# 用 TiDB 构建 Graph RAG 应用程序 ## 概述 TiDB 向量搜索功能使你能够基于数据的含义而非数据的字面数值进行搜索。它还支持构建由生成式人工智能驱动的应用程序，或者对文本、图像、视频、音频或任何类型的数据实现语义搜索或相似性搜索。向量搜索利用嵌入空间中的距离来表示相似性。将向量嵌入存储在 TiDB 中后，若要查找相关数据，只需搜索向量嵌入的最近邻。作为具备向量搜索功能的关系型数据库，TiDB 还允许你在一个数据库中同时存储数据及其向量嵌入。你可以将它们存储在同一张表中，或者分开存储在不同表中以构建知识图谱，随后使用向量搜索查询和 JOIN 操作来检索数据。 ![52%](https://lab-static.pingcap.com/quick-demo/TiDBAsVectorStoreForRAG_aa_001_20240610.png) ### 实验时长 + 完成此练习大约需要 90 分钟。 ### 任务 + 连接到 TiDB Cloud Serverless 集群，并通过 MySQL 客户端处理向量数据。 + 在 TiDB Cloud Serverless 中使用向量函数。 + 创建并使用 HNSW（分层可导航）索引，以实现快速的近似最近邻搜索。 + 利用 TiDB 作为向量存储来支持创建 AI 应用程序，使它们能够响应超出 LLM 知识库范围的查询。这些应用程序也称为检索增强生成 (RAG) 应用程序。 + 使用知识图谱方法来增强您的 RAG 应用程序。 ### 先决条件 + 本次实验将依赖您自己的 OpenAI API Key。 > [!TIP] > 点击 **开始** 启动实验环境。 > 环境准备就绪后，点击底部工具栏中的 **前进** 进入 **模块 1**。 > 然后，按照实验指南的步骤连接到实践环境并开始你的任务。 ## 模块 1：TiDB 向量数据类型与向量函数介绍 ![48%](https://lab-static.pingcap.com/quick-demo/TiDBVectors_aa_001_20240610.png) ### 步骤 1. 登录 Linux 机器 + 点击这里 - [终端 1](ssh://monitor1-ssh#1) 打开一个新的终端，连接到为你的实践实验分配的虚拟机。 + 登录后显示的横幅： ```output Last login: Mon Jun 3 01:38:55 2024 from ... [rocky@ip-10-90-1-234 ~]$ ``` ### 步骤 2. 连接到 TiDB Cloud Serverless 数据库 + 以下操作在远程 Linux 上执行。使用以下命令连接到 TiDB Cloud Serverless。（若要退出 MySQL 客户端，使用 `EXIT`。） ```bash mysql \ --comments -u 'serverless_username' -p'serverless_password' \ -h serverless_host -P serverless_port \ --ssl-mode=VERIFY_IDENTITY --ssl-ca=/etc/pki/tls/certs/ca-bundle.crt ``` ```output mysql: [Warning] Using a password on the command line interface can be insecure. Welcome to the MySQL monitor. Commands end with ; or \g. Your MySQL connection id is 2168969 Server version: 5.7.28-TiDB-Serverless TiDB Server (Apache License 2.0) Community Edition, MySQL 5.7 compatible Copyright (c) 2000, 2024, Oracle and/or its affiliates. Oracle is a registered trademark of Oracle Corporation and/or its affiliates. Other names may be trademarks of their respective owners. Type 'help;' or '\h' for help. Type '\c' to clear the current input statement. mysql> ``` ### 步骤 3. 创建并查询向量 + 创建一个新的 Schema 并切换到新的 Schema。 ```sql DROP DATABASE IF EXISTS vs_example; CREATE DATABASE vs_example; USE vs_example; ``` ```output mysql> DROP DATABASE IF EXISTS vs_example; Query OK, 0 rows affected (0.00 sec) mysql> CREATE DATABASE vs_example; Query OK, 0 rows affected (0.12 sec) mysql> USE vs_example; Database changed ``` + 使用 `VECTOR(3)` 创建一个具有 3 维向量列的表，并向其中插入 3 行。 ```sql CREATE TABLE vector_table ( id BIGINT AUTO_RANDOM PRIMARY KEY, doc TEXT, embedding VECTOR(3)); ``` ```output Query OK, 0 rows affected, 1 warning (0.13 sec) ``` ```sql INSERT INTO vector_table (doc, embedding) VALUES ('apple', '[1,1,1]'), ('banana', '[1,1,2]'), ('cat', '[2, 2, 3]'); ``` ```output Query OK, 3 rows affected (0.08 sec) Records: 3 Duplicates: 0 Warnings: 0 ``` ```sql SELECT doc, embedding FROM vector_table ORDER BY embedding; ``` ```output +--------+-----------+ | doc | embedding | +--------+-----------+ | apple | [1,1,1] | | banana | [1,1,2] | | cat | [2,2,3] | +--------+-----------+ 3 rows in set (0.00 sec) ``` ### 步骤 4. 对向量使用函数 + 对向量数据类型使用受支持的向量函数。 + 获取向量字段与特定向量 `[3,2,1]` 之间的向量余弦距离。 ```sql SELECT doc, embedding, VEC_COSINE_DISTANCE(embedding, '[3,2,1]') AS distance_to_321 FROM vector_table ORDER BY distance_to_321; ``` ```output +--------+-----------+---------------------+ | doc | embedding | distance_to_321 | +--------+-----------+---------------------+ | apple | [1,1,1] | 0.07417990022744858 | | cat | [2,2,3] | 0.15733515938218623 | | banana | [1,1,2] | 0.23623738417402662 | +--------+-----------+---------------------+ 3 rows in set (0.01 sec) ``` + 针对 `[3,2,1]` 在向量列上测试余弦距离、L1 距离（曼哈顿距离）、L2 距离（平方欧几里得距离）和负内积。 ```sql SELECT embedding, VEC_COSINE_DISTANCE(embedding, '[3,2,1]') AS cos_to_321, VEC_L1_DISTANCE(embedding, '[3,2,1]') AS l1_to_321, VEC_L2_DISTANCE(embedding, '[3,2,1]') AS l2_to_321, VEC_NEGATIVE_INNER_PRODUCT(embedding, '[3,2,1]') AS nip_to_321 FROM vector_table; ``` ```output +-----------+---------------------+-----------+-------------------+------------+ | embedding | cos_to_321 | l1_to_321 | l2_to_321 | nip_to_321 | +-----------+---------------------+-----------+-------------------+------------+ | [1,1,1] | 0.07417990022744858 | 3 | 2.23606797749979 | -6 | | [1,1,2] | 0.23623738417402662 | 4 | 2.449489742783178 | -7 | | [2,2,3] | 0.15733515938218623 | 3 | 2.23606797749979 | -13 | +-----------+---------------------+-----------+-------------------+------------+ 3 rows in set (0.01 sec) ``` + 其他可用的向量函数，如维数、向量与文本之间的转换、欧几里得范数。 ```sql SELECT embedding, VEC_DIMS(embedding) AS "#_of_dim", VEC_AS_TEXT(embedding) AS to_text, VEC_FROM_TEXT(VEC_AS_TEXT(embedding)) AS embedding_clone, VEC_L2_NORM(embedding) AS l2_norm FROM vector_table ORDER BY embedding; ``` ```output +-----------+----------+---------+-----------------+--------------------+ | embedding | #_of_dim | to_text | embedding_clone | l2_norm | +-----------+----------+---------+-----------------+--------------------+ | [1,1,1] | 3 | [1,1,1] | [1,1,1] | 1.7320508075688772 | | [1,1,2] | 3 | [1,1,2] | [1,1,2] | 2.449489742783178 | | [2,2,3] | 3 | [2,2,3] | [2,2,3] | 4.123105625617661 | +-----------+----------+---------+-----------------+--------------------+ 3 rows in set (0.00 sec) ``` + 退出到 Linux OS 提示符。 ```sql EXIT ``` ```output Bye ``` ### 模块 1 结论 + 本次实践中，一共只有三条记录，如果有很多向量记录需要排序或者查找，效率如何保证？ 下一次实践中引入了 HNSW 索引，可以解决这个问题。 + 您练习了创建具有向量数据类型的 TiDB 表，并成功插入了包含向量数据的记录。 + TiDB 支持以下向量函数：`VEC_L1_DISTANCE`、`VEC_L2_DISTANCE`、`VEC_COSINE_DISTANCE`、`VEC_NEGATIVE_INNER_PRODUCT`、`VEC_DIMS`、`VEC_AS_TEXT`、`VEC_FROM_TEXT`、`VEC_L2_NORM` 等。 ## 模块 2：使用 HNSW 索引提升 K 近邻查询性能 ![54%](https://lab-static.pingcap.com/quick-demo/TiDBVectorSimilaritySearch_aa_001_20240610.png) ### 参考 + HNSW (Hierarchical Navigable Small World) 索引是一种近似最近邻搜索算法。 + HNSW 索引将高维数据点组织成图结构，其中每个节点代表一个数据点，边连接高维空间中彼此靠近的节点。 + HNSW 索引通过对该图进行分层搜索，有效地近似最近邻查询，即使在高维空间中也可以快速检索最近邻。它通常用于推荐系统、相似性搜索和信息检索等应用。 ### 步骤 1. 连接到 TiDB Cloud Serverless 数据库 + 以下操作均在实验提供的远程 Linux 上进行，使用以下命令连接 TiDB Cloud Serverless。（要退出 MySQL 客户端可在 MySQL 提示符下使用 `EXIT` 命令。） ```bash mysql \ --comments -u 'serverless_username' -p'serverless_password' \ -h serverless_host -P serverless_port \ -D vs_example \ --ssl-mode=VERIFY_IDENTITY --ssl-ca=/etc/pki/tls/certs/ca-bundle.crt ``` ```output mysql: [Warning] Using a password on the command line interface can be insecure. Welcome to the MySQL monitor. Commands end with ; or \g. Your MySQL connection id is 2168969 Server version: 5.7.28-TiDB-Serverless TiDB Server (Apache License 2.0) Community Edition, MySQL 5.7 compatible Copyright (c) 2000, 2024, Oracle and/or its affiliates. Oracle is a registered trademark of Oracle Corporation and/or its affiliates. Other names may be trademarks of their respective owners. Type 'help;' or '\h' for help. Type '\c' to clear the current input statement. mysql> ``` ### 步骤 2. 创建具有向量数据类型的测试表 + 将数据（32768 行）填充到示例表 `vector_table_noidx` 中。 ```sql USE vs_example; DROP SEQUENCE IF EXISTS vs_seq1; CREATE SEQUENCE vs_seq1; DROP TABLE IF EXISTS vector_table_noidx; CREATE TABLE vector_table_noidx ( id BIGINT AUTO_RANDOM PRIMARY KEY, doc TEXT, embedding VECTOR(3)); INSERT INTO vector_table_noidx (doc, embedding) VALUES ('dog', '[-1, -2, -1]'); INSERT INTO vector_table_noidx (doc, embedding) SELECT NEXTVAL(vs_seq1), VEC_FROM_TEXT(CONCAT('[',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),']')) FROM vector_table_noidx; INSERT INTO vector_table_noidx (doc, embedding) SELECT NEXTVAL(vs_seq1), VEC_FROM_TEXT(CONCAT('[',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),']')) FROM vector_table_noidx; INSERT INTO vector_table_noidx (doc, embedding) SELECT NEXTVAL(vs_seq1), VEC_FROM_TEXT(CONCAT('[',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),']')) FROM vector_table_noidx; INSERT INTO vector_table_noidx (doc, embedding) SELECT NEXTVAL(vs_seq1), VEC_FROM_TEXT(CONCAT('[',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),']')) FROM vector_table_noidx; INSERT INTO vector_table_noidx (doc, embedding) SELECT NEXTVAL(vs_seq1), VEC_FROM_TEXT(CONCAT('[',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),']')) FROM vector_table_noidx; INSERT INTO vector_table_noidx (doc, embedding) SELECT NEXTVAL(vs_seq1), VEC_FROM_TEXT(CONCAT('[',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),']')) FROM vector_table_noidx; INSERT INTO vector_table_noidx (doc, embedding) SELECT NEXTVAL(vs_seq1), VEC_FROM_TEXT(CONCAT('[',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),']')) FROM vector_table_noidx; INSERT INTO vector_table_noidx (doc, embedding) SELECT NEXTVAL(vs_seq1), VEC_FROM_TEXT(CONCAT('[',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),']')) FROM