my harness for niri
codex vibecoded embeddings
+1137
-13
+9
.env.example
+9
.env.example
···
5
5
# Enable/disable model reasoning traces (thinking) across requests + stream output.
6
6
ENABLE_THINKING=true
7
7
8
+
# ── Memory embeddings ────────────────────────────────────────────────────────
9
+
# Used for semantic memory reranking and low-information chatter suppression.
10
+
# The sqlite-vec table is fixed at 3072 dimensions; changing dimensions requires
11
+
# deleting/rebuilding memory_chunk_vec, memory_prototype_vec, and embedding meta.
12
+
EMBEDDING_API_KEY=
13
+
EMBEDDING_BASE_URL=https://openrouter.ai/api/v1
14
+
EMBEDDING_MODEL=google/gemini-embedding-2-preview
15
+
EMBEDDING_DIMENSIONS=3072
16
+
8
17
# Used when the primary endpoint is unreachable or rate-limited (429/5xx).
9
18
# Supports any OpenAI-compatible provider (OpenRouter, LM Studio, etc.).
10
19
FALLBACK_OPENAI_BASE_URL=http://localhost:1234/v1
+548
-1
package-lock.json
+548
-1
package-lock.json
···
15
15
"dependencies": {
16
16
"@fastify/static": "^9.1.3",
17
17
"@niri/chat-client": "*",
18
+
"@openrouter/sdk": "^0.12.21",
18
19
"better-sqlite3": "^12.8.0",
19
20
"discord.js": "^14.25.1",
20
21
"fastify": "^5.8.4",
21
22
"node-pty": "^1.1.0",
22
-
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23
+
"openai": "^6.33.0",
24
+
"sqlite-vec": "^0.1.9"
23
25
},
24
26
"devDependencies": {
25
27
"@types/better-sqlite3": "^7.6.13",
···
74
76
"version": "7.29.0",
75
77
"dev": true,
76
78
"license": "MIT",
79
+
"peer": true,
77
80
"dependencies": {
78
81
"@babel/code-frame": "^7.29.0",
79
82
"@babel/generator": "^7.29.0",
···
439
442
"url": "https://github.com/discordjs/discord.js?sponsor"
440
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}
441
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},
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+
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841
+
"dev": true,
842
+
"license": "MIT",
843
+
"optional": true,
844
+
"os": [
845
+
"win32"
846
+
],
847
+
"engines": {
848
+
"node": ">=18"
849
+
}
850
+
},
851
+
"node_modules/@esbuild/win32-ia32": {
852
+
"version": "0.27.7",
853
+
"resolved": "https://registry.npmjs.org/@esbuild/win32-ia32/-/win32-ia32-0.27.7.tgz",
854
+
"integrity": "sha512-SmwKXe6VHIyZYbBLJrhOoCJRB/Z1tckzmgTLfFYOfpMAx63BJEaL9ExI8x7v0oAO3Zh6D/Oi1gVxEYr5oUCFhw==",
855
+
"cpu": [
856
+
"ia32"
857
+
],
858
+
"dev": true,
859
+
"license": "MIT",
860
+
"optional": true,
861
+
"os": [
862
+
"win32"
863
+
],
864
+
"engines": {
865
+
"node": ">=18"
866
+
}
867
+
},
868
+
"node_modules/@esbuild/win32-x64": {
869
+
"version": "0.27.7",
870
+
"resolved": "https://registry.npmjs.org/@esbuild/win32-x64/-/win32-x64-0.27.7.tgz",
871
+
"integrity": "sha512-56hiAJPhwQ1R4i+21FVF7V8kSD5zZTdHcVuRFMW0hn753vVfQN8xlx4uOPT4xoGH0Z/oVATuR82AiqSTDIpaHg==",
872
+
"cpu": [
873
+
"x64"
874
+
],
875
+
"dev": true,
876
+
"license": "MIT",
877
+
"optional": true,
878
+
"os": [
879
+
"win32"
880
+
],
881
+
"engines": {
882
+
"node": ">=18"
883
+
}
884
+
},
457
885
"node_modules/@fastify/accept-negotiator": {
458
886
"version": "2.0.1",
459
887
"funding": [
···
667
1095
"resolved": "apps/web",
668
1096
"link": true
669
1097
},
1098
+
"node_modules/@openrouter/sdk": {
1099
+
"version": "0.12.24",
1100
+
"resolved": "https://registry.npmjs.org/@openrouter/sdk/-/sdk-0.12.24.tgz",
1101
+
"integrity": "sha512-pHybqvkWZUvg/uM99+ShHgDKg/iA3MU2MZdlZvoenmK/3cnG6jW1zROb+tyd6iEAZwcqb2+2QoahgLbJgo/9Sw==",
1102
+
"hasInstallScript": true,
1103
+
"license": "Apache-2.0",
1104
+
"dependencies": {
1105
+
"zod": "^3.25.0 || ^4.0.0"
1106
+
}
1107
+
},
670
1108
"node_modules/@pinojs/redact": {
671
1109
"version": "0.4.0",
672
1110
"license": "MIT"
···
811
1249
"node_modules/@types/node": {
812
1250
"version": "25.5.2",
813
1251
"license": "MIT",
1252
+
"peer": true,
814
1253
"dependencies": {
815
1254
"undici-types": "~7.18.0"
816
1255
}
···
818
1257
"node_modules/@types/react": {
819
1258
"version": "19.2.14",
820
1259
"license": "MIT",
1260
+
"peer": true,
821
1261
"dependencies": {
822
1262
"csstype": "^3.2.2"
823
1263
}
···
1034
1474
}
1035
1475
],
1036
1476
"license": "MIT",
1477
+
"peer": true,
1037
1478
"dependencies": {
1038
1479
"baseline-browser-mapping": "^2.10.12",
1039
1480
"caniuse-lite": "^1.0.30001782",
···
1571
2012
"version": "1.0.0",
1572
2013
"license": "MIT"
1573
2014
},
2015
+
"node_modules/fsevents": {
2016
+
"version": "2.3.3",
2017
+
"resolved": "https://registry.npmjs.org/fsevents/-/fsevents-2.3.3.tgz",
2018
+
"integrity": "sha512-5xoDfX+fL7faATnagmWPpbFtwh/R77WmMMqqHGS65C3vvB0YHrgF+B1YmZ3441tMj5n63k0212XNoJwzlhffQw==",
2019
+
"dev": true,
2020
+
