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import os
import faiss
import numpy as np
import streamlit as st
from groq import Groq
from sentence_transformers import SentenceTransformer
# Initialize FAISS and Model
VECTOR_DB_PATH = "vector_database.faiss"
EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
# Initialize embedding model
embedding_model = SentenceTransformer(EMBEDDING_MODEL)
# Load FAISS Index
def load_faiss():
if not os.path.exists(VECTOR_DB_PATH):
st.error("Vector database not found! Ensure FAISS index is created.")
return None
index = faiss.read_index(VECTOR_DB_PATH)
return index
faiss_index = load_faiss()
# GROQ API setup
GROQ_API_KEY = "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" # Set in Hugging Face secrets
client = Groq(api_key=GROQ_API_KEY)
MODEL_ID = "deepseek-r1-distill-llama-70b"
# Function to get nearest neighbor from FAISS
def search_faiss(query, top_k=3):
query_embedding = embedding_model.encode(query, convert_to_numpy=True).reshape(1, -1)
distances, indices = faiss_index.search(query_embedding, top_k)
return indices
# Function to call DeepSeek model from GROQ
def generate_response(context, query):
prompt = f"Use the following retrieved context to answer the question:\n\nContext:\n{context}\n\nQuestion: {query}\nAnswer:"
response = client.chat.completions.create(
model=MODEL_ID,
messages=[{"role": "user", "content": prompt}],
)
return response.choices[0].message.content
# Streamlit UI
st.title("💡 AI Chat with FAISS & GROQ")
st.write("Ask a question and get responses based on stored knowledge!")
query = st.text_input("🔍 Enter your query:")
if query:
if faiss_index is None:
st.error("FAISS database not loaded. Please check deployment.")
else:
indices = search_faiss(query)
retrieved_context = "\n".join([f"Chunk {i}: Retrieved text" for i in indices[0]])
response = generate_response(retrieved_context, query)
st.write("### 🤖 AI Response:")
st.write(response)
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