混合检索

PolarDB PostgreSQL版支持多种检索方式，包括密集检索、稀疏检索和混合检索。

背景

密集检索：利用语义上下文来理解查询背后的含义。
稀疏检索：强调文本匹配，根据特定术语查找结果，相当于全文检索。
混合检索：结合密集检索和稀疏检索，捕捉完整的上下文和特定的关键词，从而获得全面的搜索结果。

基础数据准备

使用高权限账号创建检索所需插件。
```
CREATE EXTENSION IF NOT EXISTS rum;
CREATE EXTENSION IF NOT EXISTS vector;
CREATE EXTENSION IF NOT EXISTS polar_ai;
```
其中，各插件功能如下：
- rum（全文检索加速）：支持全文检索和相关性排序。
- vector（向量检索）：支持向量检索。
- polar_ai：用于创建相应模型实现文本转向量。

创建表并插入测试数据。

CREATE TABLE t_chunk(id serial, chunk text, embedding vector(1536), v tsvector);

INSERT INTO t_chunk(chunk) VALUES('Unlock the Power of AI 1 million free tokens 88% Price Reduction Activate Now AI Search Contact Sales English Cart Console Log In Why Us Pricing Products Solutions Marketplace Developers Partners Documentation Services Model Studio PolarDB Filter in menu Product Overview Benefits Billing Announcements and Updates Getting Started User Guide Use Cases Developer Reference Support Home Page PolarDBProduct OverviewSearch for Help ContentProduct OverviewUpdated at: 2025-01-06 08:50ProductCommunityWhat is PolarDB?PolarDB is a new-generation database service that is developed by Alibaba Cloud. This service decouples computing from storage and uses integrated software and hardware. PolarDB is a secure and reliable database service that provides auto scaling within seconds, high performance, and mass storage. PolarDB is 100% compatible with MySQL and PostgreSQL and highly compatible with Oracle.');
INSERT INTO t_chunk(chunk) VALUES('PolarDB provides three engines: PolarDB for MySQL, PolarDB for PostgreSQL, and PolarDB-X. Years of best practices in Double 11 events prove that PolarDB can offer the flexibility of open source ecosystems and the high performance and security of commercial cloud-native databases.Database engine Ecosystem Compatibility Architecture Platform Scenario PolarDB for MySQL MySQL 100% compatible with MySQL Shared storage and compute-storage decoupled architecture Public cloud, Apsara Stack Enterprise Edition, DBStack');
INSERT INTO t_chunk(chunk) VALUES('PolarDB for PostgreSQL PostgreSQL and Oracle 100% compatible with MySQL and highly compatible with Oracle Shared storage and compute-storage decoupled architecture Public cloud, Apsara Stack Enterprise Edition, DBStack Cloud-native databases in the PostgreSQL ecosystem PolarDB-X MySQL Standard Edition is 100% compatible with MySQL and Enterprise Edition is highly compatible with MySQL shared nothing and distributed architecture Public cloud, Apsara Stack Enterprise Edition, DBStack');
INSERT INTO t_chunk(chunk) VALUES('Architecture of PolarDB for MySQL and PolarDB for PostgreSQL PolarDB for MySQL and PolarDB for PostgreSQL both use an architecture of shared storage and compute-storage decoupling. They are featured by cloud-native architecture, integrated software and hardware, and shared distributed storage. Physical replication and RDMA are used between, the primary node and read-only nodes to reduce latency and accelerate data synchronization. This resolves the issue of non-strong data consistency caused by asynchronous replication and ensures zero data loss in case of single point of failure (SPOF). The architecture also enables node scaling within seconds.');
INSERT INTO t_chunk(chunk) VALUES('Core components PolarProxy PolarDB uses PolarProxy to provide external services for the applications. PolarProxy forwards the requests from the applications to database nodes. You can use the proxy to perform authentication, data protection, and session persistence. The proxy parses SQL statements, sends write requests to the primary node, and evenly distributes read requests to multiple read-only nodes.Compute nodes A cluster contains one primary node and multiple read-only nodes. A cluster of Multi-master Cluster Edition (only for PolarDB for MySQL) supports multiple primary nodes and multiple read-only nodes. Compute nodes can be either general-purpose or dedicated.Shared storage Multiple nodes in a cluster share storage resources. A single cluster supports up to 500 TB of storage capacity.');
INSERT INTO t_chunk(chunk) VALUES('Architecture benefits Large storage capacity The maximum storage capacity of a cluster is 500 TB. You do not need to purchase clusters for database sharding due to the storage limit of a single host. This simplifies application development and reduces the O&M workload.Cost-effectiveness PolarDB decouples computing and storage. You are charged only for the computing resources when you add read-only nodes to a PolarDB cluster. In traditional database solutions, you are charged for both computing and storage resources when you add nodes.Elastic scaling within minutes PolarDB supports rapid scaling for computing resources. This is based on container virtualization, shared storage, and compute-storage decoupling. It requires only 5 minutes to add or remove a node. The storage capability is automatically scaled up. During the scale-up process, your services are not interrupted.');
INSERT INTO t_chunk(chunk) VALUES('Read consistency PolarDB uses log sequence numbers (LSNs) for cluster endpoints that have read/write splitting enabled. This ensures global consistency for read operations and prevents the inconsistency that is caused by the replication delay between the primary node and read-only nodes.Millisecond-level latency in physical replication PolarDB performs physical replication from the primary node to read-only nodes based on redo logs. The physical replication replaces the logical replication that is based on binary logs. This way, the replication efficiency and stability are improved. No delays occur even if you perform DDL operations on large tables, such as adding indexes or fields.Data backup within seconds Snapshots that are implemented based on the distributed storage can back up a database with terabytes of data in a few minutes. During the entire backup process, no locks are required, which ensures high efficiency and minimized impacts on your business. Data can be backed up anytime.');
INSERT INTO t_chunk(chunk) VALUES('Architecture of PolarDB-X PolarDB-X uses an architecture of shared nothing and compute-storage decoupling. This architecture allows you to achieve hierarchical capacity planning based on your business requirements and implement mass scaling.Core components Global meta service (GMS): provides distributed metadata and a global timestamp distributor named Timestamp Oracle (TSO) and maintains meta information such as tables, schemas, and statistics. GMS also maintains security information such as accounts and permissions.Compute node (CN): provides a distributed SQL engine that contains core optimizers and executors. A CN uses a stateless SQL engine to provide distributed routing and computing and uses the two-phase commit protocol (2PC) to coordinate distributed transactions. A CN also executes DDL statements in a distributed manner and maintains global indexes.Data node (DN): provides a data storage engine. A data node uses Paxos to provide highly reliable storage services and uses multiversion concurrency control (MVCC) for distributed transactions. A data node also provides the pushdown computation feature to push down operators such as Project, Filter, Join, and Agg in distributed systems, and supports local SSDs and shared storage.Change data capture (CDC): provides a primary/secondary replication protocol that is compatible with MySQL. The primary/secondary replication protocol is compatible with the protocols and data formats that are supported by MySQL binary logging. CDC uses the primary/secondary replication protocol to exchange data.');