vector_table_noidx; INSERT INTO vector_table_noidx (doc, embedding) SELECT NEXTVAL(vs_seq1), VEC_FROM_TEXT(CONCAT('[',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),']')) FROM vector_table_noidx; INSERT INTO vector_table_noidx (doc, embedding) SELECT NEXTVAL(vs_seq1), VEC_FROM_TEXT(CONCAT('[',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),']')) FROM vector_table_noidx; INSERT INTO vector_table_noidx (doc, embedding) SELECT NEXTVAL(vs_seq1), VEC_FROM_TEXT(CONCAT('[',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),']')) FROM vector_table_noidx; INSERT INTO vector_table_noidx (doc, embedding) SELECT NEXTVAL(vs_seq1), VEC_FROM_TEXT(CONCAT('[',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),']')) FROM vector_table_noidx; INSERT INTO vector_table_noidx (doc, embedding) SELECT NEXTVAL(vs_seq1), VEC_FROM_TEXT(CONCAT('[',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),']')) FROM vector_table_noidx; INSERT INTO vector_table_noidx (doc, embedding) SELECT NEXTVAL(vs_seq1), VEC_FROM_TEXT(CONCAT('[',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),']')) FROM vector_table_noidx; ANALYZE TABLE vector_table_noidx; INSERT INTO vector_table_noidx (doc, embedding) SELECT NEXTVAL(vs_seq1), VEC_FROM_TEXT(CONCAT('[',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),',',CONVERT(RAND(), CHAR),']')) FROM vector_table_noidx; ANALYZE TABLE vector_table_noidx; ``` ```output ... Query OK, 16384 rows affected (0.06 sec) Records: 16384 Duplicates: 0 Warnings: 0 Query OK, 0 rows affected (0.10 sec) ``` ### 步骤 3. 运行 K 近邻搜索并查看执行计划 + 查看以下查询。它显示了使用余弦距离的典型 K 最近邻搜索。 + 该算法在数据集 - 表 `vector_table_noidx` 中寻找距离参考节点 `[-1,-1,-1]` 最近的节点。 ```sql SELECT doc, embedding FROM vector_table_noidx ORDER BY VEC_COSINE_DISTANCE(embedding, '[-1, -1, -1]') LIMIT 1; ``` ```output +------+------------+ | doc | embedding | +------+------------+ | dog | [-1,-2,-1] | +------+------------+ 1 row in set (0.03 sec) ``` + 查看执行计划，验证查询中是否使用了 `FullTableScan` 。考虑到表 `vector_table_noidx` 仅包含 32768 行数据，响应时间相当迅速，但必须考虑潜在的性能影响，尤其是在处理数十亿行数据时，这种影响重大且不容忽视。注意执行计划输出底部的操作，其中估计行数 (`estRows`) 远大于 `1.00`。 ```sql EXPLAIN SELECT doc, embedding FROM vector_table_noidx ORDER BY VEC_COSINE_DISTANCE(embedding, '[-1, -1, -1]') LIMIT 1; ``` ```output +--------------------------------+----------+-----------+--------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------+ | id | estRows | task | access object | operator info | +--------------------------------+----------+-----------+--------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Projection_13 | 1.00 | root | | vs_example.vector_table_noidx.doc, vs_example.vector_table_noidx.embedding | | └─TopN_7 | 1.00 | root | | Column#4, offset:0, count:1 | | └─Projection_14 | 1.00 | root | | vs_example.vector_table_noidx.doc, vs_example.vector_table_noidx.embedding, vec_cosine_distance(vs_example.vector_table_noidx.embedding, [3,2,1])->Column#4 | | └─TableReader_12 | 1.00 | root | | data:TopN_11 | | └─TopN_11 | 1.00 | cop[tikv] | | vec_cosine_distance(vs_example.vector_table_noidx.embedding, [3,2,1]), offset:0, count:1 | | └─TableFullScan_10 | 10000.00 | cop[tikv] | table:vector_table_noidx | keep order:false, stats:pseudo | +--------------------------------+----------+-----------+--------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------+ 6 rows in set, 1 warning (0.01 sec) ``` ### 步骤 4. 在向量数据类型上创建带有 HNSW 索引的表 + 创建一个新表 `vector_table_with_index_cos`，并在列 `embedding` 上定义 `HNSW` 索引，并使用表 `vector_table_noidx` 中的相同数据填充该表。`HNSW` 索引提供了良好的性能和近似的准确度（在大多数情况下大于 98%）。请注意，创建 `HNSW` 索引的 `COMMENT "HNSW()"` 语法将来可能会发生变化。 + 创建带有 `cosine distance` HNSW 索引的表。 ```sql DROP TABLE IF EXISTS vector_table_with_index_cos; CREATE TABLE vector_table_with_index_cos ( id BIGINT AUTO_RANDOM PRIMARY KEY, doc TEXT, embedding VECTOR(3), VECTOR INDEX embedding_idx ((VEC_COSINE_DISTANCE(embedding))) ); INSERT INTO vector_table_with_index_cos (doc, embedding) SELECT doc, embedding FROM vector_table_noidx; ANALYZE TABLE vector_table_with_index_cos; ``` ```output ... Query OK, 32768 rows affected (0.03 sec) Records: 32768 Duplicates: 0 Warnings: 0 Query OK, 0 rows affected (0.08 sec) ``` + 由于 HNSW 索引依赖 TiFlash 副本, 查看 TiFlash 副本是否已经就位。留意当你看到和展示的输出相同的结果，也就是 `AVAILABLE` 和 `PROGRESS` 均为 1 时说明副本就位了。如果它们小于 1，你可以再次执行以下命令观察进度。副本就为的速度会很快。 ```sql SELECT * FROM information_schema.tiflash_replica WHERE TABLE_SCHEMA = 'vs_example'; ``` ```output +--------------+-----------------------------+----------+---------------+-----------------+-----------+----------+ | TABLE_SCHEMA | TABLE_NAME | TABLE_ID | REPLICA_COUNT | LOCATION_LABELS | AVAILABLE | PROGRESS | +--------------+-----------------------------+----------+---------------+-----------------+-----------+----------+ | vs_example | vector_table_with_index_cos | 183 | 2 | | 1 | 1 | +--------------+-----------------------------+----------+---------------+-----------------+-----------+----------+ ``` ### 步骤 5. 对带索引的表运行 K 近邻查询 + 对表 `vector_table_with_index_cos` 运行相同的 k 最近邻搜索。 ```sql SELECT doc, embedding FROM vector_table_with_index_cos ORDER BY VEC_COSINE_DISTANCE(embedding, '[-1, -1, -1]') LIMIT 1; ``` ```output +------+------------+ | doc | embedding | +------+------------+ | dog | [-1,-2,-1] | +------+------------+ 1 row in set (0.03 sec) ``` ### 步骤 6. 查看执行计划 + 查看执行计划，验证查询中是否使用了近似最近邻索引，即输出中 `access object` 列下的 `index:embedding_idx(embedding)` 和 `operator info` 列下的 `annIndex:COSINE` 运算符。在大规模运行的生产环境中，表 `vector_table_with_index_cos` 在处理此查询时预计会优于 `vector_table_noidx`。注意 `explain` 输出底部的操作，其中预估行数 `estRows` 只有 `1.00`，这比步骤 2 的效率要高得多。除了索引优化之外，你还可以在输出中的 `task` 列下找到 `mpp[tiflash]` 任务，这是 TiDB 实现的另一个列式存储查询优化。HNSW 索引是异步构建的。在我们的实验中，你可能需要等待长达 1 分钟，查询优化器才会选择使用索引来处理你的查询。 ```sql EXPLAIN SELECT doc, embedding FROM vector_table_with_index_cos ORDER BY VEC_COSINE_DISTANCE(embedding, '[-1, -1, -1]') LIMIT 1; ``` ```output +--------------------------------------+---------+--------------+-------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | id | estRows | task | access object | operator info | +--------------------------------------+---------+--------------+-------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Projection_28 | 1.00 | root | | vs_example.vector_table_with_index_cos.doc, vs_example.vector_table_with_index_cos.embedding | | └─TopN_10 | 1.00 | root | | Column#5, offset:0, count:1 | | └─Projection_29 | 1.00 | root | | vs_example.vector_table_with_index_cos.doc, vs_example.vector_table_with_index_cos.embedding, vec_cosine_distance(vs_example.vector_table_with_index_cos.embedding, [-1,-1,-1])->Column#5 | | └─TableReader_25 | 1.00 | root | | MppVersion: 1, data:ExchangeSender_24 | | └─ExchangeSender_24 | 1.00 | mpp[tiflash] | | ExchangeType: PassThrough | | └─Projection_26 | 1.00 | mpp[tiflash] | | vs_example.vector_table_with_index_cos.doc, vs_example.vector_table_with_index_cos.embedding | | └─TopN_23 | 1.00 | mpp[tiflash] | | Column#4, offset:0, count:1 | | └─Projection_27 | 1.00 | mpp[tiflash] | | vs_example.vector_table_with_index_cos.doc, vs_example.vector_table_with_index_cos.embedding, vec_cosine_distance(vs_example.vector_table_with_index_cos.embedding, [-1,-1,-1])->Column#4 | | └─TableFullScan_22 | 1.00 | mpp[tiflash] | table:vector_table_with_index_cos, index:embedding_idx(embedding) | keep order:false, stats:pseudo, annIndex:COSINE(embedding..[-1,-1,-1], limit:1) | +--------------------------------------+---------+--------------+-------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ 9 rows in set (0.01 sec) ``` ### 步骤 7. 尝试 L2 距离索引 + 除了`余弦距离`之外，还支持`L2距离`索引。 + 由于这次搜索算法改为 `L2 距离`，请检查执行计划以验证针对表 `vector_table_with_index_cos` 的查询中没有使用近似最近邻索引 `annIndex`，而是使用了 `TableFullScan`。 ```sql EXPLAIN SELECT doc, embedding FROM vector_table_with_index_cos ORDER BY VEC_L2_DISTANCE(embedding, '[-1, -1, -1]') LIMIT 1; ``` ```output +--------------------------------------+----------+--------------+-----------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | id | estRows | task | access object | operator info | +--------------------------------------+----------+--------------+-----------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Projection_28 | 1.00 | root | | vs_example.vector_table_with_index_cos.doc, vs_example.vector_table_with_index_cos.embedding | | └─TopN_9 | 1.00 | root | | Column#5, offset:0, count:1 | | └─Projection_29 | 1.00 | root | | vs_example.vector_table_with_index_cos.doc, vs_example.vector_table_with_index_cos.embedding, vec_l2_distance(vs_example.vector_table_with_index_cos.embedding, [-1,-1,-1])->Column#5 | | └─TableReader_20 | 1.00 | root | | MppVersion: 1, data:ExchangeSender_19 | | └─ExchangeSender_19 | 1.00 | mpp[tiflash] | | ExchangeType: PassThrough | | └─Projection_26 | 1.00 | mpp[tiflash] | | vs_example.vector_table_with_index_cos.doc, vs_example.vector_table_with_index_cos.embedding | | └─TopN_18 | 1.00 | mpp[tiflash] | | Column#4, offset:0, count:1 | | └─Projection_27 | 10000.00 | mpp[tiflash] | | vs_example.vector_table_with_index_cos.doc, vs_example.vector_table_with_index_cos.embedding, vec_l2_distance(vs_example.vector_table_with_index_cos.embedding, [-1,-1,-1])->Column#4 | | └─TableFullScan_16 | 10000.00 | mpp[tiflash] | table:vector_table_with_index_cos | keep order:false, stats:pseudo | +--------------------------------------+----------+--------------+-----------------------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ 9 rows in set (0.88 sec) ``` + 创建一个具有 `L2 距离` HNSW 索引的表，并使用表 `vector_table_with_index_cos` 中的相同数据填充它。 ```sql DROP TABLE IF EXISTS vector_table_with_index_l2; CREATE TABLE vector_table_with_index_l2 ( id BIGINT AUTO_RANDOM PRIMARY KEY, doc TEXT, embedding VECTOR(3), VECTOR INDEX embedding_idx ((VEC_L2_DISTANCE(embedding))) ); INSERT INTO vector_table_with_index_l2 (doc, embedding) SELECT doc, embedding FROM vector_table_with_index_cos; ANALYZE TABLE vector_table_with_index_l2; ``` ```output Query OK, 32768 rows affected (0.62 sec) Records: 32768 Duplicates: 0 Warnings: 0 Query OK, 0 rows affected (0.09 sec) ``` + 由于 HNSW 索引依赖 TiFlash 副本, 查看 TiFlash 副本是否已经就位。这次请留意表 `vector_table_with_index_l2` 的 `AVAILABLE` 和 `PROGRESS`。 ```sql SELECT * FROM information_schema.tiflash_replica WHERE TABLE_SCHEMA = 'vs_example'; ``` ```output +--------------+-----------------------------+----------+---------------+-----------------+-----------+----------+ | TABLE_SCHEMA | TABLE_NAME | TABLE_ID | REPLICA_COUNT | LOCATION_LABELS | AVAILABLE | PROGRESS | +--------------+-----------------------------+----------+---------------+-----------------+-----------+----------+ | vs_example | vector_table_with_index_cos | 183 | 2 | | 1 | 1 | | vs_example | vector_table_with_index_l2 | 186 | 2 | | 1 | 1 | +--------------+-----------------------------+----------+---------------+-----------------+-----------+----------+ 2 rows in set (0.07 sec) ``` + 对表 `vector_table_with_index_l2` 执行查询并检查执行计划以确认 `EXPLAIN` 输出表底部的运算符现在使用近似最近邻索引(`index:embedding_idx(embedding)`) 。 ```sql SELECT doc, embedding FROM vector_table_with_index_l2 ORDER BY VEC_L2_DISTANCE(embedding, '[-1, -1, -1]') LIMIT 1; ``` ```output +------+------------+ | doc | embedding | +------+------------+ | dog | [-1,-2,-1] | +------+------------+ 1 row in set (0.10 sec) ``` ```bash EXPLAIN SELECT doc, embedding FROM vector_table_with_index_l2 ORDER BY VEC_L2_DISTANCE(embedding, '[-1, -1, -1]') LIMIT 1; ``` ```output +--------------------------------------+---------+--------------+------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | id | estRows | task | access object | operator info | +--------------------------------------+---------+--------------+------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Projection_28 | 1.00 | root | | vs_example.vector_table_with_index_l2.doc, vs_example.vector_table_with_index_l2.embedding | | └─TopN_10 | 1.00 | root | | Column#5, offset:0, count:1 | | └─Projection_29 | 1.00 | root | | vs_example.vector_table_with_index_l2.doc, vs_example.vector_table_with_index_l2.embedding, vec_l2_distance(vs_example.vector_table_with_index_l2.embedding, [-1,-1,-1])->Column#5 | | └─TableReader_25 | 1.00 | root | | MppVersion: 1, data:ExchangeSender_24 | | └─ExchangeSender_24 | 1.00 | mpp[tiflash] | | ExchangeType: PassThrough | | └─Projection_26 | 1.00 | mpp[tiflash] | | vs_example.vector_table_with_index_l2.doc, vs_example.vector_table_with_index_l2.embedding | | └─TopN_23 | 1.00 | mpp[tiflash] | | Column#4, offset:0, count:1 | | └─Projection_27 | 1.00 | mpp[tiflash] | | vs_example.vector_table_with_index_l2.doc, vs_example.vector_table_with_index_l2.embedding, vec_l2_distance(vs_example.vector_table_with_index_l2.embedding, [-1,-1,-1])->Column#4 | | └─TableFullScan_22 | 1.00 | mpp[tiflash] | table:vector_table_with_index_l2, index:embedding_idx(embedding) | keep order:false, stats:pseudo, annIndex:L2(embedding..[-1,-1,-1], limit:1) | +--------------------------------------+---------+--------------+------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ 9 rows in set (0.01 sec) ``` + 退出 MySQL 客户端，回到远程 Linux 操作系统提示符。 ```sql EXIT ``` ```output Bye ``` ### 模块 2 结论 + 在本次练习中，您练习了在 TiDB 表上创建 HNSW 索引，以确保大数据集上的 K 邻近搜索性能。 + TiDB 支持以下 HNSW：`余弦距离`、`平方距离 (L2)`。 + 使用 TiDB 作为向量存储可以带来以下直接的优势： + 高效的近似最近邻搜索。 + 零计划内和计划外停机时间。（有关 TiDB 的零停机能力，请通过其他动手实验了解更多信息。） + 关系数据和向量数据的统一存储，并且几乎无限扩容。 + 有关列式存储 TiFlash 的更多信息，请查看其他动手实验 - TiDB Cloud Serverless 入门或注册线上自学课程 [TiDB 介绍](https://eng.edu.pingcap.com/catalog/info/id:203). + 在下一次的实践中，我们将应用 TiDB 的向量搜索功能来解决一个实际问题。 ## 模块 3：使用 TiDB 支持 RAG 应用程序的语义搜索 + 通过前两个演示中掌握的概念和技能，你现在可以进行一个实际示例操作。与前两个演示中的三维向量不同，这次你将为 RAG 应用程序使用 1536 维向量。在本演示中，RAG 应用程序由 TiDB 作为向量存储提供支持，OpenAI 提供LLM。 + 在本模块中，需要一个 OpenAI API KEY。 ![48%](https://lab-static.pingcap.com/quick-demo/TiDBAsVectorStoreForRAGDetails_aa_001_20240610.png) ### Tips + RAG 是一种大模型提示丰富和补充的方法，通过在提示中加入额外的上下文，增强了大型语言模型 (LLM) 生成用户友好响应的能力。 + 使用附加上下文增强提示的关键步骤在于进行语义搜索，这涉及在 TiDB 等向量存储中执行 K 最近邻搜索。 步骤 1. 连接到 TiDB Cloud Serverless 数据库 + 连接到指定的 TiDB Cloud Serverless 实例，并确认 `semantic_search_test` 已经提前创建。 ```bash mysql \ --comments -u 'serverless_username' -p'serverless_password' \ -h serverless_host -P serverless_port \ --ssl-mode=VERIFY_IDENTITY --ssl-ca=/etc/pki/tls/certs/ca-bundle.crt ``` ### 步骤 2. 查看现有数据 + 数据库中只有一个表 `llama_index_rag_test`。 ```sql USE semantic_search_test; SHOW TABLES; ``` ```output +--------------------------------+ | Tables_in_semantic_search_test | +--------------------------------+ | llama_index_rag_test | +--------------------------------+ 1 row in set (0.00 sec) ``` + 查看表数据。请注意， `embedding` 列是`document` 列的向量嵌入，并且 `embedding` 列上有一个 `Cosine Distance` 索引。 ```sql SHOW CREATE TABLE llama_index_rag_test\G ``` ```output *************************** 1. row *************************** Table: llama_index_rag_test Create Table: CREATE TABLE `llama_index_rag_test` ( `id` varchar(36) NOT NULL, `embedding` vector(1536) NOT NULL COMMENT 'hnsw(distance=cosine)', `document` text DEFAULT NULL, `meta` json DEFAULT NULL, `create_time` datetime DEFAULT CURRENT_TIMESTAMP, `update_time` datetime DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP, PRIMARY KEY (`id`) /*T![clustered_index] CLUSTERED */ ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_bin 1 row in set (0.01 sec) ``` + 表 `llama_index_rag_test` 包含 2024 年 6 月从 “https://docs.pingcap.com” 爬取的 99 篇文档，涵盖 TiDB 8.1 版本的以下主题：TiDB 概述、TiDB 快速入门、TiDB HTAP 快速入门、迁移到 TiDB。这些记录是使用 LlamaIndex 框架和自定义配置的 OpenAI API 密钥创建的。PingCAP 培训与认证团队处理了此过程并为你提供了数据，但如果你有 OpenAI 账户，在后续步骤中你将有能力自行完成。 ```sql SELECT COUNT(DISTINCT document), COUNT(DISTINCT embedding) FROM llama_index_rag_test; ``` ```output +--------------------------+---------------------------+ | COUNT(DISTINCT document) | COUNT(DISTINCT embedding) | +--------------------------+---------------------------+ | 99 | 99 | +--------------------------+---------------------------+ 1 row in set (0.02 sec) ``` + 验证 `document` 列的平均长度（超过 3000 字节）以及 `embedding` 列中其嵌入向量的维度数量（每个嵌入为 1536 维）。 ```sql SELECT AVG(LENGTH(document)) AS avg_document_length, MAX(VEC_DIMS(embedding)) AS max_no_of_dimensions, MIN(VEC_DIMS(embedding)) AS min_no_of_dimensions FROM llama_index_rag_test; ``` ```output +---------------------+----------------------+----------------------+ | avg_document_length | max_no_of_dimensions | min_no_of_dimensions | +---------------------+----------------------+----------------------+ | 3518.3333 | 1536 | 1536 | +---------------------+----------------------+----------------------+ 1 row in set (0.01 sec) ``` + 查看其中一条记录以获得一些基本了解。 ```sql SELECT SUBSTR(document,1,57) AS CONTENT, ' ', embedding AS EMBEDDING FROM llama_index_rag_test ORDER BY create_time LIMIT 1\G ``` ```output *************************** 1. row *************************** CONTENT: Users can elastically scale TiDB to meet the requirements : EMBEDDING: [-0.0027229728,-0.016953643,0.023380065,-0.021632409,-0.009694677,0.020834267,-0.02741206,0.0009624152,-0.029558789,-0.036274195,-0.0010510021,0.023999315,0.0132725565,0.001702933,-0.004558356,0.017504087,0.0037292512,-0.003866862,-0.004056077,-0.022802101,-0.0114767365,0.0022619767,-0.0079951845,0.004035435,-0.021536082,0.008903416,0.047283053,-0.009667155,0.0038634217,-0.000537972,0.03498065,-0.02752215,-0.019237982,0.004045756,-0.010135031,-0.006708524,0.0098254075, . . . -0.0036398042,0.0026696485,0.023063561,-0.022760818,-0.006822053,-0.020820506,-0.047971107,-0.013912447,0.04571429,-0.0026042834,0.0021794103,-0.0511912,0.009082309,-0.026283653,-0.025636882,-0.019361833,-0.020297585,0.0021484478,-0.022636969,-0.024811218,0.011655631,0.00072159636,0.024687368,-0.004241851,-0.018976523,-0.014270235,0.014696828,0.0033301802,0.014724351,-0.015591298,-0.0116212275] 1 row in set (0.01 sec) ``` ### 步骤 3. 