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new b9129dcf5a3 refine vector search doc (#2955)
b9129dcf5a3 is described below
commit b9129dcf5a30e1bb20d6a23b0269bea1053b4d68
Author: zhiqiang <[email protected]>
AuthorDate: Fri Oct 10 16:39:03 2025 +0800
refine vector search doc (#2955)
## Versions
- [X] dev
- [ ] 3.0
- [ ] 2.1
- [ ] 2.0
## Languages
- [X] Chinese
- [X] English
## Docs Checklist
- [ ] Checked by AI
- [ ] Test Cases Built
---
docs/ai/vector-search.md | 81 +++++++++++++-------
.../current/ai/vector-search.md | 89 +++++++++++++++-------
2 files changed, 115 insertions(+), 55 deletions(-)
diff --git a/docs/ai/vector-search.md b/docs/ai/vector-search.md
index 8d5431c3fc1..50be502b274 100644
--- a/docs/ai/vector-search.md
+++ b/docs/ai/vector-search.md
@@ -34,9 +34,8 @@ Vector retrieval in RAG is not limited to text; it naturally
extends to multimod
Starting from version 2.0, Apache Doris supports nearest-neighbor search based
on vector distance. Performing vector search with SQL is natural and simple:
-```
-SELECT id,
- l2_distance(embedding, [1.0, 2.0, xxx, 10.0]) AS distance
+```sql
+SELECT id, l2_distance(embedding, [1.0, 2.0, xxx, 10.0]) AS distance
FROM vector_table
ORDER BY distance
LIMIT 10;
@@ -50,7 +49,7 @@ From version 4.0, Apache Doris officially supports ANN
search. No additional dat
Using the common [SIFT](http://corpus-texmex.irisa.fr/) dataset as an example,
you can create a table like this:
-```
+```sql
CREATE TABLE sift_1M (
id int NOT NULL,
embedding array<float> NOT NULL COMMENT "",
@@ -87,10 +86,11 @@ Import via S3 TVF:
```sql
INSERT INTO sift_1M
SELECT *
-FROM S3("uri" =
-"https://selectdb-customers-tools-bj.oss-cn-beijing.aliyuncs.com/sift_database.tsv";,
"format" = "csv");
+FROM S3(
+ "uri" =
"https://selectdb-customers-tools-bj.oss-cn-beijing.aliyuncs.com/sift_database.tsv";,
+ "format" = "csv");
-select count(*) from sift_1M
+SELECT count(*) FROM sift_1M
+----------+
| count(*) |
@@ -101,8 +101,15 @@ select count(*) from sift_1M
The SIFT dataset ships with a ground-truth set for result validation. Pick one
query vector and first run an exact Top-N using the precise distance:
-```
-SELECT id, l2_distance(embedding,
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44,35,50,45,9,0,0,0,4,0,4,56,18,0,3,9,16,17,59,10,10,8,57,57,100,105,125,41,1,0,6,92,8,14,73,125,29,7,0,5,0,0,8,124,66,6,3,1,63,5,0,1,49,32,17,35,125,21,0,3,2,12,6,109,21,0,0,35,74,125,14,23,0,0,6,50,25,70,64,7,59,18,7,16,22,5,0,1,125,23,1,0,7,30,14,32,4,0,2,2,59,125,19,4,0,0,2,1,6,53,33,2])
as distance FROM sift_1M ORDER BY distance limit 10
+```sql
+SELECT id,
+ L2_distance(
+ embedding,
+
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44,35,50,45,9,0,0,0,4,0,4,56,18,0,3,9,16,17,59,10,10,8,57,57,100,105,125,41,1,0,6,92,8,14,73,125,29,7,0,5,0,0,8,124,66,6,3,1,63,5,0,1,49,32,17,35,125,21,0,3,2,12,6,109,21,0,0,35,74,125,14,23,0,0,6,50,25,70,64,7,59,18,7,16,22,5,0,1,125,23,1,0,7,30,14,32,4,0,2,2,59,125,19,4,0,0,2,1,6,53,33,2]
