Create neural_searcher.py

neural_searcher.py

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+from qdrant_client import QdrantClient
+from sentence_transformers import SentenceTransformer
+from qdrant_client.models import Filter
+
+class NeuralSearcher:
+    def __init__(self, collection_name):
+        self.collection_name = collection_name
+        # Initialize encoder model
+        self.model = SentenceTransformer("all-MiniLM-L6-v2", device="cpu")
+        # initialize Qdrant client
+        self.qdrant_client = QdrantClient("http://localhost:6333")
+        
+    def search(self, text: str, city: str):
+        # Convert text query into vector
+        vector = self.model.encode(text).tolist()
+    
+        city_of_interest = city
+    
+        # Define a filter for cities
+        city_filter = Filter(**{
+            "must": [{
+                "key": "city", # Store city information in a field of the same name 
+                "match": { # This condition checks if payload field has the requested value
+                    "value": city_of_interest
+                }
+            }]
+        })
+    
+        search_result = self.qdrant_client.search(
+            collection_name=self.collection_name,
+            query_vector=vector,
+            query_filter=city_filter,
+            limit=5
+        )
+        # `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