"hasInstallScript": true,
2021
+
"license": "MIT",
2022
+
"optional": true,
2023
+
"os": [
2024
+
"darwin"
2025
+
],
2026
+
"engines": {
2027
+
"node": "^8.16.0 || ^10.6.0 || >=11.0.0"
2028
+
}
2029
+
},
1574
2030
"node_modules/gensync": {
1575
2031
"version": "1.0.0-beta.2",
1576
2032
"dev": true,
···
2877
3333
"version": "4.0.4",
2878
3334
"dev": true,
2879
3335
"license": "MIT",
3336
+
"peer": true,
2880
3337
"engines": {
2881
3338
"node": ">=12"
2882
3339
},
···
3018
3475
"node_modules/react": {
3019
3476
"version": "19.2.4",
3020
3477
"license": "MIT",
3478
+
"peer": true,
3021
3479
"engines": {
3022
3480
"node": ">=0.10.0"
3023
3481
}
···
3405
3863
"node": ">= 10.x"
3406
3864
}
3407
3865
},
3866
+
"node_modules/sqlite-vec": {
3867
+
"version": "0.1.9",
3868
+
"resolved": "https://registry.npmjs.org/sqlite-vec/-/sqlite-vec-0.1.9.tgz",
3869
+
"integrity": "sha512-L7XJWRIBNvR9O5+vh1FQ+IGkh/3D2AzVksW5gdtk28m78Hy8skFD0pqReKH1Yp0/BUKRGcffgKvyO/EON5JXpA==",
3870
+
"license": "MIT OR Apache",
3871
+
"optionalDependencies": {
3872
+
"sqlite-vec-darwin-arm64": "0.1.9",
3873
+
"sqlite-vec-darwin-x64": "0.1.9",
3874
+
"sqlite-vec-linux-arm64": "0.1.9",
3875
+
"sqlite-vec-linux-x64": "0.1.9",
3876
+
"sqlite-vec-windows-x64": "0.1.9"
3877
+
}
3878
+
},
3879
+
"node_modules/sqlite-vec-darwin-arm64": {
3880
+
"version": "0.1.9",
3881
+
"resolved": "https://registry.npmjs.org/sqlite-vec-darwin-arm64/-/sqlite-vec-darwin-arm64-0.1.9.tgz",
3882
+
"integrity": "sha512-jSsZpE42OfBkGL/ItyJTVCUwl6o6Ka3U5rc4j+UBDIQzC1ulSSKMEhQLthsOnF/MdAf1MuAkYhkdKmmcjaIZQg==",
3883
+
"cpu": [
3884
+
"arm64"
3885
+
],
3886
+
"license": "MIT OR Apache",
3887
+
"optional": true,
3888
+
"os": [
3889
+
"darwin"
3890
+
]
3891
+
},
3892
+
"node_modules/sqlite-vec-darwin-x64": {
3893
+
"version": "0.1.9",
3894
+
"resolved": "https://registry.npmjs.org/sqlite-vec-darwin-x64/-/sqlite-vec-darwin-x64-0.1.9.tgz",
3895
+
"integrity": "sha512-KDlVyqQT7pnOhU1ymB9gs7dMbSoVmKHitT+k1/xkjarcX8bBqPxWrGlK/R+C5WmWkfvWwyq5FfXfiBYCBs6PlA==",
3896
+
"cpu": [
3897
+
"x64"
3898
+
],
3899
+
"license": "MIT OR Apache",
3900
+
"optional": true,
3901
+
"os": [
3902
+
"darwin"
3903
+
]
3904
+
},
3905
+
"node_modules/sqlite-vec-linux-arm64": {
3906
+
"version": "0.1.9",
3907
+
"resolved": "https://registry.npmjs.org/sqlite-vec-linux-arm64/-/sqlite-vec-linux-arm64-0.1.9.tgz",
3908
+
"integrity": "sha512-5wXVJ9c9kR4CHm/wVqXb/R+XUHTdpZ4nWbPHlS+gc9qQFVHs92Km4bPnCKX4rtcPMzvNis+SIzMJR1SCEwpuUw==",
3909
+
"cpu": [
3910
+
"arm64"
3911
+
],
3912
+
"license": "MIT OR Apache",
3913
+
"optional": true,
3914
+
"os": [
3915
+
"linux"
3916
+
]
3917
+
},
3918
+
"node_modules/sqlite-vec-linux-x64": {
3919
+
"version": "0.1.9",
3920
+
"resolved": "https://registry.npmjs.org/sqlite-vec-linux-x64/-/sqlite-vec-linux-x64-0.1.9.tgz",
3921
+
"integrity": "sha512-w3tCH8xK2finW8fQJ/m8uqKodXUZ9KAuAar2UIhz4BHILfpE0WM/MTGCRfa7RjYbrYim5Luk3guvMOGI7T7JQA==",
3922
+
"cpu": [
3923
+
"x64"
3924
+
],
3925
+
"license": "MIT OR Apache",
3926
+
"optional": true,
3927
+
"os": [
3928
+
"linux"
3929
+
]
3930
+
},
3931
+
"node_modules/sqlite-vec-windows-x64": {
3932
+
"version": "0.1.9",
3933
+
"resolved": "https://registry.npmjs.org/sqlite-vec-windows-x64/-/sqlite-vec-windows-x64-0.1.9.tgz",
3934
+
"integrity": "sha512-y3gEIyy/17bq2QFPQOWLE68TYWcRZkBQVA2XLrTPHNTOp55xJi/BBBmOm40tVMDMjtP+Elpk6UBUXdaq+46b0Q==",
3935
+
"cpu": [
3936
+
"x64"
3937
+
],
3938
+
"license": "MIT OR Apache",
3939
+
"optional": true,
3940
+
"os": [
3941
+
"win32"
3942
+
]
3943
+
},
3408
3944
"node_modules/statuses": {
3409
3945
"version": "2.0.2",
3410
3946
"license": "MIT",
···
3543
4079
"version": "4.21.0",
3544
4080
"dev": true,
3545
4081
"license": "MIT",
4082
+
"peer": true,
3546
4083
"dependencies": {
3547
4084
"esbuild": "~0.27.0",
3548
4085
"get-tsconfig": "^4.7.5"
···
3738
4275
"version": "7.3.2",
3739
4276
"dev": true,
3740
4277
"license": "MIT",
4278
+
"peer": true,
3741
4279
"dependencies": {
3742
4280
"esbuild": "^0.27.0",
3743
4281
"fdir": "^6.5.0",
···
3848
4386
"version": "3.1.1",
3849
4387
"dev": true,
3850
4388
"license": "ISC"
4389
+
},
4390
+
"node_modules/zod": {
4391
+
"version": "4.4.1",
4392
+
"resolved": "https://registry.npmjs.org/zod/-/zod-4.4.1.tgz",
4393
+
"integrity": "sha512-a6ENMBBGZBsnlSebQ/eKCguSBeGKSf4O7BPnqVPmYGtpBYI7VSqoVqw+QcB7kPRjbqPwhYTpFbVj/RqNz/CT0Q==",
4394
+
"license": "MIT",
4395
+
"funding": {
4396
+
"url": "https://github.com/sponsors/colinhacks"
4397
+
}
3851
4398
},
3852
4399
"node_modules/zwitch": {
3853
4400
"version": "2.0.4",
+2
-1
package.json
+2
-1
package.json
+47
src/db.ts
+47
src/db.ts
···
1
1
import Database from "better-sqlite3"
2
2
import fs from "fs"
3
3
