Anthropic: Allow the use of kwargs consistent with ChatOpenAI. (#9515)

- Description: ~~Creates a new root_validator in `_AnthropicCommon` that
allows the use of `model_name` and `max_tokens` keyword arguments.~~
Adds pydantic field aliases to support `model_name` and `max_tokens` as
keyword arguments. Ultimately, this makes `ChatAnthropic` more
consistent with `ChatOpenAI`, making the two classes more
interchangeable for the developer.
  - Issue: https://github.com/langchain-ai/langchain/issues/9510

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>

libs/langchain/langchain/chat_models/anthropic.py

@@ -36,6 +36,12 @@ class ChatAnthropic(BaseChatModel, _AnthropicCommon):
             model = ChatAnthropic(model="<model_name>", anthropic_api_key="my-api-key")
     """
 
+    class Config:
+        """Configuration for this pydantic object."""
+
+        allow_population_by_field_name = True
+        arbitrary_types_allowed = True
+
     @property
     def lc_secrets(self) -> Dict[str, str]:
         return {"anthropic_api_key": "ANTHROPIC_API_KEY"}

libs/langchain/langchain/llms/anthropic.py

@@ -21,10 +21,10 @@ from langchain.utils.utils import build_extra_kwargs
 class _AnthropicCommon(BaseLanguageModel):
     client: Any = None  #: :meta private:
     async_client: Any = None  #: :meta private:
-    model: str = "claude-2"
+    model: str = Field(default="claude-2", alias="model_name")
     """Model name to use."""
 
-    max_tokens_to_sample: int = 256
+    max_tokens_to_sample: int = Field(default=256, alias="max_tokens")
     """Denotes the number of tokens to predict per generation."""
 
     temperature: Optional[float] = None
@@ -144,6 +144,7 @@ class Anthropic(LLM, _AnthropicCommon):
 
             import anthropic
             from langchain.llms import Anthropic
+
             model = Anthropic(model="<model_name>", anthropic_api_key="my-api-key")
 
             # Simplest invocation, automatically wrapped with HUMAN_PROMPT
@@ -157,6 +158,12 @@ class Anthropic(LLM, _AnthropicCommon):
             response = model(prompt)
     """
 
+    class Config:
+        """Configuration for this pydantic object."""
+
+        allow_population_by_field_name = True
+        arbitrary_types_allowed = True
+
     @root_validator()
     def raise_warning(cls, values: Dict) -> Dict:
         """Raise warning that this class is deprecated."""

libs/langchain/tests/integration_tests/chat_models/test_anthropic_2.py

@@ -8,6 +8,18 @@ from langchain.chat_models import ChatAnthropic
 os.environ["ANTHROPIC_API_KEY"] = "foo"
 
 
+@pytest.mark.requires("anthropic")
+def test_anthropic_model_name_param() -> None:
+    llm = ChatAnthropic(model_name="foo")
+    assert llm.model == "foo"
+
+
+@pytest.mark.requires("anthropic")
+def test_anthropic_model_param() -> None:
+    llm = ChatAnthropic(model="foo")
+    assert llm.model == "foo"
+
+
 @pytest.mark.requires("anthropic")
 def test_anthropic_model_kwargs() -> None:
     llm = ChatAnthropic(model_kwargs={"foo": "bar"})

libs/langchain/tests/integration_tests/llms/test_anthropic.py

@@ -9,6 +9,18 @@ from langchain.schema import LLMResult
 from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
 
 
+@pytest.mark.requires("anthropic")
+def test_anthropic_model_name_param() -> None:
+    llm = Anthropic(model_name="foo")
+    assert llm.model == "foo"
+
+
+@pytest.mark.requires("anthropic")
+def test_anthropic_model_param() -> None:
+    llm = Anthropic(model="foo")
+    assert llm.model == "foo"
+
+
 def test_anthropic_call() -> None:
     """Test valid call to anthropic."""
     llm = Anthropic(model="claude-instant-1")
@@ -24,7 +36,7 @@ def test_anthropic_streaming() -> None:
     assert isinstance(generator, Generator)
 
     for token in generator:
-        assert isinstance(token["completion"], str)
+        assert isinstance(token, str)
 
 
 def test_anthropic_streaming_callback() -> None:

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

@@ -6,9 +6,7 @@ from unittest.mock import MagicMock, patch
 import pytest
 
 from langchain.adapters.openai import convert_dict_to_message
-from langchain.chat_models.openai import (
-    ChatOpenAI,
-)
+from langchain.chat_models.openai import ChatOpenAI
 from langchain.schema.messages import (
     AIMessage,
     FunctionMessage,
@@ -17,6 +15,14 @@ from langchain.schema.messages import (
 )
 
 
+@pytest.mark.requires("openai")
+def test_openai_model_param() -> None:
+    llm = ChatOpenAI(model="foo")
+    assert llm.model_name == "foo"
+    llm = ChatOpenAI(model_name="foo")
+    assert llm.model_name == "foo"
+
+
 def test_function_message_dict_to_function_message() -> None:
     content = json.dumps({"result": "Example #1"})
     name = "test_function"