feat: Add support for dynamic providers in Astra DB Comp (#4627)

* feat: Add support for dynamic providers in Astra DB Comp

* [autofix.ci] apply automated fixes

* Make sure we return a default dict

* Rename params in starter template

* Update test_vector_store_rag.py

---------

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>

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

@@ -1,6 +1,8 @@
 import os
+from collections import defaultdict
 
 import orjson
+from astrapy import DataAPIClient
 from astrapy.admin import parse_api_endpoint
 from langchain_astradb import AstraDBVectorStore
 
@@ -29,39 +31,45 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
 
     _cached_vector_store: AstraDBVectorStore | None = None
 
-    VECTORIZE_PROVIDERS_MAPPING = {
+    VECTORIZE_PROVIDERS_MAPPING = defaultdict(
-        "Azure OpenAI": ["azureOpenAI", ["text-embedding-3-small", "text-embedding-3-large", "text-embedding-ada-002"]],
+        list,
-        "Hugging Face - Dedicated": ["huggingfaceDedicated", ["endpoint-defined-model"]],
+        {
-        "Hugging Face - Serverless": [
+            "Azure OpenAI": [
-            "huggingface",
+                "azureOpenAI",
-            [
+                ["text-embedding-3-small", "text-embedding-3-large", "text-embedding-ada-002"],
-                "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",
             ],
-        ],
+            "Hugging Face - Dedicated": ["huggingfaceDedicated", ["endpoint-defined-model"]],
-        "Jina AI": [
+            "Hugging Face - Serverless": [
-            "jinaAI",
+                "huggingface",
-            [
+                [
-                "jina-embeddings-v2-base-en",
+                    "sentence-transformers/all-MiniLM-L6-v2",
-                "jina-embeddings-v2-base-de",
+                    "intfloat/multilingual-e5-large",
-                "jina-embeddings-v2-base-es",
+                    "intfloat/multilingual-e5-large-instruct",
-                "jina-embeddings-v2-base-code",
+                    "BAAI/bge-small-en-v1.5",
-                "jina-embeddings-v2-base-zh",
+                    "BAAI/bge-base-en-v1.5",
+                    "BAAI/bge-large-en-v1.5",
+                ],
             ],
-        ],
+            "Jina AI": [
-        "Mistral AI": ["mistral", ["mistral-embed"]],
+                "jinaAI",
-        "NVIDIA": ["nvidia", ["NV-Embed-QA"]],
+                [
-        "OpenAI": ["openai", ["text-embedding-3-small", "text-embedding-3-large", "text-embedding-ada-002"]],
+                    "jina-embeddings-v2-base-en",
-        "Upstage": ["upstageAI", ["solar-embedding-1-large"]],
+                    "jina-embeddings-v2-base-de",
-        "Voyage AI": [
+                    "jina-embeddings-v2-base-es",
-            "voyageAI",
+                    "jina-embeddings-v2-base-code",
-            ["voyage-large-2-instruct", "voyage-law-2", "voyage-code-2", "voyage-large-2", "voyage-2"],
+                    "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"],
+            ],
+        },
+    )
 
     inputs = [
         SecretStrInput(
@@ -109,7 +117,7 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
             value="Embedding Model",
         ),
         HandleInput(
-            name="embedding",
+            name="embedding_model",
             display_name="Embedding Model",
             input_types=["Embeddings"],
             info="Allows an embedding model configuration.",
@@ -247,15 +255,52 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
 
