Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from datasets import load_dataset
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+from groq import Groq
+
+# Load dataset
+@st.cache_data
+def load_data():
+    dataset = load_dataset("FreedomIntelligence/RAG-Instruct", split="train")
+    df = pd.DataFrame(dataset)
+    return df[["instruction", "response"]]
+
+# Generate embeddings and index
+@st.cache_resource
+def setup_faiss(data):
+    model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+    embeddings = model.encode(data["instruction"].tolist())
+    index = faiss.IndexFlatL2(embeddings.shape[1])
+    index.add(np.array(embeddings))
+    return model, index, embeddings
+
+# Retrieve relevant context
+def retrieve_context(query, model, index, data, top_k=1):
+    query_vec = model.encode([query])
+    distances, indices = index.search(np.array(query_vec), top_k)
+    results = [data.iloc[i]["instruction"] + "\n\n" + data.iloc[i]["response"] for i in indices[0]]
+    return "\n\n".join(results)
+
+# Call Groq LLM
+def query_groq(context, query):
+    prompt = f"Context:\n{context}\n\nQuestion: {query}\n\nAnswer:"
+    client = Groq(api_key=st.secrets["GROQ_API_KEY"])
+    response = client.chat.completions.create(
+        messages=[{"role": "user", "content": prompt}],
+        model="llama-3-70b-8192"
+    )
+    return response.choices[0].message.content
+
+# Streamlit UI
+st.set_page_config(page_title="RAG Demo with Groq", layout="wide")
+st.title("🧠 RAG App using Groq API + RAG-Instruct Dataset")
+
+data = load_data()
+model, index, _ = setup_faiss(data)
+
+st.markdown("Ask a question based on the instruction-response knowledge base.")
+
+# Optional queries
+optional_queries = [
+    "How to use a specific API function?",
+    "Explain how to fine-tune a model.",
+    "What is the difference between pretraining and finetuning?",
+    "How does retrieval-augmented generation work?",
+    "Explain self-supervised learning."
+]
+
+query = st.text_input("Enter your question:", value=optional_queries[0])
+if st.button("Ask"):
+    with st.spinner("Retrieving and generating response..."):
+        context = retrieve_context(query, model, index, data)
+        answer = query_groq(context, query)
+    st.subheader("📄 Retrieved Context")
+    st.write(context)
+    st.subheader("💬 Answer from Groq LLM")
+    st.write(answer)
+
+st.markdown("### Optional Queries to Try:")
+st.write(", ".join(optional_queries))