import path from "path"
4
+
import * as sqliteVec from "sqlite-vec"
4
5
import { fileURLToPath } from "url"
5
6
6
7
const HOME_DIR = path.resolve(fileURLToPath(import.meta.url), "../../home")
7
8
const DB_PATH = path.join(HOME_DIR, "niri.db")
9
+
export const MEMORY_EMBEDDING_DIMENSIONS = 3072
8
10
9
11
let db: Database.Database
12
+
let vecAvailable = false
10
13
11
14
function ensureWritableDirOrThrow(dirPath: string, purpose: string): void {
12
15
try {
···
47
50
48
51
db.pragma("journal_mode = WAL")
49
52
db.pragma("foreign_keys = ON")
53
+
try {
54
+
sqliteVec.load(db)
55
+
vecAvailable = true
56
+
} catch (err: any) {
57
+
vecAvailable = false
58
+
console.warn(`[db] sqlite-vec unavailable: ${err?.message ?? String(err)}`)
59
+
}
50
60
51
61
db.exec(`
52
62
create table if not exists conversations (
···
186
196
insert into memory_chunks_fts(rowid, title, heading_path, chunk_text, tags)
187
197
values (new.id, new.title, new.heading_path, new.chunk_text, new.tags);
188
198
end;
199
+
200
+
create table if not exists memory_embedding_meta (
201
+
chunk_id integer primary key references memory_chunks(id) on delete cascade,
202
+
model text not null,
203
+
dimensions integer not null,
204
+
content_hash text not null,
205
+
updated_at text not null default (datetime('now'))
206
+
);
207
+
208
+
create index if not exists idx_memory_embedding_meta_model
209
+
on memory_embedding_meta(model, dimensions);
210
+
211
+
create table if not exists memory_embedding_prototypes (
212
+
id integer primary key,
213
+
name text not null unique,
214
+
category text not null,
215
+
model text not null,
216
+
dimensions integer not null,
217
+
content_hash text not null,
218
+
updated_at text not null default (datetime('now'))
219
+
);
189
220
`)
221
+
222
+
if (vecAvailable) {
223
+
db.exec(`
224
+
create virtual table if not exists memory_chunk_vec using vec0(
225
+
embedding float[${MEMORY_EMBEDDING_DIMENSIONS}] distance_metric=cosine
226
+
);
227
+
228
+
create virtual table if not exists memory_prototype_vec using vec0(
229
+
embedding float[${MEMORY_EMBEDDING_DIMENSIONS}] distance_metric=cosine
230
+
);
231
+
`)
232
+
}
190
233
191
234
console.log("[db] ready")
192
235
}
···
224
267
if (!db) throw new Error("Database not initialized")
225
268
return db
226
269
}
270
+
271
+
export function isVecAvailable(): boolean {
272
+
return vecAvailable
273
+
}
+39
src/embeddings.ts
+39
src/embeddings.ts
···
1
+
import OpenAI from "openai"
2
+
import { MEMORY_EMBEDDING_DIMENSIONS } from "./db.js"
3
+
4
+
export const EMBEDDING_MODEL = process.env.EMBEDDING_MODEL ?? "google/gemini-embedding-2-preview"
5
+
export const EMBEDDING_DIMENSIONS = parseInt(
6
+
process.env.EMBEDDING_DIMENSIONS ?? String(MEMORY_EMBEDDING_DIMENSIONS),
7
+
10,
8
+
)
9
+
10
+
const EMBEDDING_BASE_URL = process.env.EMBEDDING_BASE_URL ?? "https://openrouter.ai/api/v1"
11
+
const EMBEDDING_API_KEY = process.env.EMBEDDING_API_KEY
12
+
13
+
const embeddingClient = EMBEDDING_API_KEY
14
+
? new OpenAI({
15
+
baseURL: EMBEDDING_BASE_URL,
16
+
apiKey: EMBEDDING_API_KEY,
17
+
defaultHeaders: {
18
+
...(process.env.EMBEDDING_OPENAI_REFERER ? { "HTTP-Referer": process.env.EMBEDDING_OPENAI_REFERER } : {}),
19
+
...(process.env.EMBEDDING_TITLE ? { "X-Title": process.env.EMBEDDING_TITLE } : {}),
20
+
},
21
+
})
22
+
: null
23
+
24
+
export function embeddingsConfigured(): boolean {
25
+
return Boolean(embeddingClient) && EMBEDDING_DIMENSIONS > 0
26
+
}
27
+
28
+
export async function embedTexts(texts: string[]): Promise<number[][]> {
29
+
if (!embeddingClient || texts.length === 0) return []
30
+
31
+
const response = await embeddingClient.embeddings.create({
32
+
model: EMBEDDING_MODEL,
33
+
input: texts,
34
+
dimensions: EMBEDDING_DIMENSIONS,
35
+
encoding_format: "float",
36
+
})
37
+
38
+
return response.data.map((item) => item.embedding)
39
+
}
+40
-1
src/memory.test.ts
+40
-1
src/memory.test.ts
···
27
27
- [channel/staying up till 1 billion oclock/#niri] [2026-05-01 03:11:14.553Z] @meowskullz: awa
28
28
29
29
pending preview:
30
-
- (none)`
30
+
- 1499679672404021248 [mention] [channel/staying up till 1 billion oclock/#niri] [2026-05-01 03:11:14.553Z] @meowskullz: awa`
31
31
32
32
assert.deepEqual(__memoryTest.memoryQueryForUserMessage(batch), {
33
33
sender: "meowskullz",
34
34
source: "channel/staying up till 1 billion oclock/#niri",
35
35
body: "awa",
36
+
})
37
+
})
38
+
39
+
test("latestMemoryRecallQuery skips ambient discord batch without pending items", () => {
40
+
const batch = `[user/discord] [discord batch] 2026-05-01T03:10:50.162Z -> 2026-05-01T03:11:22.198Z
41
+
new_messages=1 channels=1 pending_inbox=0 scope=configured+dm
