Compare commits
13 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| 52a9e3aaa4 | |||
| a7aec4ee29 | |||
| dda2d4c2a3 | |||
| 58e0e488e4 | |||
| 8dfcd06752 | |||
| 14f6cdd313 | |||
| 73d6ee0f2a | |||
| bee4085666 | |||
| 3b5c71de7c | |||
| 8229e02a52 | |||
| a6fb8ae828 | |||
| d1230bcaad | |||
| 2d49c9aa80 |
234
package-lock.json
generated
234
package-lock.json
generated
@@ -1,12 +1,12 @@
|
||||
{
|
||||
"name": "@ztimson/ai-utils",
|
||||
"version": "1.0.6",
|
||||
"version": "1.2.6",
|
||||
"lockfileVersion": 3,
|
||||
"requires": true,
|
||||
"packages": {
|
||||
"": {
|
||||
"name": "@ztimson/ai-utils",
|
||||
"version": "1.0.6",
|
||||
"version": "1.2.6",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"@anthropic-ai/sdk": "^0.102.0",
|
||||
@@ -57,34 +57,38 @@
|
||||
}
|
||||
},
|
||||
"node_modules/@emnapi/core": {
|
||||
"version": "1.11.1",
|
||||
"resolved": "https://registry.npmjs.org/@emnapi/core/-/core-1.11.1.tgz",
|
||||
"integrity": "sha512-RSvbQmHzdKzNsLYa/wHrbc3KN4sYLKAdPZxqiM2HATqv/SBk2/ENSHpvXGaLOMcsAyz0poEGqkmmKYG3OWiJEQ==",
|
||||
"version": "2.0.0-alpha.3",
|
||||
"resolved": "https://registry.npmjs.org/@emnapi/core/-/core-2.0.0-alpha.3.tgz",
|
||||
"integrity": "sha512-AZypUeJ/yByuxyS7BlSNRDOMLMlROYtjYdIAuBmJssVz1UJDSeYxLrdizhXCFYhedC5bqd/ASy8EuNXbVVXp9g==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"@emnapi/wasi-threads": "1.2.2",
|
||||
"@emnapi/wasi-threads": "2.0.1",
|
||||
"tslib": "^2.4.0"
|
||||
}
|
||||
},
|
||||
"node_modules/@emnapi/runtime": {
|
||||
"version": "1.11.2",
|
||||
"resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.11.2.tgz",
|
||||
"integrity": "sha512-kyOl3X0DuTiT1h2ft8r2fYO8JYtU9a9Xis/zBSiGArNaagCOWx90N1k2wxp18czFDH+OgcWGb5ZP/XMt3dcyPA==",
|
||||
"version": "2.0.0-alpha.3",
|
||||
"resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-2.0.0-alpha.3.tgz",
|
||||
"integrity": "sha512-hFPAhMUjJD9BSyCANEISPOogeXC9Zo9ZQl7L6vKnaVsMkCtzznaW/naYypeyl0Gv5rYfWYsZbpixTMpjDJzQeA==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"tslib": "^2.4.0"
|
||||
}
|
||||
},
|
||||
"node_modules/@emnapi/wasi-threads": {
|
||||
"version": "1.2.2",
|
||||
"resolved": "https://registry.npmjs.org/@emnapi/wasi-threads/-/wasi-threads-1.2.2.tgz",
|
||||
"integrity": "sha512-c95qOXkHdydNKhscBTebqEC1CVAZpyqOfVfBzQ1qgzyl3gfeldUjIggDbIZgDKsHLgnsM+igH7TJ/eAasaVuMA==",
|
||||
"version": "2.0.1",
|
||||
"resolved": "https://registry.npmjs.org/@emnapi/wasi-threads/-/wasi-threads-2.0.1.tgz",
|
||||
"integrity": "sha512-9DsSk+o5NBX0CCJT8s0EROGSGxjR/tKu6aBTaVyq+SjAEQH4XcdcRxPBRzsBLizTTJ49MJjF+jgu3qnO9GLQcQ==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"tslib": "^2.4.0"
|
||||
}
|
||||
@@ -573,6 +577,16 @@
|
||||
"url": "https://opencollective.com/libvips"
|
||||
}
|
||||
},
|
||||
"node_modules/@img/sharp-wasm32/node_modules/@emnapi/runtime": {
|
||||
"version": "1.11.3",
|
||||
"resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.11.3.tgz",
|
||||
"integrity": "sha512-Xz4Tpyki7XyrpbUK1jR1AhdAdaXyhhY4lZ3neLodmhpuWfy2PAQN5B46sAiU4liOXGLkHypn/qU+jvfWSCYYLA==",
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"dependencies": {
|
||||
"tslib": "^2.4.0"
|
||||
}
|
||||
},
|
||||
"node_modules/@img/sharp-win32-arm64": {
|
||||
"version": "0.34.5",
|
||||
"resolved": "https://registry.npmjs.org/@img/sharp-win32-arm64/-/sharp-win32-arm64-0.34.5.tgz",
|
||||
@@ -681,22 +695,25 @@
|
||||
}
|
||||
},
|
||||
"node_modules/@napi-rs/wasm-runtime": {
|
||||
"version": "1.1.6",
|
||||
"resolved": "https://registry.npmjs.org/@napi-rs/wasm-runtime/-/wasm-runtime-1.1.6.tgz",
|
||||
"integrity": "sha512-ZLv/JdUfkvOy9eCnnBaGfiO+XimbjebAeO+MRQqD/B+FR1tnRN0tpKSJHRbE8sFfS6aqsXZ67TQjfwfsxULVbg==",
|
||||
"version": "1.2.0",
|
||||
"resolved": "https://registry.npmjs.org/@napi-rs/wasm-runtime/-/wasm-runtime-1.2.0.tgz",
|
||||
"integrity": "sha512-kDoONqMa+VnZ4vvvu/ZUurpJ4gkZU57e7g69qpNgWhYcZFPUHZM2CEMKm+cG6ufDVALbjMvfmMjFVqaK7uEMnA==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"dependencies": {
|
||||
"@tybys/wasm-util": "^0.10.3"
|
||||
},
|
||||
"engines": {
|
||||
"node": "^20.19.0 || ^22.13.0 || >=23.5.0"
|
||||
},
|
||||
"funding": {
|
||||
"type": "github",
|
||||
"url": "https://github.com/sponsors/Brooooooklyn"
|
||||
},
|
||||
"peerDependencies": {
|
||||
"@emnapi/core": "^1.7.1",
|
||||
"@emnapi/runtime": "^1.7.1"
|
||||
"@emnapi/core": "^2.0.0-alpha.3",
|
||||
"@emnapi/runtime": "^2.0.0-alpha.3"
|
||||
}
|
||||
},
|
||||
"node_modules/@oxc-project/types": {
|
||||
@@ -1007,6 +1024,18 @@
|
||||
"node": "^20.19.0 || >=22.12.0"
|
||||
}
|
||||
},
|
||||
"node_modules/@rolldown/binding-wasm32-wasi/node_modules/@emnapi/core": {
|
||||
"version": "1.11.1",
|
||||
"resolved": "https://registry.npmjs.org/@emnapi/core/-/core-1.11.1.tgz",
|
||||
"integrity": "sha512-RSvbQmHzdKzNsLYa/wHrbc3KN4sYLKAdPZxqiM2HATqv/SBk2/ENSHpvXGaLOMcsAyz0poEGqkmmKYG3OWiJEQ==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"dependencies": {
|
||||
"@emnapi/wasi-threads": "1.2.2",
|
||||
"tslib": "^2.4.0"
|
||||
}
|
||||
},
|
||||
"node_modules/@rolldown/binding-wasm32-wasi/node_modules/@emnapi/runtime": {
|
||||
"version": "1.11.1",
|
||||
"resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.11.1.tgz",
|
||||
@@ -1018,6 +1047,17 @@
|
||||
"tslib": "^2.4.0"
|
||||
}
|
||||
},
|
||||
