Add Amazon Bedrock Support

pyproject.toml

@@ -90,6 +90,7 @@ langfuse = "^1.0.13"
 pillow = "^10.0.0"
 metal-sdk = "^2.2.0"
 markupsafe = "^2.1.3"
+boto3 = "^1.28.63"
 
 
 [tool.poetry.group.dev.dependencies]

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

@@ -0,0 +1,50 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.llms.bedrock import Bedrock
+from langchain.llms.base import BaseLLM
+
+
+class AmazonBedrockComponent(CustomComponent):
+    display_name: str = "Amazon Bedrock"
+    description: str = "LLM model from Amazon Bedrock."
+
+    def build_config(self):
+        return {
+            "credentials_profile_name": {"display_name": "Credentials Profile Name", "password": True},
+            "model_id": {
+                "display_name": "Model Id",
+                "options": [
+                    "ai21.j2-grande-instruct",
+                    "ai21.j2-jumbo-instruct",
+                    "ai21.j2-mid",
+                    "ai21.j2-mid-v1",
+                    "ai21.j2-ultra",
+                    "ai21.j2-ultra-v1",
+                    "anthropic.claude-instant-v1",
+                    "anthropic.claude-v1",
+                    "anthropic.claude-v2",
+                    "cohere.command-text-v14",
+                ],
+            },
+            "model_kwargs": {
+                "display_name": "Model Keyword Arguments",
+                "field_type": "code",
+            },
+            "code": {"show": False},
+        }
+
+    def build(
+        self,
+        credentials_profile_name: str,
+        model_id: str = "anthropic.claude-instant-v1",
+        model_kwargs: Optional[dict] = None,
+    ) -> BaseLLM:
+        try:
+            output = Bedrock(
+                credentials_profile_name=credentials_profile_name,
+                model_id=model_id,
+                model_kwargs=model_kwargs,
+            )
+        except Exception as e:
+            raise ValueError("Could not connect to Test Endpoints API.") from e
+        return output