查看 RAG 应用程序中的嵌入向量 + 如果你拥有一个嵌入向量（例如来自关于 TiDB 的问题），你可以像我们在演示 2 中展示的那样，通过 K 近邻搜索执行余弦相似性搜索。区别在于维度：在演示 2 中，我们使用的是三维，而现在我们使用的是 1536 维。 + 例如，以下 1536 维向量是使用 OpenAI 的 `text-embedding-3-small` 模型从问题 `How to migrate to TiDB from MySQL?` 嵌入得到的。有关如何执行嵌入的更多信息，请查看 [OpenAI documentation](https://platform.openai.com/docs/guides/embeddings)。 ```output 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``` ### 步骤 4. 基于嵌入向量执行语义相似性搜索 + 执行语义相似性搜索，以获取与问题 `How to migrate to TiDB from MySQL?` 最接近的 3 篇 `document`。查看检索到的文档，确保它们与 `How to migrate to TiDB from MySQL?`的主题相关。将它们作为额外的上下文提示提供给你的 LLM，可以增强其为你的问题提供更精确回复的能力。你可以看出它们来自 `TiDB Data Migration` 部分。为确保简洁，输出已被截断。 ```sql PAGER less -S SELECT document FROM llama_index_rag_test ORDER BY VEC_COSINE_DISTANCE(embedding, 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LIMIT 3\G ``` + 按 Up 和 Down 箭头键滚动查看输出。预期会出现与 `dmctl`, `TiDB Data Migration`, `dm-task.yaml` 相关的内容。按 Q 键返回 `mysql>` 提示符。 ### 步骤 5. 退出 TiDB Cloud Serverless 连接 + 现在你已经掌握了关于向量数据库和相似性搜索的所有必备知识，下一步我们将整合所有内容，构建一个简单的 Python RAG 问答应用程序，由 TiDB 作为向量存储，OpenAI Agent 作为 LLM。退出到远程 Linux 的操作系统提示符。 ```sql EXIT; ``` ```output mysql> EXIT; Bye ``` ### 步骤 6. 为 RAG 应用程序准备环境 + 从这一步开始，你需要在远程 Linux 上将自己的 OpenAI API KEY 设置为环境变量，以使 LlamaIndex 查询引擎正常工作。如果你无法提供自己的 OpenAI API KEY，实践实验到此结束。不过，你可以继续向下滚动查看提供的输出。 + 设置 [OpenAI API key](https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key)。在远程 Linux 上执行以下命令，将 `${OPENAI_API_KEY}` 替换为实际值。 ```bash export OPENAI_API_KEY=${OPENAI_API_KEY} ``` + 为了将 TiDB 用作向量存储并通过高效的向量索引检索向量，你不必直接使用 SQL，TiDB 支持 Python 以及以下框架：TiDB Vector Client, SQLAlchemy, Peewee, Django, LangChain, 和 LlamaIndex。在本演示中，我们使用 [TiDB as a vector store for LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/vector_stores/TiDBVector/). + 在远程 Linux 上安装所需的库。 ```bash pip install --upgrade pip pip install \ "joblib>=1.3.0" \ "click==8.1.7" \ "PyMySQL==1.1.0" \ "SQLAlchemy==2.0.30" \ "tidb-vector==0.0.9" \ "openai==1.27.0" \ "llama-index==0.10.68" \ "llama-index-readers-web==0.1.23" \ "llama-index-vector-stores-tidbvector==0.1.2" \ "httpx==0.27.2" ``` ```output ... Successfully installed PyYAML-6.0.1 aiohttp-3.9.5 aiosignal-1.3.1 annotated-types-0.7.0 anyio-4.4.0 async-timeout-4.0.3 attrs-23.2.0 beautifulsoup4-4.12.3 certifi-2024.6.2 charset-normalizer-3.3.2 chromedriver-autoinstaller-0.6.4 click-8.1.7 cssselect-1.2.0 dataclasses-json-0.6.6 deprecated-1.2.14 dirtyjson-1.0.8 distro-1.9.0 exceptiongroup-1.2.1 feedfinder2-0.0.4 feedparser-6.0.11 filelock-3.14.0 frozenlist-1.4.1 fsspec-2024.6.0 greenlet-3.0.3 h11-0.14.0 html2text-2020.1.16 httpcore-1.0.5 httpx-0.27.0 jieba3k-0.35.1 joblib-1.4.2 llama-index-0.10.43 llama-index-agent-openai-0.2.7 llama-index-cli-0.1.12 llama-index-core-0.10.43 llama-index-embeddings-openai-0.1.10 llama-index-indices-managed-llama-cloud-0.1.6 llama-index-legacy-0.9.48 llama-index-llms-openai-0.1.22 llama-index-multi-modal-llms-openai-0.1.6 llama-index-program-openai-0.1.6 llama-index-question-gen-openai-0.1.3 llama-index-readers-file-0.1.23 llama-index-readers-llama-parse-0.1.4 llama-index-readers-web-0.1.18 llama-index-vector-stores-tidbvector-0.1.2 llama-parse-0.4.4 llamaindex-py-client-0.1.19 lxml-5.2.2 marshmallow-3.21.3 multidict-6.0.5 mypy-extensions-1.0.0 nest-asyncio-1.6.0 networkx-3.2.1 newspaper3k-0.2.8 nltk-3.8.1 numpy-1.26.4 openai-1.33.0 outcome-1.3.0.post0 packaging-24.0 pandas-2.2.2 peewee-3.17.5 pillow-10.3.0 playwright-1.44.0 pydantic-2.7.3 pydantic-core-2.18.4 pyee-11.1.0 pymysql-1.1.1 pypdf-4.2.0 python-dateutil-2.9.0.post0 regex-2024.5.15 requests-2.32.3 requests-file-2.1.0 selenium-4.21.0 sgmllib3k-1.0.0 sniffio-1.3.1 sortedcontainers-2.4.0 soupsieve-2.5 spider-client-0.0.11 sqlalchemy-2.0.30 striprtf-0.0.26 tenacity-8.3.0 tidb-vector-0.0.9 tiktoken-0.7.0 tinysegmenter-0.3 tldextract-5.1.2 tqdm-4.66.4 trio-0.25.1 trio-websocket-0.11.1 typing-extensions-4.12.2 typing-inspect-0.9.0 tzdata-2024.1 wrapt-1.16.0 wsproto-1.2.0 yarl-1.9.4 ``` ### 步骤 7. 创建简单的 RAG 聊天机器人应用程序 + 创建 `tidb-chat` 程序。请注意，它连接到 TiDB 并使用 `semantic_search_test.llama_index_rag_test` 表作为向量存储。并且它要求成功完成步骤 2。 ```bash cat > tidb-chat.py <<'EOF' import os import click from sqlalchemy import URL from llama_index.core import VectorStoreIndex, StorageContext from llama_index.vector_stores.tidbvector import TiDBVectorStore from llama_index.readers.web import SimpleWebPageReader tidb_connection_url = URL( "mysql+pymysql", username='serverless_username', password='serverless_password', host='serverless_host', port=serverless_port, database="semantic_search_test", query={"ssl_verify_cert": True, "ssl_verify_identity": True}, ) tidbvec = TiDBVectorStore( connection_string=tidb_connection_url, table_name="llama_index_rag_test", distance_strategy="cosine", vector_dimension=1536, drop_existing_table=False, ) tidb_vec_index = VectorStoreIndex.from_vector_store(tidbvec) storage_context = StorageContext.from_defaults(vector_store=tidbvec) query_engine = tidb_vec_index.as_query_engine(streaming=True) @click.command() def start_chat(): while True: question = click.prompt("Enter your question") response = query_engine.query(question) click.echo(response) if __name__ == '__main__': start_chat() EOF ls -l tidb-chat.py ``` ```output -rw-r--r--. 1 rocky rocky 1157 Jun 20 14:57 tidb-chat.py ``` ### 步骤 8. 运行简单的 RAG 聊天机器人应用程序 + 运行 `tidb-chat` 程序，享受您的聊天机器人。 ```bash python tidb-chat.py ``` ```output Enter your question: ``` + 在你刚刚构建的 `tidb-chat` 程序上尝试以下问题。 ```bash what is a dm-task.yaml file? ``` ```output Enter your question: what is a dm-task.yaml file? A `dm-task.yaml` file is a configuration file used for defining the settings and parameters required for a migration task in a DM (Data Migration) cluster. This file contains the necessary information such as the data source, target database, replication rules, filtering criteria, and other configurations essential for executing a migration task. It serves as a blueprint for DM to understand how to handle the data migration process effectively. ``` ```bash How to migrate to TiDB from MySQL? ``` ```output Enter your question: How to migrate to TiDB from MySQL? To migrate to TiDB from MySQL, you can follow these steps: 1. Export all data from MySQL. 2. Import full data to TiDB. 3. Replicate incremental data to TiDB by adding the data source, adding a replication task, checking the migration task status, monitoring the task, and viewing logs. ``` + 如果有时间，尝试更多问题。按 Ctrl + C 组合键退出到操作系统提示符。 ### 步骤 9. 从空数据库重新开始 + 一步到位重新开始。在这一步中，你首先在 TiDB 环境中创建一个空数据库。随后，你利用 LlamaIndex 框架直接从提供的 URL 自动构建向量存储。这种自动化简化了向量存储的创建过程。 + 在远程 Linux 上，在 TiDB 中预配置一个名为 `chatdb` 的新数据库，用作向量存储。然后，退出到操作系统提示符。 ```bash mysql \ --comments -u 'serverless_username' -p'serverless_password' \ -h serverless_host -P serverless_port \ --ssl-mode=VERIFY_IDENTITY --ssl-ca=/etc/pki/tls/certs/ca-bundle.crt ``` ```sql DROP DATABASE IF EXISTS chatdb; CREATE DATABASE chatdb; EXIT; ``` + 创建一个新的 Python 聊天程序 `tidb-chat-v2.py`，使用 `chatdb`作为向量存储。 查看代码，确认 `URL()` 构造函数中使用的数据库是 `chatdb`。在执行过程中，程序将创建 `llama_index_rag_test` 表作为向量存储，并向其中填充文档和相应的嵌入。 ```bash cat > tidb-chat-v2.py <<'EOF' import os import click from sqlalchemy import URL from llama_index.core import VectorStoreIndex, StorageContext from llama_index.vector_stores.tidbvector import TiDBVectorStore from llama_index.readers.web import SimpleWebPageReader tidb_connection_url = URL( "mysql+pymysql", username='serverless_username', password='serverless_password', host='serverless_host', port=serverless_port, database='chatdb', query={"ssl_verify_cert": True, "ssl_verify_identity": True}, ) tidbvec = TiDBVectorStore( connection_string=tidb_connection_url, table_name="llama_index_rag_test", distance_strategy="cosine", vector_dimension=1536, drop_existing_table=True, ) tidb_vec_index = VectorStoreIndex.from_vector_store(tidbvec) storage_context = StorageContext.from_defaults(vector_store=tidbvec) query_engine = tidb_vec_index.as_query_engine(streaming=True) def prepare_data(url): documents = SimpleWebPageReader(html_to_text=True).load_data([url,]) tidb_vec_index.from_documents(documents, storage_context=storage_context, show_progress=True) urls = [ 'https://docs.pingcap.com/tidb/stable/overview', 'https://docs.pingcap.com/tidb/stable/quick-start-with-tidb', 'https://docs.pingcap.com/tidb/stable/quick-start-with-htap', 'https://docs.pingcap.com/tidb/stable/basic-sql-operations', 'https://docs.pingcap.com/tidb/stable/explore-htap', 'https://docs.pingcap.com/tidb/stable/migration-overview', 'https://docs.pingcap.com/tidb/stable/migration-tools', 'https://docs.pingcap.com/tidb/stable/data-import-best-practices', 'https://docs.pingcap.com/tidb/stable/migrate-aurora-to-tidb', 'https://docs.pingcap.com/tidb/stable/migrate-small-mysql-to-tidb', 'https://docs.pingcap.com/tidb/stable/migrate-large-mysql-to-tidb', 'https://docs.pingcap.com/tidb/stable/migrate-small-mysql-shards-to-tidb', 'https://docs.pingcap.com/tidb/stable/migrate-large-mysql-shards-to-tidb', 'https://docs.pingcap.com/tidb/stable/migrate-from-mariadb', 'https://docs.pingcap.com/tidb/stable/migrate-from-csv-files-to-tidb', 'https://docs.pingcap.com/tidb/stable/migrate-from-sql-files-to-tidb', 'https://docs.pingcap.com/tidb/stable/migrate-from-parquet-files-to-tidb', 'https://docs.pingcap.com/tidb/stable/migrate-from-tidb-to-tidb', 'https://docs.pingcap.com/tidb/stable/migrate-from-tidb-to-mysql', ] def start_chat(urls): for url in urls: prepare_data(url) while True: question = click.prompt("Enter your question") response = query_engine.query(question) click.echo(response) if __name__ == '__main__': start_chat(urls) EOF ``` + 运行新版本的聊天机器人应用程序。 ```bash python tidb-chat-v2.py ``` ```output Parsing nodes: 100%|██████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 128.14it/s] Generating embeddings: 100%|███████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 5.69it/s] Parsing nodes: 100%|███████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 64.40it/s] Generating embeddings: 100%|███████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 12.48it/s] Parsing nodes: 100%|███████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 98.69it/s] Generating embeddings: 100%|███████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 12.08it/s] Parsing nodes: 100%|██████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 159.58it/s] Generating embeddings: 100%|███████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 4.51it/s] Parsing nodes: 100%|███████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 99.84it/s] Generating embeddings: 100%|███████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 5.01it/s] Parsing nodes: 100%|██████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 176.32it/s] Generating embeddings: 100%|███████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 7.16it/s] Parsing nodes: 100%|██████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 140.53it/s] Generating embeddings: 100%|███████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 10.30it/s] Parsing nodes: 100%|███████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 84.42it/s] Generating embeddings: 100%|███████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 8.75it/s] Parsing nodes: 100%|███████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 59.69it/s] Generating embeddings: 100%|███████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 11.87it/s] Parsing nodes: 100%|██████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 120.75it/s] Generating embeddings: 100%|███████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 9.85it/s] Parsing nodes: 100%|███████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 58.71it/s] Generating embeddings: 100%|███████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 10.82it/s] Parsing nodes: 100%|███████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 78.74it/s] Generating embeddings: 100%|███████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 9.22it/s] Parsing nodes: 100%|███████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 45.15it/s] Generating embeddings: 100%|███████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 15.58it/s] Parsing nodes: 100%|███████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 72.00it/s] Generating embeddings: 100%|███████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 14.08it/s] Parsing nodes: 100%|███████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 94.23it/s] Generating embeddings: 100%|███████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 11.66it/s] Parsing nodes: 100%|██████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 130.31it/s] Generating embeddings: 100%|███████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 9.57it/s] Parsing nodes: 100%|██████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 108.44it/s] Generating embeddings: 100%|███████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 9.51it/s] Parsing nodes: 100%|███████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 76.71it/s] Generating embeddings: 100%|███████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 10.05it/s] Parsing nodes: 100%|███████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 95.09it/s] Generating embeddings: 100%|███████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 11.96it/s] ``` ```output Enter your question: ``` + 如果有时间，尝试一些问题。完成测试后，按 Ctrl + C 组合键退出到操作系统提示符。 ### 步骤 10. 查看数据库中填充的数据 + 连接到指定的 TiDB Cloud Serverless 实例，并确认在 `chatdb` 数据库下已创建 `llama_index_rag_test` 表。 ```bash mysql \ --comments -u 'serverless_username' -p'serverless_password' \ -h serverless_host -P serverless_port \ --ssl-mode=VERIFY_IDENTITY --ssl-ca=/etc/pki/tls/certs/ca-bundle.crt \ -D chatdb ``` ```sql USE chatdb; SHOW TABLES; ``` ```output +----------------------+ | Tables_in_chatdb | +----------------------+ | llama_index_rag_test | +----------------------+ 1 row in set (0.00 sec) ``` + 验证表数据。请注意，`embedding` 列是 `document` 列的向量嵌入，并且 `embedding` 列上有一个`Cosine Distance` 索引。 ```sql SHOW CREATE TABLE llama_index_rag_test\G ``` ```output *************************** 1. row *************************** Table: llama_index_rag_test Create Table: CREATE TABLE `llama_index_rag_test` ( `id` varchar(36) NOT NULL, `embedding` vector(1536) NOT NULL COMMENT 'hnsw(distance=cosine)', `document` text DEFAULT NULL, `meta` json DEFAULT NULL, `create_time` datetime DEFAULT CURRENT_TIMESTAMP, `update_time` datetime DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP, PRIMARY KEY (`id`) /*T![clustered_index] CLUSTERED */ ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_bin 1 row in set (0.01 sec) ``` + 验证 `llama_index_rag_test` 表的 `document` 和 `embedding` 列已填充数据。文档的长度可能会随时间变化。因此，此处显示的文档数量可能与你的结果不同。 ```sql SELECT COUNT(DISTINCT document), COUNT(DISTINCT embedding) FROM llama_index_rag_test; ``` ```output +--------------------------+---------------------------+ | COUNT(DISTINCT document) | COUNT(DISTINCT embedding) | +--------------------------+---------------------------+ | 99 | 99 | +--------------------------+---------------------------+ 1 row in set (0.02 sec) ``` + 退出到操作系统提示符。 ```sql EXIT ``` ```output Bye ``` ### 模块 3 结论 我们还未完成。在接下来的实践环节中，你将利用 LLM 协助构建知识图谱。随后，你将利用这个知识图谱增强聊天程序，从基本的 RAG 过渡到 Graph RAG. ## 模块 4：构建简单的 GraphRAG 聊天机器人 通过之前的实践，你已经了解到如何使用 RAG 从外部知识源检索相关信息，使大语言模型能够回答来自之前未见过的文档集合的问题。然而，当处理针对整个文本语料库的宽泛问题时，RAG 就显得力不从心。使用知识图谱可以将你的 RAG 应用从简单的 RAG 升级为 Graph RAG，以便获取最相关的知识，而不是为大语言模型提示增强找到最相似的文本。 在本实践中，需要一个支持 `gpt-4o` 的 OpenAI API KEY。 ![48%](https://lab-static.pingcap.com/quick-demo/TiDBAsGraphStoreForRAGDetails_aa_001_20240610.png) ### 提示 + 在本次实践中，你将使用基于 Python 的开源 Graph RAG 方法实现，从 TiDB 数据库 `chatdb` 中的 Wiki 页面构建有关 `TiDB` 的知识图谱。 + 基于 Python 的实现使用 TiDB 中的两个表来表示图，即 `entities` 和 `relationships`。知识文档及其向量嵌入存储在 `entities` 表中。 + 基于嵌入的向量搜索作为图的入口点，而 `JOIN` 操作在图中遍历以检索最相关的知识节点。 ### 步骤 1. 连接到 TiDB Cloud Serverless 数据库 + 连接到指定的 TiDB Cloud Serverless 实例，并确保它有一个名为 `chatdb` 的数据库。 ```bash mysql \ --comments -u 'serverless_username' -p'serverless_password' \ -h serverless_host -P serverless_port \ --ssl-mode=VERIFY_IDENTITY --ssl-ca=/etc/pki/tls/certs/ca-bundle.crt ``` ```sql DROP DATABASE IF EXISTS chatdb; CREATE DATABASE chatdb; EXIT ``` ### 步骤 2. 准备应用程序环境 + 安装所需的 Python 模块。 ```bash pip install \ click==8.1.7 \ PyMySQL==1.1.0 \ SQLAlchemy==2.0.30 \ tidb-vector==0.0.14 \ pydantic==2.7.1 pydantic_core==2.18.2 \ dspy-ai==2.4.9 \ langchain-community==0.2.0 \ wikipedia==1.4.0 \ pyvis==0.3.1 \ openai==1.27.0 \ httpx==0.27.2 ``` ```output ... Successfully installed Mako-1.3.6 PyMySQL-1.1.0 SQLAlchemy-2.0.30 aiohappyeyeballs-2.4.3 aiohttp-3.11.0 aiosignal-1.3.1 alembic-1.14.0 annotated-types-0.7.0 anyio-4.6.2.post1 asttokens-2.4.1 async-timeout-4.0.3 backoff-2.2.1 beautifulsoup4-4.12.3 certifi-2024.8.30 click-8.1.7 colorlog-6.9.0 dataclasses-json-0.6.7 datasets-2.14.7 decorator-5.1.1 dill-0.3.7 distro-1.9.0 dspy-ai-2.4.9 exceptiongroup-1.2.2 executing-2.1.0 filelock-3.16.1 frozenlist-1.5.0 fsspec-2023.10.0 greenlet-3.1.1 h11-0.14.0 httpcore-1.0.6 httpx-0.27.2 huggingface-hub-0.26.2 ipython-8.18.1 jedi-0.19.2 joblib-1.3.2 jsonpatch-1.33 jsonpickle-4.0.0 langchain-0.2.17 langchain-community-0.2.0 langchain-core-0.2.43 langchain-text-splitters-0.2.4 langsmith-0.1.143 marshmallow-3.23.1 matplotlib-inline-0.1.7 multidict-6.1.0 multiprocess-0.70.15 mypy-extensions-1.0.0 networkx-3.2.1 numpy-1.26.4 openai-1.27.0 optuna-4.1.0 orjson-3.10.11 packaging-24.2 pandas-2.2.3 parso-0.8.4 pexpect-4.9.0 prompt-toolkit-3.0.48 propcache-0.2.0 ptyprocess-0.7.0 pure-eval-0.2.3 pyarrow-18.0.0 pyarrow-hotfix-0.6 pydantic-2.7.1 pydantic-core-2.18.2 pygments-2.18.0 python-dateutil-2.9.0.post0 pyvis-0.3.1 regex-2024.11.6 requests-toolbelt-1.0.0 sniffio-1.3.1 soupsieve-2.6 stack-data-0.6.3 structlog-24.4.0 tenacity-8.5.0 tidb-vector-0.0.14 tqdm-4.67.0 traitlets-5.14.3 typing-extensions-4.12.2 typing-inspect-0.9.0 tzdata-2024.2 ujson-5.10.0 wcwidth-0.2.13 wikipedia-1.4.0 xxhash-3.5.0 yarl-1.17.1 ``` + 在环境中设置 [OpenAI API key](https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key) 。将 `${OPENAI_API_KEY}` 替换为实际值，然后在远程 Linux 上执行以下命令。如果在上一步实践中已完成设置，则可跳过此步骤。 ```bash export OPENAI_API_KEY=${OPENAI_API_KEY} echo $OPENAI_API_KEY ``` ### 步骤 3. 为聊天机器人构建知识图谱 + 通过 "TiDB" 的维基百科加载器查询来构建知识图谱。 + 要创建名为 `build-graph.py`的 Python 脚本，请执行以下命令。您可以点击下方代码区域，轻松复制整个 `cat` 命令。 ```bash cat > build-graph.py <<'EOF' import os import pymysql import dspy import enum import openai import tidb_vector from pymysql import Connection from pymysql.cursors import DictCursor from dspy.functional import TypedPredictor from pydantic import BaseModel, Field from typing import Mapping, Any, Optional, List from langchain_community.document_loaders import WikipediaLoader from pyvis.network import Network from IPython.display import HTML from sqlalchemy import ( Column, Integer, String, Text, JSON, ForeignKey, BLOB, Enum as SQLEnum, DateTime, URL, create_engine, or_, ) from sqlalchemy.orm import relationship, Session, declarative_base, joinedload from tidb_vector.sqlalchemy import VectorType, VectorAdaptor class Entity(BaseModel): """List of entities extracted from the text to form the knowledge graph""" name: str = Field( description="Name of the entity, it should be a clear and concise term" ) description: str = Field( description=( "Description of the entity, it should be a complete and comprehensive sentence, not few words. " "Sample description of entity 'TiDB in-place upgrade': " "'Upgrade TiDB component binary files to achieve upgrade, generally use rolling upgrade method'" ) ) class Relationship(BaseModel): """List of relationships extracted from the text to form the knowledge graph""" source_entity: str = Field( description="Source entity name of the relationship, it should an existing entity in the Entity list" ) target_entity: str = Field( description="Target entity name of the relationship, it should an existing entity in the Entity list" ) relationship_desc: str = Field( description=( "Description of the relationship, it should be a complete and comprehensive sentence, not few words. " "Sample relationship description: 'TiDB will release a new LTS version every 6 months.'" ) ) class KnowledgeGraph(BaseModel): """Graph representation of the knowledge for text.""" entities: List[Entity] = Field( description="List of entities in the knowledge graph" ) relationships: List[Relationship] = Field( description="List of relationships in the knowledge graph" ) class ExtractGraphTriplet(dspy.Signature): text = dspy.InputField( desc="a paragraph of text to extract entities and relationships to form a knowledge graph" ) knowledge: KnowledgeGraph = dspy.OutputField( desc="Graph representation of the knowledge extracted from the text." ) class Extractor(dspy.Module): def __init__(self): super().__init__() self.prog_graph = TypedPredictor(ExtractGraphTriplet) def forward(self, text): return self.prog_graph( text=text, config={ "response_format": {"type": "json_object"}, }, ) def interactive_graph(kg: KnowledgeGraph) -> str: net = Network(notebook=True, cdn_resources="remote") node_map = {} for index in range(len(kg.entities)): node_map[kg.entities[index].name] = index net.add_node( index, label=kg.entities[index].name, title=kg.entities[index].description ) for index in range(len(kg.relationships)): relation = kg.relationships[index] src_index = node_map[relation.source_entity] target_index = node_map[relation.target_entity] net.add_edge(src_index, target_index) filename = "kg.html" net.save_graph(filename) return filename def get_query_embedding(query: str): open_ai_client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY")) response = open_ai_client.embeddings.create( input=[query], model="text-embedding-3-small" ) return response.data[0].embedding def generate_result(query: str, entities, relationships): open_ai_client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY")) entities_prompt = "\n".join( map(lambda e: f'(Name: "{e.name}", Description: "{e.description}")', entities) ) relationships_prompt = "\n".join( map(lambda r: f'"{r.relationship_desc}"', relationships) ) response = open_ai_client.chat.completions.create( model="gpt-4o", messages=[ { "role": "system", "content": "Please carefully think the user's " + "question and ONLY use the content below to generate answer:\n" + f"Entities: {entities_prompt}, Relationships: {relationships_prompt}", }, {"role": "user", "content": query}, ], ) return response.choices[0].message.content def get_db_url(): return URL( drivername="mysql+pymysql", username="serverless_username", password="serverless_password", host="serverless_host", port=serverless_port, database="chatdb", query={"ssl_verify_cert": True, "ssl_verify_identity": True}, ) engine = create_engine(get_db_url(), pool_recycle=300) Base = declarative_base() class DatabaseEntity(Base): id = Column(Integer, primary_key=True) name = Column(String(512)) description = Column(Text) description_vec = Column(VectorType(1536)) __tablename__ = "entities" class DatabaseRelationship(Base): id = Column(Integer, primary_key=True) source_entity_id = Column(Integer, ForeignKey("entities.id")) target_entity_id = Column(Integer, ForeignKey("entities.id")) relationship_desc = Column(Text) source_entity = relationship("DatabaseEntity", foreign_keys=[source_entity_id]) target_entity = relationship("DatabaseEntity", foreign_keys=[target_entity_id]) __tablename__ = "relationships" def clean_knowledge_graph(kg: KnowledgeGraph) -> KnowledgeGraph: entity_name_set = set(map(lambda e: e.name, kg.entities)) kg.relationships = list( filter( lambda r: r.source_entity in entity_name_set and r.target_entity in entity_name_set, kg.relationships, ) ) return kg def save_knowledge_graph(kg: KnowledgeGraph): data_entities = list( map( lambda e: DatabaseEntity( name=e.name, description=e.description, description_vec=get_query_embedding(e.description), ), kg.entities, ) ) with Session(engine) as session: session.add_all(data_entities) session.flush() entity_id_map = dict(map(lambda e: (e.name, e.id), data_entities)) print(entity_id_map) data_relationships = list( map( lambda r: DatabaseRelationship( source_entity_id=entity_id_map[r.source_entity], target_entity_id=entity_id_map[r.target_entity], relationship_desc=r.relationship_desc, ), kg.relationships, ) ) session.add_all(data_relationships) session.commit() def retrieve_entities_relationships(question_embedding) -> (List[DatabaseEntity], List[DatabaseRelationship]): with Session(engine) as session: entity = ( session.query(DatabaseEntity) .order_by( DatabaseEntity.description_vec.cosine_distance(question_embedding) ) .limit(1) .first() ) entities = {entity.id: entity} relationships = ( session.query(DatabaseRelationship) .options( joinedload(DatabaseRelationship.source_entity), joinedload(DatabaseRelationship.target_entity), ) .filter( or_( DatabaseRelationship.source_entity == entity, DatabaseRelationship.target_entity == entity, ) ) ) for r in relationships: entities.update( { r.source_entity.id: r.source_entity, r.target_entity.id: r.target_entity, } ) return entities.values(), relationships extractor = Extractor() Base.metadata.create_all(engine) # Create Index VectorAdaptor(engine).create_vector_index( DatabaseEntity.description_vec, tidb_vector.DistanceMetric.COSINE, skip_existing=True # For L2 distance, use tidb_vector.DistanceMetric.L2 ) open_ai_client = dspy.OpenAI( model="gpt-4o", api_key=os.getenv("OPENAI_API_KEY"), max_tokens=4096 ) dspy.settings.configure(lm=open_ai_client) wiki = WikipediaLoader(query="TiDB").load() pred = extractor(text=wiki[0].page_content) knowledge_graph = clean_knowledge_graph(pred.knowledge) interactive_graph(knowledge_graph) save_knowledge_graph(knowledge_graph) EOF ls -l build-graph.py ``` ```output -rw-r--r--. 