Update knowledge_base.py

knowledge_base.py

@@ -32,20 +32,16 @@ def create_faiss_index(texts):
     return index, texts
 
 # Search the FAISS index
-def search_faiss(faiss_index, stored_texts, query, top_k=3):
+    def search_faiss(faiss_index, stored_texts, query, top_k=3):
     """
-    Search the FAISS index for the most relevant texts based on the query.
+    Search FAISS for the most relevant texts.
     """
-    # Load the same model used for indexing
-    model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+    from sentence_transformers import SentenceTransformer
 
-    # Encode the query into an embedding
+    model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
     query_embedding = model.encode([query])
 
-    # Search the FAISS index
     distances, indices = faiss_index.search(query_embedding, top_k)
-
-    # Retrieve the corresponding texts
     results = [stored_texts[i] for i in indices[0] if i < len(stored_texts)]
 
     return results