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b5c68be 8a73d8b b5c68be 8a73d8b b5c68be 8a73d8b 1317a05 b5c68be 8a73d8b b5c68be 8a73d8b b5c68be 8a73d8b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 | import streamlit as st
import os
from PyPDF2 import PdfReader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_groq import ChatGroq
from langchain_classic.chains import create_retrieval_chain
from langchain_classic.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from transformers import AutoTokenizer
# Automatically fetch the key from Hugging Face Secrets
GROQ_API_KEY = os.getenv("RAGpdf")
# ==========================================
# Core Pipeline Functions
# ==========================================
def extract_text_from_pdf(pdf_file):
pdf_reader = PdfReader(pdf_file)
text = ""
for page in pdf_reader.pages:
if page.extract_text():
text += page.extract_text()
return text
def tokenize_and_chunk_text(text):
model_name = "sentence-transformers/all-MiniLM-L6-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
text_splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(
tokenizer, chunk_size=500, chunk_overlap=50
)
return text_splitter.split_text(text)
def create_embeddings_and_vectorstore(text_chunks):
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
return FAISS.from_texts(texts=text_chunks, embedding=embeddings)
def query_and_generate_response(user_query, vectorstore, api_key):
llm = ChatGroq(groq_api_key=api_key, model_name="llama-3.1-8b-instant", temperature=0.3)
prompt = ChatPromptTemplate.from_template(
"""
Answer the question based only on the provided context.
If the answer is not in the context, say "I cannot answer this based on the provided document."
Context:
{context}
Question: {input}
"""
)
document_chain = create_stuff_documents_chain(llm, prompt)
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
retrieval_chain = create_retrieval_chain(retriever, document_chain)
return retrieval_chain.invoke({"input": user_query})["answer"]
# ==========================================
# Streamlit User Interface
# ==========================================
st.title("📄 RAG App with Groq & Llama-3")
st.write("Upload a PDF, process it, and ask questions!")
if not GROQ_API_KEY:
st.error("🚨 GROQ_API_KEY is missing! Please set it in your Space Settings under Secrets.")
st.stop()
pdf_file = st.file_uploader("Upload your PDF document", type="pdf")
if pdf_file:
if st.button("Process Document"):
with st.spinner("Extracting text..."):
raw_text = extract_text_from_pdf(pdf_file)
with st.spinner("Tokenizing and Chunking..."):
chunks = tokenize_and_chunk_text(raw_text)
with st.spinner("Creating Embeddings..."):
st.session_state.vectorstore = create_embeddings_and_vectorstore(chunks)
st.success("Document processed and vector store built successfully!")
if "vectorstore" in st.session_state:
st.divider()
user_query = st.text_input("Ask a question about your document:")
if user_query:
with st.spinner("Generating answer..."):
answer = query_and_generate_response(
user_query,
st.session_state.vectorstore,
GROQ_API_KEY
)
st.write("**Response:**")
st.write(answer) |