fixing the retriever from pinecone

app.py

@@ -39,17 +39,16 @@ def rename(orig_author: str):
 @cl.on_chat_start  # marks a function that will be executed at the start of a user session
 async def start_chat():
 
-    msg = cl.Message(content=f"Building Index...")
+    msg = cl.Message(content=f"Initializing the Application...")
     await msg.send()
 
     # load documents from Arxiv
     axloader = ArxivLoader()
     axloader.main()
 
-    # build index in Pinecone
+    # load embedder and the retriever
     pi = PineconeIndexer()
     pi.load_embedder()
-    pi.index_documents(axloader.documents)
     retriever=pi.get_vectorstore().as_retriever()
     print(pi.index.describe_index_stats())
 
@@ -59,7 +58,7 @@ async def start_chat():
         temperature=0
     )
 
-    msg.content = f"Index built!"
+    msg = cl.Message(content=f"Application is ready !")
     await msg.send()
 
     cl.user_session.set("llm", llm)
@@ -86,4 +85,9 @@ async def main(message: cl.Message):
 
     answer = retrieval_augmented_qa_chain.invoke({"question" : message.content})
     
-    await cl.Message(content=answer["response"].content).send()
+    await cl.Message(content=answer["response"].content).send()
+
+
+
+
+

chainlit.md

@@ -1,3 +1,5 @@
 # Pythonic RAGA with LangChain & Pinecone
 
 This application leverages Chainlit, OpenAI, LangChain, Pinecone and Hugging Face to build a basic RAQA (Retrieval Augmented Question Answering) application based on a Pinecone index containing documents with arxiv papers about nuclear fission.
+
+Await for the `The application is ready !` message before starting to use the app.

utils.py

@@ -104,10 +104,10 @@ class PineconeIndexer:
                 metric=metric,
                 dimension=n_dims
             )
+
+            self.arxiv_loader = ArxivLoader()
         
         self.index = pinecone.Index(index_name)
-        self.arxiv_loader = ArxivLoader()
-
 
     def load_embedder(self):
         """"""