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/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(