+ ) AS distance
+FROM sift_1m
+ORDER BY distance
+LIMIT 10;
--------------
+--------+----------+
@@ -124,8 +131,15 @@ SELECT id, l2_distance(embedding,
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44
When using `l2_distance` or `inner_product`, Doris computes the distance
between the query vector and all 1,000,000 candidate vectors, then applies a
TopN operator globally. Using `l2_distance_approximate` /
`inner_product_approximate` triggers the index path:
-```
-SELECT id, l2_distance_approximate(embedding,
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44,35,50,45,9,0,0,0,4,0,4,56,18,0,3,9,16,17,59,10,10,8,57,57,100,105,125,41,1,0,6,92,8,14,73,125,29,7,0,5,0,0,8,124,66,6,3,1,63,5,0,1,49,32,17,35,125,21,0,3,2,12,6,109,21,0,0,35,74,125,14,23,0,0,6,50,25,70,64,7,59,18,7,16,22,5,0,1,125,23,1,0,7,30,14,32,4,0,2,2,59,125,19,4,0,0,2,1,6,53,33,2])
as distance FROM sift_1M ORDER BY distance limit 10
+```sql
+SELECT id,
+ l2_distance_approximate(
+ embedding,
+
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44,35,50,45,9,0,0,0,4,0,4,56,18,0,3,9,16,17,59,10,10,8,57,57,100,105,125,41,1,0,6,92,8,14,73,125,29,7,0,5,0,0,8,124,66,6,3,1,63,5,0,1,49,32,17,35,125,21,0,3,2,12,6,109,21,0,0,35,74,125,14,23,0,0,6,50,25,70,64,7,59,18,7,16,22,5,0,1,125,23,1,0,7,30,14,32,4,0,2,2,59,125,19,4,0,0,2,1,6,53,33,2]
+ ) AS distance
+FROM sift_1m
+ORDER BY distance
+LIMIT 10;
--------------
+--------+----------+
@@ -157,8 +171,13 @@ Beyond the common TopN nearest neighbor search (returning
the closest N records)
Example SQL:
-```
-SELECT count(*) FROM sift_1M WHERE l2_distance_approximate(embedding,
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44,35,50,45,9,0,0,0,4,0,4,56,18,0,3,9,16,17,59,10,10,8,57,57,100,105,125,41,1,0,6,92,8,14,73,125,29,7,0,5,0,0,8,124,66,6,3,1,63,5,0,1,49,32,17,35,125,21,0,3,2,12,6,109,21,0,0,35,74,125,14,23,0,0,6,50,25,70,64,7,59,18,7,16,22,5,0,1,125,23,1,0,7,30,14,32,4,0,2,2,59,125,19,4,0,0,2,1,6,53,33,2])
> 300
+```sql
+SELECT count(*)
+FROM sift_1m
+WHERE l2_distance_approximate(
+ embedding,
+
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44,35,50,45,9,0,0,0,4,0,4,56,18,0,3,9,16,17,59,10,10,8,57,57,100,105,125,41,1,0,6,92,8,14,73,125,29,7,0,5,0,0,8,124,66,6,3,1,63,5,0,1,49,32,17,35,125,21,0,3,2,12,6,109,21,0,0,35,74,125,14,23,0,0,6,50,25,70,64,7,59,18,7,16,22,5,0,1,125,23,1,0,7,30,14,32,4,0,2,2,59,125,19,4,0,0,2,1,6,53,33,2])
+ > 300
--------------
+----------+
@@ -175,8 +194,15 @@ These range-based vector searches are also accelerated by
the ANN index: the ind
Compound Search combines an ANN TopN search with a range predicate in the same
SQL statement, returning the TopN results that also satisfy a distance
constraint.