42
+
43
+
recent messages:
44
+
- [channel/meowskullz's server/#ai-sister-yap] [2026-05-01 08:01:35.639Z] @rose: foxie emoji
45
+
46
+
pending preview:
47
+
- (none)`
48
+
49
+
const conversation: Message[] = [
50
+
{ role: "user", content: "what did we talk about when lisya was buying monero" },
51
+
{ role: "assistant", content: "..." },
52
+
{ role: "user", content: batch },
53
+
]
54
+
55
+
assert.equal(
56
+
__memoryTest.latestMemoryRecallQuery(conversation),
57
+
"what did we talk about when lisya was buying monero",
58
+
)
59
+
})
60
+
61
+
test("memoryQueryForUserMessage ignores ambient discord batch recent messages", () => {
62
+
const batch = `[user/discord] [discord batch] 2026-05-01T03:10:50.162Z -> 2026-05-01T03:11:22.198Z
63
+
new_messages=1 channels=1 pending_inbox=0 scope=configured+dm
64
+
65
+
recent messages:
66
+
- [channel/meowskullz's server/#ai-sister-yap] [2026-05-01 08:01:35.639Z] @rose: foxie emoji
67
+
68
+
pending preview:
69
+
- (none)`
70
+
71
+
assert.deepEqual(__memoryTest.memoryQueryForUserMessage(batch), {
72
+
sender: null,
73
+
source: null,
74
+
body: batch,
36
75
})
37
76
})
38
77
+452
-10
src/memory.ts
+452
-10
src/memory.ts
···
3
3
import { createHash } from "crypto"
4
4
import { fileURLToPath } from "url"
5
5
import type { Message } from "./types.js"
6
-
import { getDb } from "./db.js"
6
+
import { getDb, isVecAvailable, MEMORY_EMBEDDING_DIMENSIONS } from "./db.js"
7
+
import { EMBEDDING_DIMENSIONS, EMBEDDING_MODEL, embeddingsConfigured, embedTexts } from "./embeddings.js"
7
8
import { recordMetric } from "./metrics.js"
8
9
9
10
const HOME_DIR = path.resolve(fileURLToPath(import.meta.url), "../../home")
···
22
23
const MEMORY_RECALL_PER_EXTRA_PERSON_CHARS = 400
23
24
const MEMORY_QUERY_TOKEN_LIMIT = 12
24
25
const MEMORY_RECALL_COOLDOWN_TURNS = 7
26
+
const MEMORY_EMBEDDING_BATCH_SIZE = 24
27
+
const MEMORY_SEMANTIC_MIN_SIMILARITY = 0.18
28
+
const MEMORY_SEMANTIC_STRONG_SIMILARITY = 0.32
29
+
const MEMORY_CHATTER_SIMILARITY_THRESHOLD = 0.74
30
+
const MEMORY_RECALL_INTENT_SIMILARITY_THRESHOLD = 0.55
25
31
const SCHEDULED_HEARTBEAT_CONTENT = "Scheduled heartbeat."
32
+
const MEMORY_EMBEDDING_PROTOTYPES = [
33
+
{ id: 1, name: "affection-love", category: "chatter", text: "i love you so much sweetie <33" },
34
+
{ id: 2, name: "cat-greeting", category: "chatter", text: "boop mraow meow hi sweetie" },
35
+
{ id: 3, name: "celebration", category: "chatter", text: "yay yayy lets gooooo <33" },
36
+
{ id: 4, name: "goodnight", category: "chatter", text: "goodnight sweet dreams rest well" },
37
+
{ id: 101, name: "who-person", category: "recall_intent", text: "who is this person what do i know about them" },
38
+
{ id: 102, name: "past-event", category: "recall_intent", text: "what happened before remember when that event happened" },
39
+
{ id: 103, name: "task-context", category: "recall_intent", text: "what context do i need for this task or project" },
40
+
{ id: 104, name: "system-lesson", category: "recall_intent", text: "what lesson or instruction should i remember here" },
41
+
] as const
26
42
const MEMORY_STOP_WORDS = new Set([
27
43
"a",
28
44
"an",
···
103
119
headingPath: string | null
104
120
text: string
105
121
rank: number
122
+
semanticDistance?: number
123
+
semanticSimilarity?: number
106
124
}
107
125
108
126
export type MemorySearchResult = {
···
140
158
senderMatch: boolean
141
159
}
142
160
161
+
type MemoryEmbeddingRow = {
162
+
chunkId: number
163
+
path: string
164
+
kind: MemoryKind
165
+
documentTitle: string
166
+
title: string
167
+
headingPath: string | null
168
+
text: string
169
+
tags: string | null
170
+
model: string | null
171
+
dimensions: number | null
172
+
contentHash: string | null
173
+
}
174
+
175
+
type SemanticQuerySignal = {
176
+
vector: number[]
177
+
chatterSimilarity: number | null
178
+
recallIntentSimilarity: number | null
179
+
}
180
+
143
181
export type AliasMap = Record<string, string[]>
144
182
145
183
function normalizeText(value: string): string {
···
297
335
return createHash("sha1").update(content).digest("hex")
298
336
}
299
337
338
+
function embeddingInputHash(content: string): string {
339
+
return createHash("sha256").update(content).digest("hex")
340
+
}
341
+
342
+
function vectorParam(vector: number[]): Float32Array {
343
+
return new Float32Array(vector)
344
+
}
345
+
346
+
function embeddingTextForChunk(row: {
347
+
path: string
348
+
kind: MemoryKind
349
+
documentTitle: string
350
+
title: string
351
+
headingPath: string | null
352
+
text: string
353
+
tags?: string | null
354
+
}): string {
355
+
const relativePath = path.relative(HOME_DIR, row.path)
356
+
return [
