Update app.py

app.py

@@ -25,18 +25,6 @@ print("CHECK - Pinecone vector db setup")
 openai.api_key = os.environ.get("OPENAI_API_KEY")
 embeddings = OpenAIEmbeddings()
 
-# initialize pinecone db
-index_name = "kellogg-markstrat"
-
-pinecone.init(
-    api_key=os.getenv("PINECONE_API_KEY"),  # find at app.pinecone.io
-	environment=os.getenv("PINECONE_ENV"),  # next to api key in console
-)
-
-# load existing index
-vectorsearch = Pinecone.from_existing_index(index_name, embeddings)
-retriever = vectorsearch.as_retriever()
-
 print("CHECK - setting up conversational retrieval agent")
 
 # callback handler for streaming
@@ -85,7 +73,19 @@ def predict(message, model_type):
 	# Create a Queue
 	q = Queue()
 	job_done = object()
-	
+
+    # initialize pinecone db
+    index_name = "kellogg-markstrat"
+
+    pinecone.init(
+        api_key=os.getenv("PINECONE_API_KEY"),  # find at app.pinecone.io
+    	environment=os.getenv("PINECONE_ENV"),  # next to api key in console
+    )
+
+    # load existing index
+    vectorsearch = Pinecone.from_existing_index(index_name, embeddings)
+    retriever = vectorsearch.as_retriever()
+    
 	# conversational retrieval agent component construction - memory, prompt template, agent, agent executor
 	# specifying LLM to use
 	if (model_type==1):