Update app.py

app.py

@@ -32,9 +32,15 @@ persisted_vectordb_location = './report_10K_db'
 print("before")
 vectorstore_persisted = Chroma(
     collection_name=collection_name_qna,
-    persist_directory=persisted_vectordb_location,
+    persist_directory='./report_10K_db',
     embedding_function=embedding_model
 )
+
+vectorstore_retriever = vectorstore_persisted.as_retriever(
+    search_type='similarity',
+    search_kwargs={'k': 5}
+)
+
 print("after")
 # Prepare the logging functionality
 
@@ -100,7 +106,7 @@ def predict(user_input,company):
 
     # Create context_for_query
     user_input = user_input
-    relevant_document_chunks = vectorstore_persisted.similarity_search(user_input,k=5,filter={"source":filter_company})
+    relevant_document_chunks = vectorstore_retriever.similarity_search(user_input,k=5,filter={"source":filter_company})
     context_list = [d.page_content for d in relevant_document_chunks]
     context_for_query = ". ".join(context_list)