mirror of
https://github.com/labring/FastGPT.git
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yanzhicong
d23c72690e
* feat: add openGauss DataVec as vector database backend Add openGauss with DataVec extension as a new vector storage option alongside PGVector and Milvus. Includes vector DB controller, Docker Compose deployment configs (CN/Global), deploy generation scripts, and test templates. * test: add opengauss vectorDB integration entry * test: adjust vector env template for opengauss run * fix: ts --------- Co-authored-by: archer <545436317@qq.com>
84 lines
2.6 KiB
TypeScript
84 lines
2.6 KiB
TypeScript
export const DatasetVectorDbName = 'fastgpt';
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export const DatasetVectorTableName = 'modeldata';
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export const PG_ADDRESS = process.env.PG_URL;
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export const OPENGAUSS_ADDRESS = process.env.OPENGAUSS_URL;
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export const OCEANBASE_ADDRESS = process.env.OCEANBASE_URL;
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export const SEEKDB_ADDRESS = process.env.SEEKDB_URL;
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export const MILVUS_ADDRESS = process.env.MILVUS_ADDRESS;
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export const MILVUS_TOKEN = process.env.MILVUS_TOKEN;
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export const VectorVQ = (() => {
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if (process.env.VECTOR_VQ_LEVEL === '32') {
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return 32;
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}
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if (process.env.VECTOR_VQ_LEVEL === '16') {
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return 16;
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}
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if (process.env.VECTOR_VQ_LEVEL === '8') {
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return 8;
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}
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if (process.env.VECTOR_VQ_LEVEL === '4') {
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return 4;
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}
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if (process.env.VECTOR_VQ_LEVEL === '2') {
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return 2;
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}
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return 32;
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})();
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/**
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* OceanBase HNSW Index Configuration
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*
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* VECTOR_VQ_LEVEL mapping:
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* - 32 (default): hnsw + inner_product
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* - 8: hnsw_sq + inner_product
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* - 1: hnsw_bq + cosine
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*
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* See https://www.oceanbase.com/docs/common-oceanbase-database-cn-1000000004920602
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* for the recommended way of choosing parameters (`m`, `ef_construction`, `ef_search`). It varies for data volume.
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*
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* HNSW_BQ requires cosine or l2 distance. inner_product is not supported up until V4.3.5 BP5 (current lts version until Jan 2026).
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* See https://www.oceanbase.com/docs/common-oceanbase-database-cn-1000000004920603
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* `HNSW_BQ distance 参数支持 l2 和 cosine。cosine 从 V4.3.5 BP4 版本开始支持。` and section `距离函数使用规则`.
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*
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* Tested on OceanBase 4.3.5-lts:
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* ```sql
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* -- HNSW_BQ + cosine: VECTOR INDEX SCAN ✓
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* CREATE VECTOR INDEX idx ON t(vec) WITH (distance=cosine, type=hnsw_bq, m=16, ef_construction=200);
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* EXPLAIN SELECT id, cosine_distance(vec, '[...]') AS score FROM t ORDER BY score ASC APPROXIMATE LIMIT 10;
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* -- |1 |└─VECTOR INDEX SCAN|t(idx)|
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* ```
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*/
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export const OceanBaseIndexConfig = (() => {
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const level = process.env.VECTOR_VQ_LEVEL;
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if (level === '1') {
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return {
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type: 'hnsw_bq' as const,
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distance: 'cosine' as const,
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distanceFunc: 'cosine_distance',
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orderDirection: 'ASC' as const,
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scoreTransform: (score: number) => 1 - score / 2
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};
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}
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if (level === '8') {
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return {
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type: 'hnsw_sq' as const,
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distance: 'inner_product' as const,
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distanceFunc: 'inner_product',
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orderDirection: 'DESC' as const,
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scoreTransform: (score: number) => score
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};
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}
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return {
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type: 'hnsw' as const,
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distance: 'inner_product' as const,
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distanceFunc: 'inner_product',
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orderDirection: 'DESC' as const,
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scoreTransform: (score: number) => score
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};
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})();
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