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import streamlit as st
from groq import Groq
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.text_splitter import CharacterTextSplitter
from langchain.document_loaders import PyPDFLoader
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
import tempfile
import os
# Set API Key
GROQ_API_KEY = os.getenv("your_groq_api_key")
# Initialize Groq client
client = Groq(api_key=GROQ_API_KEY)
# Streamlit App Title
st.title("📄 RAG Chatbot with Using Deepseek")
# File Upload
uploaded_files = st.file_uploader("📂 Upload PDFs", type=["pdf"], accept_multiple_files=True)
if uploaded_files:
with st.spinner("Processing PDFs... ⏳"):
docs = []
for file in uploaded_files:
# Save uploaded file temporarily
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_pdf:
temp_pdf.write(file.read())
temp_pdf_path = temp_pdf.name
# Load PDF and extract text
loader = PyPDFLoader(temp_pdf_path)
docs.extend(loader.load())
# Clean up temp file
os.remove(temp_pdf_path)
# Split text into smaller chunks
text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
split_docs = text_splitter.split_documents(docs)
# Create embeddings & FAISS vector store
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
vector_db = FAISS.from_documents(split_docs, embeddings)
st.success("✅ PDFs processed and stored in the vector database!")
# User Input
query = st.text_input("🔍 Ask a question:")
if query:
with st.spinner("Retrieving answer... ⏳"):
# Perform retrieval
retriever = vector_db.as_retriever()
relevant_docs = retriever.get_relevant_documents(query)
# Get context from retrieved docs
context = "\n\n".join([doc.page_content for doc in relevant_docs])
# Make API call to Groq LLaMA-70B
completion = client.chat.completions.create(
model="deepseek-r1-distill-llama-70b",
messages=[
{"role": "system", "content": "You are an AI assistant providing answers based on the given context."},
{"role": "user", "content": f"Context:\n{context}\n\nQuestion: {query}"}
],
temperature=0.6,
max_completion_tokens=1024,
top_p=0.95,
stream=True,
reasoning_format="raw"
)
# Stream response
response_text = ""
response_container = st.empty()
for chunk in completion:
response_text += chunk.choices[0].delta.content or ""
response_container.markdown(response_text)
st.success("✅ Answer generated!")
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