Update Anthropic and ChatAnthropic components

src/backend/langflow/components/llms/Anthropic.py

@@ -1,6 +1,7 @@
 from langflow import CustomComponent
-from typing import Optional, Dict, Any
+from typing import Optional
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import BaseLanguageModel,NestedDict
+from langchain_community.llms.anthropic import Anthropic
 
 
 class AnthropicComponent(CustomComponent):
@@ -20,48 +21,25 @@ class AnthropicComponent(CustomComponent):
             },
             "model_kwargs": {
                 "display_name": "Model Kwargs",
-                "field_type": 'dict',
+                "field_type": 'NestedDict',
                 "advanced": True,
             },
             "temperature": {
                 "display_name": "Temperature",
-                "type": float,
+                "field_type": "float",
             },
         }
 
     def build(
         self,
-        anthropic_api_key: Optional[str],
+        anthropic_api_key: str,
-        anthropic_api_url: Optional[str],
+        anthropic_api_url: str,
-        model_kwargs: Optional[Dict[str, Any]],
+        model_kwargs: Optional[NestedDict],
         temperature: Optional[float] = None,
     ) -> BaseLanguageModel:
-        # The actual builder method should return an instance of the Anthropic class
-        # Here we are returning a placeholder class as the Anthropic class is not defined
-        # This is to comply with the type hints required by the CustomComponent
-        class Anthropic(BaseLanguageModel):
-            def __init__(
-                self,
-                api_key: Optional[str],
-                api_url: Optional[str],
-                model_kwargs: Optional[Dict[str, Any]] = None,
-                temperature: Optional[float] = None,
-            ):
-                # Initialize Anthropic model with the provided arguments
-                super().__init__()
-                self.api_key = api_key
-                self.api_url = api_url
-                self.model_kwargs = model_kwargs
-                self.temperature = temperature
-
-            def __call__(self, prompt: str) -> str:
-                # The logic to call the Anthropic model would go here
-                # This is a placeholder implementation
-                return "This is a simulated response from the Anthropic model."
-
         return Anthropic(
-            api_key=anthropic_api_key,
+            anthropic_api_key=anthropic_api_key,
-            api_url=anthropic_api_url,
+            anthropic_api_url=anthropic_api_url,
             model_kwargs=model_kwargs,
             temperature=temperature,
         )

src/backend/langflow/components/llms/ChatAnthropic.py

@@ -2,7 +2,7 @@
 from langflow import CustomComponent
 from typing import Optional, Union, Callable
 from langflow.field_typing import BaseLanguageModel
-
+from langchain_community.chat_models.anthropic import ChatAnthropic
 class ChatAnthropicComponent(CustomComponent):
     display_name = "ChatAnthropic"
     description = "`Anthropic` chat large language models."
@@ -12,12 +12,12 @@ class ChatAnthropicComponent(CustomComponent):
         return {
             "anthropic_api_key": {
                 "display_name": "Anthropic API Key",
-                "type": str,
+                "field_type": "str",
                 "password": True,
             },
             "anthropic_api_url": {
                 "display_name": "Anthropic API URL",
-                "type": str,
+                "field_type": "str",
             },
             "model_kwargs": {
                 "display_name": "Model Kwargs",
@@ -26,7 +26,7 @@ class ChatAnthropicComponent(CustomComponent):
             },
             "temperature": {
                 "display_name": "Temperature",
-                "type": float,
+                "field_type": "float",
             },
         }
 
@@ -37,10 +37,9 @@ class ChatAnthropicComponent(CustomComponent):
         model_kwargs: dict = {},
         temperature: Optional[float] = None,
     ) -> Union[BaseLanguageModel, Callable]:
-        from langchain.model_io.models.chat.integrations import ChatAnthropic  # Importing here due to potential local scope requirements
 
         return ChatAnthropic(
-            anthropic_api_key=anthropic_api_key.get_secret_value() if anthropic_api_key else None,
+            anthropic_api_key=anthropic_api_key,
             anthropic_api_url=anthropic_api_url,
             model_kwargs=model_kwargs,
             temperature=temperature,