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import streamlit as st
import zipfile
import os
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import HuggingFaceEmbeddings
st.set_page_config(page_title="RAG Search", page_icon="🔍")
# --- 1️⃣ Define correct paths ---
ROOT_DIR = "/app" # Hugging Face Space root
ZIP_PATH = os.path.join(ROOT_DIR, "chroma_db.zip")
DB_PATH = os.path.join(ROOT_DIR, "chroma_db")
# --- 2️⃣ Extract only once per app session ---
if "db_ready" not in st.session_state:
if not os.path.exists(DB_PATH):
if os.path.exists(ZIP_PATH):
st.info("📦 Extracting Chroma DB for the first time...")
with zipfile.ZipFile(ZIP_PATH, "r") as zip_ref:
zip_ref.extractall(DB_PATH)
st.success("✅ Database extracted successfully!")
else:
st.error(f"❌ Database zip not found at: {ZIP_PATH}")
st.stop()
else:
st.info("✅ Chroma DB already extracted.")
st.session_state.db_ready = True # mark done
# --- 3️⃣ Load embeddings (CPU-only) ---
@st.cache_resource(show_spinner=False)
def load_embeddings():
return HuggingFaceEmbeddings(
model_name="mixedbread-ai/mxbai-embed-large-v1",
model_kwargs={"device": "cpu"}
)
embeddings = load_embeddings()
# --- 4️⃣ Load Chroma DB (cached) ---
@st.cache_resource(show_spinner=False)
def load_vectordb():
return Chroma(persist_directory=DB_PATH, embedding_function=embeddings)
vectordb = load_vectordb()
# --- 5️⃣ Query input ---
query = st.text_input("Enter your query:", "What is SystemVerilog interface?")
if st.button("Search"):
st.write("🔎 Searching your local vector database...")
results = vectordb.similarity_search(query, k=3)
if results:
for i, doc in enumerate(results):
st.subheader(f"Result {i+1}")
st.write(doc.page_content)
st.caption(doc.metadata)
st.markdown("---")
else:
st.warning("⚠️ No results found.")
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