Update app.py

app.py

@@ -41,19 +41,19 @@ def invoke(openai_api_key, use_rag, prompt):
                      temperature = 0)
     if (use_rag):
         # Document loading, splitting, and storage
-        loader = GenericLoader(YoutubeAudioLoader([YOUTUBE_URL_01,
-                                                   YOUTUBE_URL_02], YOUTUBE_DIR), 
-                               OpenAIWhisperParser())
-        docs = loader.load()
-        text_splitter = RecursiveCharacterTextSplitter(chunk_overlap = 150,
-                                                       chunk_size = 1500)
-        splits = text_splitter.split_documents(docs)
-        vector_db = Chroma.from_documents(documents = splits, 
-                                          embedding = OpenAIEmbeddings(), 
-                                          persist_directory = CHROMA_DIR)
+        #loader = GenericLoader(YoutubeAudioLoader([YOUTUBE_URL_01,
+        #                                           YOUTUBE_URL_02], YOUTUBE_DIR), 
+        #                       OpenAIWhisperParser())
+        #docs = loader.load()
+        #text_splitter = RecursiveCharacterTextSplitter(chunk_overlap = 150,
+        #                                               chunk_size = 1500)
+        #splits = text_splitter.split_documents(docs)
+        #vector_db = Chroma.from_documents(documents = splits, 
+        #                                  embedding = OpenAIEmbeddings(), 
+        #                                  persist_directory = CHROMA_DIR)
         # Document retrieval
-        #vector_db = Chroma(embedding_function = OpenAIEmbeddings(),
-        #                   persist_directory = CHROMA_DIR)
+        vector_db = Chroma(embedding_function = OpenAIEmbeddings(),
+                           persist_directory = CHROMA_DIR)
         rag_chain = RetrievalQA.from_chain_type(llm, 
                                                 chain_type_kwargs = {"prompt": RAG_CHAIN_PROMPT}, 
                                                 retriever = vector_db.as_retriever(search_kwargs = {"k": 3}),