"node_modules/@rolldown/binding-wasm32-wasi/node_modules/@emnapi/wasi-threads": {
|
||||
"version": "1.2.2",
|
||||
"resolved": "https://registry.npmjs.org/@emnapi/wasi-threads/-/wasi-threads-1.2.2.tgz",
|
||||
"integrity": "sha512-c95qOXkHdydNKhscBTebqEC1CVAZpyqOfVfBzQ1qgzyl3gfeldUjIggDbIZgDKsHLgnsM+igH7TJ/eAasaVuMA==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"optional": true,
|
||||
"dependencies": {
|
||||
"tslib": "^2.4.0"
|
||||
}
|
||||
},
|
||||
"node_modules/@rolldown/binding-win32-arm64-msvc": {
|
||||
"version": "1.1.5",
|
||||
"resolved": "https://registry.npmjs.org/@rolldown/binding-win32-arm64-msvc/-/binding-win32-arm64-msvc-1.1.5.tgz",
|
||||
@@ -1408,18 +1448,18 @@
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/@ztimson/utils": {
|
||||
"version": "0.29.5",
|
||||
"resolved": "https://registry.npmjs.org/@ztimson/utils/-/utils-0.29.5.tgz",
|
||||
"integrity": "sha512-8mUuhi//3agwrueR006emOvJu1JXxVEryJmD3nkEmK4yQ9qS24oZijdoaiLRid/6xc75j3Fk6YjmnYmjq61iPQ==",
|
||||
"version": "0.29.7",
|
||||
"resolved": "https://registry.npmjs.org/@ztimson/utils/-/utils-0.29.7.tgz",
|
||||
"integrity": "sha512-cjQ9+RjC5X7gKNA/hJHDf7OtyYCa+5E0PDc76lIaATwNAxXCSx2IO9r2wHiHtZGV5bldjnFmw7aOV8Jmq7SgKQ==",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"var-persist": "^1.0.1"
|
||||
}
|
||||
},
|
||||
"node_modules/acorn": {
|
||||
"version": "8.17.0",
|
||||
"resolved": "https://registry.npmjs.org/acorn/-/acorn-8.17.0.tgz",
|
||||
"integrity": "sha512-xRQbDb9BnwDafYNn6Vwl839DYVjqXYb1XVGtWAZ1kcDc6iwAL4hg3B1dZlRiuENFeO2H53gFG3in621AdERVAg==",
|
||||
"version": "8.18.0",
|
||||
"resolved": "https://registry.npmjs.org/acorn/-/acorn-8.18.0.tgz",
|
||||
"integrity": "sha512-lGq+9yr1/GuAWaVYIHRjvvySG5/4VfKIvC8EWxStPdcDh/Ka7FG3twP6v4d5BkravUilhIAsG4Qj83t02LWUPQ==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"bin": {
|
||||
@@ -1504,9 +1544,9 @@
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/brace-expansion": {
|
||||
"version": "2.1.2",
|
||||
"resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-2.1.2.tgz",
|
||||
"integrity": "sha512-w5JZcKgdhDOgOwm8H+KgbosopHMuGcl6qbulwjtz3SM7I7P3yW1eAjzMPLrIE+NQ9vjgANKHWeMHnrT0OXW1oA==",
|
||||
"version": "2.1.3",
|
||||
"resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-2.1.3.tgz",
|
||||
"integrity": "sha512-DRdx5neNsG/QXbniLFWi2YmC/68oeOOmKz6zOjVk6ZS1ZLXgLIKqVEc6hWsmkjBbgii0SwaBTcJ5XKj5gzY/4A==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
@@ -2009,9 +2049,9 @@
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/exsolve": {
|
||||
"version": "1.1.0",
|
||||
"resolved": "https://registry.npmjs.org/exsolve/-/exsolve-1.1.0.tgz",
|
||||
"integrity": "sha512-D+42+T12DdIlJM3uepa55qGiL3sYdLBOxIl2ifQCzCHz4c7eiolaHsi3BIqEr7JxBzxv2pYZQX9kw16ziMcEmw==",
|
||||
"version": "1.1.1",
|
||||
"resolved": "https://registry.npmjs.org/exsolve/-/exsolve-1.1.1.tgz",
|
||||
"integrity": "sha512-9U/jZUgjnSGyntRr6y5Muu1MJcwFl6kPu7k8qLF0IMNfLqvw0NZ4nnVDq0RVoZ0RvCyumib4Ez3KYrVfilrw+g==",
|
||||
"dev": true,
|
||||
"license": "MIT"
|
||||
},
|
||||
@@ -2382,9 +2422,9 @@
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/lightningcss": {
|
||||
"version": "1.32.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss/-/lightningcss-1.32.0.tgz",
|
||||
"integrity": "sha512-NXYBzinNrblfraPGyrbPoD19C1h9lfI/1mzgWYvXUTe414Gz/X1FD2XBZSZM7rRTrMA8JL3OtAaGifrIKhQ5yQ==",
|
||||
"version": "1.33.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss/-/lightningcss-1.33.0.tgz",
|
||||
"integrity": "sha512-WkUDrojuJs0xkgGf2udWxa3yGBRxPtxUkB79i6aCZLRgc7PM8fZe9TosfPDcvEpQZbuFASnHYmRLBLUbmLOIIA==",
|
||||
"dev": true,
|
||||
"license": "MPL-2.0",
|
||||
"dependencies": {
|
||||
@@ -2398,23 +2438,23 @@
|
||||
"url": "https://opencollective.com/parcel"
|
||||
},
|
||||
"optionalDependencies": {
|
||||
"lightningcss-android-arm64": "1.32.0",
|
||||
"lightningcss-darwin-arm64": "1.32.0",
|
||||
"lightningcss-darwin-x64": "1.32.0",
|
||||
"lightningcss-freebsd-x64": "1.32.0",
|
||||
"lightningcss-linux-arm-gnueabihf": "1.32.0",
|
||||
"lightningcss-linux-arm64-gnu": "1.32.0",
|
||||
"lightningcss-linux-arm64-musl": "1.32.0",
|
||||
"lightningcss-linux-x64-gnu": "1.32.0",
|
||||
"lightningcss-linux-x64-musl": "1.32.0",
|
||||
"lightningcss-win32-arm64-msvc": "1.32.0",
|
||||
"lightningcss-win32-x64-msvc": "1.32.0"
|
||||
"lightningcss-android-arm64": "1.33.0",
|
||||
"lightningcss-darwin-arm64": "1.33.0",
|
||||
"lightningcss-darwin-x64": "1.33.0",
|
||||
"lightningcss-freebsd-x64": "1.33.0",
|
||||
"lightningcss-linux-arm-gnueabihf": "1.33.0",
|
||||
"lightningcss-linux-arm64-gnu": "1.33.0",
|
||||
"lightningcss-linux-arm64-musl": "1.33.0",
|
||||
"lightningcss-linux-x64-gnu": "1.33.0",
|
||||
"lightningcss-linux-x64-musl": "1.33.0",
|
||||
"lightningcss-win32-arm64-msvc": "1.33.0",
|
||||
"lightningcss-win32-x64-msvc": "1.33.0"
|
||||
}
|
||||
},
|
||||
"node_modules/lightningcss-android-arm64": {
|
||||
"version": "1.32.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-android-arm64/-/lightningcss-android-arm64-1.32.0.tgz",
|
||||
"integrity": "sha512-YK7/ClTt4kAK0vo6w3X+Pnm0D2cf2vPHbhOXdoNti1Ga0al1P4TBZhwjATvjNwLEBCnKvjJc2jQgHXH0NEwlAg==",
|
||||
"version": "1.33.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-android-arm64/-/lightningcss-android-arm64-1.33.0.tgz",
|
||||
