Create app.py

app.py

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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:[email protected]/?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}")