Upload 2 files

app.py

@@ -0,0 +1,72 @@
+import streamlit as st
+from langchain_groq import ChatGroq
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_text_splitters 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 PyPDFLoader
+
+import os
+from dotenv import load_dotenv
+import tempfile
+import time
+
+load_dotenv()
+
+## Langsmith Tracking
+os.environ['LANGCHAIN_API_KEY'] = os.getenv('LANGCHAIN_API_KEY')
+os.environ['LANGCHAIN_TRACING_V2'] = 'true'
+os.environ['LANGCHAIN_PROJECT'] = "Simple Q&A Chatbot With OpenAI"
+os.environ['GROQ_API_KEY'] = os.getenv('GROQ_API_KEY')
+os.environ["HF_TOKEN"] = os.getenv('HF_TOKEN')
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+llm = ChatGroq(model="llama-3.1-70b-Versatile")
+
+prompt = ChatPromptTemplate.from_template(
+    """
+    Answer the question based on provided context only.
+    Please provide the most accurate response based on the question
+    <context>
+    {context}
+    </context>
+    Question: {input}
+    """
+)
+
+def create_vector_embeddings(pdf_file_path):
+    st.session_state.embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+    st.session_state.loader = PyPDFLoader(pdf_file_path)
+    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)
+    st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)
+
+uploaded_file = st.file_uploader("Upload a PDF", type="pdf", key="pdf_uploader")
+
+user_prompt = st.text_input("Enter your Query about PDF here:")
+
+if st.button("Document Embedding") and uploaded_file is not None:
+    with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
+        tmp_file.write(uploaded_file.getvalue())
+        tmp_file_path = tmp_file.name
+    create_vector_embeddings(tmp_file_path)
+    st.write("Vector Database is ready")
+
+if user_prompt and "vectors" in st.session_state:
+    document_chain = create_stuff_documents_chain(llm, prompt)
+    retriever = st.session_state.vectors.as_retriever()
+    retrieval_chain = create_retrieval_chain(retriever, document_chain)
+    
+    start = time.process_time()
+    response = retrieval_chain.invoke({"input": user_prompt})
+    st.write(f"Response Time: {time.process_time() - start}")
+    
+    st.write(response["answer"])
+    
+    with st.expander("Document Similarity Search"):
+        for i, doc in enumerate(response["context"]):
+            st.write(doc.page_content)
+            st.write("---------------------------------------")

requirements.txt

@@ -0,0 +1,11 @@
+langchain_groq
+langchain
+python-dotenv
+langchain_community
+langchain_core
+streamlit
+langchain_huggingface
+langchain-text-splitters
+pypdf
+faiss-cpu
+langchain-openai