"integrity": "sha512-gEpRTalKdosp4Bb8qWtc2iOgE5SeIHlpS1up9bFq2wAyYhl1UdTObYiHe98zEM9SQvSoqQZ1IQD0JNpg3Ml5pg==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
],
|
||||
@@ -2433,9 +2473,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/lightningcss-darwin-arm64": {
|
||||
"version": "1.32.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-darwin-arm64/-/lightningcss-darwin-arm64-1.32.0.tgz",
|
||||
"integrity": "sha512-RzeG9Ju5bag2Bv1/lwlVJvBE3q6TtXskdZLLCyfg5pt+HLz9BqlICO7LZM7VHNTTn/5PRhHFBSjk5lc4cmscPQ==",
|
||||
"version": "1.33.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-darwin-arm64/-/lightningcss-darwin-arm64-1.33.0.tgz",
|
||||
"integrity": "sha512-Sciaz8eenNTKn9b3t7+xr0ipTp9YxKQY4npwQ3mrRuL0BAVHBLyZxofhaKBAVtzmtRZ/zTyo0/to4B1uWG/Djg==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
],
|
||||
@@ -2454,9 +2494,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/lightningcss-darwin-x64": {
|
||||
"version": "1.32.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-darwin-x64/-/lightningcss-darwin-x64-1.32.0.tgz",
|
||||
"integrity": "sha512-U+QsBp2m/s2wqpUYT/6wnlagdZbtZdndSmut/NJqlCcMLTWp5muCrID+K5UJ6jqD2BFshejCYXniPDbNh73V8w==",
|
||||
"version": "1.33.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-darwin-x64/-/lightningcss-darwin-x64-1.33.0.tgz",
|
||||
"integrity": "sha512-Z5UPAxzrjlWNNyGy6i65cJzzvgJ5D3T6wMvs+gWpY9d7qRhANrxqAp6LhxIgZhWEw18RfJTGcRxjuLIBr+m8XQ==",
|
||||
"cpu": [
|
||||
"x64"
|
||||
],
|
||||
@@ -2475,9 +2515,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/lightningcss-freebsd-x64": {
|
||||
"version": "1.32.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-freebsd-x64/-/lightningcss-freebsd-x64-1.32.0.tgz",
|
||||
"integrity": "sha512-JCTigedEksZk3tHTTthnMdVfGf61Fky8Ji2E4YjUTEQX14xiy/lTzXnu1vwiZe3bYe0q+SpsSH/CTeDXK6WHig==",
|
||||
"version": "1.33.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-freebsd-x64/-/lightningcss-freebsd-x64-1.33.0.tgz",
|
||||
"integrity": "sha512-QQM/Ti/hQajJwCY+RiWuCZ9sdtI/XQk7nDK5vC8kkdwixezOlDgvDx7+RT+QjK6FcFT4MpsuoBnHIo/O3StRRg==",
|
||||
"cpu": [
|
||||
"x64"
|
||||
],
|
||||
@@ -2496,9 +2536,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/lightningcss-linux-arm-gnueabihf": {
|
||||
"version": "1.32.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-linux-arm-gnueabihf/-/lightningcss-linux-arm-gnueabihf-1.32.0.tgz",
|
||||
"integrity": "sha512-x6rnnpRa2GL0zQOkt6rts3YDPzduLpWvwAF6EMhXFVZXD4tPrBkEFqzGowzCsIWsPjqSK+tyNEODUBXeeVHSkw==",
|
||||
"version": "1.33.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-linux-arm-gnueabihf/-/lightningcss-linux-arm-gnueabihf-1.33.0.tgz",
|
||||
"integrity": "sha512-N7FVBe6iS24MlM6R/4RBTxGhQheZGs7tiQ9U32UtF75NzP5Q7xWPRqLBCKxlRQRk3rY1jCIPLzx7WzOhuUIRLQ==",
|
||||
"cpu": [
|
||||
"arm"
|
||||
],
|
||||
@@ -2517,9 +2557,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/lightningcss-linux-arm64-gnu": {
|
||||
"version": "1.32.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-linux-arm64-gnu/-/lightningcss-linux-arm64-gnu-1.32.0.tgz",
|
||||
"integrity": "sha512-0nnMyoyOLRJXfbMOilaSRcLH3Jw5z9HDNGfT/gwCPgaDjnx0i8w7vBzFLFR1f6CMLKF8gVbebmkUN3fa/kQJpQ==",
|
||||
"version": "1.33.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-linux-arm64-gnu/-/lightningcss-linux-arm64-gnu-1.33.0.tgz",
|
||||
"integrity": "sha512-j2v/itmy4HlNxlc6voKXYgBqNi0Ng2LShg4z7GufpEgs05P+2suBVyi9I6YHq5uoVFx9ETin3eCEhLVyXGQnKg==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
],
|
||||
@@ -2541,9 +2581,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/lightningcss-linux-arm64-musl": {
|
||||
"version": "1.32.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-linux-arm64-musl/-/lightningcss-linux-arm64-musl-1.32.0.tgz",
|
||||
"integrity": "sha512-UpQkoenr4UJEzgVIYpI80lDFvRmPVg6oqboNHfoH4CQIfNA+HOrZ7Mo7KZP02dC6LjghPQJeBsvXhJod/wnIBg==",
|
||||
"version": "1.33.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-linux-arm64-musl/-/lightningcss-linux-arm64-musl-1.33.0.tgz",
|
||||
"integrity": "sha512-yiO5ROMuYQgXbC60yjZU5CYSFZGKXL0HFATXt9mHJn1+zW55oCtMI9NfcVhYLMFDL7gV7oBPon/EmMMGg2OvtQ==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
],
|
||||
@@ -2565,9 +2605,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/lightningcss-linux-x64-gnu": {
|
||||
"version": "1.32.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-linux-x64-gnu/-/lightningcss-linux-x64-gnu-1.32.0.tgz",
|
||||
"integrity": "sha512-V7Qr52IhZmdKPVr+Vtw8o+WLsQJYCTd8loIfpDaMRWGUZfBOYEJeyJIkqGIDMZPwPx24pUMfwSxxI8phr/MbOA==",
|
||||
"version": "1.33.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-linux-x64-gnu/-/lightningcss-linux-x64-gnu-1.33.0.tgz",
|
||||
"integrity": "sha512-ar+Ju7LmcN0Jo4FpL4hpFybwNG9/3A/Br5KW2n2jyODg3MEZXaDYADdemoNS+BDNfMgKvylJLj4S5tyRActuAg==",
|
||||
"cpu": [
|
||||
"x64"
|
||||
],
|
||||
@@ -2589,9 +2629,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/lightningcss-linux-x64-musl": {
|
||||
"version": "1.32.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-linux-x64-musl/-/lightningcss-linux-x64-musl-1.32.0.tgz",
|
||||
"integrity": "sha512-bYcLp+Vb0awsiXg/80uCRezCYHNg1/l3mt0gzHnWV9XP1W5sKa5/TCdGWaR/zBM2PeF/HbsQv/j2URNOiVuxWg==",
|
||||
"version": "1.33.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-linux-x64-musl/-/lightningcss-linux-x64-musl-1.33.0.tgz",
|
||||
