bump docs version + describe oversampling

qdrant-landing/content/documentation/guides/quantization.md

@@ -191,7 +191,8 @@ POST /collections/{collection_name}/points/search
     "params": {
         "quantization": {
             "ignore": false,
-            "rescore": true
+            "rescore": true,
+            "oversampling": 2.0
         }
     },
     "vector": [0.2, 0.1, 0.9, 0.7],
@@ -212,6 +213,7 @@ client.search(
         quantization=models.QuantizationSearchParams(
             ignore=False,
             rescore=True,
+            oversampling=2.0,
         )
     )
 )
@@ -224,6 +226,11 @@ This can improve the search quality, but may slightly decrease the search speed,
 It is recommended to disable rescore only if the original vectors are stored on a slow storage (e.g. HDD or network storage).
 By default, rescore is enabled.
 
+**Available as of v1.3.0**
+
+`oversampling` - Defines how many extra vectors should be pre-selected using quantized index, and then re-scored using original vectors.
+For example, if oversampling is 2.4 and limit is 100, then 240 vectors will be pre-selected using quantized index, and then top-100 will be returned after re-scoring.
+Oversampling is useful if you want to tune the tradeoff between search speed and search quality in the query time.
 
 ## Quantization tips