feat: bedrock access with aws access key

src/backend/base/langflow/components/embeddings/AmazonBedrockEmbeddings.py

@@ -2,6 +2,7 @@
 
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import Embeddings
+from langflow.inputs import SecretStrInput
 from langflow.io import DropdownInput, MessageTextInput, Output
 
 
@@ -19,32 +20,50 @@ class AmazonBedrockEmbeddingsComponent(LCModelComponent):
             options=["amazon.titan-embed-text-v1"],
             value="amazon.titan-embed-text-v1",
         ),
+        SecretStrInput(name="aws_access_key", display_name="Access Key"),
+        SecretStrInput(name="aws_secret_key", display_name="Secret Key"),
         MessageTextInput(
             name="credentials_profile_name",
             display_name="Credentials Profile Name",
+            advanced=True,
         ),
-        MessageTextInput(
-            name="endpoint_url",
-            display_name="Bedrock Endpoint URL",
-        ),
-        MessageTextInput(
-            name="region_name",
-            display_name="AWS Region",
-        ),
+        MessageTextInput(name="region_name", display_name="Region Name", value="us-east-1"),
+        MessageTextInput(name="endpoint_url", display_name=" Endpoint URL", advanced=True),
     ]
 
     outputs = [
         Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
     ]
 
     def build_embeddings(self) -> Embeddings:
-        try:
-            output = BedrockEmbeddings(
-                credentials_profile_name=self.credentials_profile_name,
-                model_id=self.model_id,
-                endpoint_url=self.endpoint_url,
-                region_name=self.region_name,
-            )  # type: ignore
-        except Exception as e:
-            raise ValueError("Could not connect to Amazon Bedrock API.") from e
+        if self.aws_access_key:
+            import boto3
+
+            session = boto3.Session(
+                aws_access_key_id=self.aws_access_key,
+                aws_secret_access_key=self.aws_secret_key,
+            )
+        elif self.credentials_profile_name:
+            import boto3
+
+            session = boto3.Session(profile_name=self.credentials_profile_name)
+        else:
+            import boto3
+
+            session = boto3.Session()
+
+        client_params = {}
+        if self.endpoint_url:
+            client_params["endpoint_url"] = self.endpoint_url
+        if self.region_name:
+            client_params["region_name"] = self.region_name
+
+        boto3_client = session.client("bedrock-runtime", **client_params)
+        output = BedrockEmbeddings(
+            credentials_profile_name=self.credentials_profile_name,
+            client=boto3_client,
+            model_id=self.model_id,
+            endpoint_url=self.endpoint_url,
+            region_name=self.region_name,
+        )  # type: ignore
         return output

src/backend/base/langflow/components/models/AmazonBedrockModel.py

@@ -2,7 +2,7 @@
 
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import LanguageModel
-from langflow.inputs import MessageTextInput
+from langflow.inputs import MessageTextInput, SecretStrInput
 from langflow.io import DictInput, DropdownInput
 
 
@@ -51,27 +51,46 @@ class AmazonBedrockComponent(LCModelComponent):
             ],
             value="anthropic.claude-3-haiku-20240307-v1:0",
         ),
-        MessageTextInput(name="credentials_profile_name", display_name="Credentials Profile Name"),
+        SecretStrInput(name="aws_access_key", display_name="Access Key"),
+        SecretStrInput(name="aws_secret_key", display_name="Secret Key"),
+        MessageTextInput(name="credentials_profile_name", display_name="Credentials Profile Name", advanced=True),
         MessageTextInput(name="region_name", display_name="Region Name", value="us-east-1"),
         DictInput(name="model_kwargs", display_name="Model Kwargs", advanced=True, is_list=True),
         MessageTextInput(name="endpoint_url", display_name="Endpoint URL", advanced=True),
     ]
 
     def build_model(self) -> LanguageModel:  # type: ignore[type-var]
-        model_id = self.model_id
-        credentials_profile_name = self.credentials_profile_name
-        region_name = self.region_name
-        model_kwargs = self.model_kwargs
-        endpoint_url = self.endpoint_url
-        stream = self.stream
+        if self.aws_access_key:
+            import boto3
+
+            session = boto3.Session(
+                aws_access_key_id=self.aws_access_key,
+                aws_secret_access_key=self.aws_secret_key,
+            )
+        elif self.credentials_profile_name:
+            import boto3
+
+            session = boto3.Session(profile_name=self.credentials_profile_name)
+        else:
+            import boto3
+
+            session = boto3.Session()
+
+        client_params = {}
+        if self.endpoint_url:
+            client_params["endpoint_url"] = self.endpoint_url
+        if self.region_name:
+            client_params["region_name"] = self.region_name
+
+        boto3_client = session.client("bedrock-runtime", **client_params)
         try:
             output = ChatBedrock(  # type: ignore
-                credentials_profile_name=credentials_profile_name,
-                model_id=model_id,
-                region_name=region_name,
-                model_kwargs=model_kwargs,
-                endpoint_url=endpoint_url,
-                streaming=stream,
+                client=boto3_client,
+                model_id=self.model_id,
+                region_name=self.region_name,
+                model_kwargs=self.model_kwargs,
+                endpoint_url=self.endpoint_url,
+                streaming=self.stream,
             )
         except Exception as e:
             raise ValueError("Could not connect to AmazonBedrock API.") from e

src/backend/base/langflow/services/settings/constants.py

@@ -22,4 +22,6 @@
     "VECTARA_CUSTOMER_ID",
     "VECTARA_CORPUS_ID",
     "VECTARA_API_KEY",
+    "AWS_ACCESS_KEY_ID",
+    "AWS_SECRET_ACCESS_KEY",
 ]