From 1316db4bacbe8b43ee2fe59743de530df9683870 Mon Sep 17 00:00:00 2001 From: Andrey Vasnetsov Date: Fri, 23 Jun 2023 20:15:28 +0200 Subject: [PATCH] bump docs version + describe oversampling --- qdrant-landing/config.toml | 2 +- .../content/documentation/guides/quantization.md | 9 ++++++++- 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/qdrant-landing/config.toml b/qdrant-landing/config.toml index 874b22156..bf63461ce 100644 --- a/qdrant-landing/config.toml +++ b/qdrant-landing/config.toml @@ -53,7 +53,7 @@ keywords = "search engine, vector database, neural network, matching, filter, Sa githubDocPrefix = "https://github.com/qdrant/landing_page/tree/master/qdrant-landing/content/" - docVersion = "v1.2.x" + docVersion = "v1.3.x" cloudDocVersion = "v0.1.x" googleTagManager = "GTM-KRLCXD5" diff --git a/qdrant-landing/content/documentation/guides/quantization.md b/qdrant-landing/content/documentation/guides/quantization.md index 655e1c1c7..fb71ae96e 100644 --- a/qdrant-landing/content/documentation/guides/quantization.md +++ b/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