VectorSearchSpace

KIT · interface · Type contract

Embedding space identity and width; vectors from another space must never be mixed in.

Import

TypeScript

import type { VectorSearchSpace } from '@jangts/agent-core/kits/vector-search';

JavaScript

/** @typedef {import('@jangts/agent-core/kits/vector-search').VectorSearchSpace} VectorSearchSpace */

Other public entry points:@jangts/agent-core/types, @jangts/agent-core/kits, @jangts/agent-core/kits/vector-search.

Definition

interface VectorSearchSpace

Field structure

Field	Type	Required	Description
identity	string	Required	Read-only. Non-blank space name, for example model@revision; reported back by VectorSearch.
dimensions	number	Required	Read-only. Expected vector length, 1..16384.

Examples

• VectorSearch: bounded exact cosine retrieval: Given the same vector space, filter documents first and then rank by cosine similarity.

First retrieve documentation fragments in the same space

Beginner · Node.js

Two-dimensional vectors are used to show the contract; they do not represent real text embeddings or retrieval quality.

TypeScript

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 }));

JavaScript

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 }));

Add permissions, a threshold, and document grouping to the same search

Configuration · Node.js

TypeScript

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 }));

JavaScript

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 }));

Related policy source

• Vector validation, normalization, filtering, and grouped ordering