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import os
import faiss
import pickle
import streamlit as st
from groq import Groq
from sentence_transformers import SentenceTransformer
# Initialize the Groq client for Llama LLM
GROQ_API_KEY = "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"
client = Groq(
api_key=GROQ_API_KEY,
)
# Load FAISS index and metadata
def load_faiss_index(index_path, metadata_path):
if not os.path.exists(index_path) or not os.path.exists(metadata_path):
raise FileNotFoundError("FAISS index or metadata file not found!")
index = faiss.read_index(index_path)
with open(metadata_path, "rb") as f:
metadata = pickle.load(f)
return index, metadata
# Load the FAISS index and metadata
index, metadata = load_faiss_index("faiss_index.bin", "metadata.pkl")
# Initialize the SentenceTransformer for embedding
embedder = SentenceTransformer("all-MiniLM-L6-v2")
# Streamlit UI Configuration
st.set_page_config(page_title="RAG Chatbot: Healthcare, Education & Finance", layout="wide")
st.title("🤖 RAG Chatbot: Healthcare, Education & Finance")
st.markdown("Welcome to the **AI Chatbot**! Ask anything about **healthcare**, **education**, or **finance**.")
# Initialize session state for chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Input field for user queries
user_input = st.text_input("💬 Type your message:", placeholder="Ask me anything...")
# Function to generate Llama LLM responses using Groq API
def generate_llama_response(user_query, context):
response = client.chat.completions.create(
messages=[
{"role": "system", "content": "You are an expert assistant in Healthcare, Education, and Finance."},
{"role": "user", "content": f"Context: {context}\n\nQuery: {user_query}"}
],
model="llama-3.3-70b-versatile",
stream=False,
)
return response.choices[0].message.content
# Function to handle user queries and retrieve responses
def handle_query(user_query):
# Embed the user query and search FAISS
query_vector = embedder.encode([user_query])
_, top_indices = index.search(query_vector, k=3)
relevant_contexts = [metadata[i] for i in top_indices[0]]
# Combine retrieved chunks into context
context = "\n".join(relevant_contexts)
# Generate response from Llama LLM
response = generate_llama_response(user_query, context)
# Save user input and response to FAISS for tracking
user_vector = embedder.encode([user_query])
index.add(user_vector)
metadata.append(user_query)
faiss.write_index(index, "faiss_index.bin")
with open("metadata.pkl", "wb") as f:
pickle.dump(metadata, f)
return response
# Process user input
if user_input:
# Display user message
st.session_state.messages.append({"role": "user", "content": user_input})
# Get bot response
bot_response = handle_query(user_input)
st.session_state.messages.append({"role": "assistant", "content": bot_response})
# Display chat messages
for message in st.session_state.messages:
if message["role"] == "user":
st.markdown(f"**You:** {message['content']}")
else:
st.markdown(f"**Bot:** {message['content']}")
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