-```
-SELECT id, l2_distance_approximate(embedding,
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44,35,50,45,9,0,0,0,4,0,4,56,18,0,3,9,16,17,59,10,10,8,57,57,100,105,125,41,1,0,6,92,8,14,73,125,29,7,0,5,0,0,8,124,66,6,3,1,63,5,0,1,49,32,17,35,125,21,0,3,2,12,6,109,21,0,0,35,74,125,14,23,0,0,6,50,25,70,64,7,59,18,7,16,22,5,0,1,125,23,1,0,7,30,14,32,4,0,2,2,59,125,19,4,0,0,2,1,6,53,33,2])
as dist FROM sift_1M WHERE l2_distance_approximate(embedding,
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44,35,50,4 [...]
+```sql
+SELECT id,
+ l2_distance_approximate(
+ embedding,
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44,35,50,45,9,0,0,0,4,0,4,56,18,0,3,9,16,17,59,10,10,8,57,57,100,105,125,41,1,0,6,92,8,14,73,125,29,7,0,5,0,0,8,124,66,6,3,1,63,5,0,1,49,32,17,35,125,21,0,3,2,12,6,109,21,0,0,35,74,125,14,23,0,0,6,50,25,70,64,7,59,18,7,16,22,5,0,1,125,23,1,0,7,30,14,32,4,0,2,2,59,125,19,4,0,0,2,1,6,53,33,2])
as dist
+FROM sift_1M
+WHERE l2_distance_approximate(
+ embedding,
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44,35,50,45,9,0,0,0,4,0,4,56,18,0,3,9,16,17,59,10,10,8,57,57,100,105,125,41,1,0,6,92,8,14,73,125,29,7,0,5,0,0,8,124,66,6,3,1,63,5,0,1,49,32,17,35,125,21,0,3,2,12,6,109,21,0,0,35,74,125,14,23,0,0,6,50,25,70,64,7,59,18,7,16,22,5,0,1,125,23,1,0,7,30,14,32,4,0,2,2,59,125,19,4,0,0,2,1,6,53,33,2])
+ > 300
+ORDER BY dist limit 10
--------------
+--------+----------+
@@ -206,21 +232,21 @@ This refers to applying other predicates before the ANN
TopN and returning the T
Example with a small 8-D vector and a text filter:
-```
-create table ann_with_fulltext (
- id int not null,
- embedding array<float> not null,
- comment String not null,
- value int null,
+```sql
+CREATE TABLE ann_with_fulltext (
+ id int NOT NULL,
+ embedding array<float> NOT NULL,
+ comment String NOT NULL,
+ value int NULL,
INDEX idx_comment(`comment`) USING INVERTED PROPERTIES("parser" = "english")
COMMENT 'inverted index for comment',
INDEX ann_embedding(`embedding`) USING ANN
PROPERTIES("index_type"="hnsw","metric_type"="l2_distance","dim"="8")
-) duplicate key (`id`)
-distributed by hash(`id`) buckets 1
-properties("replication_num"="1");
+) DUPLICATE KEY (`id`)
+DISTRIBUTED BY HASH(`id`) BUCKETS 1
+PROPERTIES("replication_num"="1");
```
Insert sample data and search only within rows where `comment` contains
“music”:
-```
+```sql
INSERT INTO ann_with_fulltext VALUES
(1, [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8], 'this is about music', 10),
(2, [0.2,0.1,0.5,0.3,0.9,0.4,0.7,0.1], 'sports news today', 20),
@@ -259,7 +285,7 @@ With FLAT encoding, an HNSW index (raw vectors plus graph
structure) may consume
Vector quantization compresses float32 storage to reduce memory. Doris
currently supports two scalar quantization schemes: INT8 and INT4 (SQ8 / SQ4).
Example using SQ8:
-```
+```sql
CREATE TABLE sift_1M (
id int NOT NULL,
embedding array<float> NOT NULL COMMENT "",
@@ -343,13 +369,14 @@ Disable query profiling for ultra latency-sensitive
queries.
2. ANN index is only supported on DuplicateKey table model.
3. Doris uses pre-filter semantics (predicates applied before ANN TopN). If
predicates include columns without secondary indexes that can precisely locate
rows (e.g., no inverted index), Doris falls back to brute force to preserve
correctness.