357
+
`kind: ${row.kind}`,
358
+
`file: ${relativePath}`,
359
+
`document: ${row.documentTitle}`,
360
+
`title: ${row.title}`,
361
+
row.headingPath ? `section: ${row.headingPath}` : null,
362
+
row.tags ? `tags: ${row.tags}` : null,
363
+
"",
364
+
row.text,
365
+
]
366
+
.filter((part): part is string => part !== null)
367
+
.join("\n")
368
+
}
369
+
300
370
function chunkLargeSection(text: string, maxChars = 900): string[] {
301
371
const paragraphs = text
302
372
.split(/\n\s*\n/g)
···
405
475
if (content.startsWith(MEMORY_RECALL_HEADER)) return true
406
476
if (content.startsWith("[system]")) return true
407
477
if (content.includes("scan snapshot:") && !content.includes("[discord batch]")) return true
478
+
if (/\[discord batch\]/i.test(content) && conciseDiscordBatchMemoryQuery(content) === null) return true
408
479
return false
409
480
}
410
481
···
506
577
const withoutWakeEnvelope = raw.replace(/^\[(wake|incoming|harness restarted)[^\n]*\]\s*/gi, "").trim()
507
578
if (!/\[discord batch\]/i.test(withoutWakeEnvelope)) return null
508
579
509
-
const recent = extractBulletSection(withoutWakeEnvelope, "recent messages")
510
580
const pending = extractBulletSection(withoutWakeEnvelope, "pending preview")
511
-
const selected = (recent.length > 0 ? recent : pending).slice(-3)
581
+
const selected = pending.filter((entry) => !/^\(none\)$/i.test(entry)).slice(-3)
512
582
if (selected.length === 0) return null
513
583
514
584
const senders: string[] = []
515
585
const sources: string[] = []
516
586
const bodies: string[] = []
517
-
const entryPattern = /^\[([^\]]+)\]\s+\[[^\]]+\]\s+@([^:]+):\s*(.*)$/i
587
+
const entryPattern = /(?:^|\s)\[([^\]]+)\]\s+\[[^\]]+\]\s+@([^:]+):\s*(.*)$/i
518
588
for (const entry of selected) {
519
589
const match = entry.match(entryPattern)
520
590
if (!match) {
···
798
868
})()
799
869
800
870
if (updates.length === 0 && removedPaths.length === 0) {
871
+
await syncMemoryEmbeddings()
801
872
return
802
873
}
874
+
875
+
await syncMemoryEmbeddings()
876
+
}
877
+
878
+
let embeddingSkipWarned = false
879
+
880
+
async function syncMemoryEmbeddings(): Promise<void> {
881
+
if (!isVecAvailable()) return
882
+
if (!embeddingsConfigured()) {
883
+
if (!embeddingSkipWarned) {
884
+
console.warn("[memory] embeddings disabled: set EMBEDDING_API_KEY")
885
+
embeddingSkipWarned = true
886
+
}
887
+
return
888
+
}
889
+
if (EMBEDDING_DIMENSIONS !== MEMORY_EMBEDDING_DIMENSIONS) {
890
+
if (!embeddingSkipWarned) {
891
+
console.warn(
892
+
`[memory] embeddings disabled: EMBEDDING_DIMENSIONS=${EMBEDDING_DIMENSIONS} but sqlite-vec table is ${MEMORY_EMBEDDING_DIMENSIONS}`,
893
+
)
894
+
embeddingSkipWarned = true
895
+
}
896
+
return
897
+
}
898
+
899
+
const db = getDb()
900
+
try {
901
+
await syncMemoryEmbeddingPrototypes()
902
+
} catch (err: any) {
903
+
console.warn(`[memory] prototype embedding sync failed: ${err?.message ?? String(err)}`)
904
+
return
905
+
}
906
+
db.prepare("delete from memory_embedding_meta where chunk_id not in (select id from memory_chunks)").run()
907
+
db.prepare("delete from memory_chunk_vec where rowid not in (select id from memory_chunks)").run()
908
+
909
+
const rows = db
910
+
.prepare(`
911
+
select
912
+
c.id as chunkId,
913
+
d.path as path,
914
+
d.kind as kind,
915
+
d.title as documentTitle,
916
+
c.title as title,
917
+
c.heading_path as headingPath,
918
+
c.chunk_text as text,
919
+
c.tags as tags,
920
+
m.model as model,
921
+
m.dimensions as dimensions,
922
+
m.content_hash as contentHash
923
+
from memory_chunks c
924
+
join memory_documents d on d.id = c.document_id
925
+
left join memory_embedding_meta m on m.chunk_id = c.id
926
+
order by d.kind, d.path, c.chunk_index
927
+
`)
928
+
.all() as MemoryEmbeddingRow[]
929
+
930
+
const pending = rows
931
+
.map((row) => {
932
+
const text = embeddingTextForChunk(row)
933
+
return { ...row, embeddingText: text, embeddingHash: embeddingInputHash(text) }
934
+
})
935
+
.filter(
936
+
(row) =>
937
+
row.model !== EMBEDDING_MODEL ||
938
+
row.dimensions !== MEMORY_EMBEDDING_DIMENSIONS ||
939
+
row.contentHash !== row.embeddingHash,
940
+
)
941
+
942
+
if (pending.length === 0) return
943
+
944
+
const upsertMeta = db.prepare(`
945
+
insert into memory_embedding_meta (chunk_id, model, dimensions, content_hash, updated_at)
946
+
values (?, ?, ?, ?, datetime('now'))
947
+
on conflict(chunk_id) do update set
948
+
model = excluded.model,
949
+
dimensions = excluded.dimensions,
950
+
content_hash = excluded.content_hash,
951
+
updated_at = datetime('now')
952
+
`)
953
+
const upsertVector = db.prepare("insert or replace into memory_chunk_vec(rowid, embedding) values (?, ?)")
