Update app.py

app.py

@@ -7,12 +7,13 @@ from pinecone import Pinecone, ServerlessSpec
 from haystack.components.embedders import OpenAIDocumentEmbedder, OpenAITextEmbedder
 from haystack.components.writers import DocumentWriter
 from haystack_integrations.document_stores.pinecone import PineconeDocumentStore
-from haystack_integrations.components.retrievers.pinecone import PineconeEmbeddingRetriever # Corrected import
+from haystack_integrations.components.retrievers.pinecone import PineconeEmbeddingRetriever
 from haystack import Pipeline
 from haystack.components.generators import OpenAIGenerator
 from haystack.components.builders import PromptBuilder
 from haystack.components.converters import TextFileToDocument
 from haystack.components.preprocessors import DocumentSplitter
+from haystack.utils import Secret # Import the Secret class
 
 # --- Logging ---
 logging.basicConfig(stream=sys.stdout, level=logging.INFO)
@@ -40,7 +41,7 @@ if index_name not in [idx['name'] for idx in pc.list_indexes()]:
         metric="euclidean",
         spec=ServerlessSpec(cloud="aws", region="us-east-1")
     )
-    
+
 # --- Document Loading and Processing ---
 os.makedirs("data/paul_graham", exist_ok=True)
 file_path = "data/paul_graham/paul_graham_essay.txt"
@@ -51,7 +52,7 @@ if not os.path.exists(file_path):
         f.write(r.text)
 
 # --- Haystack Pipeline for Indexing ---
-document_store = PineconeDocumentStore(api_key=api_key, index=index_name)
+document_store = PineconeDocumentStore(api_key=Secret.from_env_var("PINECONE_API_KEY"), index=index_name) # Use Secret object
 
 indexing_pipeline = Pipeline()
 indexing_pipeline.add_component("converter", TextFileToDocument())
@@ -84,7 +85,7 @@ query_pipeline = Pipeline()
 query_pipeline.add_component("embedder", OpenAITextEmbedder())
 query_pipeline.add_component("retriever", PineconeEmbeddingRetriever(document_store=document_store))
 query_pipeline.add_component("prompt_builder", PromptBuilder(template=template))
-query_pipeline.add_component("llm", OpenAIGenerator(api_key=openai_api_key))
+query_pipeline.add_component("llm", OpenAIGenerator(api_key=Secret.from_env_var("OPENAI_API_KEY"))) # Use Secret object
 
 query_pipeline.connect("embedder.query", "retriever.query")
 query_pipeline.connect("retriever.documents", "prompt_builder.documents")