components: simplify astra vectorize (#2370)

* Add ProviderKey as text field since it's the only auth entry available
today
* Hide authentication and external key as advanced fields
* Make provider name and model required
* Make provider as dropdown
* Add complete list of models in the help (better to not hardcode models
for now)

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

@@ -1,6 +1,6 @@
 from typing import Any
 from langflow.custom import Component
-from langflow.inputs.inputs import DictInput, SecretStrInput, MessageTextInput
+from langflow.inputs.inputs import DictInput, SecretStrInput, MessageTextInput, DropdownInput
 from langflow.template.field.base import Output
 
 
@@ -10,32 +10,58 @@ class AstraVectorize(Component):
     documentation: str = "https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html"
     icon = "AstraDB"
 
+    VECTORIZE_PROVIDERS_MAPPING = {
+        "Azure OpenAI": ["azureOpenAI", ["text-embedding-3-small", "text-embedding-3-large", "text-embedding-ada-002"]],
+        "Hugging Face - Dedicated": ["huggingfaceDedicated", ["endpoint-defined-model"]],
+        "Hugging Face - Serverless": ["huggingface",
+                                      ["sentence-transformers/all-MiniLM-L6-v2", "intfloat/multilingual-e5-large",
+                                       "intfloat/multilingual-e5-large-instruct", "BAAI/bge-small-en-v1.5",
+                                       "BAAI/bge-base-en-v1.5", "BAAI/bge-large-en-v1.5"]],
+        "Jina AI": ["jinaAI", ["jina-embeddings-v2-base-en", "jina-embeddings-v2-base-de", "jina-embeddings-v2-base-es",
+                               "jina-embeddings-v2-base-code", "jina-embeddings-v2-base-zh"]],
+        "Mistral AI": ["mistral", ["mistral-embed"]],
+        "NVIDIA": ["nvidia", ["NV-Embed-QA"]],
+        "OpenAI": ["openai", ["text-embedding-3-small", "text-embedding-3-large", "text-embedding-ada-002"]],
+        "Upstage": ["upstageAI", ["solar-embedding-1-large"]],
+        "Voyage AI": ["voyageAI",
+                      ["voyage-large-2-instruct", "voyage-law-2", "voyage-code-2", "voyage-large-2", "voyage-2"]]
+    }
+    VECTORIZE_MODELS_STR = "\n\n".join([provider + ": " + (', '.join(models[1])) for provider, models in VECTORIZE_PROVIDERS_MAPPING.items()])
+
     inputs = [
-        MessageTextInput(
+        DropdownInput(
             name="provider",
             display_name="Provider name",
-            info="The embedding provider to use.",
+            options=VECTORIZE_PROVIDERS_MAPPING.keys(),
+            value="",
         ),
         MessageTextInput(
             name="model_name",
             display_name="Model name",
-            info="The embedding model to use.",
+            info=f"The embedding model to use for the selected provider. Each provider has a different set of models "
+                 f"available (full list at https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html):\n\n{VECTORIZE_MODELS_STR}",
+            required=True
+        ),
+        MessageTextInput(
+            name="api_key_name",
+            display_name="API Key name",
+            info="The name of the embeddings provider API key stored on Astra. If set, it will override the 'ProviderKey' in the authentication parameters."
         ),
         DictInput(
             name="authentication",
-            display_name="Authentication",
-            info="Authentication parameters. Use the Astra Portal to add the embedding provider integration to your Astra organization.",
+            display_name="Authentication parameters",
             is_list=True,
+            advanced=True,
         ),
         SecretStrInput(
             name="provider_api_key",
             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="model_parameters",
             display_name="Model parameters",
-            info="Additional model parameters.",
             advanced=True,
             is_list=True,
         ),
@@ -45,12 +71,17 @@ class AstraVectorize(Component):
     ]
 
     def build_options(self) -> dict[str, Any]:
+        provider_value = self.VECTORIZE_PROVIDERS_MAPPING[self.provider][0]
+        authentication = {**self.authentication}
+        api_key_name = self.api_key_name
+        if api_key_name:
+            authentication["providerKey"] = api_key_name
         return {
             # must match exactly astra CollectionVectorServiceOptions
             "collection_vector_service_options": {
-                "provider": self.provider,
+                "provider": provider_value,
                 "modelName": self.model_name,
-                "authentication": self.authentication,
+                "authentication": authentication,
                 "parameters": self.model_parameters,
             },
             "collection_embedding_api_key": self.provider_api_key,

src/backend/base/langflow/components/langchain_utilities/FirecrawlScrapeApi.py

@@ -2,8 +2,6 @@ from typing import Optional
 from firecrawl.firecrawl import FirecrawlApp
 from langflow.custom import CustomComponent
 from langflow.schema import Data
-from langflow.services.database.models.base import orjson_dumps
-import json
 
 class FirecrawlScrapeApi(CustomComponent):
     display_name: str = "FirecrawlScrapeApi"