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import streamlit as st
import os
import tempfile
from langchain_community.vectorstores import FAISS
from langchain_groq import ChatGroq
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_core.runnables import RunnablePassthrough
from langchain.document_loaders import PyPDFLoader
from langchain import hub
# Set API key (replace with your actual key)
os.environ["GROQ_API_KEY"] = "your_groq_api_key"
# Streamlit UI
st.title("📄 PDF Chatbot with RAG")
st.write("Upload a PDF and ask questions!")
# File uploader
uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
if uploaded_file:
# Save uploaded PDF temporarily
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
temp_file.write(uploaded_file.read())
temp_file_path = temp_file.name
# Load and process PDF
loader = PyPDFLoader(temp_file_path)
docs = loader.load()
# Initialize LLM and Embeddings
llm = ChatGroq(model="llama3-8b-8192")
model_name = "BAAI/bge-small-en"
hf_embeddings = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs={'device': 'cpu'},
encode_kwargs={'normalize_embeddings': True}
)
# Split text
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
splits = text_splitter.split_documents(docs)
# Create FAISS vector store
vectorstore = FAISS.from_documents(documents=splits, embedding=hf_embeddings)
retriever = vectorstore.as_retriever()
# Load RAG prompt
prompt = hub.pull("rlm/rag-prompt")
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
# RAG Chain
rag_chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| prompt
| llm
)
# User Query
user_query = st.text_input("Ask a question from the PDF:")
if user_query:
response = rag_chain.invoke(user_query)
st.write("### 🤖 AI Response:")
st.write(response)
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