         return build_config
 
+    def update_providers_mapping(self):
+        # If we don't have token or api_endpoint, we can't fetch the list of providers
+        if not self.token or not self.api_endpoint:
+            self.log("Astra DB token and API endpoint are required to fetch the list of Vectorize providers.")
+
+            return self.VECTORIZE_PROVIDERS_MAPPING
+
+        try:
+            self.log("Dynamically updating list of Vectorize providers.")
+
+            # Get the admin object
+            client = DataAPIClient(token=self.token)
+            admin = client.get_admin()
+
+            # Get the embedding providers
+            db_admin = admin.get_database_admin(self.api_endpoint)
+            embedding_providers = db_admin.find_embedding_providers().as_dict()
+
+            vectorize_providers_mapping = {}
+
+            # Map the provider display name to the provider key and models
+            for provider_key, provider_data in embedding_providers["embeddingProviders"].items():
+                display_name = provider_data["displayName"]
+                models = [model["name"] for model in provider_data["models"]]
+
+                vectorize_providers_mapping[display_name] = [provider_key, models]
+
+            # Sort the resulting dictionary
+            return defaultdict(list, dict(sorted(vectorize_providers_mapping.items())))
+        except Exception as e:  # noqa: BLE001
+            self.log(f"Error fetching Vectorize providers: {e}")
+
+            return self.VECTORIZE_PROVIDERS_MAPPING
+
     def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):
         if field_name == "embedding_choice":
             if field_value == "Astra Vectorize":
-                self.del_fields(build_config, ["embedding"])
+                self.del_fields(build_config, ["embedding_model"])
+
+                # Update the providers mapping
+                vectorize_providers = self.update_providers_mapping()
 
                 new_parameter = DropdownInput(
                     name="embedding_provider",
                     display_name="Embedding Provider",
-                    options=self.VECTORIZE_PROVIDERS_MAPPING.keys(),
+                    options=vectorize_providers.keys(),
                     value="",
                     required=True,
                     real_time_refresh=True,
@@ -276,13 +321,13 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
                 )
 
                 new_parameter = HandleInput(
-                    name="embedding",
+                    name="embedding_model",
                     display_name="Embedding Model",
                     input_types=["Embeddings"],
                     info="Allows an embedding model configuration.",
                 ).to_dict()
 
-                self.insert_in_dict(build_config, "embedding_choice", {"embedding": new_parameter})
+                self.insert_in_dict(build_config, "embedding_choice", {"embedding_model": new_parameter})
 
         elif field_name == "embedding_provider":
             self.del_fields(
@@ -290,7 +335,9 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
                 ["model", "z_01_model_parameters", "z_02_api_key_name", "z_03_provider_api_key", "z_04_authentication"],
             )
 
-            model_options = self.VECTORIZE_PROVIDERS_MAPPING[field_value][1]
+            # Update the providers mapping
+            vectorize_providers = self.update_providers_mapping()
+            model_options = vectorize_providers[field_value][1]
 
             new_parameter = DropdownInput(
                 name="model",
@@ -420,7 +467,7 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
             raise ValueError(msg) from e
 
         if self.embedding_choice == "Embedding Model":
-            embedding_dict = {"embedding": self.embedding}
+            embedding_dict = {"embedding": self.embedding_model}
         else:
             from astrapy.info import CollectionVectorServiceOptions
 

src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json

@@ -104,7 +104,7 @@
             "output_types": ["Embeddings"]
           },
           "targetHandle": {
-            "fieldName": "embedding",
+            "fieldName": "embedding_model",
             "id": "AstraDB-3buPx",
             "inputTypes": ["Embeddings"],
             "type": "other"
@@ -196,7 +196,7 @@
             "output_types": ["Embeddings"]
           },
           "targetHandle": {
-            "fieldName": "embedding",
+            "fieldName": "embedding_model",
             "id": "AstraDB-laybz",
             "inputTypes": ["Embeddings"],
             "type": "other"
@@ -1601,7 +1601,7 @@
               "ingest_data",
               "namespace",
               "embedding_service",
-              "embedding",
+              "embedding_model",
               "metric",
               "batch_size",
               "bulk_insert_batch_concurrency",
@@ -1781,23 +1781,6 @@
                 "type": "str",
                 "value": ""
               },
-              "embedding": {
-                "_input_type": "HandleInput",
-                "advanced": false,
-                "display_name": "Embedding Model",
-                "dynamic": false,
-                "info": "Allows an embedding model configuration.",
-                "input_types": ["Embeddings"],
-                "list": false,
-                "name": "embedding",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "trace_as_metadata": true,
-                "type": "other",
-                "value": ""
-              },
               "embedding_choice": {
                 "_input_type": "DropdownInput",
                 "advanced": false,
@@ -1817,6 +1800,23 @@
                 "type": "str",
                 "value": "Embedding Model"
               },
+              "embedding_model": {
+                "_input_type": "HandleInput",
+                "advanced": false,
+                "display_name": "Embedding Model",
+                "dynamic": false,
+                "info": "Allows an embedding model configuration.",
+                "input_types": ["Embeddings"],
+                "list": false,
+                "name": "embedding_model",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "trace_as_metadata": true,
+                "type": "other",
+                "value": ""
+              },
               "ingest_data": {
                 "_input_type": "DataInput",
                 "advanced": false,
@@ -2556,7 +2556,7 @@
               "ingest_data",
               "namespace",
               "embedding_service",
-              "embedding",
+              "embedding_model",
               "metric",
               "batch_size",
               "bulk_insert_batch_concurrency",
@@ -2736,23 +2736,6 @@
                 "type": "str",
                 "value": "test"
               },
-              "embedding": {
-                "_input_type": "HandleInput",
-                "advanced": false,
-                "display_name": "Embedding Model",
-                "dynamic": false,
-                "info": "Allows an embedding model configuration.",
-                "input_types": ["Embeddings"],
-                "list": false,
-                "name": "embedding",
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "trace_as_metadata": true,
-                "type": "other",
-                "value": ""
-              },
               "embedding_choice": {
                 "_input_type": "DropdownInput",
                 "advanced": false,
@@ -2772,6 +2755,23 @@
                 "type": "str",
                 "value": "Embedding Model"
               },
+              "embedding_model": {
+                "_input_type": "HandleInput",
+                "advanced": false,
+                "display_name": "Embedding Model",
+                "dynamic": false,
+                "info": "Allows an embedding model configuration.",
+                "input_types": ["Embeddings"],
+                "list": false,
+                "name": "embedding_model",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "trace_as_metadata": true,
+                "type": "other",
+                "value": ""
+              },
               "ingest_data": {
                 "_input_type": "DataInput",
                 "advanced": false,

