Create utils.py

utils.py

@@ -0,0 +1,22 @@
+class NeuralSearcher:
+    def __init__(self, collection_name, client, model):
+        self.collection_name = collection_name
+        # Initialize encoder model
+        self.model = model
+        # initialize Qdrant client
+        self.qdrant_client = client
+    def search(self, text: str):
+        # Convert text query into vector
+        vector = self.model.encode(text).tolist()
+
+        # Use `vector` for search for closest vectors in the collection
+        search_result = self.qdrant_client.search(
+            collection_name=self.collection_name,
+            query_vector=vector,
+            query_filter=None,  # If you don't want any filters for now
+            limit=1,  # 5 the most closest results is enough
+        )
+        # `search_result` contains found vector ids with similarity scores along with the stored payload
+        # In this function you are interested in payload only
+        payloads = [hit.payload for hit in search_result]
+        return payloads["content"]