Create app.py

app.py

@@ -0,0 +1,59 @@
+import streamlit as st
+import os
+from dotenv import load_dotenv
+from langchain_groq import ChatGroq
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.chains import create_retrieval_chain
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFDirectoryLoader
+
+load_dotenv()
+os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")
+groq_api_key = os.getenv("GROQ_API_KEY")
+
+llm = ChatGroq(groq_api_key=groq_api_key, model="Llama3-8b-8192")
+
+prompt = ChatPromptTemplate.from_template(
+"""
+Answer the questions based on the context only.
+Provide the answer accurately and briefly to the question
+<context>
+{context}
+<context>
+Question:{input}
+"""
+)
+
+st.title("Simple RAG Application")
+
+def create_vector_embedding():
+    if "vectors" not in st.session_state:
+        st.session_state.embeddings = HuggingFaceBgeEmbeddings()
+        st.session_state.loader = PyPDFDirectoryLoader("documents")
+        st.session_state.docs = st.session_state.loader.load()
+        st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+        st.session_state.final_documents = st.session_state.text_splitter.split_documents()
+        st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)
+
+if "vectors" not in st.session_state:
+    st.write("The vector store database is not yet ready")
+    if st.button("Create"):
+        create_vector_embedding()
+        st.write("Done")
+
+if "vectors" in st.session_state:
+    user_prompt = st.text_input("Enter your query here")
+    if user_prompt:
+        document_chain = create_stuff_documents_chain(llm, prompt)
+        retriever = st.session_state.vectors.as_retriever()
+        retrieval_chain = create_retrieval_chain(retriever, document_chain)
+        response = retrieval_chain.invoke({'input': user_prompt})
+        st.write(response['answer'])
+
+        with st.expander("Context"):
+            for i, doc in enumerate(response['context']):
+                st.write(doc.page_content)
+                st.write("\n\n")