fixes and refactory

docs/docs/components/embeddings.mdx

@@ -11,6 +11,18 @@ Used to load embedding models from [Amazon Bedrock](https://aws.amazon.com/bedro
 | `endpoint_url`             | `str`    | URL to set a specific service endpoint other than the default AWS endpoint.                                                                         |             |
 | `region_name`              | `str`    | AWS region to use, e.g., `us-west-2`. Falls back to `AWS_DEFAULT_REGION` environment variable or region specified in ~/.aws/config if not provided. |             |
 
+## Astra vectorize
+
+Used to generate server-side embeddings using [DataStax Astra](https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html).
+
+| **Parameter**      | **Type** | **Description**                                                                                                       | **Default** |
+|--------------------|----------|-----------------------------------------------------------------------------------------------------------------------|-------------|
+| `provider`         | `str`    | The embedding provider to use.                                                                                        |             |
+| `model_name`       | `str`    | The embedding model to use.                                                                                           |             |
+| `authentication`   | `dict`   | Authentication parameters. Use the Astra Portal to add the embedding provider integration to your Astra organization. |             |
+| `provider_api_key` | `str`    | An alternative to the Astra Authentication that let you use directly the API key of the provider.                     |             |
+| `model_parameters` | `dict`   | Additional model parameters.                                                                                          |             |
+
 ## Cohere Embeddings
 
 Used to load embedding models from [Cohere](https://cohere.com/).

docs/docs/components/vector-stores.mdx

@@ -9,7 +9,7 @@ The `Astra DB` initializes a vector store using Astra DB from Data. It creates A
 **Parameters:**
 
 - **Input:** Documents or Data for input.
-- **Embedding:** Embedding model Astra DB uses.
+- **Embedding or Astra vectorize:** External or server-side model Astra DB uses.
 - **Collection Name:** Name of the Astra DB collection.
 - **Token:** Authentication token for Astra DB.
 - **API Endpoint:** API endpoint for Astra DB.
@@ -40,7 +40,7 @@ The `Astra DB` initializes a vector store using Astra DB from Data. It creates A
 
 - **Search Type:** Type of search, such as Similarity or MMR.
 - **Input Value:** Value to search for.
-- **Embedding:** Embedding model Astra DB uses.
+- **Embedding or Astra vectorize:** External or server-side model Astra DB uses.
 - **Collection Name:** Name of the Astra DB collection.
 - **Token:** Authentication token for Astra DB.
 - **API Endpoint:** API endpoint for Astra DB.

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

@@ -1,41 +1,7 @@
-from typing import Optional, Dict, Any
-
-from langflow.custom import CustomComponent
-
+from typing import Any
 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, SecretStrInput
-from langflow.schema import Data
+from langflow.inputs.inputs import DictInput, SecretStrInput, StrInput
 from langflow.template.field.base import Output
-from langflow.utils.util import build_loader_repr_from_data, unescape_string
 
 
 class AstraVectorize(Component):
@@ -45,15 +11,15 @@ class AstraVectorize(Component):
     icon = "AstraDB"
 
     inputs = [
-        TextInput(
+        StrInput(
             name="provider",
             display_name="Provider name",
-            info='The provider to use.',
+            info='The embedding provider to use.',
         ),
-        TextInput(
+        StrInput(
             name="model_name",
             display_name="Model name",
-            info='The model to use.',
+            info='The embedding model to use.',
         ),
         DictInput(
             name="authentication",
@@ -63,20 +29,20 @@ class AstraVectorize(Component):
         ),
         SecretStrInput(
             name="provider_api_key",
-            display_name="Provider API Key to authenticate to the external service",
+            display_name="Provider API Key",
             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",
+            display_name="Model parameters",
             info='Additional model parameters.',
             advanced=True,
             is_list=True
         ),
     ]
     outputs = [
-        Output(display_name="Configuration", name="config", method="build", types=["dict"]),
+        Output(display_name="Vectorize", name="config", method="build", types=["dict"]),
     ]
 
     def build(self) -> dict[str, Any]:

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

@@ -106,7 +106,7 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
         ),
         HandleInput(
             name="embedding",
-            display_name="Embedding",
+            display_name="Embedding or Astra Vectorize",
             input_types=["Embeddings", "dict"],
         ),
         StrInput(

src/frontend/package-lock.json

@@ -787,6 +787,7 @@
     },
     "node_modules/@clack/prompts/node_modules/is-unicode-supported": {
       "version": "1.3.0",
+      "extraneous": true,
       "inBundle": true,
       "license": "MIT",
       "engines": {

src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx

@@ -632,7 +632,7 @@ export default function ParameterComponent({
               editNode={false}
               value={
                 !data.node!.template[name]?.value ||
-                data.node!.template[name]?.value?.toString() === "{}"
+                !Object.keys(data.node!.template[name]?.value || {}).length
                   ? {}
                   : data.node!.template[name]?.value
               }
@@ -648,9 +648,9 @@ export default function ParameterComponent({
               disabled={disabled}
               editNode={false}
               value={
-                data.node!.template[name]?.value?.length === 0 ||
-                !data.node!.template[name]?.value
-                  ? [{ "": "" }]
+                !data.node!.template[name]?.value ||
+                !Object.keys(data.node!.template[name]?.value || {}).length
+                  ? [{"":""}]
                   : convertObjToArray(data.node!.template[name]?.value, type!)
               }
               duplicateKey={errorDuplicateKey}