components: add astra vectorize

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

@@ -0,0 +1,97 @@
+from typing import Optional, Dict, Any
+
+from langflow.custom import CustomComponent
+
+from langflow.custom import Component
+from base.langflow.inputs import TextInput
+from base.langflow.template.field.base import Output
+#
+#
+# class AstraVectorize(Component):
+#     display_name = "Astra Vectorize"
+#     description = "Configuration options for Astra Vectorize server-side embeddings."
+#     documentation = "..."
+#     icon = "AstraDB" # TODO: New icon?
+#
+#     inputs = [
+#         TextInput(
+#             name="provider",
+#             display_name="Provider",
+#         )
+#     ]
+#     outputs = [
+#         Output(display_name="Vectorize_configuration", name="embeddings", method="build"),
+#     ]
+#
+#     def build(
+#         self,
+#     ) -> Dict[str, Any]:
+#         return {
+#             "provider": self.provider
+#         }
+
+
+from langflow.custom import Component
+from langflow.inputs.inputs import DataInput, IntInput, TextInput, DictInput
+from langflow.schema import Data
+from langflow.template.field.base import Output
+from langflow.utils.util import build_loader_repr_from_data, unescape_string
+
+
+class AstraVectorize(Component):
+    display_name: str = "Astra Vectorize"
+    description: str = "Configuration options for Astra Vectorize server-side embeddings."
+    documentation: str = "https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html"
+    icon = "AstraDB"
+
+    inputs = [
+        TextInput(
+            name="provider",
+            display_name="Provider name",
+            info='The provider to use.',
+        ),
+        TextInput(
+            name="model_name",
+            display_name="Model name",
+            info='The model to use.',
+        ),
+        DictInput(
+            name="authentication",
+            display_name="Authentication",
+            info='Authentication parameters. Use the Astra Portal to add the embedding provider integration to your Astra organization.',
+            is_list=True
+        ),
+        DictInput(
+            name="authentication2",
+            display_name="Authentication",
+            info='Authentication parameters. Use the Astra Portal to add the embedding provider integration to your Astra organization.',
+            is_list=False
+        ),
+        TextInput(
+            name="provider_api_key",
+            display_name="Provider API Key to authenticate to the external service",
+            info='An alternative to the Astra Authentication that let you use directly the API key of the provider.',
+            advanced=True
+        ),
+        DictInput(
+            name="parameters",
+            display_name="Additional model parameters",
+            info='Additional model parameters.',
+            advanced=True,
+            is_list=True
+        ),
+    ]
+    outputs = [
+        Output(display_name="Configuration", name="config", method="build", types=["dict"]),
+    ]
+
+    def build(self) -> dict[str, Any]:
+        return {
+            "collection_vector_service_options": {
+                "provider": self.provider,
+                "model_name": self.model_name,
+                "authentication": self.authentication,
+                "parameters": self.parameters
+            },
+            "collection_embedding_api_key": self.provider_api_key
+        }

src/backend/base/langflow/components/vectorstores/AstraDB.py

@@ -107,7 +107,7 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
         HandleInput(
             name="embedding",
             display_name="Embedding",
-            input_types=["Embeddings"],
+            input_types=["Embeddings", "dict"],
         ),
         StrInput(
             name="metadata_indexing_exclude",
@@ -155,8 +155,12 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
         except KeyError:
             raise ValueError(f"Invalid setup mode: {self.setup_mode}")
 
+        if isinstance(self.embedding, dict):
+            embedding_dict = {"embedding": self.embedding}
+        else:
+            embedding_dict = self.embedding.to_dict()
         vector_store_kwargs = {
-            "embedding": self.embedding,
+            **embedding_dict,
             "collection_name": self.collection_name,
             "token": self.token,
             "api_endpoint": self.api_endpoint,