Example:
- ```
+ ```sql
SELECT id, l2_distance_approximate(embedding, [xxx]) AS distance
FROM sift_1M
WHERE round(id) > 100
ORDER BY distance LIMIT 10;
```
Although `id` is a key, without a secondary index (such as an inverted
index), its predicate is applied after index analysis, so Doris falls back to
brute force to honor pre-filter semantics.
+
4. If the distance function in SQL does not match the metric type defined in
the index DDL, Doris cannot use the ANN index for TopN—even if you call
`l2_distance_approximate` / `inner_product_approximate`.
5. For metric type `inner_product`, only `ORDER BY
inner_product_approximate(...) DESC LIMIT N` (DESC required) can be accelerated
by the ANN index.
6. The first parameter of `xxx_approximate()` must be a ColumnArray, and the
second must be a CAST or ArrayLiteral. Reversing them triggers brute-force
search.
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/ai/vector-search.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/ai/vector-search.md
index 2622ad61266..152025ac735 100644
--- a/i18n/zh-CN/docusaurus-plugin-content-docs/current/ai/vector-search.md
+++ b/i18n/zh-CN/docusaurus-plugin-content-docs/current/ai/vector-search.md
@@ -27,9 +27,9 @@ under the License.
在生成式 AI
的应用中，单纯依赖大模型自身的参数“记忆”存在明显局限：一方面，模型知识具有时效性，无法覆盖最新信息；另一方面，完全依赖模型直接“生成”容易产生幻觉（Hallucination）。因此，RAG（检索增强生成）应运而生。其核心目标不是让模型凭空构造答案，而是从外部知识库中检索出与用户查询最相关的
Top-K
信息片段，作为生成依据。为实现这一点，需要一种机制衡量“用户查询”与“知识库文档”之间的语义相关性。向量表示正是常用手段：将查询与文档统一编码为语义向量后，可通过向量相似度衡量相关程度。随着预训练模型的发展，生成高质量语义向量已成主流，RAG
的检索阶段也演化为一个标准的向量相似度搜索问题——从大规模向量集合中找出与查询最相似的 K 个向量（候选知识片段）。需要注意，RAG 的向量检索不�
�文本，也可扩展到多模态：图片、语音、视频等数据同样可以编码为向量供生成模型使用。例如，用户上传图片后，系统先检索相关描述或知识片段，再辅助生成解释性内容；在医学问答中，可检索病例资料与医学文献，生成更准确的诊断建议。
## 暴力搜索
Apache Doris 自 2.0 版本起支持基于向量距离的最近邻搜索，通过 SQL 实现向量搜索是一个自然且简单的过程。
-```
-SELECT id,
- l2_distance(embedding, [1.0, 2.0, xxx, 10.0]) AS distance
+
+```sql
+SELECT id, l2_distance(embedding, [1.0, 2.0, xxx, 10.0]) AS distance
FROM vector_table
ORDER BY distance
LIMIT 10;
@@ -41,7 +41,7 @@ LIMIT 10;
Apache Doris 自 4.0 版本开始正式支持 ANN 搜索。系统未引入额外数据类型，向量仍以定长数组存储；针对向量距离检索，我们基于 Faiss
实现了新的 ANN 索引类型。
以下以常见的 [SIFT](http://corpus-texmex.irisa.fr/) 数据集为例，建表示例如下：
-```
+```sql
CREATE TABLE sift_1M (
id int NOT NULL,
embedding array<float> NOT NULL COMMENT "",
@@ -77,8 +77,9 @@ PROPERTIES (
```sql
INSERT INTO sift_1M
SELECT *
