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fix: memory cursor and extract prompt

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ikechan8370 2025-11-05 15:02:52 +08:00
parent 660de1b462
commit 0550e6d492
7 changed files with 257 additions and 48 deletions
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39
models/memory/database.js
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@ -5,6 +5,7 @@ import path from 'path'
import ChatGPTConfig from '../../config/config.js'
const META_VECTOR_DIM_KEY = 'group_vec_dimension'
const META_VECTOR_MODEL_KEY = 'group_vec_model'
const META_GROUP_TOKENIZER_KEY = 'group_memory_tokenizer'
const META_USER_TOKENIZER_KEY = 'user_memory_tokenizer'
const TOKENIZER_DEFAULT = 'unicode61'
@ -14,6 +15,7 @@ const PLUGIN_ROOT = path.resolve('./plugins/chatgpt-plugin')
let dbInstance = null
let cachedVectorDimension = null
let cachedVectorModel = null
let userMemoryFtsConfig = {
tokenizer: TOKENIZER_DEFAULT,
matchQuery: null
@ -329,6 +331,17 @@ function ensureGroupFactsTable (db) {
ensureGroupFactsFtsTable(db)
}
function ensureGroupHistoryCursorTable (db) {
ensureMetaTable(db)
db.exec(`
CREATE TABLE IF NOT EXISTS group_history_cursor (
group_id TEXT PRIMARY KEY,
last_message_id TEXT,
last_timestamp INTEGER
)
`)
}
function ensureUserMemoryTable (db) {
ensureMetaTable(db)
db.exec(`
@ -550,6 +563,9 @@ function ensureUserMemoryFtsTable (db) {
}
function createVectorTable (db, dimension) {
if (!dimension || dimension <= 0) {
throw new Error(`Invalid vector dimension for table creation: ${dimension}`)
}
db.exec(`CREATE VIRTUAL TABLE vec_group_facts USING vec0(embedding float[${dimension}])`)
}
@ -559,10 +575,24 @@ function ensureVectorTable (db) {
return cachedVectorDimension
}
const stored = getMetaValue(db, META_VECTOR_DIM_KEY)
const storedModel = getMetaValue(db, META_VECTOR_MODEL_KEY)
const currentModel = ChatGPTConfig.llm?.embeddingModel || ''
if (!storedModel || storedModel !== currentModel) {
try {
db.exec('DROP TABLE IF EXISTS vec_group_facts')
} catch (err) {
logger?.warn?.('[Memory] failed to drop vec_group_facts during model change:', err)
}
setMetaValue(db, META_VECTOR_MODEL_KEY, currentModel)
setMetaValue(db, META_VECTOR_DIM_KEY, '0')
cachedVectorDimension = 0
cachedVectorModel = currentModel
return cachedVectorDimension
}
let dimension = stored ? parseInt(stored, 10) : null
if (!dimension || Number.isNaN(dimension) || dimension <= 0) {
dimension = resolvePreferredDimension()
setMetaValue(db, META_VECTOR_DIM_KEY, dimension.toString())
cachedVectorDimension = 0
return cachedVectorDimension
}
const exists = db.prepare(`
SELECT name FROM sqlite_master
@ -572,6 +602,7 @@ function ensureVectorTable (db) {
createVectorTable(db, dimension)
}
cachedVectorDimension = dimension
cachedVectorModel = currentModel
return dimension
}
@ -588,11 +619,15 @@ export function resetVectorTableDimension (dimension) {
}
createVectorTable(db, dimension)
setMetaValue(db, META_VECTOR_DIM_KEY, dimension.toString())
const model = ChatGPTConfig.llm?.embeddingModel || ''
setMetaValue(db, META_VECTOR_MODEL_KEY, model)
cachedVectorDimension = dimension
cachedVectorModel = model
}
function migrate (db) {
ensureGroupFactsTable(db)
ensureGroupHistoryCursorTable(db)
ensureUserMemoryTable(db)
ensureVectorTable(db)
}