A local context retrieval library that enables semantic search over your documentation. It loads documents (Markdown, JSON, Text), vectorizes them using Transformers.js, and stores vectors locally in .zvec files for fast semantic querying.
Tip
Based on this library, we provide an official context HTTP server similar to context7, used to provide AI code generation context services in MCP, Skill, and CLI, for free!
- Multi-format Loading: Automatic parsing and vectorization of Markdown, JSON, and plain text files
- Hybrid Search: Combines semantic vectors with full-text search using RRF fusion for better recall
- Two-stage Ranking: Coarse vector search followed by keyword-based reranking for precision
- Query Expansion: Extends queries with user-defined synonym maps for cross-language and domain-specific matching
npm install @antv/contextimport { Context } from '@antv/context';
// Create context (vectorsDir is optional, defaults to .context/vectors)
const ctx = await Context.create();
// Load documents into a specific library with automatic vectorization
await ctx.load('g2', './g2-docs/**/*.md');
await ctx.load('f2', './f2-docs/**/*.json');
// Query a library (default: hybrid search + reranking)
const results = await ctx.query('How to configure a line chart', { library: 'g2', topK: 5 });
// => [{ content: '...', score: 0.92, scoreMode: 'reranked', id: 'g2-docs/line.md' }, ...]
// Close when done (releases resources)
await ctx.close();| Parameter | Type | Default | Description |
|---|---|---|---|
vectorsDir |
string |
.context/vectors |
Directory to store vector files |
basePath |
string |
process.cwd() |
Base path for resolving document IDs. Set for cross-machine consistent IDs. |
onProgress |
(phase, detail) => void |
— | Progress callback for load() phases: 'load' → 'embed' → 'insert'. |
queryExpansion |
`QueryExpansionOptions | false` | false (no-op) |
ftsFields |
string[] |
['content'] |
Fields to index for Full Text Search in hybrid mode |
ftsFieldWeights |
Record<string, number> |
{ content: 1 } |
Per-field boost weights for FTS text path. Higher = more influence. |
rankConstant |
number |
60 |
RRF rank constant for hybrid search fusion. Lower = "winner-takes-all", higher = more even. |
const ctx = await Context.create({
vectorsDir: '.context/vectors',
// Boost title matches 3x over content matches
ftsFieldWeights: { content: 1, title: 3 },
// More "winner-takes-all" ranking
rankConstant: 20,
});const ctx = await Context.create({
vectorsDir: '.context/vectors',
// Define your own CN↔EN synonym bridges (no built-in defaults)
queryExpansion: {
synonyms: {
'折线图': ['line chart', '折线'],
'雷达图': ['radar chart', '蜘蛛图'],
'tooltip': ['提示框', 'hover', '悬浮'],
},
},
});
// Disable query expansion entirely
const ctxNoExpand = await Context.create({
vectorsDir: '.context/vectors',
queryExpansion: false,
});Load files into a specified library with automatic batch vectorization. Documents are embedded in batches and inserted into the vector store. A content-hash change detection mechanism re-embeds files whose content has changed since the last load.
Document IDs are derived from file paths relative to basePath for cross-machine consistency.
| Parameter | Type | Description |
|---|---|---|
library |
string |
Library name for organizing documents |
pattern |
`string | string[]` |
await ctx.load('g2', './docs/**/*.md');
await ctx.load('g2', ['./docs/**/*.md', './docs/**/*.json']);Load phases emit progress via the onProgress callback:
const ctx = await Context.create({
vectorsDir: '.context/vectors',
onProgress: (phase, detail) => {
console.log(`${phase}: ${detail.loaded}/${detail.total}`);
},
});
// Phases: 'load' → 'embed' → 'insert'Two-stage retrieval: coarse search (vector / hybrid) → reranking → final topK results.
| Parameter | Type | Default | Description |
|---|---|---|---|
library |
string |
— | Library name to query. |
topK |
number |
5 |
Number of results to return |
// Semantic search — hybrid (vector + FTS) + reranking by default
const results = await ctx.query('sankey diagram', { library: 'g2', topK: 5 });Each result includes:
| Field | Type | Description |
|---|---|---|
id |
string |
Document ID |
content |
string |
Document content |
score |
number |
Similarity score (0–1) |
meta |
Record<string, unknown> |
Front-matter metadata (if present) |
path |
string |
Original file path relative to basePath |
Close all stores and release resources. Call this when you are done using the Context instance.
await ctx.close();+------------------------------------------------------------------------+
| @antv/context |
+------------------------------------------------------------------------+
LOAD PHASE QUERY PHASE
---------- ----------
+----------+ +----------+ +----------+ +----------+
| markdown | | json | | text | | Query |
+----+-----+ +----+-----+ +----+-----+ +----+-----+
| | | |
+--------------+--------------+ |
| |
+---------v-------+ +-------v----------+
| FileLoader | | QueryExpander |
+--------+--------+ | (SynonymExpander)|
| +--------+---------+
+--------v--------+ |
| EmbedBatch | |
+--------+--------+ +--------v--------+
| | Embedder |
+--------v--------+ +--------+--------+
| .zvec | |
+-----------------+ +--------v--------+
| Vectorize |
+--------+--------+
|
+-----------v-----------+
| |
+-------v--------+ +-------v-------+
| FTS Text Path | | Vector Path |
| | | |
+-------+--------+ +-------+-------+
| |
+-----------+-----------+
|
+-----------v-----------+
| RRF Fusion |
| (rankConstant) |
+-----------+-----------+
|
+-----------v------------+
| KeywordReranker |
| (optional, 2nd stage) |
+-----------+------------+
|
v
Query Result
+------------------------------------------------------------------------+
MIT