init flag

retriever.py

@@ -18,7 +18,9 @@ class HybridRetriever:
         self.docs = docs
         self.mode = mode
         self.k = k
-        self.embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+        self.embedding_model = HuggingFaceEmbeddings(
+            model_name="sentence-transformers/all-MiniLM-L6-v2"
+        )
 
         # Initialize BM25 retriever
         self.bm25 = BM25Retriever.from_documents(docs)
@@ -52,6 +54,8 @@ class HybridRetriever:
             scores = []
             for doc in bm25_candidates:
                 doc_vec = self.doc_embeddings.get(doc.page_content)
+
+                # similarity calculation
                 if doc_vec is not None:
                     sim = np.dot(query_embedding, doc_vec) / (
                         np.linalg.norm(query_embedding) * np.linalg.norm(doc_vec)
@@ -80,6 +84,7 @@ class GuestInfoHybridTool(Tool):
     output_type = "string"
 
     def __init__(self, docs, mode="rerank"):
+        self.is_initialized = False # Flag to check if the tool is initialized
         self.retriever = HybridRetriever(docs, mode=mode)
 
     def forward(self, query: str):