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# Import necessary libraries
import streamlit as st
import os
from dotenv import load_dotenv
from langchain_groq import ChatGroq
from langchain_chroma import Chroma
from langchain_community.document_loaders import WebBaseLoader, MongodbLoader
from langchain_core.prompts import ChatPromptTemplate
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain.chains import create_retrieval_chain, create_history_aware_retriever
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.prompts import MessagesPlaceholder
# Load environment variables
load_dotenv()
groq_api_key = os.getenv('GROQ_API_KEY')
hf_token = os.getenv('HF_TOKEN')
# Initialize the ChatGroq model
llm = ChatGroq(groq_api_key=groq_api_key, model_name="llama3-8b-8192")
# Initialize embeddings
from langchain_huggingface.embeddings import HuggingFaceEmbeddings
embeddings = HuggingFaceEmbeddings(model_name='all-MiniLM-L6-v2')
# MongoDB data loading setup
loader = MongodbLoader(
connection_string="mongodb+srv://deshcode0:helloworld@deshcode0.ftigm.mongodb.net/?retryWrites=true&w=majority&appName=deshcode0",
db_name="sample_mflix",
collection_name="movies",
field_names = ["_id", "plot", "genres", "runtime", "cast", "poster", "title", "fullplot", "languages", "released", "directors", "rated", "awards", "lastupdated", "year", "imdb", "countries", "type", "tomatoes", "num_mflix_comments"],
)
docs = loader.load()
# Split documents and initialize Chroma vector store
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
splits = text_splitter.split_documents(docs)
vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings)
retriever = vectorstore.as_retriever()
# Define prompt templates
system_prompt = (
"You are an assistant for question-answering tasks. "
"Use the following pieces of retrieved context to answer "
"the question. If you don't know the answer, say that you "
"don't know. Use three sentences maximum and keep the "
"answer concise.\n\n{context}"
)
qa_prompt = ChatPromptTemplate.from_messages(
[
("system", system_prompt),
MessagesPlaceholder("chat_history"),
("human", "{input}"),
]
)
# Initialize the retrieval chain
question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
rag_chain = create_retrieval_chain(retriever, question_answer_chain)
# Streamlit App
st.title("LLM-Powered Question Answering with Memory")
# Initialize session state for chat history
if "chat_history" not in st.session_state:
st.session_state.chat_history = []
# Sidebar for question input
st.sidebar.title("Ask a Question")
question = st.sidebar.text_input("Enter your question:")
# Retrieve and display the answer
if question:
# Add question to chat history
st.session_state.chat_history.append(HumanMessage(content=question))
# Retrieve answer with context from chat history
response = rag_chain.invoke({"input": question, "chat_history": st.session_state.chat_history})
# Display the answer
st.write("**Answer:**")
st.write(response['answer'])
# Add answer to chat history
st.session_state.chat_history.append(AIMessage(content=response['answer']))
# Display chat history in the main app
st.write("## Chat History")
for message in st.session_state.chat_history:
if isinstance(message, HumanMessage):
st.write(f"**You:** {message.content}")
elif isinstance(message, AIMessage):
st.write(f"**Bot:** {message.content}")
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