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xiaoyun-memory

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xiaoyun-memory

A small, local-first memory search service for Chinese text. It runs on Node's built-in node:sqlite, keeps everything on disk/in memory on your machine, and exposes three things:

  • an HTTP API (/search, /get, /stats),

  • a stdio MCP server (memory_search, memory_get) so an agent can call it as a tool,

  • a tiny schema you can point at your own table.

It was extracted from a personal assistant's memory pipeline. The interesting part is not FTS5 or RRF (both are well documented) — it is the glue: bigram tokenization for Chinese, candidate recall by FTS, optional vector recall, rank fusion, and a set of small guards that keep latency predictable.

Why bigram

SQLite's unicode61 tokenizer treats a run of Chinese characters as one token, so MATCH '天气' finds nothing unless the whole run matches. trigram needs at least three characters. A bigram index (天气 气很 很好 …) gives two-character recall with a modest size cost: on a 1.07M-character corpus it was ~7.3 MB and answered in ~0.2 ms, roughly 30× faster than a LIKE '%…%' scan.

Related MCP server: memory-agent

Quick start

node examples/seed.mjs demo.sqlite3
MEM_DB=./demo.sqlite3 MEM_PORT=3505 node src/server.mjs
curl "http://127.0.0.1:3505/search?q=香菜&limit=3"

MCP:

MEM_API=http://127.0.0.1:3505 node src/mcp.mjs

The wrapper lazily starts src/server.mjs if the API is not up (local URLs only).

API

Method

Path

Notes

GET

/search?q=…&limit=5

returns {query, count, results:[{id,kind,title,content,importance,source,observed_at,sensitive,score}]}

GET

/get?id=…

one row, 404 if missing

GET

/stats

{total, db, fts, hybrid}

limit is clamped to 1..20. Query text is normalized (NFKC, zero-width characters removed) before tokenization.

Environment

Variable

Default

Meaning

MEM_DB

— (required)

path to the SQLite file

MEM_PORT / MEM_HOST

3505 / 127.0.0.1

HTTP listener

MEM_TOKEN

—

if set, require header x-mem-token

MEM_FTS

on

0 disables the in-memory bigram index (falls back to LIKE)

MEM_FTS_REFRESH_MS

60000

how often to sync the index with the table

MEM_HYBRID

off

1 enables vector recall + RRF

MEM_MODEL_DIR

—

directory with an ONNX export of a sentence-embedding model

MEM_HYBRID_GATE

30

if lexical top1 scores ≥ this, skip the vector pass

MEM_EXPAND

off

1 enables query expansion rules

MEM_EXPAND_FILE

config/expand.example.json

JSON rule table

Schema

create table memories (
  id text primary key,
  kind text,
  title text,
  content text,
  importance real default 0.5,
  source text,
  observed_at text,
  sensitive integer default 0,
  superseded_by text
);
create table mem_vectors (id text primary key, vec blob); -- optional, float32 LE, 512-d

Rows with superseded_by is not null are ignored everywhere.

Tests

npm test        # node --test test/retrieval.test.mjs test/fts.test.mjs

Tests build a throwaway database in the OS temp dir, so they never touch your data.

More measurements: docs/notes/fts5-bigram.md, docs/notes/hybrid-eval.md, and docs/lessons.md.

What this is not

  • Not a database. It reads an existing SQLite file; writes are your job.

  • Not a hosted service. It binds to loopback by default.

  • Not a model. Vector recall is optional and expects you to bring a model directory.

License

MIT.

This server cannot be deployed

Context All Your Agents Can ShareAgents share what they learn, hand off work, and hold it when you switch models. Start with 5 GB free.fulcradynamics.comAd

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

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