src/backend/base/langflow/initial_setup/starter_projects/vector_store_rag.py

@@ -20,7 +20,7 @@ def ingestion_graph():
     openai_embeddings = OpenAIEmbeddingsComponent()
     vector_store = AstraVectorStoreComponent()
     vector_store.set(
-        embedding=openai_embeddings.build_embeddings,
+        embedding_model=openai_embeddings.build_embeddings,
         ingest_data=text_splitter.split_text,
     )
 
@@ -34,7 +34,7 @@ def rag_graph():
     rag_vector_store = AstraVectorStoreComponent()
     rag_vector_store.set(
         search_input=chat_input.message_response,
-        embedding=openai_embeddings.build_embeddings,
+        embedding_model=openai_embeddings.build_embeddings,
     )
 
     parse_data = ParseDataComponent()

src/backend/tests/integration/components/astra/test_astra_component.py

@@ -48,7 +48,7 @@ async def test_base(astradb_client: AstraDB):
             "token": application_token,
             "api_endpoint": api_endpoint,
             "collection_name": BASIC_COLLECTION,
-            "embedding": ComponentInputHandle(
+            "embedding_model": ComponentInputHandle(
                 clazz=OpenAIEmbeddingsComponent,
                 inputs={"openai_api_key": get_openai_api_key()},
                 output_name="embeddings",
@@ -79,7 +79,7 @@ async def test_astra_embeds_and_search():
             "ingest_data": ComponentInputHandle(
                 clazz=TextToData, inputs={"text_data": ["test1", "test2"]}, output_name="from_text"
             ),
-            "embedding": ComponentInputHandle(
+            "embedding_model": ComponentInputHandle(
                 clazz=OpenAIEmbeddingsComponent,
                 inputs={"openai_api_key": get_openai_api_key()},
                 output_name="embeddings",

src/backend/tests/unit/initial_setup/starter_projects/test_vector_store_rag.py

@@ -31,7 +31,7 @@ def ingestion_graph():
     )
     vector_store = AstraVectorStoreComponent(_id="vector-store-123")
     vector_store.set(
-        embedding=openai_embeddings.build_embeddings,
+        embedding_model=openai_embeddings.build_embeddings,
         ingest_data=text_splitter.split_text,
         api_endpoint="https://astra.example.com",
         token="token",  # noqa: S106
@@ -53,7 +53,7 @@ def rag_graph():
         search_input=chat_input.message_response,
         api_endpoint="https://astra.example.com",
         token="token",  # noqa: S106
-        embedding=openai_embeddings.build_embeddings,
+        embedding_model=openai_embeddings.build_embeddings,
     )
     # Mock search_documents
     rag_vector_store.set_on_output(