Create app.py

app.py

@@ -0,0 +1,88 @@
+import streamlit as st
+import pandas as pd
+import os
+import faiss
+import pickle
+from sentence_transformers import SentenceTransformer
+from groq import Groq
+
+# Load environment variables
+from dotenv import load_dotenv
+load_dotenv()
+
+# Setup Groq client
+client = Groq(api_key=os.getenv("GROQ_API_KEY"))
+MODEL_NAME = "llama-3-70b-8192"  # Or use "llama-3-8b-8192", "llama-3-3b-8192"
+
+# Load dataset
+@st.cache_data
+def load_data():
+    url = "https://huggingface.co/datasets/llmware/rag_instruct_benchmark_tester/resolve/main/rag_instruct_benchmark_tester.csv"
+    df = pd.read_csv(url)
+    return df
+
+# Build or load FAISS index
+@st.cache_resource
+def load_embeddings(df):
+    embed_model = SentenceTransformer('all-MiniLM-L6-v2')
+    context_list = df['context'].tolist()
+    embeddings = embed_model.encode(context_list, show_progress_bar=True)
+
+    index = faiss.IndexFlatL2(embeddings[0].shape[0])
+    index.add(embeddings)
+
+    return index, embeddings, embed_model
+
+# Retrieve top k similar context passages
+def retrieve_context(query, embed_model, index, df, k=3):
+    query_embedding = embed_model.encode([query])
+    D, I = index.search(query_embedding, k)
+    context_passages = df.iloc[I[0]]['context'].tolist()
+    return context_passages
+
+# Ask Groq LLM
+def ask_groq(query, context):
+    prompt = f"""You are a helpful assistant. Use the provided context to answer the question.
+
+Context:
+{context}
+
+Question:
+{query}
+
+Answer:"""
+    response = client.chat.completions.create(
+        messages=[{"role": "user", "content": prompt}],
+        model=MODEL_NAME
+    )
+    return response.choices[0].message.content
+
+# Streamlit UI
+st.title("📚 RAG App with Groq API")
+st.markdown("Use this Retrieval-Augmented Generation app to ask enterprise, legal, and financial questions.")
+
+df = load_data()
+index, embeddings, embed_model = load_embeddings(df)
+
+sample_queries = df['query'].dropna().unique().tolist()
+
+query = st.text_input("Enter your question:", "")
+if st.button("Use Random Sample"):
+    import random
+    query = random.choice(sample_queries)
+    st.session_state["query"] = query
+    st.experimental_rerun()
+
+if query:
+    st.markdown(f"**Your Query:** {query}")
+    with st.spinner("Retrieving relevant context..."):
+        contexts = retrieve_context(query, embed_model, index, df)
+        combined_context = "\n\n".join(contexts)
+    with st.spinner("Getting answer from Groq..."):
+        answer = ask_groq(query, combined_context)
+    st.markdown("### 💡 Answer")
+    st.write(answer)
+    st.markdown("### 📄 Retrieved Context")
+    for i, ctx in enumerate(contexts, 1):
+        st.markdown(f"**Context {i}:**")
+        st.write(ctx)