Update vector_store.py

vector_store.py

@@ -1,4 +1,3 @@
-# vector_store.py
 import os
 import logging
 import numpy as np
@@ -9,20 +8,20 @@ from sentence_transformers import SentenceTransformer
 
 logger = logging.getLogger(__name__)
 
-# ------------------ Constants ------------------
+# Constants
 STORAGE_DIR = "storage"
 EMB_FILE = os.path.join(STORAGE_DIR, "embeddings_float16.npz")
 FAISS_FILE = os.path.join(STORAGE_DIR, "faiss_index.idx")
 
 
-# ------------------ Model Init ------------------
+# Model Init
 def init_model(model_name: str = "sentence-transformers/all-MiniLM-L6-v2"):
     """Initialize SentenceTransformer model (cached by HuggingFace)."""
     logger.info(f"Loading embedding model: {model_name}")
     return SentenceTransformer(model_name)
 
 
-# ------------------ Embeddings ------------------
+# Embeddings
 def build_embeddings(documents: List[Dict], model) -> np.ndarray:
     """
     Creates or loads embeddings.
@@ -52,7 +51,7 @@ def build_embeddings(documents: List[Dict], model) -> np.ndarray:
     return embeddings.astype(np.float32)
 
 
-# ------------------ FAISS Index ------------------
+# FAISS Index
 def build_faiss_index(embeddings: np.ndarray):
     """
     Create or load a FAISS index.
@@ -77,7 +76,7 @@ def build_faiss_index(embeddings: np.ndarray):
     return index
 
 
-# ------------------ BM25 ------------------
+# BM25
 def build_bm25(documents: List[Dict]):
     """
     Build BM25 sparse index.
@@ -90,11 +89,7 @@ def build_bm25(documents: List[Dict]):
     return bm25
 
 
-# ============================================================
-#                  🔍 SEARCH METHODS
-# ============================================================
-
-# -------- Semantic Search (via FAISS) --------
+# Semantic Search (via FAISS)
 def semantic_search(query: str, model, faiss_index, documents, k=5):
     """
     Returns top-k documents ranked by dense semantic similarity (FAISS).
@@ -114,7 +109,7 @@ def semantic_search(query: str, model, faiss_index, documents, k=5):
     return results
 
 
-# -------- BM25 Search --------
+# BM25 Search
 def bm25_search(query: str, bm25, documents, k=5):
     """
     Returns top-k documents ranked by sparse lexical BM25 similarity.
@@ -132,7 +127,7 @@ def bm25_search(query: str, bm25, documents, k=5):
     return results
 
 
-# -------- Hybrid Search (FAISS + BM25) --------
+# Hybrid Search (FAISS + BM25)
 def hybrid_search(query: str, model, faiss_index, bm25, documents, k=5, alpha=0.5):
     """
     Combines semantic FAISS + lexical BM25 search.