Update app.py

app.py

@@ -45,37 +45,40 @@ def invoke(openai_api_key, use_rag, prompt):
                      openai_api_key = openai_api_key, 
                      temperature = 0)
     if (use_rag):
-        # Document loading
-        #docs = []
-        # Load PDF
-        #loader = PyPDFLoader(PDF_URL)
-        #docs.extend(loader.load())
-        # Load Web
-        #loader = WebBaseLoader(WEB_URL_1)
-        #docs.extend(loader.load())
-        # Load YouTube
-        #loader = GenericLoader(YoutubeAudioLoader([YOUTUBE_URL_1,
-        #                                           YOUTUBE_URL_2,
-        #                                           YOUTUBE_URL_3], YOUTUBE_DIR), 
-        #                       OpenAIWhisperParser())
-        #docs.extend(loader.load())
-        # Document splitting
-        #text_splitter = RecursiveCharacterTextSplitter(chunk_overlap = 150,
-        #                                               chunk_size = 1500)
-        #splits = text_splitter.split_documents(docs)
-        # Document storage
-        #vector_db = Chroma.from_documents(documents = splits, 
-        #                                  embedding = OpenAIEmbeddings(disallowed_special = ()), 
-        #                                  persist_directory = CHROMA_DIR)
-        # Document retrieval
-        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}), 
-                                                return_source_documents = True)
-        result = rag_chain({"query": prompt})
-        result = result["result"]
+        try:
+            # Document loading
+            #docs = []
+            # Load PDF
+            #loader = PyPDFLoader(PDF_URL)
+            #docs.extend(loader.load())
+            # Load Web
+            #loader = WebBaseLoader(WEB_URL_1)
+            #docs.extend(loader.load())
+            # Load YouTube
+            #loader = GenericLoader(YoutubeAudioLoader([YOUTUBE_URL_1,
+            #                                           YOUTUBE_URL_2,
+            #                                           YOUTUBE_URL_3], YOUTUBE_DIR), 
+            #                       OpenAIWhisperParser())
+            #docs.extend(loader.load())
+            # Document splitting
+            #text_splitter = RecursiveCharacterTextSplitter(chunk_overlap = 150,
+            #                                               chunk_size = 1500)
+            #splits = text_splitter.split_documents(docs)
+            # Document storage
+            #vector_db = Chroma.from_documents(documents = splits, 
+            #                                  embedding = OpenAIEmbeddings(disallowed_special = ()), 
+            #                                  persist_directory = CHROMA_DIR)
+            # Document retrieval
+            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}), 
+                                                    return_source_documents = True)
+            result = rag_chain({"query": prompt})
+            result = result["result"]
+        except Exception as e
+            returns e
     else:
         chain = LLMChain(llm = llm, prompt = LLM_CHAIN_PROMPT)
         result = chain.run({"question": prompt})