Update app.py

app.py

@@ -1,6 +1,12 @@
 import streamlit as st
 from langchain_core.messages import AIMessage, HumanMessage
 from langchain_community.document_loaders import WebBaseLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain_openai import OpenAIEmbeddings, ChatOpenAI
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.chains import create_history_aware_retriever, create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
 
 
 def get_response(user_input):
@@ -8,18 +14,66 @@ def get_response(user_input):
 
 def get_vector_store_from_url(url):
     loader = WebBaseLoader(url)
-    documents = loader.load()
-    return documents
+    document = loader.load()
+    
+    # split the document into chunks
+    text_splitter = RecursiveCharacterTextSplitter()
+    document_chunks = text_splitter.split_documents(document)
+    
+    # create a vectorstore from the chunks
+    vector_store = Chroma.from_documents(document_chunks, OpenAIEmbeddings())
+
+    return vector_store
+    
+
+def get_context_retriever_chain(vector_store):
+    llm = ChatOpenAI()
+    
+    retriever = vector_store.as_retriever()
+    
+    prompt = ChatPromptTemplate.from_messages([
+      MessagesPlaceholder(variable_name="chat_history"),
+      ("user", "{input}"),
+      ("user", "Given the above conversation, generate a search query to look up in order to get information relevant to the conversation")
+    ])
+    
+    retriever_chain = create_history_aware_retriever(llm, retriever, prompt)
+    
+    return retriever_chain
+    
 
+def get_conversational_rag_chain(retriever_chain): 
+    
+    llm = ChatOpenAI()
+    
+    prompt = ChatPromptTemplate.from_messages([
+      ("system", "Answer the user's questions based on the below context:\n\n{context}"),
+      MessagesPlaceholder(variable_name="chat_history"),
+      ("user", "{input}"),
+    ])
+    
+    stuff_documents_chain = create_stuff_documents_chain(llm,prompt)
+    
+    return create_retrieval_chain(retriever_chain, stuff_documents_chain)
+
+def get_response(user_input):
+    retriever_chain = get_context_retriever_chain(st.session_state.vector_store)
+    conversation_rag_chain = get_conversational_rag_chain(retriever_chain)
+    
+    response = conversation_rag_chain.invoke({
+        "chat_history": st.session_state.chat_history,
+        "input": user_query
+    })
+    
+    return response['answer']
+
+    
 # app config
 st.set_page_config(page_title= "Chat with Websites", page_icon="🤖")
 st.title("Chat with Websites")
 
 
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = [ 
-    AIMessage(content = "Hello, I am a bot. How can I help you"),
-]
+
 
 
 #sidebar
@@ -33,6 +87,14 @@ if (website_url is None or website_url == "") or (openai_apikey is None or opena
     
 
 else:
+    
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = [ 
+        AIMessage(content = "Hello, I am a bot. How can I help you"),
+        ]
+
+    if "vector_store" not in st.session_state:
+        st.session_state.vector_store = get_vectorstore_from_url(website_url)
 
     documents = get_vector_store_from_url(website_url)
     with st.sidebar: