Update app.py

app.py

@@ -6,7 +6,11 @@ from openai import OpenAI
 from llama_index.node_parser import SemanticSplitterNodeParser
 from llama_index.embeddings import OpenAIEmbedding
 from llama_index.ingestion import IngestionPipeline
-from pinecone import PineconeClient, Index, create_index
+
+from pinecone.grpc import PineconeGRPC
+from pinecone import ServerlessSpec
+
+from llama_index.vector_stores import PineconeVectorStore
 
 # Set OpenAI API key from Streamlit secrets
 pinecone_api_key = os.getenv("PINECONE_API_KEY") 
@@ -15,18 +19,19 @@ openai_api_key = os.getenv("OPENAI_API_KEY")
 # Initialize OpenAI client
 client = OpenAI(api_key=openai_api_key)
 
-# Initialize Pinecone connection
-pinecone_client = PineconeClient(api_key=pinecone_api_key)
-index_name = "annualreport"
 
-# Check if the index exists, if not, create it
-if index_name not in pinecone_client.list_indexes():
-    create_index(name=index_name, dimension=1536)  # Dimension should match your embedding model
+# Initialize connection to Pinecone
+pc = PineconeGRPC(api_key=pinecone_api_key)
+index_name = "anualreport"
 
-# Initialize Pinecone index
-pinecone_index = Index(index_name)
+# Initialize your index
+pinecone_index = pc.Index(index_name)
+
+# Initialize VectorStore
 vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
 
+pinecone_index.describe_index_stats()
+
 # Initialize vector index and retriever
 vector_index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
 retriever = VectorIndexRetriever(index=vector_index, similarity_top_k=5)