updates search

doc_searcher.py

@@ -22,24 +22,39 @@ class DocSearcher:
                 models.Prefetch(
                     query=dense_query,
                     using=DENSE_MODEL,
-                    limit=10000
+                    params=models.SearchParams(
+                    quantization=models.QuantizationSearchParams(
+                            rescore=False,
+                        ),
+                    ),
+                    limit=200
                 ),
                 models.Prefetch(
                     query=models.SparseVector(**sparse_query.as_object()),
                     using=SPARSE_MODEL,
-                    limit=10000
+                    params=models.SearchParams(
+                    quantization=models.QuantizationSearchParams(
+                            rescore=False,
+                        ),
+                    ),
+                    limit=200
                 )
         ]
 
         search_result = self.qdrant_client.query_points(
             collection_name= self.collection_name,
+            search_params=models.SearchParams(
+                hnsw_ef=128,
+                quantization=models.QuantizationSearchParams(
+                        rescore=True,
+                    ),
+                ),
             prefetch=prefetch,
             query=models.FusionQuery(
                 fusion=models.Fusion.RRF,
             ),
-            with_payload=False,
-            score_threshold=0.8,
-            limit = 5
+            with_payload=True,
+            limit = 10
         ).points
 
         data = []

suggestion_searcher.py

@@ -21,25 +21,41 @@ class SuggestionSearcher:
         prefetch = [
                 models.Prefetch(
                     query=dense_query,
+                    params=models.SearchParams(
+                        quantization=models.QuantizationSearchParams(
+                            rescore=False,
+                        ),
+                    ),
                     using=DENSE_MODEL,
-                    limit=100
+                    limit=10000
                 ),
                 models.Prefetch(
                     query=models.SparseVector(**sparse_query.as_object()),
+                    params=models.SearchParams(
+                        quantization=models.QuantizationSearchParams(
+                            rescore=False,
+                        ),
+                    ),
                     using=SPARSE_MODEL,
-                    limit=100
+                    limit=10000
                 )
         ]
 
         search_result = self.qdrant_client.query_points(
             collection_name= self.collection_name,
             search_params=models.SearchParams(
-            quantization=models.QuantizationSearchParams(rescore=False),hnsw_ef=64, exact=False),
+                hnsw_ef=128, 
+                exact=False, 
+                quantization=models.QuantizationSearchParams(
+                        rescore=True,
+                    ),
+                ),
             prefetch=prefetch,
             query=models.FusionQuery(
                 fusion=models.Fusion.RRF,
             ),
-            with_payload=True,
+            with_payload=False,
+            score_threshold=0.8,
             limit = 5
         ).points