954
+
955
+
let embedded = 0
956
+
for (let i = 0; i < pending.length; i += MEMORY_EMBEDDING_BATCH_SIZE) {
957
+
const batch = pending.slice(i, i + MEMORY_EMBEDDING_BATCH_SIZE)
958
+
let vectors: number[][]
959
+
try {
960
+
vectors = await embedTexts(batch.map((row) => row.embeddingText))
961
+
} catch (err: any) {
962
+
console.warn(`[memory] embedding batch failed: ${err?.message ?? String(err)}`)
963
+
return
964
+
}
965
+
966
+
db.transaction(() => {
967
+
batch.forEach((row, index) => {
968
+
const vector = vectors[index]
969
+
if (!vector) return
970
+
if (vector.length !== MEMORY_EMBEDDING_DIMENSIONS) {
971
+
throw new Error(`embedding dimension mismatch: got ${vector.length}, expected ${MEMORY_EMBEDDING_DIMENSIONS}`)
972
+
}
973
+
upsertVector.run(BigInt(row.chunkId), vectorParam(vector))
974
+
upsertMeta.run(row.chunkId, EMBEDDING_MODEL, MEMORY_EMBEDDING_DIMENSIONS, row.embeddingHash)
975
+
embedded += 1
976
+
})
977
+
})()
978
+
}
979
+
980
+
console.log(`[memory] embedded chunks=${embedded} model=${EMBEDDING_MODEL} dimensions=${MEMORY_EMBEDDING_DIMENSIONS}`)
981
+
}
982
+
983
+
async function syncMemoryEmbeddingPrototypes(): Promise<void> {
984
+
const db = getDb()
985
+
const rows = db
986
+
.prepare("select id, name, category, model, dimensions, content_hash as contentHash from memory_embedding_prototypes")
987
+
.all() as Array<{
988
+
id: number
989
+
name: string
990
+
category: string
991
+
model: string
992
+
dimensions: number
993
+
contentHash: string
994
+
}>
995
+
const known = new Map(rows.map((row) => [row.id, row]))
996
+
const pending = MEMORY_EMBEDDING_PROTOTYPES.map((prototype) => ({
997
+
...prototype,
998
+
hash: embeddingInputHash(`${prototype.category}\n${prototype.name}\n${prototype.text}`),
999
+
})).filter((prototype) => {
1000
+
const row = known.get(prototype.id)
1001
+
return (
1002
+
!row ||
1003
+
row.name !== prototype.name ||
1004
+
row.category !== prototype.category ||
1005
+
row.model !== EMBEDDING_MODEL ||
1006
+
row.dimensions !== MEMORY_EMBEDDING_DIMENSIONS ||
1007
+
row.contentHash !== prototype.hash
1008
+
)
1009
+
})
1010
+
1011
+
if (pending.length === 0) return
1012
+
1013
+
const vectors = await embedTexts(pending.map((prototype) => prototype.text))
1014
+
const upsertPrototype = db.prepare(`
1015
+
insert into memory_embedding_prototypes (id, name, category, model, dimensions, content_hash, updated_at)
1016
+
values (?, ?, ?, ?, ?, ?, datetime('now'))
1017
+
on conflict(id) do update set
1018
+
name = excluded.name,
1019
+
category = excluded.category,
1020
+
model = excluded.model,
1021
+
dimensions = excluded.dimensions,
1022
+
content_hash = excluded.content_hash,
1023
+
updated_at = datetime('now')
1024
+
`)
1025
+
const upsertVector = db.prepare("insert or replace into memory_prototype_vec(rowid, embedding) values (?, ?)")
1026
+
1027
+
db.transaction(() => {
1028
+
pending.forEach((prototype, index) => {
1029
+
const vector = vectors[index]
1030
+
if (!vector) return
1031
+
if (vector.length !== MEMORY_EMBEDDING_DIMENSIONS) {
1032
+
throw new Error(`prototype embedding dimension mismatch: got ${vector.length}, expected ${MEMORY_EMBEDDING_DIMENSIONS}`)
1033
+
}
1034
+
upsertVector.run(BigInt(prototype.id), vectorParam(vector))
1035
+
upsertPrototype.run(
1036
+
prototype.id,
1037
+
prototype.name,
1038
+
prototype.category,
1039
+
EMBEDDING_MODEL,
1040
+
MEMORY_EMBEDDING_DIMENSIONS,
1041
+
prototype.hash,
1042
+
)
1043
+
})
1044
+
})()
803
1045
}
804
1046
805
1047
function senderHandles(profile: MemorySearchProfile): string[] {
···
820
1062
return out
821
1063
}
822
1064
1065
+
function targetPersonHandles(profile: MemorySearchProfile): string[] {
1066
+
return profile.bodyPeople.length > 0 ? profile.bodyPeople : allPersonHandles(profile)
1067
+
}
1068
+
823
1069
function hitMatchesHandle(hit: MemoryHit, handle: string): boolean {
824
1070
const titleHaystack = `${hit.documentTitle} ${hit.title} ${hit.headingPath ?? ""} ${basenameWithoutExt(hit.path)}`.toLowerCase()
825
1071
const pathHaystack = hit.path.toLowerCase()
826
1072
return titleHaystack.includes(handle) || pathHaystack.includes(`/${handle}.md`)
1073
+
}
1074
+
1075
+
function hitMentionsHandle(hit: MemoryHit, handle: string): boolean {
1076
+
return hitMatchesHandle(hit, handle) || hitSearchHaystack(hit).includes(handle)
827
1077
}
828
1078
829
1079
function scoreMemoryHit(hit: MemoryHit, profile: MemorySearchProfile): number {
···
856
1106
}
857
1107
}
858
1108
1109
+
if (profile.bodyPeople.length > 0) {
1110
+
const targetHandles = targetPersonHandles(profile)
1111
+
const matchesTarget = targetHandles.some((handle) => hitMatchesHandle(hit, handle))
1112
+
if (matchesTarget) {
1113
+
score -= 4
1114
+
} else if (hit.kind === "people") {
1115
+
score += 3
1116
+
} else if (hit.kind === "core" && !profileHasCoreIntent(profile)) {
1117
+
score += 3.5
1118
+
}
1119
+
}
1120
+
859
1121
return score
860
1122
}
861
1123
···
915
1177
return false
916
1178
}
917
1179
918
-
function searchMemory(
1180
+
function profileHasExplicitRecallIntent(profile: MemorySearchProfile): boolean {
1181
+
if (profile.bodyPeople.length > 0) return true
1182
+
if (profile.eventQuery) return true
1183
+
if (/\b(who is|who's|tell me about|remember|recall|what happened|what do i know|context)\b/.test(profile.normalized)) {
1184
+
return true
1185
+
}
1186
+
return false
1187
+
}
1188
+
1189
+
function profileHasCoreIntent(profile: MemorySearchProfile): boolean {
1190
+
return /\b(core|identity|system|environment|lesson|rule|instruction|workflow|tool|config|memory)\b/.test(profile.normalized)
1191
+
}
1192
+
1193
+
async function semanticQuerySignal(memoryQuery: string): Promise<SemanticQuerySignal | null> {
1194
+
if (!isVecAvailable() || !embeddingsConfigured() || EMBEDDING_DIMENSIONS !== MEMORY_EMBEDDING_DIMENSIONS) return null
1195
+
const [vector] = await embedTexts([memoryQuery])
1196
+
if (!vector || vector.length !== MEMORY_EMBEDDING_DIMENSIONS) return null
1197
+
1198
+
const rows = getDb()
1199
+
.prepare(`
1200
+
select p.category as category, v.distance as distance
1201
+
from memory_prototype_vec v
1202
+
join memory_embedding_prototypes p on p.id = v.rowid
1203
+
where v.embedding match ?