"integrity": "sha512-RYiYbkokw0trfKqqzfF55lginwEPrD3OJDfTuJzFs1MK6iFnDenaz1fqLLtX4ITG3OktJQXOeTaw1awrBAlZPw==",
|
||||
"cpu": [
|
||||
"x64"
|
||||
],
|
||||
@@ -2613,9 +2653,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/lightningcss-win32-arm64-msvc": {
|
||||
"version": "1.32.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-win32-arm64-msvc/-/lightningcss-win32-arm64-msvc-1.32.0.tgz",
|
||||
"integrity": "sha512-8SbC8BR40pS6baCM8sbtYDSwEVQd4JlFTOlaD3gWGHfThTcABnNDBda6eTZeqbofalIJhFx0qKzgHJmcPTnGdw==",
|
||||
"version": "1.33.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-win32-arm64-msvc/-/lightningcss-win32-arm64-msvc-1.33.0.tgz",
|
||||
"integrity": "sha512-1K+MPfLSFVpphzpdbfkhlWk6wBrTObBzS2T6db10PNOZgR9GoVsAWzwNyuhUYYbTp23j+4RrncfujZ4uAzXvwA==",
|
||||
"cpu": [
|
||||
"arm64"
|
||||
],
|
||||
@@ -2634,9 +2674,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/lightningcss-win32-x64-msvc": {
|
||||
"version": "1.32.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-win32-x64-msvc/-/lightningcss-win32-x64-msvc-1.32.0.tgz",
|
||||
"integrity": "sha512-Amq9B/SoZYdDi1kFrojnoqPLxYhQ4Wo5XiL8EVJrVsB8ARoC1PWW6VGtT0WKCemjy8aC+louJnjS7U18x3b06Q==",
|
||||
"version": "1.33.0",
|
||||
"resolved": "https://registry.npmjs.org/lightningcss-win32-x64-msvc/-/lightningcss-win32-x64-msvc-1.33.0.tgz",
|
||||
"integrity": "sha512-OlEICDx/Xl0FqSp4bry8zFnCvGpig3Gl4gCquvYwHuqJKEC1+n9NgDniFvqHGmMv1ZkqDJrDqKKSykTDX+ehuA==",
|
||||
"cpu": [
|
||||
"x64"
|
||||
],
|
||||
@@ -2794,9 +2834,9 @@
|
||||
}
|
||||
},
|
||||
"node_modules/mdurl": {
|
||||
"version": "2.0.0",
|
||||
"resolved": "https://registry.npmjs.org/mdurl/-/mdurl-2.0.0.tgz",
|
||||
"integrity": "sha512-Lf+9+2r+Tdp5wXDXC4PcIBjTDtq4UKjCPMQhKIuzpJNW0b96kVqSwW0bT7FhRSfmAiFYgP+SCRvdrDozfh0U5w==",
|
||||
"version": "2.1.0",
|
||||
"resolved": "https://registry.npmjs.org/mdurl/-/mdurl-2.1.0.tgz",
|
||||
"integrity": "sha512-1+HBaOx0zi/dQWht8rNv9MYf9qqpqL/kxI0hXImU6Y547zM6Sni8BQibt7ifgMcYtQg41ao3Ivd6cnSM86inpg==",
|
||||
"dev": true,
|
||||
"license": "MIT"
|
||||
},
|
||||
@@ -2971,9 +3011,9 @@
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/nanoid": {
|
||||
"version": "3.3.15",
|
||||
"resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.15.tgz",
|
||||
"integrity": "sha512-y7Wygv/7mEOvxTuEQDB8StXdMRBWf1kR/tlhAzBRUFkB2jfcLOAxO/SHmOO2zgz1pVgK29/kyupn059/bCHdjA==",
|
||||
"version": "3.3.16",
|
||||
"resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.16.tgz",
|
||||
"integrity": "sha512-bzlKTyNJ7+LdGIIwy8ijFpIqEQIvafahV7eYykJ8Cvh42EdJeODoJ6gUJXpQJvej1BddH8OqTXZNE/KfbWAu8Q==",
|
||||
"dev": true,
|
||||
"funding": [
|
||||
{
|
||||
@@ -3092,9 +3132,9 @@
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/openai": {
|
||||
"version": "6.46.0",
|
||||
"resolved": "https://registry.npmjs.org/openai/-/openai-6.46.0.tgz",
|
||||
"integrity": "sha512-DFg6jEPT2RO+oAyXtddeUJU8zkGy1OQ1AjGzNIJUMQG03TTqvCpy9tBpQ+2VVVnvrl3E56F8GEin2JYtWpITtA==",
|
||||
"version": "6.49.0",
|
||||
"resolved": "https://registry.npmjs.org/openai/-/openai-6.49.0.tgz",
|
||||
"integrity": "sha512-aYCc0C6L864eR6WSYIwQGyXriw/nIyZx0ObvhzOEVuk0zoBDpynjSbrionWI7q65B5H8jJX0DXR9snEzM6bfPg==",
|
||||
"license": "Apache-2.0",
|
||||
"peerDependencies": {
|
||||
"@aws-sdk/credential-provider-node": ">=3.972.0 <4",
|
||||
@@ -3232,9 +3272,9 @@
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/postcss": {
|
||||
"version": "8.5.17",
|
||||
"resolved": "https://registry.npmjs.org/postcss/-/postcss-8.5.17.tgz",
|
||||
"integrity": "sha512-J7EF+8X+CzRPaJPOv9Ck2wNWJvGnnl3PcNPAdGg6GTLjyVpyQ0yATMSXRFRV01BviT/9Gwuc3rjEyJbDJG9a4w==",
|
||||
"version": "8.5.25",
|
||||
"resolved": "https://registry.npmjs.org/postcss/-/postcss-8.5.25.tgz",
|
||||
"integrity": "sha512-DTPx3RWSSnWyzLxQnlH0rJP+EW5ekl16ZU4/psbIhA0e53kJfdgaN5vKM+xP7yJtXVu+nfdVFmlgFDEKAe4Pyw==",
|
||||
"dev": true,
|
||||
"funding": [
|
||||
{
|
||||
@@ -3252,7 +3292,7 @@
|
||||
],
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"nanoid": "^3.3.12",
|
||||
"nanoid": "^3.3.16",
|
||||
"picocolors": "^1.1.1",
|
||||
"source-map-js": "^1.2.1"
|
||||
},
|
||||
@@ -3787,9 +3827,9 @@
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/undici": {
|
||||
"version": "7.28.0",
|
||||
"resolved": "https://registry.npmjs.org/undici/-/undici-7.28.0.tgz",
|
||||
"integrity": "sha512-cRZYrTDwWznlnRiPjggAGxZXanty6M8RV1ff8Wm4LWXBp7/IG8v5DnOm74DtUBp9OONpK75YlPnIjQqX0dBDtA==",
|
||||
"version": "7.29.0",
|
||||
"resolved": "https://registry.npmjs.org/undici/-/undici-7.29.0.tgz",
|
||||
"integrity": "sha512-IDxfleLmmbSskfWSUATiN1nfn2rDuvnMOqb5CWR92iIfojA0Ud+ulOAAEQ57LPr9rWmsreUyf5lwyao+7GNNVw==",
|
||||
"license": "MIT",
|
||||
"engines": {
|
||||
"node": ">=20.18.1"
|
||||
@@ -3981,16 +4021,16 @@
|
||||
}
|
||||
},
|
||||
"node_modules/vite": {
|
||||
"version": "8.1.4",
|
||||
"resolved": "https://registry.npmjs.org/vite/-/vite-8.1.4.tgz",
|
||||
"integrity": "sha512-bTT9PsdWO+MQMNG9ZXIP/qM9wGh37DFxTV/sPq9cFpHr3w4jkgef032PkAL9jAqhk3Nz8NQw3O8n6/xFkqO4QQ==",
|
||||
"version": "8.1.5",
|
||||
"resolved": "https://registry.npmjs.org/vite/-/vite-8.1.5.tgz",
|
||||
"integrity": "sha512-7ULLwsCdYx/nRyrpiEwvqb5TFHrMVZyBt+rg/OAXT7rgj/z+DtTDyKFeLAdDkubDVDKD8jOsndmy7m55XcfUsw==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"lightningcss": "^1.32.0",
|
||||
"picomatch": "^4.0.5",
|
||||
"postcss": "^8.5.16",
|
||||
