Update vector_store.py

vector_store.py

@@ -1,8 +1,7 @@
 import faiss
-import numpy as np
 from langchain.vectorstores import FAISS
 from langchain.schema import Document
-from utils.embeddings import embedding_model
+from embeddings import embedding_model  # Correct the import to directly reference the embeddings.py file
 
 # Initialize FAISS index
 dim = 384  # MiniLM embedding size
@@ -14,7 +13,7 @@ def add_document(text, doc_id):
     doc = Document(page_content=text, metadata={"id": doc_id})
     vector_db.add_documents([doc])
 
-def search(query, top_k=3):
+def search(query):
     """Search FAISS index for similar documents."""
-    results = vector_db.similarity_search(query, k=top_k)
+    results = vector_db.similarity_search(query, k=3)
     return results