1204
+
and k = ?
1205
+
order by v.distance
1206
+
`)
1207
+
.all(vectorParam(vector), 8) as Array<{ category: string; distance: number }>
1208
+
1209
+
let chatterSimilarity: number | null = null
1210
+
let recallIntentSimilarity: number | null = null
1211
+
for (const row of rows) {
1212
+
const similarity = 1 - row.distance
1213
+
if (row.category === "chatter") chatterSimilarity = Math.max(chatterSimilarity ?? -Infinity, similarity)
1214
+
if (row.category === "recall_intent") {
1215
+
recallIntentSimilarity = Math.max(recallIntentSimilarity ?? -Infinity, similarity)
1216
+
}
1217
+
}
1218
+
1219
+
return {
1220
+
vector,
1221
+
chatterSimilarity,
1222
+
recallIntentSimilarity,
1223
+
}
1224
+
}
1225
+
1226
+
function shouldSkipForSemanticChatter(profile: MemorySearchProfile, signal: SemanticQuerySignal | null): boolean {
1227
+
if (!signal) return false
1228
+
if (profileHasExplicitRecallIntent(profile)) return false
1229
+
if (profile.bodyTokens.length > 5) return false
1230
+
const chatter = signal.chatterSimilarity ?? 0
1231
+
const recallIntent = signal.recallIntentSimilarity ?? 0
1232
+
return chatter >= MEMORY_CHATTER_SIMILARITY_THRESHOLD && recallIntent < MEMORY_RECALL_INTENT_SIMILARITY_THRESHOLD
1233
+
}
1234
+
1235
+
function semanticDistancesForQuery(vector: number[], limit: number): Map<number, number> {
1236
+
if (!isVecAvailable()) return new Map()
1237
+
const rows = getDb()
1238
+
.prepare(`
1239
+
select rowid as chunkId, distance
1240
+
from memory_chunk_vec
1241
+
where embedding match ?
1242
+
and k = ?
1243
+
order by distance
1244
+
`)
1245
+
.all(vectorParam(vector), limit) as Array<{ chunkId: number; distance: number }>
1246
+
return new Map(rows.map((row) => [row.chunkId, row.distance]))
1247
+
}
1248
+
1249
+
function exactPersonHits(profile: MemorySearchProfile): MemoryHit[] {
1250
+
if (profile.bodyPeople.length === 0) return []
1251
+
const db = getDb()
1252
+
const hits: MemoryHit[] = []
1253
+
const seen = new Set<number>()
1254
+
const stmt = db.prepare(`
1255
+
select
1256
+
c.id as chunkId,
1257
+
d.path as path,
1258
+
d.kind as kind,
1259
+
d.title as documentTitle,
1260
+
c.title as title,
1261
+
c.heading_path as headingPath,
1262
+
c.chunk_text as text,
1263
+
-10.0 as rank
1264
+
from memory_chunks c
1265
+
join memory_documents d on d.id = c.document_id
1266
+
where d.kind = 'people'
1267
+
and lower(d.path) like ?