"rolldown": "~1.1.4",
|
||||
"postcss": "^8.5.17",
|
||||
"rolldown": "~1.1.5",
|
||||
"tinyglobby": "^0.2.17"
|
||||
},
|
||||
"bin": {
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "@ztimson/ai-utils",
|
||||
"version": "1.1.0",
|
||||
"version": "1.2.11",
|
||||
"description": "AI Utility library",
|
||||
"author": "Zak Timson",
|
||||
"license": "MIT",
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import * as os from 'node:os';
|
||||
import LLM, {AnthropicConfig, OllamaConfig, OpenAiConfig, LLMRequest} from './llm';
|
||||
import LLM, {AnthropicConfig, OpenAiConfig, LLMRequest} from './llm';
|
||||
import { Audio } from './audio.ts';
|
||||
import {Vision} from './vision.ts';
|
||||
|
||||
@@ -18,7 +18,7 @@ export type AiOptions = {
|
||||
embedder?: string;
|
||||
/** Large language models, first is default */
|
||||
llm?: Omit<LLMRequest, 'model'> & {
|
||||
models: {[model: string]: AnthropicConfig | OllamaConfig | OpenAiConfig};
|
||||
models: {[model: string]: AnthropicConfig | OpenAiConfig};
|
||||
}
|
||||
/** OCR model: eng, eng_best, eng_fast */
|
||||
ocr?: string;
|
||||
|
||||
@@ -3,6 +3,8 @@ export * from './antrhopic';
|
||||
export * from './audio';
|
||||
export * from './llm';
|
||||
export * from './memory';
|
||||
export * from './memory-cache';
|
||||
export * from './memory-graph';
|
||||
export * from './open-ai';
|
||||
export * from './provider';
|
||||
export * from './tools';
|
||||
|
||||
334
src/kd-tree.ts
Normal file
334
src/kd-tree.ts
Normal file
@@ -0,0 +1,334 @@
|
||||
export type DistanceMetric = "euclidean" | "cosine";
|
||||
|
||||
export interface KDPoint<T = unknown> {
|
||||
vector: number[];
|
||||
payload: T;
|
||||
}
|
||||
|
||||
export interface KNNResult<T = unknown> {
|
||||
point: KDPoint<T>;
|
||||
distance: number;
|
||||
}
|
||||
|
||||
interface KDNode<T> {
|
||||
point: KDPoint<T>;
|
||||
axis: number;
|
||||
left: KDNode<T> | null;
|
||||
right: KDNode<T> | null;
|
||||
}
|
||||
|
||||
// ─── Distance helpers ─────────────────────────────────────────────────────────
|
||||
|
||||
function euclidean(a: number[], b: number[]): number {
|
||||
let sum = 0;
|
||||
for (let i = 0; i < a.length; i++) {
|
||||
const d = a[i] - b[i];
|
||||
sum += d * d;
|
||||
}
|
||||
return Math.sqrt(sum);
|
||||
}
|
||||
|
||||
function cosine(a: number[], b: number[]): number {
|
||||
let dot = 0, normA = 0, normB = 0;
|
||||
for (let i = 0; i < a.length; i++) {
|
||||
dot += a[i] * b[i];
|
||||
normA += a[i] * a[i];
|
||||
normB += b[i] * b[i];
|
||||
}
|
||||
const denom = Math.sqrt(normA) * Math.sqrt(normB);
|
||||
return denom === 0 ? 1 : 1 - dot / denom; // distance = 1 - similarity
|
||||
}
|
||||
|
||||
/**
|
||||
* Keeps the k closest candidates in memory, evicts the furthest when full
|
||||
*/
|
||||
class BoundedMaxHeap<T> {
|
||||
private heap: KNNResult<T>[] = [];
|
||||
|
||||
constructor(private readonly k: number) {}
|
||||
|
||||
get size(): number { return this.heap.length; }
|
||||
|
||||
get worstDistance(): number {
|
||||
return this.heap.length < this.k ? Infinity : this.heap[0].distance;
|
||||
}
|
||||
|
||||
push(item: KNNResult<T>): void {
|
||||
if (this.heap.length < this.k) {
|
||||
this.heap.push(item);
|
||||
this.bubbleUp(this.heap.length - 1);
|
||||
} else if (item.distance < this.heap[0].distance) {
|
||||
this.heap[0] = item;
|
||||
this.sinkDown(0);
|
||||
}
|
||||
}
|
||||
|
||||
toSortedArray(): KNNResult<T>[] {
|
||||
return [...this.heap].sort((a, b) => a.distance - b.distance);
|
||||
}
|
||||
|
||||
private bubbleUp(i: number): void {
|
||||
while (i > 0) {
|
||||
const parent = (i - 1) >> 1;
|
||||
if (this.heap[parent].distance >= this.heap[i].distance) break;
|
||||
[this.heap[parent], this.heap[i]] = [this.heap[i], this.heap[parent]];
|
||||
i = parent;
|
||||
}
|
||||
}
|
||||
|
||||
private sinkDown(i: number): void {
|
||||
const n = this.heap.length;
|
||||
while (true) {
|
||||
let largest = i;
|
||||
const l = 2 * i + 1, r = 2 * i + 2;
|
||||
if (l < n && this.heap[l].distance > this.heap[largest].distance) largest = l;
|
||||
if (r < n && this.heap[r].distance > this.heap[largest].distance) largest = r;
|
||||
if (largest === i) break;
|
||||
[this.heap[largest], this.heap[i]] = [this.heap[i], this.heap[largest]];
|
||||
i = largest;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* K-D Tree for efficient nearest-neighbor search over high-dimensional vectors / embeddings.
|
||||
*
|
||||
* Supports:
|
||||
* - Insertion of labeled points
|
||||
* - k-nearest-neighbor (KNN) search
|
||||
* - Radius search (all points within a given distance)
|
||||
* - Euclidean and cosine distance metrics
|
||||
* - Bulk construction (balanced tree) for best query performance
|
||||
*/
|
||||
export class KDTree<T = unknown> {
|
||||
private root: KDNode<T> | null = null;
|
||||
private _size = 0;
|
||||
private readonly dims: number;
|
||||
private readonly distanceFn: (a: number[], b: number[]) => number;
|
||||
|
||||
/**
|
||||
* @param dims Dimensionality of all vectors (must be consistent).
|
||||
* @param metric Distance metric to use. Default: "euclidean".
|
||||
* @param points Optional initial set of points. Builds a balanced tree
|
||||
* in O(n log² n) — prefer this over inserting one-by-one
|
||||
* when you have a large corpus.
