Fix example code for Astra DB pipeline (#481)

integrations/astra/examples/example.py

@@ -22,16 +22,14 @@
 logger.info(file_paths)
 
 collection_name = os.getenv("COLLECTION_NAME", "haystack_vector_search")
-keyspace_name = os.getenv("KEYSPACE_NAME", "recommender_demo")
 
-# Make sure ASTRA_API_ENDPOINT and ASTRA_TOKEN environment variables are set before proceeding
+# Make sure ASTRA_DB_API_ENDPOINT and ASTRA_DB_APPLICATION_TOKEN environment variables are set before proceeding
 
 # We support many different databases. Here, we load a simple and lightweight in-memory database.
 document_store = AstraDocumentStore(
-    astra_collection=collection_name,
-    astra_keyspace=keyspace_name,
+    collection_name=collection_name,
     duplicates_policy=DuplicatePolicy.OVERWRITE,
-    embedding_dim=384,
+    embedding_dimension=384,
 )
 
 # Create components and an indexing pipeline that converts txt files to documents,

integrations/astra/examples/pipeline_example.py

@@ -30,16 +30,14 @@
 """
 
 collection_name = os.getenv("COLLECTION_NAME", "haystack_vector_search")
-keyspace_name = os.getenv("KEYSPACE_NAME", "recommender_demo")
 
-# Make sure ASTRA_API_ENDPOINT and ASTRA_TOKEN environment variables are set before proceeding
+# Make sure ASTRA_DB_API_ENDPOINT and ASTRA_DB_APPLICATION_TOKEN environment variables are set before proceeding
 
 # We support many different databases. Here, we load a simple and lightweight in-memory database.
 document_store = AstraDocumentStore(
-    astra_collection=collection_name,
-    astra_keyspace=keyspace_name,
+    collection_name=collection_name,
     duplicates_policy=DuplicatePolicy.SKIP,
-    embedding_dim=384,
+    embedding_dimension=384,
 )
 
 
@@ -74,7 +72,7 @@
 )
 rag_pipeline.add_component(instance=AstraEmbeddingRetriever(document_store=document_store), name="retriever")
 rag_pipeline.add_component(instance=PromptBuilder(template=prompt_template), name="prompt_builder")
-rag_pipeline.add_component(instance=OpenAIGenerator(api_key=os.environ.get("OPENAI_API_KEY")), name="llm")
+rag_pipeline.add_component(instance=OpenAIGenerator(), name="llm")
 rag_pipeline.add_component(instance=AnswerBuilder(), name="answer_builder")
 rag_pipeline.connect("embedder", "retriever")
 rag_pipeline.connect("retriever", "prompt_builder.documents")

integrations/astra/examples/requirements.txt

@@ -1,3 +1,4 @@
 haystack-ai==2.0.0b4
 sentence_transformers==2.2.2
-openai==1.6.1
+openai==1.6.1
+astrapy>=0.7.0