-FROM S3("uri" =
-"https://selectdb-customers-tools-bj.oss-cn-beijing.aliyuncs.com/sift_database.tsv";,
"format" = "csv");
+FROM S3(
+ "uri" =
"https://selectdb-customers-tools-bj.oss-cn-beijing.aliyuncs.com/sift_database.tsv";,
+ "format" = "csv");
select count(*) from sift_1M
--------------
@@ -90,8 +91,16 @@ select count(*) from sift_1M
+----------+
```
SIFT 数据集同时发布了一组 ground truth，用于校验结果。下面选取一组向量，先使用精确距离函数进行 TopN 召回：
-```
-SELECT id, l2_distance(embedding,
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44,35,50,45,9,0,0,0,4,0,4,56,18,0,3,9,16,17,59,10,10,8,57,57,100,105,125,41,1,0,6,92,8,14,73,125,29,7,0,5,0,0,8,124,66,6,3,1,63,5,0,1,49,32,17,35,125,21,0,3,2,12,6,109,21,0,0,35,74,125,14,23,0,0,6,50,25,70,64,7,59,18,7,16,22,5,0,1,125,23,1,0,7,30,14,32,4,0,2,2,59,125,19,4,0,0,2,1,6,53,33,2])
as distance FROM sift_1M ORDER BY distance limit 10
+
+```sql
+SELECT id,
+ L2_distance(
+ embedding,
+
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44,35,50,45,9,0,0,0,4,0,4,56,18,0,3,9,16,17,59,10,10,8,57,57,100,105,125,41,1,0,6,92,8,14,73,125,29,7,0,5,0,0,8,124,66,6,3,1,63,5,0,1,49,32,17,35,125,21,0,3,2,12,6,109,21,0,0,35,74,125,14,23,0,0,6,50,25,70,64,7,59,18,7,16,22,5,0,1,125,23,1,0,7,30,14,32,4,0,2,2,59,125,19,4,0,0,2,1,6,53,33,2]
+ ) AS distance
+FROM sift_1m
+ORDER BY distance
+LIMIT 10;
--------------
+--------+----------+
@@ -110,9 +119,17 @@ SELECT id, l2_distance(embedding,
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44
+--------+----------+
10 rows in set (0.29 sec)
```
+
当使用 `l2_distance` 或 `inner_product` 时，Doris 需要计算查询向量与 1,000,000 个候选向量之间的距离，再通过
TopN 算子得到全局结果。使用 `l2_distance_approximate` / `inner_product_approximate`
可触发索引执行路径：
-```
-SELECT id, l2_distance_approximate(embedding,
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44,35,50,45,9,0,0,0,4,0,4,56,18,0,3,9,16,17,59,10,10,8,57,57,100,105,125,41,1,0,6,92,8,14,73,125,29,7,0,5,0,0,8,124,66,6,3,1,63,5,0,1,49,32,17,35,125,21,0,3,2,12,6,109,21,0,0,35,74,125,14,23,0,0,6,50,25,70,64,7,59,18,7,16,22,5,0,1,125,23,1,0,7,30,14,32,4,0,2,2,59,125,19,4,0,0,2,1,6,53,33,2])
as distance FROM sift_1M ORDER BY distance limit 10
+```sql
+SELECT id,
+ l2_distance_approximate(
+ embedding,
+
