Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,42 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+from langchain.vectorstores import Chroma
+from langchain.embeddings import HuggingFaceEmbeddings
+
+# ------------------------------------
+# 1. Streamlit basic setup
+# ------------------------------------
+st.set_page_config(page_title="RAG Search", page_icon="🔍")
+
+DB_PATH = "path_to_your_existing_db"  # your existing SQLite3/Chroma folder
+query = st.text_input("Enter your query:", "what is verilog")
+
+# ------------------------------------
+# 2. Load embedding model (only for query)
+# ------------------------------------
+embeddings = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
+
+# ------------------------------------
+# 3. Load Chroma database (NO embedding function)
+# ------------------------------------
+vectordb = Chroma(persist_directory=DB_PATH)
+
+# ------------------------------------
+# 4. Search process
+# ------------------------------------
+if st.button("Search"):
+    st.write("🔎 Searching your local vector database...")
+
+    # Convert query → embedding vector
+    query_vector = embeddings.embed_query(query)
+
+    # Use precomputed embeddings in Chroma to find similarity
+    results = vectordb.similarity_search_by_vector(query_vector, 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 matching results found.")