasoning models only, like OpenAI o1, o3, and o4-mini.
 
     Currently supported values are low, medium, and high. Reducing reasoning effort 
     can result in faster responses and fewer tokens used on reasoning in a response.
-    
+
     .. versionadded:: 0.2.14
     """
+    reasoning: Optional[dict[str, Any]] = None
+    """Reasoning parameters for reasoning models, i.e., OpenAI o-series models (o1, o3,
+    o4-mini, etc.). For use with the Responses API.
+
+    Example:
+
+    .. code-block:: python
+
+        reasoning={
+            "effort": "medium",  # can be "low", "medium", or "high"
+            "summary": "auto",  # can be "auto", "concise", or "detailed"
+        }
+
+    .. versionadded:: 0.3.24
+    """
     tiktoken_model_name: Optional[str] = None
     """The model name to pass to tiktoken when using this class. 
     Tiktoken is used to count the number of tokens in documents to constrain 
@@ -556,11 +572,41 @@ class BaseChatOpenAI(BaseChatModel):
     However this does not prevent a user from directly passed in the parameter during
     invocation. 
     """
+
+    include: Optional[list[str]] = None
+    """Additional fields to include in generations from Responses API.
+
+    Supported values:
+
+    - ``"file_search_call.results"``
+    - ``"message.input_image.image_url"``
+    - ``"computer_call_output.output.image_url"``
+    - ``"reasoning.encrypted_content"``
+    - ``"code_interpreter_call.outputs"``
+
+    .. versionadded:: 0.3.24
+    """
+
     service_tier: Optional[str] = None
     """Latency tier for request. Options are 'auto', 'default', or 'flex'. Relevant
     for users of OpenAI's scale tier service.
     """
 
+    store: Optional[bool] = None
+    """If True, the Responses API may store response data for future use. Defaults to
+    True.
+
+    .. versionadded:: 0.3.24
+    """
+
+    truncation: Optional[str] = None
+    """Truncation strategy (Responses API). Can be ``"auto"`` or ``"disabled"``
+    (default). If ``"auto"``, model may drop input items from the middle of the
+    message sequence to fit the context window.
+
+    .. versionadded:: 0.3.24
+    """
+
     use_responses_api: Optional[bool] = None
     """Whether to use the Responses API instead of the Chat API.
 
@@ -685,7 +731,11 @@ def _default_params(self) -> dict[str, Any]:
             "n": self.n,
             "temperature": self.temperature,
             "reasoning_effort": self.reasoning_effort,
+            "reasoning": self.reasoning,
+            "include": self.include,
             "service_tier": self.service_tier,
+            "truncation": self.truncation,
+            "store": self.store,
         }
 
         params = {
@@ -3134,7 +3184,7 @@ def _construct_responses_api_payload(
     for legacy_token_param in ["max_tokens", "max_completion_tokens"]:
         if legacy_token_param in payload:
             payload["max_output_tokens"] = payload.pop(legacy_token_param)
-    if "reasoning_effort" in payload:
+    if "reasoning_effort" in payload and "reasoning" not in payload:
         payload["reasoning"] = {"effort": payload.pop("reasoning_effort")}
 
     payload["input"] = _construct_responses_api_input(messages)

libs/partners/openai/tests/integration_tests/chat_models/test_responses_api.py

@@ -323,7 +323,7 @@ def test_route_from_model_kwargs() -> None:
 
 @pytest.mark.flaky(retries=3, delay=1)
 def test_computer_calls() -> None:
-    llm = ChatOpenAI(model="computer-use-preview", model_kwargs={"truncation": "auto"})
+    llm = ChatOpenAI(model="computer-use-preview", truncation="auto")
     tool = {
         "type": "computer_use_preview",
         "display_width": 1024,
@@ -354,10 +354,10 @@ def test_file_search() -> None:
 
 
 def test_stream_reasoning_summary() -> None:
-    reasoning = {"effort": "medium", "summary": "auto"}
-
     llm = ChatOpenAI(
-        model="o4-mini", use_responses_api=True, model_kwargs={"reasoning": reasoning}
+        model="o4-mini",
+        use_responses_api=True,
+        reasoning={"effort": "medium", "summary": "auto"},
     )
     message_1 = {"role": "user", "content": "What is 3^3?"}
     response_1: Optional[BaseMessageChunk] = None
@@ -465,7 +465,8 @@ def test_mcp_builtin_zdr() -> None:
     llm = ChatOpenAI(
         model="o4-mini",
         use_responses_api=True,
-        model_kwargs={"store": False, "include": ["reasoning.encrypted_content"]},
+        store=False,
+        include=["reasoning.encrypted_content"],
     )
 
     llm_with_tools = llm.bind_tools(