Files
memind/temporal-recall-service/adapters/chat.mjs
T
john 212ff3ff80 Add User Model Service and Temporal Recall for MeMind V0.1.
Introduce UMS ingest/snapshot pipeline, Context Planner with multi-source recall, runtime context injection, canonical user mapping, and session snapshot loading on auth/me.

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-09-03 23:25:18 +08:00

108 lines
3.5 KiB
JavaScript

import crypto from 'node:crypto';
import {
expandObservedFetchRange,
extractEventTime,
itemMatchesTimeWindow,
} from '../event-time-extract.mjs';
function newTimelineId(sourceRef) {
const hash = crypto.createHash('sha256').update(`timeline-v1|${sourceRef}`).digest('hex');
return [
hash.slice(0, 8),
hash.slice(8, 12),
`4${hash.slice(13, 16)}`,
hash.slice(16, 20),
hash.slice(20, 32),
].join('-');
}
function parseUserMessage(row) {
try {
return typeof row.user_message_json === 'string'
? JSON.parse(row.user_message_json)
: row.user_message_json;
} catch {
return null;
}
}
function extractText(message) {
if (!message) return '';
if (typeof message === 'string') return message.trim();
if (typeof message.content === 'string') return message.content.trim();
if (Array.isArray(message.content)) {
return message.content
.map((part) => (typeof part === 'string' ? part : part?.text ?? ''))
.join('')
.trim();
}
if (typeof message.text === 'string') return message.text.trim();
return '';
}
function scoreTextImportance(text, expandedQueries = []) {
let score = 0.5;
const t = String(text ?? '');
if (/重要|紧急|安排|会议|待办|记得|跟进|截止|确认/.test(t)) score += 0.18;
for (const q of expandedQueries) {
if (q && t.includes(q)) score += 0.05;
}
return Math.min(0.95, score);
}
function matchesExpanded(text, expandedQueries) {
if (!expandedQueries?.length) return true;
return expandedQueries.some((q) => q && text.includes(q));
}
/**
* @param {import('mysql2/promise').Pool} pool
* @param {{ userId: string, retrieval: object, time: object, sessionId?: string }} ctx
*/
export async function searchChat(pool, ctx) {
if (!pool?.query) return [];
let sql = `
SELECT id, user_message_json, created_at, agent_session_id
FROM h5_agent_runs
WHERE user_id = ? AND created_at >= ? AND created_at < ?`;
const fetchRange = expandObservedFetchRange(ctx.time, ctx.temporalMode);
const params = [ctx.userId, new Date(fetchRange.start).getTime(), new Date(fetchRange.end).getTime()];
if (ctx.sessionId) {
sql += ' AND agent_session_id = ?';
params.push(ctx.sessionId);
}
sql += ' ORDER BY created_at ASC LIMIT 300';
const [rows] = await pool.query(sql, params);
const items = [];
for (const row of rows) {
const message = parseUserMessage(row);
const text = extractText(message);
if (text.length < 2) continue;
if (!matchesExpanded(text, ctx.retrieval.expanded_queries) && text.length < 12) continue;
const observed = new Date(Number(row.created_at)).toISOString();
const extracted = extractEventTime(text, observed);
const sourceRef = `chat:run:${row.id}`;
const item = {
timeline_item_id: newTimelineId(sourceRef),
user_id: ctx.userId,
source: 'chat',
type: /待办|记得|别忘了|跟进|截止|安排/.test(text) ? 'commitment' : 'mention',
event_time: extracted.event_time,
observed_time: observed,
title: text.slice(0, 80),
content: text.slice(0, 8192),
importance: scoreTextImportance(text, ctx.retrieval.expanded_queries),
confidence: extracted.event_time ? extracted.confidence : 0.88,
source_ref: sourceRef,
participants: [],
status: extracted.status === 'planned' ? 'planned' : 'mentioned',
metadata: { agent_session_id: row.agent_session_id },
};
if (!itemMatchesTimeWindow(item, ctx.time, ctx.temporalMode)) continue;
items.push(item);
}
return items;
}