feat: use lighter embedding model

app.py

@@ -71,4 +71,4 @@ with st.sidebar:
 
             st.write("filename:", uploaded_file.name)
             process_pdf(temp_file, vector_store)
-            st.success("PDFs uploaded successfully. ✅")
+            st.success("PDFs processed successfully. ✅")

vector_store.py

@@ -9,17 +9,13 @@ from langchain_text_splitters import RecursiveCharacterTextSplitter
 
 @st.cache_resource()
 def load_embedding_model(model):
-    """
-    sentence-transformers/all-mpnet-base-v2
-    sentence-transformers/all-MiniLM-L6-v2
-    """
     model = HuggingFaceEmbeddings(model_name=model)
     return model
 
 
 @st.cache_resource()
 def load_vector_store():
-    model = load_embedding_model("sentence-transformers/all-mpnet-base-v2")
+    model = load_embedding_model("sentence-transformers/all-MiniLM-L12-v2")
 
     vector_store = Chroma(
         collection_name="main_store",