VectorSearchSpace
KIT · interface · 类型合同
Embedding space identity and width; vectors from another space must never be mixed in.
导入
ts
import type { VectorSearchSpace } from '@jangts/agent-core/kits/vector-search';js
/** @typedef {import('@jangts/agent-core/kits/vector-search').VectorSearchSpace} VectorSearchSpace */其他公开入口：@jangts/agent-core/types、@jangts/agent-core/kits、@jangts/agent-core/kits/vector-search。
定义
ts
interface VectorSearchSpace字段结构
| 字段 | 类型 | 必需 | 说明 |
|---|---|---|---|
identity | string | 必需 | 只读。Non-blank space name, for example model@revision; reported back by VectorSearch. |
dimensions | number | 必需 | 只读。Expected vector length, 1..16384. |
使用示例
- VectorSearch：有边界的精确余弦检索：给定同一个向量空间，先过滤文档，再按余弦相似度排序。
先在同一空间检索说明片段
入门 · Node.js
二维向量用于展示合同；不代表真实文本 embedding 或检索质量。
ts
import { VectorSearch } from '@jangts/agent-core/kits/vector-search';
const index = new VectorSearch<{ id: string; text: string }>({ identity: 'demo-direction-v1', dimensions: 2 });
index.upsert({ document: { id: 'workflow', text: '工作流说明' }, vector: [1, 0] });
index.upsert({ document: { id: 'model', text: '模型说明' }, vector: [0, 1] });
console.log(index.search({ vector: [0.9, 0.1], limit: 2, filter: () => true }));js
import { VectorSearch } from '@jangts/agent-core/kits/vector-search';
const index = new VectorSearch({ identity: 'demo-direction-v1', dimensions: 2 });
index.upsert({ document: { id: 'workflow', text: '工作流说明' }, vector: [1, 0] });
index.upsert({ document: { id: 'model', text: '模型说明' }, vector: [0, 1] });
console.log(index.search({ vector: [0.9, 0.1], limit: 2, filter: () => true }));相同 search 增加权限、阈值和文档分组
配置 · Node.js
ts
import { VectorSearch } from '@jangts/agent-core/kits/vector-search';
type Chunk = { id: string; documentId: string; audience: string; text: string };
const index = new VectorSearch<Chunk>({ identity: 'demo-direction-v1', dimensions: 2 });
index.upsert({ document: { id: 'manual-1', documentId: 'manual', audience: 'public', text: '工作流定义' }, vector: [1, 0] });
index.upsert({ document: { id: 'manual-2', documentId: 'manual', audience: 'public', text: '工作流调用' }, vector: [0.95, 0.05] });
index.upsert({ document: { id: 'private-1', documentId: 'private', audience: 'owner', text: '内部说明' }, vector: [1, 0] });
console.log(index.search({ vector: [1, 0], limit: 5, minScore: 0.5, filter: row => row.audience === 'public', groupBy: row => row.documentId }));js
import { VectorSearch } from '@jangts/agent-core/kits/vector-search';
const index = new VectorSearch({ identity: 'demo-direction-v1', dimensions: 2 });
index.upsert({ document: { id: 'manual-1', documentId: 'manual', audience: 'public', text: '工作流定义' }, vector: [1, 0] });
index.upsert({ document: { id: 'manual-2', documentId: 'manual', audience: 'public', text: '工作流调用' }, vector: [0.95, 0.05] });
index.upsert({ document: { id: 'private-1', documentId: 'private', audience: 'owner', text: '内部说明' }, vector: [1, 0] });
console.log(index.search({ vector: [1, 0], limit: 5, minScore: 0.5, filter: row => row.audience === 'public', groupBy: row => row.documentId }));