Update AmazonBedrock and add AzureOpenAI model

src/backend/langflow/components/models/AmazonBedrock.py

@@ -60,6 +60,7 @@ class AmazonBedrockComponent(CustomComponent):
             )  # type: ignore
         except Exception as e:
             raise ValueError("Could not connect to AmazonBedrock API.") from e
-        message = output.invoke(input=inputs)
-        self.status = message
-        return message
+        message = output.invoke(inputs)
+        result = message.content if hasattr(message, "content") else message
+        self.status = result
+        return result

src/backend/langflow/components/models/AzureOpenAI.py

@@ -0,0 +1,104 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.llms.base import BaseLanguageModel
+from langchain_openai import AzureChatOpenAI
+
+
+class AzureChatOpenAIComponent(CustomComponent):
+    display_name: str = "AzureOpenAI model"
+    description: str = "LLM model from Azure OpenAI."
+    documentation: str = (
+        "https://python.langchain.com/docs/integrations/llms/azure_openai"
+    )
+    beta = False
+
+    AZURE_OPENAI_MODELS = [
+        "gpt-35-turbo",
+        "gpt-35-turbo-16k",
+        "gpt-35-turbo-instruct",
+        "gpt-4",
+        "gpt-4-32k",
+        "gpt-4-vision",
+    ]
+
+    AZURE_OPENAI_API_VERSIONS = [
+        "2023-03-15-preview",
+        "2023-05-15",
+        "2023-06-01-preview",
+        "2023-07-01-preview",
+        "2023-08-01-preview",
+        "2023-09-01-preview",
+        "2023-12-01-preview",
+    ]
+
+    def build_config(self):
+        return {
+            "model": {
+                "display_name": "Model Name",
+                "value": self.AZURE_OPENAI_MODELS[0],
+                "options": self.AZURE_OPENAI_MODELS,
+                "required": True,
+            },
+            "azure_endpoint": {
+                "display_name": "Azure Endpoint",
+                "required": True,
+                "info": "Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`",
+            },
+            "azure_deployment": {
+                "display_name": "Deployment Name",
+                "required": True,
+            },
+            "api_version": {
+                "display_name": "API Version",
+                "options": self.AZURE_OPENAI_API_VERSIONS,
+                "value": self.AZURE_OPENAI_API_VERSIONS[-1],
+                "required": True,
+                "advanced": True,
+            },
+            "api_key": {"display_name": "API Key", "required": True, "password": True},
+            "temperature": {
+                "display_name": "Temperature",
+                "value": 0.7,
+                "field_type": "float",
+                "required": False,
+            },
+            "max_tokens": {
+                "display_name": "Max Tokens",
+                "value": 1000,
+                "required": False,
+                "field_type": "int",
+                "advanced": True,
+                "info": "Maximum number of tokens to generate.",
+            },
+            "code": {"show": False},
+            "inputs": {"display_name": "Input"},
+            "inputs": {"display_name": "Input"},
+        }
+
+    def build(
+        self,
+        model: str,
+        azure_endpoint: str,
+        inputs: str,
+        azure_deployment: str,
+        api_key: str,
+        api_version: str,
+        temperature: float = 0.7,
+        max_tokens: Optional[int] = 1000,
+    ) -> BaseLanguageModel:
+        try:
+            output = AzureChatOpenAI(
+                model=model,
+                azure_endpoint=azure_endpoint,
+                azure_deployment=azure_deployment,
+                api_version=api_version,
+                api_key=api_key,
+                temperature=temperature,
+                max_tokens=max_tokens,
+            )
+        except Exception as e:
+            raise ValueError("Could not connect to AzureOpenAI API.") from e
+        message = output.invoke(inputs)
+        result = message.content if hasattr(message, "content") else message
+        self.status = result
+        return result

src/backend/langflow/components/models/OpenAI.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from langchain_community.chat_models.openai import ChatOpenAI
+from langchain_openai import ChatOpenAI
 from langflow import CustomComponent
 from langflow.field_typing import NestedDict, Text