1 rocky rocky 8777 Jun 20 15:02 build-graph.py ``` + 运行 Python 脚本 `build-graph.py` 来构建图谱。它将在 `chatdb` 数据库下创建 `entities` 和 `relationships` 表。等待脚本执行完毕，这大约需要 2 分钟。您得到的输出可能与显示的输出不完全相同。 ```bash python build-graph.py ``` ```output {'TiDB': 90001, 'PingCAP': 90002, 'Apache 2.0': 90003, "Google's Spanner and F1 papers": 90004, 'TiDB 7.5 GA': 90005, 'TiDB 7.1 GA': 90006, 'TiDB 6.0 GA': 90007, 'TiDB 5.0 GA': 90008, 'TiDB 4.0 GA': 90009, 'TiDB 3.0 GA': 90010, 'TiDB 2.0 GA': 90011, 'TiDB 1.0 GA': 90012, 'Horizontal scalability': 90013, 'MySQL compatibility': 90014, 'Distributed transactions with strong consistency': 90015, 'Cloud native': 90016, 'TiKV': 90017, 'Real-time HTAP': 90018, 'TiFlash': 90019, 'High availability': 90020, 'Raft consensus algorithm': 90021, 'Kubernetes with Operator': 90022, 'TiDB Operator': 90023, 'TiUP': 90024, 'TiDB Ansible': 90025, 'Docker': 90026, 'TiDB Data Migration (DM)': 90027, 'Backup & Restore (BR)': 90028, 'Dumpling': 90029} ``` ### 步骤 4. 检查已构建的知识图谱 + 连接到 `chatdb` 数据库。 ```bash mysql \ --comments -u 'serverless_username' -p'serverless_password' \ -h serverless_host -P serverless_port \ --ssl-mode=VERIFY_IDENTITY --ssl-ca=/etc/pki/tls/certs/ca-bundle.crt \ -D chatdb ``` ```output mysql: [Warning] Using a password on the command line interface can be insecure. Welcome to the MySQL monitor. Commands end with ; or \g. Your MySQL connection id is 2168969 Server version: 5.7.28-TiDB-Serverless TiDB Server (Apache License 2.0) Community Edition, MySQL 5.7 compatible Copyright (c) 2000, 2024, Oracle and/or its affiliates. Oracle is a registered trademark of Oracle Corporation and/or its affiliates. Other names may be trademarks of their respective owners. Type 'help;' or '\h' for help. Type '\c' to clear the current input statement. mysql> ``` + 查看由 Python 脚本填充数据的 `chatdb.entities` 表。请注意，在`description_vec`列上声明了 HNSW 索引。您得到的输出可能与显示的输出不完全相同。 ```sql SHOW CREATE TABLE entities\G ``` ```output *************************** 1. row *************************** Table: entities Create Table: CREATE TABLE `entities` ( `id` int(11) NOT NULL AUTO_INCREMENT, `name` varchar(512) DEFAULT NULL, `description` text DEFAULT NULL, `description_vec` vector(1536) DEFAULT NULL COMMENT 'HNSW(distance=cosine)', PRIMARY KEY (`id`) /*T![clustered_index] CLUSTERED */, VECTOR INDEX `vec_idx_description_vec` ((VEC_COSINE_DISTANCE(`description_vec`))) ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_bin AUTO_INCREMENT=60001 1 row in set (0.02 sec) ``` ```bash SELECT id, name, SUBSTR(description, 1,30) AS desc_head, VEC_DIMS(description_vec) FROM entities ORDER BY name; ``` ```output +----+--------------------------------------------------+--------------------------------+---------------------------+ | id | name | desc_head | VEC_DIMS(description_vec) | +----+--------------------------------------------------+--------------------------------+---------------------------+ | 3 | Apache 2.0 | Apache 2.0 is the license unde | 1536 | | 26 | Backup & Restore (BR) | Backup & Restore (BR) is a dis | 1536 | | 31 | Cloud Native Computing Foundation (CNCF) | The Cloud Native Computing Fou | 1536 | | 18 | Cloud native | TiDB is designed to work in th | 1536 | | 17 | Distributed transactions with strong consistency | TiDB internally shards a table | 1536 | | 24 | Docker | Docker can be used to deploy T | 1536 | | 4 | Google's Spanner and F1 papers | Google's Spanner and F1 papers | 1536 | | 20 | High availability | TiDB uses the Raft consensus a | 1536 | | 15 | Horizontal scalability | TiDB can expand both SQL proce | 1536 | | 21 | Kubernetes with Operator | TiDB can be deployed in a Kube | 1536 | | 16 | MySQL compatibility | TiDB acts like it is a MySQL 8 | 1536 | | 2 | PingCAP | PingCAP is the primary develop | 1536 | | 29 | Raft consensus algorithm | The Raft consensus algorithm i | 1536 | | 19 | Real-time HTAP | TiDB supports both online tran | 1536 | | 1 | TiDB | TiDB is an open-source NewSQL | 1536 | | 14 | TiDB 1.0 GA | TiDB 1.0 GA was released on Oc | 1536 | | 13 | TiDB 2.0 GA | TiDB 2.0 GA was released on Ap | 1536 | | 12 | TiDB 3.0 GA | TiDB 3.0 GA was released on Ju | 1536 | | 11 | TiDB 4.0 GA | TiDB 4.0 GA was released on Ma | 1536 | | 10 | TiDB 5.0 GA | TiDB 5.0 GA was released on Ap | 1536 | | 9 | TiDB 6.0 GA | TiDB 6.0 GA was released on Ap | 1536 | | 8 | TiDB 7.1 GA | TiDB 7.1 GA was released on Ma | 1536 | | 7 | TiDB 7.5 GA | TiDB 7.5 GA was released on De | 1536 | | 6 | TiDB 8.1 GA | TiDB 8.1 GA was released on Ma | 1536 | | 5 | TiDB 8.5 GA | TiDB 8.5 GA was released on De | 1536 | | 23 | TiDB Ansible | TiDB can be deployed using Ans | 1536 | | 25 | TiDB Data Migration (DM) | TiDB Data Migration (DM) is su | 1536 | | 30 | TiDB Operator | TiDB Operator is a method of p | 1536 | | 28 | TiFlash | TiFlash is a columnstore stora | 1536 | | 27 | TiKV | TiKV is the storage layer of T | 1536 | | 22 | TiUP | TiDB 4.0 introduces TiUP, a cl | 1536 | +----+--------------------------------------------------+--------------------------------+---------------------------+ 31 rows in set (0.34 sec) ``` + 查看 `chatdb.relationships` 表的架构，并通过 `entities` 和 `relationshipts` 表的连接获取关系数据。您得到的输出可能与显示的输出不完全相同。 ```sql SHOW CREATE TABLE relationships\G ``` ```output *************************** 1. row *************************** Table: relationships Create Table: CREATE TABLE `relationships` ( `id` int(11) NOT NULL AUTO_INCREMENT, `source_entity_id` int(11) DEFAULT NULL, `target_entity_id` int(11) DEFAULT NULL, `relationship_desc` text DEFAULT NULL, PRIMARY KEY (`id`) /*T![clustered_index] CLUSTERED */, KEY `fk_1` (`source_entity_id`), KEY `fk_2` (`target_entity_id`), CONSTRAINT `fk_1` FOREIGN KEY (`source_entity_id`) REFERENCES `chatdb`.`entities` (`id`), CONSTRAINT `fk_2` FOREIGN KEY (`target_entity_id`) REFERENCES `chatdb`.`entities` (`id`) ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_bin AUTO_INCREMENT=60001 1 row in set (0.00 sec) ``` ```sql SELECT source.name AS src_name, SUBSTR(r.relationship_desc, 1, 50) relation_head, target.name AS tgt_name FROM entities AS source, entities AS target, relationships as r WHERE source.id = r.source_entity_id AND target.id = r.target_entity_id ORDER BY src_name; ``` ```output +--------------------------+----------------------------------------------------+--------------------------------------------------+ | src_name | relation_head | tgt_name | +--------------------------+----------------------------------------------------+--------------------------------------------------+ | Raft consensus algorithm | The Raft consensus algorithm is used by TiDB to en | High availability | | TiDB | TiDB acts like it is a MySQL 8.0 server to applica | MySQL compatibility | | TiDB | TiDB supports both online transaction processing ( | Real-time HTAP | | TiDB | Dumpling is a data export tool for TiDB. | Dumpling | | TiDB | Backup & Restore (BR) is a distributed backup and | Backup & Restore (BR) | | TiDB | TiDB Data Migration (DM) is a tool for replicating | TiDB Data Migration (DM) | | TiDB | Docker can be used to deploy TiDB in a containeriz | Docker | | TiDB | TiDB can be deployed using Ansible with a TiDB Ans | TiDB Ansible | | TiDB | TiUP is a cluster operation and maintenance tool i | TiUP | | TiDB | TiDB can be deployed in a Kubernetes-enabled cloud | Kubernetes with Operator | | TiDB | TiDB uses the Raft consensus algorithm to ensure d | High availability | | TiDB | TiDB can expand both SQL processing and storage ca | Horizontal scalability | | TiDB | TiDB acts like it is a MySQL 8.0 server to applica | MySQL compatibility | ... | TiDB 5.0 GA | TiDB 5.0 GA was released on April 7, 2021. | TiDB | | TiDB 5.0 GA | TiDB 5.0 GA was released on April 7, 2021. | TiDB | | TiDB 6.0 GA | TiDB 6.0 GA was released on April 7, 2022. | TiDB | | TiDB 6.0 GA | TiDB 6.0 GA was released on April 7, 2022. | TiDB | | TiDB 6.0 GA | TiDB 6.0 GA was released on April 7, 2022. | TiDB | | TiDB 7.1 GA | TiDB 7.1 GA was released on May 31, 2023. | TiDB | | TiDB 7.1 GA | TiDB 7.1 GA was released on May 31, 2023. | TiDB | | TiDB 7.1 GA | TiDB 7.1 GA was released on May 31, 2023. | TiDB | | TiDB 7.5 GA | TiDB 7.5 GA was released on December 1, 2023. | TiDB | | TiDB 7.5 GA | TiDB 7.5 GA was released on December 1, 2023. | TiDB | | TiDB 7.5 GA | TiDB 7.5 GA was released on December 1, 2023. | TiDB | | TiDB Operator | TiDB Operator was originally developed by PingCAP | PingCAP | | TiDB Operator | TiDB Operator was originally developed by PingCAP | PingCAP | | TiDB Operator | TiDB Operator is a method of packaging, deploying, | Kubernetes with Operator | | TiFlash | TiFlash is a columnstore storage engine used by Ti | Real-time HTAP | | TiKV | TiKV, the storage layer of TiDB, became a Cloud Na | Cloud native | +--------------------------+----------------------------------------------------+--------------------------------------------------+ 81 rows in set (0.01 sec) ``` + 退出到远程 Linux 的操作系统提示符。 ```sql EXIT ``` ### 步骤 5. 创建 GraphRAG 聊天机器人程序 + 创建 Python 脚本 `test-graph.py`。 ```bash cat > test-graph.py <<'EOF' import openai import os import click from sqlalchemy import ( Column, Integer, String, Text, ForeignKey, URL, create_engine, or_, ) from typing import Mapping, Any, Optional, List from sqlalchemy.orm import relationship, Session, declarative_base, joinedload from tidb_vector.sqlalchemy import VectorType def get_db_url(): return URL( drivername="mysql+pymysql", username="serverless_username", password="serverless_password", host="serverless_host", port=serverless_port, database="chatdb", query={"ssl_verify_cert": True, "ssl_verify_identity": True}, ) engine = create_engine(get_db_url(), pool_recycle=300) Base = declarative_base() class DatabaseEntity(Base): id = Column(Integer, primary_key=True) name = Column(String(512)) description = Column(Text) description_vec = Column(VectorType(1536)) __tablename__ = "entities" class DatabaseRelationship(Base): id = Column(Integer, primary_key=True) source_entity_id = Column(Integer, ForeignKey("entities.id")) target_entity_id = Column(Integer, ForeignKey("entities.id")) relationship_desc = Column(Text) source_entity = relationship("DatabaseEntity", foreign_keys=[source_entity_id]) target_entity = relationship("DatabaseEntity", foreign_keys=[target_entity_id]) __tablename__ = "relationships" def get_query_embedding(query: str): open_ai_client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY")) response = open_ai_client.embeddings.create( input=[query], model="text-embedding-3-small" ) return response.data[0].embedding def retrieve_entities_relationships( question_embedding, ) -> (List[DatabaseEntity], List[DatabaseRelationship]): with Session(engine) as session: entity = ( session.query(DatabaseEntity) .order_by( DatabaseEntity.description_vec.cosine_distance(question_embedding) ) .limit(1) .first() ) entities = {entity.id: entity} relationships = ( session.query(DatabaseRelationship) .options( joinedload(DatabaseRelationship.source_entity), joinedload(DatabaseRelationship.target_entity), ) .filter( or_( DatabaseRelationship.source_entity == entity, DatabaseRelationship.target_entity == entity, ) ) ) for r in relationships: entities.update( { r.source_entity.id: r.source_entity, r.target_entity.id: r.target_entity, } ) return entities.values(), relationships def generate_result(query: str, entities, relationships): open_ai_client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY")) entities_prompt = "\n".join( map(lambda e: f'(Name: "{e.name}", Description: "{e.description}")', entities) ) relationships_prompt = "\n".join( map(lambda r: f'"{r.relationship_desc}"', relationships) ) response = open_ai_client.chat.completions.create( model="gpt-4o", messages=[ { "role": "system", "content": "Please carefully think the user's " + "question and ONLY use the content below to generate answer:\n" + f"Entities: {entities_prompt}, Relationships: {relationships_prompt}", }, {"role": "user", "content": query}, ], ) return response.choices[0].message.content @click.command() def start_chat(): while True: question = click.prompt("Enter your question") question_embedding = get_query_embedding(question) entities, relationships = retrieve_entities_relationships(question_embedding) result = generate_result(question, entities, relationships) click.echo(result) if __name__ == '__main__': start_chat() EOF ls -l test-graph.py ``` ```output -rw-r--r--. 1 rocky rocky 4154 Jun 20 15:14 test-graph.py ``` ### 步骤 6. 运行 GraphRAG 聊天机器人程序 + 运行脚本 `test-graph.py`. ```bash python test-graph.py ``` ```output Enter your question: ``` + 尝试问题 1，该知识不在我们的 TiDB 知识图谱中： "What is the relationship between raccoon and umbrella?" ```bash What is the relationship between raccoon and umbrella? ``` ```output Enter your question: What is the relationship between raccoon and umbrella? The provided information does not contain any details about raccoons or umbrellas. Therefore, I can't provide a relationship between these two based on the given content. ``` + 尝试问题 2，该知识包含在我们的 TiDB 知识图谱中： "What is the relationship between PD and Raft?" ```bash What is the relationship between PD and Raft? ``` ```output Enter your question: What is the relationship between PD and Raft? The Raft consensus algorithm is used by TiDB to ensure data availability and replication. However, the relationship specifically between PD and Raft is not detailed in the provided content. Generally speaking, in TiDB, PD (Placement Driver) handles metadata management and scheduling, and works in conjunction with the Raft consensus algorithm to maintain high availability and data consistency through leader election and automatic failover mechanisms. ``` + 尝试问题 3，该知识包含在我们的 TiDB 知识图谱中："Which tools or solutions are most relevant for migrating to TiDB?" ```bash Which tools or solutions are most relevant for migrating to TiDB? ``` ```output Enter your question: Which tools or solutions are most relevant for migrating to TiDB? The most relevant tool for migrating to TiDB is **TiDB Data Migration (DM)**. This tool is specifically designed for replicating data from sharded MySQL or MariaDB tables to TiDB, facilitating near real-time analytical workloads. ``` + 尝试您在模块 3 中问过的相同问题。这次使用的是 Graph RAG 而非简单的 RAG，比较两个答案，哪个对您更喜欢？请注意，您得到的输出可能与显示的输出不完全相同。 ```bash How to migrate to TiDB from MySQL? ``` ```output Enter your question: How to migrate to TiDB from MySQL? To migrate to TiDB from MySQL, you can use TiDB Data Migration (DM). TiDB DM is a tool designed specifically for replicating data from sharded MySQL or MariaDB tables to TiDB, allowing near real-time analytical workloads. Here are the general steps to perform the migration: 1. **Install TiDB DM**: Set up TiDB Data Migration by following the official installation guide provided by PingCAP. 2. **Configure Source Database**: Prepare your MySQL or MariaDB databases with the necessary configurations and privileges required for data replication. 3. **Deploy and Configure DM Workers and Master**: Deploy TiDB DM components, including DM workers and the DM master. Configure these components by defining the source database configurations, replication tasks, and target TiDB cluster details. 4. **Create Data Migration Tasks**: Define and create data migration tasks that detail what data from the source databases should be replicated to TiDB. This includes sharding rules if applicable. 5. **Start Data Migration**: Initiate the data migration tasks to begin replicating data from the MySQL or MariaDB source tables to TiDB. 6. **Monitor and Manage**: Monitor the migration process using provided tools and logs to ensure it progresses as expected. Make adjustments and handle any issues that arise. By following these steps with TiDB Data Migration (DM), you can efficiently migrate your data from MySQL to TiDB while maintaining data integrity and allowing for real-time workload processing on TiDB. For detailed instructions and best practices, refer to the official TiDB documentation. ``` + 如果有时间，尝试更多问题。按 Ctrl + C 组合键退出到操作系统提示符。 + 从远程 Linux 注销。 ```bash exit ``` ## 结论 在现实世界中，我们在本练习中使用了相同的技术，将 TiDB 用作向量和图存储，并使用 RAG GRAPH 创建了一个理解 TiDB 知识的在线助手，比如右下角的 `Ask AI` 。 K 近邻（KNN）搜索是指在向量空间中为给定点找到 K 个最近点的问题。解决这个问题最直接的方法是暴力搜索，即计算向量空间中所有点与参考点之间的距离。这种方法能保证完美的准确性，但对于实际应用来说通常太慢。因此，近邻搜索问题通常使用近似算法来解决。 在 TiDB 中，您可以创建并利用向量搜索索引进行此类近似最近邻（ANN）搜索。通过使用向量搜索索引，向量搜索查询可以在毫秒内完成，从而助力您的 RAG 或 Graph RAG 应用程序。 此外，选择分布式 SQL 数据库 TiDB 作为向量存储，您可立即获得其固有优势：可无限扩展以容纳无数量限制的向量（最大维度为 2048，在我们的示例中嵌入文档为 1536 维），确保持续可用且性能不间断。更多详细信息，请查看其他实验。 下次实验再见，再会！