Update app.py

app.py

@@ -17,7 +17,7 @@ load_dotenv()
 # Set up logging
 logging.basicConfig(
     level=logging.INFO,
-    format="%(asctime)s - %(levelname)s - %(message)s"
+    format='%(asctime)s - %(levelname)s - %(message)s'
 )
 
 # Function to extract text from PDF files
@@ -40,38 +40,36 @@ def get_text_chunks(text):
     chunks = text_splitter.split_text(text)
     return chunks
 
-# Function to create a FAISS vectorstore with rate-limiting and retry logic
+# Function to create a FAISS vectorstore with batching
 def get_vectorstore(text_chunks):
     cohere_api_key = os.getenv("COHERE_API_KEY")
     embeddings = CohereEmbeddings(model="embed-english-v3.0", cohere_api_key=cohere_api_key)
     
-    vectorstore = None
-    batch_size = 10  # Process chunks in batches of 10
+    # Batch processing to respect Cohere's rate limit
+    batch_size = 40
+    all_embeddings = []
+
     for i in range(0, len(text_chunks), batch_size):
-        batch = text_chunks[i:i+batch_size]
-        retry_count = 0
-        
-        while retry_count < 5:  # Retry up to 5 times
-            try:
-                if vectorstore is None:
-                    vectorstore = FAISS.from_texts(texts=batch, embedding=embeddings)
-                else:
-                    vectorstore.add_texts(batch, embedding=embeddings)
-                break  # Exit retry loop if successful
-            except Exception as e:
-                if "rate limit" in str(e).lower():
-                    logging.warning(f"Rate limit exceeded. Retrying batch {i//batch_size + 1} in {2 ** retry_count} seconds...")
-                    time.sleep(2 ** retry_count)  # Exponential backoff
-                    retry_count += 1
-                else:
-                    raise e  # Raise other errors
+        batch = text_chunks[i:i + batch_size]
+        logging.info(f"Processing batch {i // batch_size + 1}: {len(batch)} texts")
+        try:
+            batch_embeddings = embeddings.embed_documents(batch)
+            all_embeddings.extend(batch_embeddings)
+        except Exception as e:
+            logging.error(f"Error embedding batch {i // batch_size + 1}: {e}")
+            st.error(f"An error occurred while embedding batch {i // batch_size + 1}.")
+        if i + batch_size < len(text_chunks):  # Enforce delay only if more batches remain
+            logging.info("Waiting for 60 seconds to respect API rate limits...")
+            time.sleep(60)  # Wait for 60 seconds
+
+    vectorstore = FAISS.from_texts_with_embeddings(texts=text_chunks, embeddings=all_embeddings)
     return vectorstore
 
 # Function to set up the conversational retrieval chain
 def get_conversation_chain(vectorstore):
     try:
         llm = ChatGroq(model="llama-3.1-70b-versatile", temperature=0.5)
-        memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+        memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
         
         conversation_chain = ConversationalRetrievalChain.from_llm(
             llm=llm,
@@ -88,8 +86,8 @@ def get_conversation_chain(vectorstore):
 # Handle user input
 def handle_userinput(user_question):
     if st.session_state.conversation is not None:
-        response = st.session_state.conversation({"question": user_question})
-        st.session_state.chat_history = response["chat_history"]
+        response = st.session_state.conversation({'question': user_question})
+        st.session_state.chat_history = response['chat_history']
 
         for i, message in enumerate(st.session_state.chat_history):
             if i % 2 == 0:
@@ -126,5 +124,5 @@ def main():
                 vectorstore = get_vectorstore(text_chunks)
                 st.session_state.conversation = get_conversation_chain(vectorstore)
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()