|
||||
*/
|
||||
constructor(
|
||||
dims: number,
|
||||
metric: DistanceMetric = "euclidean",
|
||||
points?: KDPoint<T>[]
|
||||
) {
|
||||
this.dims = dims;
|
||||
this.distanceFn = metric === "cosine" ? cosine : euclidean;
|
||||
|
||||
if (points && points.length > 0) {
|
||||
this.validateAll(points);
|
||||
this.root = this.buildBalanced([...points], 0);
|
||||
this._size = points.length;
|
||||
}
|
||||
}
|
||||
|
||||
/** Total number of points stored in the tree. */
|
||||
get size(): number { return this._size; }
|
||||
|
||||
// ── Insertion ──────────────────────────────────────────────────────────────
|
||||
|
||||
/**
|
||||
* Insert a single point. O(log n) average, O(n) worst case on skewed data.
|
||||
* For bulk loading prefer passing points to the constructor.
|
||||
*/
|
||||
insert(point: KDPoint<T>): void {
|
||||
this.validate(point);
|
||||
this.root = this.insertNode(this.root, point, 0);
|
||||
this._size++;
|
||||
}
|
||||
|
||||
// ── KNN search ─────────────────────────────────────────────────────────────
|
||||
|
||||
/**
|
||||
* Find the k nearest neighbors to `query`.
|
||||
* Returns results sorted by distance ascending.
|
||||
*/
|
||||
knn(query: number[], k: number): KNNResult<T>[] {
|
||||
if (k <= 0) throw new RangeError("k must be a positive integer");
|
||||
this.validateVector(query);
|
||||
|
||||
const heap = new BoundedMaxHeap<T>(k);
|
||||
this.searchKNN(this.root, query, k, heap, 0);
|
||||
return heap.toSortedArray();
|
||||
}
|
||||
|
||||
/**
|
||||
* Nearest single neighbor. Convenience wrapper around knn(query, 1).
|
||||
* Returns null if the tree is empty.
|
||||
*/
|
||||
nearest(query: number[]): KNNResult<T> | null {
|
||||
const results = this.knn(query, 1);
|
||||
return results[0] ?? null;
|
||||
}
|
||||
|
||||
// ── Radius search ──────────────────────────────────────────────────────────
|
||||
|
||||
/**
|
||||
* Return all points whose distance to `query` is ≤ `radius`,
|
||||
* sorted by distance ascending.
|
||||
*/
|
||||
radiusSearch(query: number[], radius: number): KNNResult<T>[] {
|
||||
if (radius < 0) throw new RangeError("radius must be non-negative");
|
||||
this.validateVector(query);
|
||||
|
||||
const results: KNNResult<T>[] = [];
|
||||
this.searchRadius(this.root, query, radius, results, 0);
|
||||
results.sort((a, b) => a.distance - b.distance);
|
||||
return results;
|
||||
}
|
||||
|
||||
// ── Conversion ─────────────────────────────────────────────────────────────
|
||||
|
||||
/** Collect all points in the tree (order not guaranteed). */
|
||||
toArray(): KDPoint<T>[] {
|
||||
const out: KDPoint<T>[] = [];
|
||||
this.collect(this.root, out);
|
||||
return out;
|
||||
}
|
||||
|
||||
/**
|
||||
* Rebuild the tree from its current points as a balanced tree.
|
||||
* Useful after many individual insertions to restore O(log n) query time.
|
||||
*/
|
||||
rebalance(): void {
|
||||
const points = this.toArray();
|
||||
this.root = points.length ? this.buildBalanced(points, 0) : null;
|
||||
}
|
||||
|
||||
// ── Private: build ─────────────────────────────────────────────────────────
|
||||
|
||||
private buildBalanced(points: KDPoint<T>[], depth: number): KDNode<T> {
|
||||
const axis = depth % this.dims;
|
||||
points.sort((a, b) => a.vector[axis] - b.vector[axis]);
|
||||
|
||||
const mid = Math.floor(points.length / 2);
|
||||
return {
|
||||
point: points[mid],
|
||||
axis,
|
||||
left: points.slice(0, mid).length
|
||||
? this.buildBalanced(points.slice(0, mid), depth + 1)
|
||||
: null,
|
||||
right: points.slice(mid + 1).length
|
||||
? this.buildBalanced(points.slice(mid + 1), depth + 1)
|
||||
: null,
|
||||
};
|
||||
}
|
||||
|
||||
// ── Private: insert ────────────────────────────────────────────────────────
|
||||
|
||||
private insertNode(
|
||||
node: KDNode<T> | null,
|
||||
point: KDPoint<T>,
|
||||
depth: number
|
||||
): KDNode<T> {
|
||||
if (node === null) {
|
||||
return { point, axis: depth % this.dims, left: null, right: null };
|
||||
}
|
||||
const axis = depth % this.dims;
|
||||
if (point.vector[axis] < node.point.vector[axis]) {
|
||||
node.left = this.insertNode(node.left, point, depth + 1);
|
||||
} else {
|
||||
node.right = this.insertNode(node.right, point, depth + 1);
|
||||
}
|
||||
return node;
|
||||
}
|
||||
|
||||
// ── Private: KNN traversal ─────────────────────────────────────────────────
|
||||
|
||||
private searchKNN(
|
||||
node: KDNode<T> | null,
|
||||
query: number[],
|
||||
k: number,
|
||||
heap: BoundedMaxHeap<T>,
|
||||
depth: number
|
||||
): void {
|
||||
if (node === null) return;
|
||||
|
||||
const dist = this.distanceFn(query, node.point.vector);
|
||||
heap.push({ point: node.point, distance: dist });
|
||||
|
||||
const axis = node.axis;
|
||||
const diff = query[axis] - node.point.vector[axis];
|
||||
const [near, far] = diff <= 0
|
||||
? [node.left, node.right]
|
||||
: [node.right, node.left];
|
||||
|
||||
this.searchKNN(near, query, k, heap, depth + 1);
|
||||
|
||||
// Only explore the far side if it could contain a closer point.
|
||||
// For cosine distance we can't prune by axis gap alone, so always explore.
