Create app.py

app.py

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+import streamlit as st
+import os
+from langchain_groq import ChatGroq
+from langchain_openai import OpenAIEmbeddings
+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
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+## load the GroqAPI Key
+os.environ['OPENAI_API_KEY']=os.getenv("OPENAI_API_KEY")
+groq_api_key = os.getenv('GROQ_API_KEY')
+
+st.title("ChatBot Demo for Error Codes")
+
+llm=ChatGroq(groq_api_key=groq_api_key,
+             model="Llama3-8b-8192")
+
+
+prompt = ChatPromptTemplate.from_template(
+    """
+Answer the question based on the provided context only.
+Please provide the most accurate response based on the question.
+<context>
+{context}
+<context>
+Question: {input}
+    """
+)
+
+
+def vector_embedding():
+
+    if "vectors" not in st.session_state:
+
+        st.session_state.embeddings = OpenAIEmbeddings()
+        st.session_state.loader = PyPDFDirectoryLoader("./data")
+        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.docs[:20])
+        st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings )
+
+
+prompt1=st.text_input("Enter your question from Documents")
+
+if st.button("Documents Embedding"):
+    vector_embedding()
+    st.write("VectorStore DB is ready")
+
+import time
+
+if prompt1:
+    start = time.process_time()
+    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': prompt1})
+    print("Response time : ", time.process_time() - start)
+    st.write(response['answer'])
+
+    # With a Streamlit expander
+    with st.expander("Document Similarity Search"):
+        # Find the relevant chunks
+        for i, doc in enumerate(response["context"]):
+            st.write(doc.page_content)
+            st.write("------------------------------------")