More improvements in the chatbot

Author: ptptiwari

python-rag-chatbot/init_rag.py

@@ -49,7 +49,7 @@
 
 # private configs
 CONFIG_PROFILE = "DEFAULT"
-COMPARTMENT_OCID = "ocid1.compartment.oc1..yourcompartmentid"
+COMPARTMENT_OCID = "ocid1.compartment.oc1..aaaaaaaajdyhd7dqnix2avhlckbhhkkcl3cujzyuz6jzyzonadca3i66pqjq"
 oci_config = oci.config.from_file("~/.oci/config", CONFIG_PROFILE)
 COHERE_API_KEY = oci_config['security_token_file']
 DEBUG = True
@@ -155,7 +155,7 @@ def initialize_rag_chain():
 
         llm = OCIGenAI(
             service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
-            compartment_id="ocid1.compartment.oc1..yourcompartmentid",
+            compartment_id="ocid1.compartment.oc1..aaaaaaaajdyhd7dqnix2avhlckbhhkkcl3cujzyuz6jzyzonadca3i66pqjq",
             model_kwargs={"max_tokens": 1000},
             auth_type='SECURITY_TOKEN',
         )

python-rag-chatbot/init_rag_streamlit_exp.py

@@ -63,7 +63,7 @@
 
 # private configs
 CONFIG_PROFILE = "DEFAULT"
-COMPARTMENT_OCID = "ocid1.compartment.oc1..yourcompartmentid"
+COMPARTMENT_OCID = "ocid1.compartment.oc1..aaaaaaaajdyhd7dqnix2avhlckbhhkkcl3cujzyuz6jzyzonadca3i66pqjq"
 oci_config = oci.config.from_file("~/.oci/config", CONFIG_PROFILE)
 COHERE_API_KEY = oci_config['security_token_file']
 
@@ -134,6 +134,7 @@ def split_in_chunks(all_pages):
     splits = post_process(splits)
 
     print(f"Splitted the pdf in {len(splits)} chunks...")
+    print("Size of splits: " + str(text_splitter.__sizeof__()) + "bytes")
 
     return splits
 
@@ -252,7 +253,7 @@ def build_llm(llm_type):
         llm = OCIGenAI(
             model_id="cohere.command",
             service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
-            compartment_id="ocid1.compartment.oc1..yourcompartmentid",
+            compartment_id="ocid1.compartment.oc1..aaaaaaaajdyhd7dqnix2avhlckbhhkkcl3cujzyuz6jzyzonadca3i66pqjq",
             model_kwargs={"max_tokens": 200},
             auth_type='SECURITY_TOKEN',
         )
@@ -284,13 +285,14 @@ def initialize_rag_chain():
 
     # 3. Load embeddings model
     embedder = create_cached_embedder()
-
+    print("Size of splits---: " + str(document_splits.__sizeof__()) + "bytes")
     # 4. Create a Vectore Store and store embeddings
     vectorstore = create_vector_store(VECTOR_STORE_NAME, document_splits, embedder)
 
     # 5. Create a retriever
     # increased num. of docs to 5 (default to 4)
     # added optionally a reranker
+
     retriever = create_retriever(vectorstore)
 
     # 6. Build the LLM

python-rag-chatbot/oracle_bot_exp.py

@@ -1,22 +1,25 @@
 import streamlit as st
-import time
+
 import traceback
 import sys
 
 from init_rag_streamlit_exp import initialize_rag_chain, get_answer
 
 from streamlit_feedback import streamlit_feedback
 
+
 def process_feedback(feedback_value):
     st.write("Feedback value:", feedback_value)
     with open("feedback.txt", "a", encoding="utf-8") as f:
         f.write(f"{feedback_value}\n")
 
+
 def reset_conversation():
     st.session_state.messages = []
     st.session_state.feedback_rendered = False
     st.session_state.feedback_key = 0
 
+
 st.title("Developing an AI bot powered by RAG and Oracle Database")
 
 # Added reset button
@@ -56,6 +59,7 @@ def _submit_feedback(feedback_value, *args, **kwargs):
                         process_feedback(feedback_value)
                         st.session_state.feedback_rendered = False
 
+
                     feedback_component = streamlit_feedback(
                         feedback_type="faces",
                         on_submit=_submit_feedback,