|
||||
const shouldExplore =
|
||||
this.distanceFn === cosine
|
||||
? true
|
||||
: Math.abs(diff) < heap.worstDistance;
|
||||
|
||||
if (shouldExplore) {
|
||||
this.searchKNN(far, query, k, heap, depth + 1);
|
||||
}
|
||||
}
|
||||
|
||||
// ── Private: radius traversal ──────────────────────────────────────────────
|
||||
|
||||
private searchRadius(
|
||||
node: KDNode<T> | null,
|
||||
query: number[],
|
||||
radius: number,
|
||||
results: KNNResult<T>[],
|
||||
depth: number
|
||||
): void {
|
||||
if (node === null) return;
|
||||
|
||||
const dist = this.distanceFn(query, node.point.vector);
|
||||
if (dist <= radius) {
|
||||
results.push({ point: node.point, distance: dist });
|
||||
}
|
||||
|
||||
const axis = node.axis;
|
||||
const diff = query[axis] - node.point.vector[axis];
|
||||
const [near, far] = diff <= 0
|
||||
? [node.left, node.right]
|
||||
: [node.right, node.left];
|
||||
|
||||
this.searchRadius(near, query, radius, results, depth + 1);
|
||||
|
||||
const shouldExplore =
|
||||
this.distanceFn === cosine ? true : Math.abs(diff) <= radius;
|
||||
|
||||
if (shouldExplore) {
|
||||
this.searchRadius(far, query, radius, results, depth + 1);
|
||||
}
|
||||
}
|
||||
|
||||
// ── Private: collect ───────────────────────────────────────────────────────
|
||||
|
||||
private collect(node: KDNode<T> | null, out: KDPoint<T>[]): void {
|
||||
if (node === null) return;
|
||||
out.push(node.point);
|
||||
this.collect(node.left, out);
|
||||
this.collect(node.right, out);
|
||||
}
|
||||
|
||||
// ── Private: validation ────────────────────────────────────────────────────
|
||||
|
||||
private validateVector(v: number[]): void {
|
||||
if (v.length !== this.dims) {
|
||||
throw new TypeError(
|
||||
`Vector length ${v.length} does not match tree dimensionality ${this.dims}`
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
private validate(point: KDPoint<T>): void {
|
||||
this.validateVector(point.vector);
|
||||
}
|
||||
|
||||
private validateAll(points: KDPoint<T>[]): void {
|
||||
for (const p of points) this.validate(p);
|
||||
}
|
||||
}
|
||||
67
src/llm.ts
67
src/llm.ts
@@ -1,5 +1,6 @@
|
||||
import {AbortablePromise, Ai} from './ai.ts';
|
||||
import {Anthropic} from './antrhopic.ts';
|
||||
import {MemoryCache} from './memory-cache.ts';
|
||||
import {OpenAi} from './open-ai.ts';
|
||||
import {LLMProvider} from './provider.ts';
|
||||
import {AiTool, AiToolArg} from './tools.ts';
|
||||
@@ -9,7 +10,6 @@ import {spawn} from 'node:child_process';
|
||||
import {Memory, MemoryManager} from './memory.ts';
|
||||
|
||||
export type AnthropicConfig = {proto: 'anthropic', token: string};
|
||||
export type OllamaConfig = {proto: 'llama', host: string};
|
||||
export type OpenAiConfig = {proto: 'openai', host?: string, token: string};
|
||||
|
||||
export type LLMMessage = {
|
||||
@@ -56,7 +56,7 @@ export type LLMRequest = {
|
||||
/** Compress old messages in the chat to free up context */
|
||||
compress?: {max: number; min: number};
|
||||
/** User's memory documents - RAG injected automatically each turn */
|
||||
memory?: Memory[];
|
||||
memory?: Memory[] | MemoryCache;
|
||||
/** Model to use for memory operations */
|
||||
memoryModel?: string;
|
||||
/** Skill documents the AI can browse and read on demand */
|
||||
@@ -95,7 +95,6 @@ class LLM {
|
||||
Object.entries(ai.options.llm.models).forEach(([model, config]) => {
|
||||
if(!this.defaultModel) this.defaultModel = model;
|
||||
if(config.proto == 'anthropic') this.models[model] = new Anthropic(this.ai, config.token, model);
|
||||
else if(config.proto == 'llama') this.models[model] = new OpenAi(this.ai, config.host, 'ignored', model, true);
|
||||
else if(config.proto == 'openai') this.models[model] = new OpenAi(this.ai, config.host || null, config.token, model);
|
||||
});
|
||||
this.memoryManager = new MemoryManager(this);
|
||||
@@ -145,7 +144,7 @@ class LLM {
|
||||
return {
|
||||
prompt: `You have access to the following skill documents, use \`read_skill\` to access them:\n${list}`,
|
||||
tools: [{
|
||||
name: 'read_skill',
|
||||
name: 'skill_read',
|
||||
description: 'Read the full content of a skill/knowledge document',
|
||||
args: {
|
||||
name: {type: 'string', description: 'Exact skill name', required: true}
|
||||
@@ -169,8 +168,14 @@ class LLM {
|
||||
}
|
||||
const m = options.model || this.defaultModel;
|
||||
if(!this.models[m]) throw new Error(`Model does not exist: ${m}`);
|
||||
let abort = () => {};
|
||||
return Object.assign(new Promise<string>(async res => {
|
||||
let request: AbortablePromise<string> | null = null;
|
||||
let aborted = false;
|
||||
const abort = () => {
|
||||
aborted = true;
|
||||
request?.abort?.();
|
||||
};
|
||||
|
||||
const promise = (async () => {
|
||||
let tools: AiTool[] = options.tools || this.ai.options.llm?.tools || [];
|
||||
const prompts: string[] = [];
|
||||
let history = options.history || [];
|
||||
@@ -192,23 +197,29 @@ class LLM {
|
||||
}
|
||||
|
||||
// Memory
|
||||
if(options.memory) {
|
||||
const relevant = await this.memoryManager.recollect(message, options.memory, 1);
|
||||
if (options.memory) {
|
||||
const mems = options.memory instanceof MemoryCache ? options.memory.memories : options.memory;
|
||||
if(mems.length) {
|
||||
const relevant = await this.memoryManager.recollect(message, options.memory, 5);
|
||||
prompts.unshift(`You have access to the following memory files:
|
||||
${options.memory.map(m => `- ${m.name}: ${m.description}`).join('\n')}
|
||||
${relevant.length ? `
|
||||
The closest memory has been added primitively:
|
||||
\`\`\`
|
||||
Name: ${relevant[0].name}
|
||||
Description: ${relevant[0].description}
|
||||
${relevant[0].content}
|
||||
\`\`\`
|
||||
`: ''}`.trim());
|
||||
tools.push(this.memoryManager.tools.read(<Memory[]>options.memory));
|
||||
${mems.map(m => `- ${m.name}: ${m.description}`).join('\n')}
|
||||
${relevant.length ? `
|
||||
Relevant memories have been preloaded:
|
||||
${relevant.map(r => `
|
||||
**${r.name}**
|
||||
${r.description}
|
||||
${r.content}
|
||||
`).join('\n---\n')}
|
||||
` : ''}`.trim());
|
||||
tools.push(this.memoryManager.tools.read(options.memory));
|
||||
}
|
||||
}
|
||||
|
||||
if(aborted) throw Object.assign(new Error('Aborted'), {name: 'AbortError'});
|
||||
|
||||
prompts.unshift(options.system || this.ai.options.llm?.system || '');
|
||||
const resp = await this.models[m].ask(message, {...options, tools, system: prompts.filter(Boolean).join('\n\n')});
|
||||
request = this.models[m].ask(message, {...options, tools, system: prompts.filter(Boolean).join('\n\n')});
|
||||
const resp = await request;
|
||||
|
||||
// Trim memory injections from history
|
||||
if(options.memory) {
|
||||
@@ -217,21 +228,23 @@ ${relevant[0].content}
|
||||
|
||||
// Auto-memorize before compressing
|
||||
if(options.compress && this.estimateTokens(history) >= options.compress.max) {
|
||||
if(options.memory) await this.memoryManager.memorize(history, options.memory, options);
|
||||
if(options.memory) await this.memoryManager.memorize(history, options.memory, {model: options.memoryModel || this.defaultModel, ...options});
|
||||
const compressed = await this.compressHistory(history, options.compress.max, options.compress.min, options);
|
||||
if(options.history) options.history.splice(0, options.history.length, ...compressed);
|
||||
}
|
||||
|
||||
return res(resp);
|
||||
}), {abort});
|
||||
return resp;
|
||||
})();
|
||||
|
||||
return Object.assign(promise, {abort});
|
||||
}
|
||||
|
||||
/**
|
||||
* Digest full conversation history into memory documents.
|
||||
* Call on session end to persist the conversation.
