From 3c91525b42ac8bc35fb72de557c3ad09dd166301 Mon Sep 17 00:00:00 2001
From: "min.tian" <min.tian.cn@gmail.com>
Date: Thu, 27 Nov 2025 17:00:26 +0800
Subject: [PATCH] fix es quant & rescore settings

Signed-off-by: min.tian <min.tian.cn@gmail.com>
---
 .../clients/elastic_cloud/elastic_cloud.py    | 43 ++++---------------
 .../frontend/config/dbCaseConfigs.py          |  7 +--
 vectordb_bench/models.py                      |  1 +
 3 files changed, 13 insertions(+), 38 deletions(-)

diff --git a/vectordb_bench/backend/clients/elastic_cloud/elastic_cloud.py b/vectordb_bench/backend/clients/elastic_cloud/elastic_cloud.py
index e89601be2..ae3350ddf 100644
--- a/vectordb_bench/backend/clients/elastic_cloud/elastic_cloud.py
+++ b/vectordb_bench/backend/clients/elastic_cloud/elastic_cloud.py
@@ -180,48 +180,21 @@ def search_embedding(
         """
         assert self.client is not None, "should self.init() first"
 
+        knn = {
+            "field": self.vector_col_name,
+            "k": k,
+            "num_candidates": self.case_config.num_candidates,
+            "filter": self.filter,
+            "query_vector": query,
+        }
         if self.case_config.use_rescore:
-            oversample_k = int(k * self.case_config.oversample_ratio)
-            oversample_num_candidates = int(self.case_config.num_candidates * self.case_config.oversample_ratio)
-            knn = {
-                "field": self.vector_col_name,
-                "k": oversample_k,
-                "num_candidates": oversample_num_candidates,
-                "filter": self.filter,
-                "query_vector": query,
-            }
-            rescore = {
-                "window_size": oversample_k,
-                "query": {
-                    "rescore_query": {
-                        "script_score": {
-                            "query": {"match_all": {}},
-                            "script": {
-                                "source": f"cosineSimilarity(params.queryVector, '{self.vector_col_name}')",
-                                "params": {"queryVector": query},
-                            },
-                        }
-                    },
-                    "query_weight": 0,
-                    "rescore_query_weight": 1,
-                },
-            }
-        else:
-            knn = {
-                "field": self.vector_col_name,
-                "k": k,
-                "num_candidates": self.case_config.num_candidates,
-                "filter": self.filter,
-                "query_vector": query,
-            }
-            rescore = None
+            knn["rescore_vector"] = {"oversample": self.case_config.oversample_ratio}
         size = k
 
         res = self.client.search(
             index=self.indice,
             knn=knn,
             routing=self.routing_key,
-            rescore=rescore,
             size=size,
             _source=False,
             docvalue_fields=[self.id_col_name],
diff --git a/vectordb_bench/frontend/config/dbCaseConfigs.py b/vectordb_bench/frontend/config/dbCaseConfigs.py
index 525e2e166..6dd8a6e19 100644
--- a/vectordb_bench/frontend/config/dbCaseConfigs.py
+++ b/vectordb_bench/frontend/config/dbCaseConfigs.py
@@ -839,7 +839,7 @@ class CaseConfigInput(BaseModel):
 )
 
 CaseConfigParamInput_IndexType_ES = CaseConfigInput(
-    label=CaseConfigParamType.IndexType,
+    label=CaseConfigParamType.index,
     inputType=InputType.Option,
     inputConfig={
         "options": [
@@ -881,7 +881,8 @@ class CaseConfigInput(BaseModel):
     label=CaseConfigParamType.use_rescore,
     inputType=InputType.Bool,
     inputConfig={"value": False},
-    isDisplayed=lambda config: config.get(CaseConfigParamType.IndexType, None) != IndexType.ES_HNSW.value,
+    isDisplayed=lambda config: config.get(CaseConfigParamType.index, None) != IndexType.ES_HNSW.value,
+    inputHelp="Recalculating scores using the original (non-quantized) vectors.",
 )
 
 CaseConfigParamInput_OversampleRatio_ES = CaseConfigInput(
@@ -889,7 +890,7 @@ class CaseConfigInput(BaseModel):
     inputType=InputType.Float,
     inputConfig={"min": 1.0, "max": 100.0, "value": 2.0},
     isDisplayed=lambda config: config.get(CaseConfigParamType.use_rescore, False),
-    inputHelp="num_oversample = oversample_ratio * top_k.",
+    inputHelp="Retrieving more candidates per shard for rescoring.",
 )
 
 CaseConfigParamInput_UseRouting_ES = CaseConfigInput(
diff --git a/vectordb_bench/models.py b/vectordb_bench/models.py
index 32eabec04..5ffd0e70f 100644
--- a/vectordb_bench/models.py
+++ b/vectordb_bench/models.py
@@ -44,6 +44,7 @@ class CaseConfigParamType(Enum):
     """
 
     IndexType = "IndexType"
+    index = "index"
     M = "M"
     EFConstruction = "efConstruction"
     ef_construction = "ef_construction"