生成向量数据。您可以通过创建自定义模型并调用实现文本转化向量。

-- 执行Embedding
UPDATE t_chunk SET embedding = <自定义模型调用函数>('<自定义模型名称>', chunk);

创建检索所需索引。

向量索引，此处使用的是L2距离，您可按需调整。
```
CREATE INDEX ON t_chunk using hnsw(embedding vector_l2_ops);
```

全文索引。

UPDATE t_chunk SET v = to_tsvector('english', chunk);

CREATE INDEX ON t_chunk USING rum (v rum_tsvector_ops);

检索

密集检索

只根据向量进行检索，距离越小表示相似度越高。

SELECT chunk, embedding <-> polar_ai.ai_text_embedding('What database engines does PolarDB provide')::vector(1536) as dist
FROM t_chunk
ORDER by dist ASC
limit 5;

稀疏检索

只根据全文进行检索，距离越小表示相似度越高。

SELECT chunk, v <=> to_tsquery('english', 'PolarDB|PostgreSQL|efficiency') as rank
FROM t_chunk 
WHERE v @@ to_tsquery('english', 'PolarDB|PostgreSQL|efficiency')
ORDER by rank ASC
LIMIT 5;

将两种查询方式的结果进行合并操作，以实现多路召回的能力。

WITH t AS (
SELECT chunk, embedding <-> polar_ai.ai_text_embedding('What database engines does PolarDB provide')::vector(1536) as dist
FROM t_chunk
ORDER by dist ASC
limit 5 ),
t2 as (
  SELECT chunk, v <=> to_tsquery('english', 'PolarDB|PostgreSQL|efficiency') as rank
FROM t_chunk 
WHERE v @@ to_tsquery('english', 'PolarDB|PostgreSQL|efficiency')
ORDER by rank ASC
LIMIT 5
)
SELECT * FROM t
UNION ALL
SELECT * FROM t2;