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44,35,50,45,9,0,0,0,4,0,4,56,18,0,3,9,16,17,59,10,10,8,57,57,100,105,125,41,1,0,6,92,8,14,73,125,29,7,0,5,0,0,8,124,66,6,3,1,63,5,0,1,49,32,17,35,125,21,0,3,2,12,6,109,21,0,0,35,74,125,14,23,0,0,6,50,25,70,64,7,59,18,7,16,22,5,0,1,125,23,1,0,7,30,14,32,4,0,2,2,59,125,19,4,0,0,2,1,6,53,33,2]
+ ) AS distance
+FROM sift_1m
+ORDER BY distance
+LIMIT 10;
--------------
+--------+----------+
@@ -139,9 +156,16 @@ Doris 中，ANN 索引建立在 segment 粒度；由于表是分布式的，各
除了常见的 TopN 最近邻搜索（即返回与目标向量最近的前 N 条记录）之外，向量检索中还有一类常见的查询方式是 基于距离阈值的范围搜索。
这类查询不返回固定数量，而是找出所有与目标向量距离满足条件的数据点。例如：查找距离大于或小于某阈值的向量。范围搜索在需要“足够相似”或“足够不相似”候选集的场景中很有用：推荐系统中可获取“接近但不完全相同”内容以增加多样性；异常检测中可定位远离正常模式的数据点。
+
一个典型的 SQL 为：
-```
-SELECT count(*) FROM sift_1M WHERE l2_distance_approximate(embedding,
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44,35,50,45,9,0,0,0,4,0,4,56,18,0,3,9,16,17,59,10,10,8,57,57,100,105,125,41,1,0,6,92,8,14,73,125,29,7,0,5,0,0,8,124,66,6,3,1,63,5,0,1,49,32,17,35,125,21,0,3,2,12,6,109,21,0,0,35,74,125,14,23,0,0,6,50,25,70,64,7,59,18,7,16,22,5,0,1,125,23,1,0,7,30,14,32,4,0,2,2,59,125,19,4,0,0,2,1,6,53,33,2])
> 300
+
+```sql
+SELECT count(*)
+FROM sift_1m
+WHERE l2_distance_approximate(
+ embedding,
+
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44,35,50,45,9,0,0,0,4,0,4,56,18,0,3,9,16,17,59,10,10,8,57,57,100,105,125,41,1,0,6,92,8,14,73,125,29,7,0,5,0,0,8,124,66,6,3,1,63,5,0,1,49,32,17,35,125,21,0,3,2,12,6,109,21,0,0,35,74,125,14,23,0,0,6,50,25,70,64,7,59,18,7,16,22,5,0,1,125,23,1,0,7,30,14,32,4,0,2,2,59,125,19,4,0,0,2,1,6,53,33,2])
+ > 300
--------------
+----------+
@@ -154,8 +178,16 @@ SELECT count(*) FROM sift_1M WHERE
l2_distance_approximate(embedding, [0,11,77
在 Doris 中，这类基于范围的向量搜索同样通过 ANN 索引 来加速执行。通过 ANN
索引，系统能够快速筛选出候选向量集合，然后再计算精确的近似距离，从而显著降低计算开销、提升查询效率。目前支持的范围查询条件包括 `>, >=, <, <=`。
## 组合搜索
Compound Search 指在同一条 SQL 中同时进行 ANN TopN 与 Range 条件过滤，返回满足范围约束的 TopN。
-```
-SELECT id, l2_distance_approximate(embedding,
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44,35,50,45,9,0,0,0,4,0,4,56,18,0,3,9,16,17,59,10,10,8,57,57,100,105,125,41,1,0,6,92,8,14,73,125,29,7,0,5,0,0,8,124,66,6,3,1,63,5,0,1,49,32,17,35,125,21,0,3,2,12,6,109,21,0,0,35,74,125,14,23,0,0,6,50,25,70,64,7,59,18,7,16,22,5,0,1,125,23,1,0,7,30,14,32,4,0,2,2,59,125,19,4,0,0,2,1,6,53,33,2])
as dist FROM sift_1M WHERE l2_distance_approximate(embedding,
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44,35,50,4 [...]
