From 814d4a3eade23a2b519ce1fbdaa08fdbf88e04fa Mon Sep 17 00:00:00 2001
From: bilal <bilalline@gmail.com>
Date: Tue, 21 Apr 2026 00:40:44 +0300
Subject: [PATCH] =?UTF-8?q?=D0=A3=D0=B4=D0=B0=D0=BB=D0=B8=D1=82=D1=8C=20ve?=
 =?UTF-8?q?ctor=5Fstore.py?=
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---
 vector_store.py | 36 ------------------------------------
 1 file changed, 36 deletions(-)
 delete mode 100644 vector_store.py

diff --git a/vector_store.py b/vector_store.py
deleted file mode 100644
index 9699984..0000000
--- a/vector_store.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import os
-from qdrant_client import QdrantClient
-from qdrant_client.models import Distance, VectorParams, PointStruct
-
-class VectorDB:
-    def __init__(self, collection_name="wifi_lab", vector_size=4096):
-        host = os.getenv("QDRANT_HOST", "localhost")
-        port = int(os.getenv("QDRANT_PORT", 6333))
-        self.client = QdrantClient(host=host, port=port)
-        self.collection_name = collection_name
-        
-        # Создаем коллекцию, если её нет (размерность 4096 для Qwen 8B Embedding)
-        self.client.recreate_collection(
-            collection_name=self.collection_name,
-            vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE),
-        )
-
-    def add_packet(self, pkt_id, vector, metadata, text):
-        self.client.upsert(
-            collection_name=self.collection_name,
-            points=[
-                PointStruct(
-                    id=pkt_id,
-                    vector=vector,
-                    payload={"metadata": metadata, "text": text}
-                )
-            ]
-        )
-
-    def find_similar(self, vector):
-        return self.client.search(
-            collection_name=self.collection_name,
-            query_vector=vector,
-            limit=3,
-            with_payload=True
-        )