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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)
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