update model

Retrieval.py

@@ -0,0 +1,34 @@
+import logging
+from model import llm, vectorstore, splitter, embedding, QA_PROMPT
+
+
+# Chain for Web 
+from langchain.chains import RetrievalQA
+
+bsic_chain = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever = vectorstore.as_retriever(search_kwargs={"k": 4}),
+    return_source_documents= True,
+    input_key="question",
+    chain_type_kwargs={"prompt": QA_PROMPT},
+)
+
+
+
+from langchain.retrievers.multi_query import MultiQueryRetriever
+# from kk import MultiQueryRetriever
+
+retriever_from_llm = MultiQueryRetriever.from_llm(
+    retriever=vectorstore.as_retriever(search_kwargs={"k": 3}), 
+    llm=llm,
+)
+
+multiQuery_chain = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever = retriever_from_llm,
+    return_source_documents= True,
+    input_key="question",
+    chain_type_kwargs={"prompt": QA_PROMPT},
+)

app.py

@@ -1,10 +1,10 @@
 import streamlit as st
-from model import Web_qa
+from Retrieval import bsic_chain, multiQuery_chain
 import time
 
 from htmlTemplates import css, bot_template, user_template, source_template
 
-st.set_page_config(page_title="Chat with ATrad",page_icon=":currency_exchange:")
+st.set_page_config(page_title="Chat with ATrad", page_icon=":currency_exchange:")
 st.write(css, unsafe_allow_html=True)
 
 def main():
@@ -17,8 +17,11 @@ def main():
         4. Source documents will be displayed in the sidebar.
     """)
 
+    # Dropdown to select model --------------------------------------------------------
+    model_selection = st.sidebar.selectbox("Select Model", ["Basic", "MultiQuery"])
+    print(model_selection)
+
     # Button to connect to Google link ------------------------------------------------
-    
     st.sidebar.markdown('<a href="https://drive.google.com/drive/folders/13v6LsaYH9wEwvqVtlLG1U4OiUHgZ7hY4?usp=sharing" target="_blank" style="display: inline-block;'
                         'background-color: #475063; color: white; padding: 10px 20px; text-align: center;border: 1px solid white;'
                         'text-decoration: none; cursor: pointer; border-radius: 5px;">Sources</a>',
@@ -27,8 +30,8 @@ def main():
     st.title("ATrad Chat App")
 
     # Chat area -----------------------------------------------------------------------
-
     user_input = st.text_input("", key="user_input",placeholder="Type your question here...")
+    
     # JavaScript code to submit the form on Enter key press
     js_submit = f"""
         document.addEventListener("keydown", function(event) {{
@@ -38,31 +41,32 @@ def main():
         }});
     """
     st.markdown(f'<script>{js_submit}</script>', unsafe_allow_html=True)
+    
     if st.button("Send"):
         if user_input:
-            
             with st.spinner('Waiting for response...'):
-            
                 # Add bot response here (you can replace this with your bot logic)
-                response, metadata, source_documents = generate_bot_response(user_input)
-                st.write(user_template.replace(
-                "{{MSG}}", user_input), unsafe_allow_html=True)
-                st.write(bot_template.replace(
-                "{{MSG}}",  response ), unsafe_allow_html=True)
+                response, metadata, source_documents = generate_bot_response(user_input, model_selection)
+                st.write(user_template.replace("{{MSG}}", user_input), unsafe_allow_html=True)
+                st.write(bot_template.replace("{{MSG}}", response), unsafe_allow_html=True)
             
                 # Source documents
-                print("metadata", metadata)
                 st.sidebar.title("Source Documents")
                 for i, doc in enumerate(source_documents, 1):
-                    tit=metadata[i-1]["source"].split("\\")[-1]
+                    tit = metadata[i-1]["source"].split("\\")[-1]
                     with st.sidebar.expander(f"{tit}"):
                         st.write(doc)  # Assuming the Document object can be directly written to display its content
 
-def generate_bot_response(user_input):
+def generate_bot_response(user_input, model):
     # Simple bot logic (replace with your actual bot logic)
     start_time = time.time()
     print(f"User Input: {user_input}")
-    res = Web_qa(user_input)
+    
+    if model == "Basic":
+        res = bsic_chain(user_input)
+    elif model == "MultiQuery":
+        res = multiQuery_chain(user_input)
+    
     response = res['result']
     metadata = [i.metadata for i in res.get("source_documents", [])]
     end_time = time.time()

model.py

@@ -68,15 +68,5 @@ from langchain.prompts import PromptTemplate
 QA_PROMPT = PromptTemplate(input_variables=["context", "question"],template=qa_template_V2,)
 
 
-# Chain for Web 
-from langchain.chains import RetrievalQA
-
-Web_qa = RetrievalQA.from_chain_type(
-    llm=llm,
-    chain_type="stuff",
-    retriever = vectorstore.as_retriever(search_kwargs={"k": 4}),
-    return_source_documents= True,
-    input_key="question",
-    chain_type_kwargs={"prompt": QA_PROMPT},
-)
+