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app.py

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+import streamlit as st
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_openai import ChatOpenAI
+from langchain_chroma import Chroma
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_huggingface.embeddings import HuggingFaceEndpointEmbeddings
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.chains.history_aware_retriever import create_history_aware_retriever
+from langchain.chains.retrieval import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+llm = ChatOpenAI(temperature=0.5, model="mistralai/mistral-7b-instruct:free",base_url="https://openrouter.ai/api/v1",api_key=os.getenv("OPENAI_API_KEY"))
+
+def get_vector_store(url):
+    loader = WebBaseLoader(url)
+    documents = loader.load()
+
+    # splitting into chunks
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    documents_chunks = text_splitter.split_documents(documents=documents)
+
+    # converting the chunks into embeddings
+    embeddings = HuggingFaceEndpointEmbeddings(huggingfacehub_api_token=os.getenv("HF_TOKEN"))
+
+    # store the embeddings in a database
+    vector_store = Chroma.from_documents(documents_chunks, embeddings)
+    return vector_store
+
+def get_context_retiriever(vector_store):
+    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=llm, retriever=retriever, prompt=prompt)
+    return retriever_chain
+
+def get_conversational_chain(retriever_chain):
+    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_documnets_chain = create_stuff_documents_chain(llm, prompt)
+    return create_retrieval_chain(retriever_chain, stuff_documnets_chain)
+
+def get_response(user_input):
+    retriever_chain = get_context_retiriever(st.session_state.vector_store)
+    conversational_chain = get_conversational_chain(retriever_chain)
+    response = conversational_chain.invoke({
+        "chat_history": st.session_state.chat_history,
+        "input": user_input
+    })
+    return response['answer']
+
+st.set_page_config(page_title="Chat with websites", page_icon="🤖")
+st.title("Chat with websites")
+st.caption("Follow me on Github: [samagra44](https://github.com/samagra44)")
+with st.sidebar:
+    st.header("Settings")
+    website_url = st.text_input("Website URL")
+
+if website_url is None or website_url == "":
+    st.info("Please enter a website URL")
+
+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_vector_store(website_url)    
+
+    user_query = st.chat_input("Type your message here...")
+    if user_query is not None and user_query != "":
+        response = get_response(user_query)
+        st.session_state.chat_history.append(HumanMessage(content=user_query))
+        st.session_state.chat_history.append(AIMessage(content=response))
+        
+    for message in st.session_state.chat_history:
+        if isinstance(message, AIMessage):
+            with st.chat_message("AI"):
+                st.write(message.content)
+        elif isinstance(message, HumanMessage):
+            with st.chat_message("Human"):
+                st.write(message.content)

requirements.txt

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+langchain
+langchain-core
+langchain-community
+langchain-openai
+langchain-chroma
+langchain-huggingface
+streamlit
+python-dotenv