multiple: add stop attribute (#22573)

libs/partners/ai21/langchain_ai21/chat_models.py

@@ -35,6 +35,8 @@ class ChatAI21(BaseChatModel, AI21Base):
         You can view the options at https://github.com/AI21Labs/ai21-python?tab=readme-ov-file#model-types"""
     num_results: int = 1
     """The number of responses to generate for a given prompt."""
+    stop: Optional[List[str]] = None
+    """Default stop sequences."""
 
     max_tokens: int = 16
     """The maximum number of tokens to generate for each response."""
@@ -97,6 +99,8 @@ def _default_params(self) -> Mapping[str, Any]:
             "top_k_return": self.top_k_return,
             "n": self.n,
         }
+        if self.stop:
+            base_params["stop_sequences"] = self.stop
 
         if self.count_penalty is not None:
             base_params["count_penalty"] = self.count_penalty.to_dict()

libs/partners/anthropic/langchain_anthropic/chat_models.py

@@ -492,6 +492,9 @@ class Config:
     max_retries: int = 2
     """Number of retries allowed for requests sent to the Anthropic Completion API."""
 
+    stop: Optional[List[str]] = Field(None, alias="stop_sequences")
+    """Default stop sequences."""
+
     anthropic_api_url: Optional[str] = Field(None, alias="base_url")
     """Base URL for API requests. Only specify if using a proxy or service emulator.
     
@@ -611,14 +614,15 @@ def _format_params(
     ) -> Dict:
         # get system prompt if any
         system, formatted_messages = _format_messages(messages)
+        stop_sequences = stop or self.stop
         rtn = {
             "model": self.model,
             "max_tokens": self.max_tokens,
             "messages": formatted_messages,
             "temperature": self.temperature,
             "top_k": self.top_k,
             "top_p": self.top_p,
-            "stop_sequences": stop,
+            "stop_sequences": stop_sequences,
             "system": system,
             **self.model_kwargs,
             **kwargs,

libs/partners/fireworks/langchain_fireworks/chat_models.py

@@ -300,6 +300,8 @@ def is_lc_serializable(cls) -> bool:
     """Number of chat completions to generate for each prompt."""
     max_tokens: Optional[int] = None
     """Maximum number of tokens to generate."""
+    stop: Optional[List[str]] = Field(None, alias="stop_sequences")
+    """Default stop sequences."""
 
     class Config:
         """Configuration for this pydantic object."""
@@ -354,6 +356,7 @@ def _default_params(self) -> Dict[str, Any]:
             "stream": self.streaming,
             "n": self.n,
             "temperature": self.temperature,
+            "stop": self.stop,
             **self.model_kwargs,
         }
         if self.max_tokens is not None:
@@ -443,8 +446,6 @@ def _create_message_dicts(
     ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
         params = self._default_params
         if stop is not None:
-            if "stop" in params:
-                raise ValueError("`stop` found in both the input and default params.")
             params["stop"] = stop
         message_dicts = [_convert_message_to_dict(m) for m in messages]
         return message_dicts, params

libs/partners/groq/langchain_groq/chat_models.py

@@ -123,6 +123,8 @@ class ChatGroq(BaseChatModel):
     """Number of chat completions to generate for each prompt."""
     max_tokens: Optional[int] = None
     """Maximum number of tokens to generate."""
+    stop: Optional[List[str]] = Field(None, alias="stop_sequences")
+    """Default stop sequences."""
     default_headers: Union[Mapping[str, str], None] = None
     default_query: Union[Mapping[str, object], None] = None
     # Configure a custom httpx client. See the
@@ -428,6 +430,7 @@ def _default_params(self) -> Dict[str, Any]:
             "stream": self.streaming,
             "n": self.n,
             "temperature": self.temperature,
+            "stop": self.stop,
             **self.model_kwargs,
         }
         if self.max_tokens is not None:
@@ -461,8 +464,6 @@ def _create_message_dicts(
     ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
         params = self._default_params
         if stop is not None:
-            if "stop" in params:
-                raise ValueError("`stop` found in both the input and default params.")
             params["stop"] = stop
         message_dicts = [_convert_message_to_dict(m) for m in messages]
         return message_dicts, params

libs/standard-tests/langchain_standard_tests/integration_tests/chat_models.py

@@ -144,6 +144,17 @@ def test_usage_metadata(
         assert isinstance(result.usage_metadata["output_tokens"], int)
         assert isinstance(result.usage_metadata["total_tokens"], int)
 
+    def test_stop_sequence(
+        self, chat_model_class: Type[BaseChatModel], chat_model_params: dict
+    ) -> None:
+        model = chat_model_class(**chat_model_params)
+        result = model.invoke("hi", stop=["you"])
+        assert isinstance(result, AIMessage)
+
+        model = chat_model_class(**chat_model_params, stop=["you"])
+        result = model.invoke("hi")
+        assert isinstance(result, AIMessage)
+
     def test_tool_message_histories_string_content(
         self,
         chat_model_class: Type[BaseChatModel],