Update knowledge_base.py

knowledge_base.py

@@ -1,19 +1,25 @@
 # Create FAISS index
 def create_faiss_index(texts):
-    # Assuming `texts` is a list of text data
+    """
+    Create a FAISS index from the provided list of texts.
+    """
     import faiss
     from sentence_transformers import SentenceTransformer
 
+    # Load pre-trained SentenceTransformer model
     model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
     embeddings = model.encode(texts)
 
+    # Create the FAISS index
     dimension = embeddings.shape[1]
     index = faiss.IndexFlatL2(dimension)
     index.add(embeddings)
 
     return index, texts
 
-    def search_faiss(faiss_index, stored_texts, query, top_k=3):
+
+# Search the FAISS index
+def search_faiss(faiss_index, stored_texts, query, top_k=3):
     """
     Search the FAISS index for the most relevant texts based on the query.
     """