1268
+
order by c.chunk_index
1269
+
`)
1270
+
1271
+
for (const handle of profile.bodyPeople) {
1272
+
const rows = stmt.all(`%/people/${handle.toLowerCase()}.md`) as MemoryHit[]
1273
+
for (const row of rows) {
1274
+
if (seen.has(row.chunkId)) continue
1275
+
seen.add(row.chunkId)
1276
+
hits.push(row)
1277
+
}
1278
+
}
1279
+
return hits
1280
+
}
1281
+
1282
+
function scoreMemoryHitWithSemantic(hit: MemoryHit, profile: MemorySearchProfile): number {
1283
+
let score = scoreMemoryHit(hit, profile)
1284
+
const coreIntent = profileHasCoreIntent(profile)
1285
+
1286
+
if (hit.semanticSimilarity !== undefined) {
1287
+
score -= hit.semanticSimilarity * 2.5
1288
+
if (hit.kind === "core" && !coreIntent && hit.semanticSimilarity < MEMORY_SEMANTIC_STRONG_SIMILARITY) {
1289
+
score += 1.25
1290
+
}
1291
+
} else {
1292
+
score += 0.75
1293
+
if (hit.kind === "core" && !coreIntent) score += 1.25
1294
+
}
1295
+
1296
+
if (hit.kind === "core" && !coreIntent) score += 0.75
1297
+
return score
1298
+
}
1299
+
1300
+
async function searchMemory(
919
1301
profile: MemorySearchProfile,
920
1302
cooldowns: Record<number, number>,
921
1303
currentTurn: number,
922
1304
limit: number,
923
-
): MemoryHit[] {
1305
+
semanticSignal: SemanticQuerySignal | null = null,
1306
+
): Promise<MemoryHit[]> {
924
1307
const db = getDb()
925
1308
const query = buildSearchQuery(profile)
926
1309
if (!query) return []
···
944
1327
`)
945
1328
.all(query, Math.max(limit * 8, limit)) as MemoryHit[]
946
1329
947
-
const ordered = rows.sort((a, b) => scoreMemoryHit(a, profile) - scoreMemoryHit(b, profile))
1330
+
for (const hit of exactPersonHits(profile)) {
1331
+
if (rows.some((row) => row.chunkId === hit.chunkId)) continue
1332
+
rows.push(hit)
1333
+
}
1334
+
1335
+
if (semanticSignal) {
1336
+
const distances = semanticDistancesForQuery(semanticSignal.vector, Math.max(limit * 16, 64))
1337
+
for (const row of rows) {
1338
+
const distance = distances.get(row.chunkId)
1339
+
if (distance === undefined) continue
1340
+
row.semanticDistance = distance
1341
+
row.semanticSimilarity = 1 - distance
1342
+
}
1343
+
}
1344
+
1345
+
const ordered = rows.sort((a, b) => scoreMemoryHitWithSemantic(a, profile) - scoreMemoryHitWithSemantic(b, profile))
948
1346
const pool =
949
1347
profile.personQuery && !profile.eventQuery
950
1348
? (() => {
···
962
1360
const seenChunkIds = new Set<number>()
963
1361
const seenPaths = new Set<string>()
964
1362
965
-
const handles = allPersonHandles(profile)
1363
+
const handles = targetPersonHandles(profile)
1364
+
const exactTargetAvailable =
1365
+
profile.bodyPeople.length > 0 && pool.some((row) => handles.some((handle) => hitMatchesHandle(row, handle)))
966
1366
if (handles.length >= 2) {
967
1367
for (const handle of handles) {
968
1368
if (deduped.length >= limit) break
···
984
1384
if (deduped.length >= limit) break
985
1385
if (seenChunkIds.has(row.chunkId)) continue
986
1386
if (isCoolingDown(row, cooldowns, currentTurn)) continue
1387
+
const exactTargetMatch = profile.bodyPeople.length > 0 && handles.some((handle) => hitMatchesHandle(row, handle))
1388
+
if (exactTargetAvailable && !exactTargetMatch && row.kind !== "journal") {
1389
+
continue
1390
+
}
1391
+
if (profile.bodyPeople.length > 0 && !profile.bodyPeople.some((handle) => hitMentionsHandle(row, handle))) {
1392
+
continue
1393
+
}
1394
+
if (
1395
+
semanticSignal &&
1396
+
row.kind === "core" &&
1397
+
!profileHasCoreIntent(profile) &&
1398
+
(row.semanticSimilarity ?? 0) < MEMORY_SEMANTIC_MIN_SIMILARITY
1399
+
) {
1400
+
continue
1401
+
}
987
1402
seenChunkIds.add(row.chunkId)
988
1403
deduped.push(row)
989
1404
}
···
1051
1466
return { messages: conversation, recalledChunkIds: [] }
1052
1467
}
1053
1468
1469
+
let semanticSignal: SemanticQuerySignal | null = null
1470
+
try {
1471
+
semanticSignal = await semanticQuerySignal(memoryQuery)
1472
+
} catch (err: any) {
1473
+
console.warn(`[memory] semantic query failed: ${err?.message ?? String(err)}`)
1474
+
}
1475
+
1476
+
if (shouldSkipForSemanticChatter(profile, semanticSignal)) {
1477
+
console.log(
1478
+
`[memory] skipped query=${JSON.stringify(trimForPrompt(normalizeText(memoryQuery), 120))} sender=${profile.sender ?? "-"} reason=semantic-chatter chatter=${semanticSignal?.chatterSimilarity?.toFixed(3) ?? "-"} recallIntent=${semanticSignal?.recallIntentSimilarity?.toFixed(3) ?? "-"}`,
1479
+
)
1480
+
return { messages: conversation, recalledChunkIds: [] }
1481
+
}
1482
+
1054
1483
const personCount = allPersonHandles(profile).length
1055
1484
const recallLimit = Math.min(
1056
1485
MEMORY_RECALL_MAX_CHUNKS_HARD_CAP,
1057
1486
personCount >= 2 ? MEMORY_RECALL_MAX_CHUNKS + (personCount - 1) : MEMORY_RECALL_MAX_CHUNKS,
1058
1487
)
1059
-
const hits = searchMemory(profile, cooldowns, currentTurn, recallLimit)
1488
+
const hits = await searchMemory(profile, cooldowns, currentTurn, recallLimit, semanticSignal)
1060
1489
const aliasInfo = profile.senderAliases.length > 0 ? ` aliases=${profile.senderAliases.join(",")}` : ""
1061
1490
const peopleInfo = profile.bodyPeople.length > 0 ? ` people=${profile.bodyPeople.join(",")}` : ""
1062
1491
const debugTag = `sender=${profile.sender ?? "-"}${aliasInfo}${peopleInfo} personQuery=${profile.personQuery} eventQuery=${profile.eventQuery}`
···
1129
1558
const profile = await buildSearchProfile({ sender: null, source: null, body: rawQuery })
1130
1559
if (profile.tokens.length === 0) return []
1131
1560
1132
-
const results = searchMemory(profile, {}, Number.POSITIVE_INFINITY, Math.max(1, Math.min(limit, 10))).map(toMemorySearchResult)
1561
+
let semanticSignal: SemanticQuerySignal | null = null
1562
+
try {
1563
+
semanticSignal = await semanticQuerySignal(rawQuery)
1564
+
} catch {
1565
+
semanticSignal = null
1566
+
}
1567
+
1568
+
const results = (await searchMemory(
1569
+
profile,
1570
+
{},
1571
+
Number.POSITIVE_INFINITY,
1572
+
Math.max(1, Math.min(limit, 10)),
1573
+
semanticSignal,
1574
+
)).map(toMemorySearchResult)
1133
1575
recordMetric({
1134
1576
type: "memory",
1135
1577
query: rawQuery,