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vector_store.py

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+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
+        )