+
+```sql
+SELECT id,
+ l2_distance_approximate(
+ embedding,
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44,35,50,45,9,0,0,0,4,0,4,56,18,0,3,9,16,17,59,10,10,8,57,57,100,105,125,41,1,0,6,92,8,14,73,125,29,7,0,5,0,0,8,124,66,6,3,1,63,5,0,1,49,32,17,35,125,21,0,3,2,12,6,109,21,0,0,35,74,125,14,23,0,0,6,50,25,70,64,7,59,18,7,16,22,5,0,1,125,23,1,0,7,30,14,32,4,0,2,2,59,125,19,4,0,0,2,1,6,53,33,2])
as dist
+FROM sift_1M
+WHERE l2_distance_approximate(
+ embedding,
[0,11,77,24,3,0,0,0,28,70,125,8,0,0,0,0,44,35,50,45,9,0,0,0,4,0,4,56,18,0,3,9,16,17,59,10,10,8,57,57,100,105,125,41,1,0,6,92,8,14,73,125,29,7,0,5,0,0,8,124,66,6,3,1,63,5,0,1,49,32,17,35,125,21,0,3,2,12,6,109,21,0,0,35,74,125,14,23,0,0,6,50,25,70,64,7,59,18,7,16,22,5,0,1,125,23,1,0,7,30,14,32,4,0,2,2,59,125,19,4,0,0,2,1,6,53,33,2])
+ > 300
+ORDER BY dist limit 10
--------------
+--------+----------+
@@ -179,17 +211,18 @@ SELECT id, l2_distance_approximate(embedding,
[0,11,77,24,3,0,0,0,28,70,125,8,0,
## 带过滤条件的 ANN 搜索
带过滤条件的 ANN 搜索是指在执行 ANN TopN 之前先应用其他谓词过滤，返回满足条件的 TopN。
下面用一个 8 维示例说明混合搜索流程。
-```
-create table ann_with_fulltext (
- id int not null,
- embedding array<float> not null,
- comment String not null,
- value int null,
- INDEX idx_comment(`comment`) USING INVERTED PROPERTIES("parser" =
"english") COMMENT 'inverted index for comment',
- INDEX ann_embedding(`embedding`) USING ANN
PROPERTIES("index_type"="hnsw","metric_type"="l2_distance","dim"="8")
- ) duplicate key (`id`)
- distributed by hash(`id`) buckets 1
- properties("replication_num"="1");
+
+```sql
+CREATE TABLE ann_with_fulltext (
+ id int NOT NULL,
+ embedding array<float> NOT NULL,
+ comment String NOT NULL,
+ value int NULL,
+ INDEX idx_comment(`comment`) USING INVERTED PROPERTIES("parser" = "english")
COMMENT 'inverted index for comment',
+ INDEX ann_embedding(`embedding`) USING ANN
PROPERTIES("index_type"="hnsw","metric_type"="l2_distance","dim"="8")
+) DUPLICATE KEY (`id`)
+DISTRIBUTED BY HASH(`id`) BUCKETS 1
+PROPERTIES("replication_num"="1");
INSERT INTO ann_with_fulltext VALUES
(1, [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8], 'this is about music', 10),
@@ -198,7 +231,7 @@ INSERT INTO ann_with_fulltext VALUES
(4, [0.05,0.06,0.07,0.08,0.09,0.1,0.2,0.3], 'politics update',40)
```
假设用户输入查询向量 `[0.1,0.1,0.2,0.2,0.3,0.3,0.4,0.4]`，只在 comment 含 “music”
的文档中检索最相似的前 2 条：
-```
+```sql
SELECT id, comment,
l2_distance_approximate(embedding, [0.1,0.1,0.2,0.2,0.3,0.3,0.4,0.4])
AS dist
FROM ann_with_fulltext
@@ -228,7 +261,7 @@ LIMIT 2;
采用 FLAT 编码时，HNSW 索引（原始向量 + 图结构）可能占用大量内存。HNSW 必须全量驻留内存才能工作，因此在超大规模数据集上易成瓶颈。
向量量化通过压缩 FLOAT32 减少内存开销。Doris 当前支持两种标量量化：INT8 与 INT4（SQ8 / SQ4）。以 SQ8 为例：
-```
+```sql
CREATE TABLE sift_1M (
id int NOT NULL,
embedding array<float> NOT NULL COMMENT "",
@@ -310,7 +343,7 @@ ANN TopN 查询返回行数很少，无需高并行度，建议 `SET parallel_pi
3. Doris 使用前过滤语意（谓词计算在AnnTopN 计算之前）当 SQL 中的谓词涉及到的列有非二级索引列时，为了保证结果的正确性，此时 Doris
会回退到暴力计算。
比如
-```
+```sql
SELECT id, l2_distance_approximate(embedding, [xxx]) AS distance
FROM sift_1M
WHERE round(id) > 100
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