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import streamlit as st
import pickle
import faiss
import numpy as np
from sentence_transformers import SentenceTransformer
from groq import Groq
import os
# --- Load secrets ---
GROQ_API_KEY = st.secrets["GROQ_API_KEY"]
# --- Init Groq client ---
client = Groq(api_key=GROQ_API_KEY)
# --- Load embeddings model ---
embed_model = SentenceTransformer("all-MiniLM-L6-v2")
# --- Load docs + FAISS index ---
@st.cache_resource
def load_data():
with open("documents.pkl", "rb") as f:
documents = pickle.load(f)
index = faiss.read_index("docs.index")
return documents, index
documents, index = load_data()
# --- Retrieval ---
def retrieve(query, k=2):
q_emb = embed_model.encode([query])
D, I = index.search(np.array(q_emb), k)
return [documents[i]["text"] for i in I[0]]
# --- RAG Query ---
def rag_query(query):
retrieved = retrieve(query)
context = "\n".join(retrieved)
completion = client.chat.completions.create(
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": f"Context:\n{context}\n\nQuestion: {query}"}
],
model="llama-3.3-70b-versatile",
)
return completion.choices[0].message.content
# --- Streamlit UI ---
st.set_page_config(page_title="RAG with Groq", page_icon="📖")
st.title("📖 Simple RAG with Groq + SentenceTransformers")
query = st.text_input("Ask a question:")
if st.button("Submit") and query:
with st.spinner("Thinking..."):
answer = rag_query(query)
st.subheader("Answer")
st.write(answer)
st.subheader("Retrieved context")
for c in retrieve(query):
st.markdown(f"- {c}")
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