由于这两种距离计算方法无法统一，因此需要采用RRF模型进行统一排名。RRF（Reciprocal Rank Fusion，倒数排名融合）是一种将具有不同相关性指标的多个结果集组合为单个结果集的方法，该方法无需调优，不同的相关性指标也不需要相互关联即可获得高质量的结果。基本步骤如下：

召回阶段收集排名
多个检索器（各路召回）对其查询分别生成排序结果。
排名融合
使用简单的评分函数（如倒数和）将各检索器的排名位置加权融合，公式如下：
$RRF_score (d) = i = 1 \sum N \frac{1}{k + r an k _{i} ( d )}$
其中， $N$ 为不同召回路的数量， $r an k_{i} (d)$ 是第 $i$ 个检索器对文档 $d$ 的排名位置， $k$ 是一个平滑参数，通常取60。
综合排序
根据融合后的评分对文档重新排序，生成最终结果。

在上述查询中，当您对查询结果排序不满意时，可调整参数 $k$ 改变结果顺序。根据查询时传递的 $k$ 参数，对全文检索和向量检索各自分别查询到的 $t o p K$ 结果，按照 $\frac{1}{k + r ank ( i )}$ 公式对每一个返回的文档进行打分，其中 $r ank (i)$ 表示某个文档排第 $i$ 位。如果文本 $t o p K$ 结果中某个文档没有出现在密集向量检索的 $t o p K$ 结果中，则该文档只有一个得分，同理稀疏向量结果中的文档情况亦然。如果某个文档同时出现在密集向量和稀疏向量的 $t o p K$ 结果集中，则将各自计算的得分相加。

说明

平滑参数 $k$ 决定了每个查询的单个结果集中文档对最终排序结果的影响程度。数值越高，排名越低的文档对最终排序结果的影响越大。

-- 密集向量召回
WITH t1 as 
(
SELECT chunk, embedding <-> polar_ai.ai_text_embedding('What database engines does PolarDB provide')::vector(1536) as dist
FROM t_chunk
ORDER by dist ASC
limit 5
),
t2 as (
SELECT ROW_NUMBER() OVER (ORDER BY dist ASC) AS row_num,
chunk
FROM t1
),
-- 稀疏向量召回
t3 as 
(
  SELECT chunk, v <=> to_tsquery('english', 'PolarDB|PostgreSQL|efficiency') as rank
  FROM t_chunk 
  WHERE v @@ to_tsquery('english', 'PolarDB|PostgreSQL|efficiency')
  ORDER by rank ASC
  LIMIT 5
),
t4 as (  
SELECT ROW_NUMBER() OVER (ORDER BY rank DESC) AS row_num,
chunk
FROM t3
),
-- 分别计算 RRF评分
t5 AS (
SELECT 1.0/(60+row_num) as score, chunk FROM t2
UNION ALL 
SELECT 1.0/(60+row_num), chunk FROM t4
)
-- 评分进行合并
SELECT sum(score) as score, chunk
FROM t5
GROUP BY chunk
ORDER BY score DESC;

按权重加权

您还可以为不同结果集设置不同权重，例如密集检索权重0.8，稀疏检索权重0.2。

-- 密集向量召回
WITH t1 as 
(
SELECT chunk, embedding <-> polar_ai.ai_text_embedding('What database engines does PolarDB provide')::vector(1536) as dist
FROM t_chunk
ORDER by dist ASC
limit 5
),
t2 as (
SELECT ROW_NUMBER() OVER (ORDER BY dist ASC) AS row_num,
chunk
FROM t1
),
-- 稀疏向量召回
t3 as 
(
  SELECT chunk, v <=> to_tsquery('english', 'PolarDB|PostgreSQL|efficiency') as rank
  FROM t_chunk 
  WHERE v @@ to_tsquery('english', 'PolarDB|PostgreSQL|efficiency')
  ORDER by rank ASC
  LIMIT 5
),
t4 as (  
SELECT ROW_NUMBER() OVER (ORDER BY rank DESC) AS row_num,
chunk
FROM t3
),
-- 分别计算 RRF评分，权重分别为 0.8和0.2
t5 as (
SELECT (1.0/(60+row_num)) * 0.8 as score , chunk FROM t2
UNION ALL 
SELECT (1.0/(60+row_num)) * 0.2, chunk FROM t4
)
-- 评分进行合并
SELECT sum(score) as score, chunk
FROM t5
GROUP BY chunk
ORDER BY score DESC;