|
||||
*/
|
||||
async updateMemory(history: LLMMessage[], memories: Memory[], options: LLMRequest = {}): Promise<void> {
|
||||
await this.memoryManager.memorize(history, memories, {model: this.defaultModel, ...options});
|
||||
async updateMemory(history: LLMMessage[], memories: Memory[] | MemoryCache, options: LLMRequest = {}): Promise<Memory[]> {
|
||||
return this.memoryManager.memorize(history, memories, {model: this.defaultModel, ...options});
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -429,9 +442,8 @@ ${relevant[0].content}
|
||||
});
|
||||
}
|
||||
|
||||
addModel(name: string, config: AnthropicConfig | OllamaConfig | OpenAiConfig, setDefault = false) {
|
||||
addModel(name: string, config: AnthropicConfig | OpenAiConfig, setDefault = false) {
|
||||
if(config.proto == 'anthropic') this.models[name] = new Anthropic(this.ai, config.token, name);
|
||||
else if(config.proto == 'llama') this.models[name] = new OpenAi(this.ai, config.host, 'not-needed', name, true);
|
||||
else if(config.proto == 'openai') this.models[name] = new OpenAi(this.ai, config.host || null, config.token, name);
|
||||
if(setDefault || !this.defaultModel) this.defaultModel = name;
|
||||
}
|
||||
@@ -443,12 +455,11 @@ ${relevant[0].content}
|
||||
}
|
||||
}
|
||||
|
||||
setModels(models: {[model: string]: AnthropicConfig | OllamaConfig | OpenAiConfig}, replace = true) {
|
||||
setModels(models: {[model: string]: AnthropicConfig | OpenAiConfig}, replace = true) {
|
||||
if(replace) this.models = {};
|
||||
Object.entries(models).forEach(([model, config]) => {
|
||||
if(!this.defaultModel) this.defaultModel = model;
|
||||
if(config.proto == 'anthropic') this.models[model] = new Anthropic(this.ai, config.token, model);
|
||||
else if(config.proto == 'llama') this.models[model] = new OpenAi(this.ai, config.host, 'not-needed', model, true);
|
||||
else if(config.proto == 'openai') this.models[model] = new OpenAi(this.ai, config.host || null, config.token, model);
|
||||
});
|
||||
this.defaultModel = Object.keys(this.models)[0] ?? '';
|
||||
|
||||
59
src/memory-cache.ts
Normal file
59
src/memory-cache.ts
Normal file
@@ -0,0 +1,59 @@
|
||||
import {KDPoint, KDTree} from './kd-tree.ts';
|
||||
import {Memory, MemoryRef} from './memory.ts';
|
||||
|
||||
export class MemoryCache {
|
||||
private tree: KDTree<MemoryRef>;
|
||||
public memories: Memory[];
|
||||
|
||||
get length() { return this.memories.length; }
|
||||
|
||||
constructor(memories: Memory[]) {
|
||||
this.memories = memories;
|
||||
this.tree = this.buildTree();
|
||||
}
|
||||
|
||||
private buildTree(): KDTree<MemoryRef> {
|
||||
const embedded = this.memories.filter(m => m.embedding?.length);
|
||||
if (!embedded.length) return new KDTree<MemoryRef>(0);
|
||||
|
||||
const dims = embedded[0].embedding.length;
|
||||
const points: KDPoint<MemoryRef>[] = embedded.map(m => ({
|
||||
vector: m.embedding,
|
||||
payload: {name: m.name, description: m.description},
|
||||
}));
|
||||
|
||||
return new KDTree<MemoryRef>(dims, 'cosine', points);
|
||||
}
|
||||
|
||||
search(query: number[], limit: number): MemoryRef[] {
|
||||
const results = this.tree.knn(query, limit);
|
||||
return results.map(r => r.point.payload);
|
||||
}
|
||||
|
||||
add(memory: Memory): void {
|
||||
this.memories.push(memory);
|
||||
this.rebuild();
|
||||
}
|
||||
|
||||
update(memory: Memory): void {
|
||||
const idx = this.memories.findIndex(m => m.name === memory.name);
|
||||
if (idx !== -1) {
|
||||
this.memories[idx] = memory;
|
||||
} else {
|
||||
this.memories.push(memory);
|
||||
}
|
||||
this.rebuild();
|
||||
}
|
||||
|
||||
remove(name: string): void {
|
||||
const idx = this.memories.findIndex(m => m.name === name);
|
||||
if (idx !== -1) {
|
||||
this.memories.splice(idx, 1);
|
||||
this.rebuild();
|
||||
}
|
||||
}
|
||||
|
||||
rebuild(): void {
|
||||
this.tree = this.buildTree();
|
||||
}
|
||||
}
|
||||
69
src/memory-graph.ts
Normal file
69
src/memory-graph.ts
Normal file
@@ -0,0 +1,69 @@
|
||||
import {MemoryCache} from './memory-cache.ts';
|
||||
import {extractMetadata, Memory, MemoryNode} from './memory.ts';
|
||||
|
||||
export function buildMemoryGraph(memories: Memory[] | MemoryCache): MemoryNode[] {
|
||||
const mems = memories instanceof MemoryCache ? memories.memories : memories;
|
||||
const nameSet = new Set(mems.map(m => m.name));
|
||||
const ghosts = new Set<string>();
|
||||
|
||||
const nodes: MemoryNode[] = mems.map(m => {
|
||||
const {links, backlinks} = extractMetadata(m.content);
|
||||
return {
|
||||
name: m.name,
|
||||
missing: false,
|
||||
links,
|
||||
backlinks,
|
||||
};
|
||||
});
|
||||
|
||||
for (const node of nodes) {
|
||||
for (const link of node.links) {
|
||||
if (!nameSet.has(link)) ghosts.add(link);
|
||||
}
|
||||
}
|
||||
|
||||
return [
|
||||
...nodes,
|
||||
...[...ghosts].map(name => ({
|
||||
name,
|
||||
missing: true,
|
||||
links: [],
|
||||
backlinks: nodes
|
||||
.filter(n => n.links.includes(name))
|
||||
.map(n => n.name),
|
||||
}))
|
||||
];
|
||||
}
|
||||
|
||||
export function renderMemoryGraph(nodes) {
|
||||
if (!nodes.length) return 'No memories yet.';
|
||||
|
||||
const groups = new Map();
|
||||
for (const node of nodes) {
|
||||
const [prefix, ...rest] = node.name.split('/');
|
||||
const group = rest.length ? prefix : 'Root';
|
||||
const label = rest.length ? rest.join('/') : node.name;
|
||||
if (!groups.has(group)) groups.set(group, []);
|
||||
groups.get(group).push({...node, label});
|
||||
}
|
||||
|
||||
const ghostCount = nodes.filter(n => n.missing).length;
|
||||
const lines = [`Memory Graph (${nodes.length} nodes, ${ghostCount} ghost${ghostCount === 1 ? '' : 's'})`, ''];
|
||||
|
||||
for (const group of [...groups.keys()].sort()) {
|
||||
const items = groups.get(group).sort((a, b) => a.label.localeCompare(b.label));
|
||||
lines.push(`${group}/`);
|
||||
items.forEach((n, i) => {
|
||||
const last = i === items.length - 1;
|
||||
const branch = last ? '└─' : '├─';
|
||||
const pad = last ? ' ' : '│ ';
|
||||
const tag = n.missing ? ' (ghost)' : '';
|
||||
lines.push(` ${branch} ${n.label}${tag}`);
|
||||
if (n.links.length) lines.push(` ${pad} → ${n.links.join(', ')}`);
|
||||
if (n.backlinks.length) lines.push(` ${pad} ← ${n.backlinks.join(', ')}`);
|
||||
});
|
||||
lines.push('');
|
||||
}
|
||||
|
||||
return lines.join('\n').trimEnd();
|
||||
}
|
||||
584
src/memory.ts
584
src/memory.ts
@@ -1,177 +1,483 @@
|
||||
// memory.ts
|
||||
import {LLMRequest, LLMMessage} from './llm.ts';
|
||||
import {MemoryCache} from './memory-cache.ts';
|
||||
import {AiTool} from './tools.ts';
|
||||
|
||||
/** Background information the AI will be fed as a knowledge document */
|
||||
export type Memory = {
|
||||
/** Memory subject */
|
||||
name: string;
|
||||
/** Short description of what this document contains - used for RAG retrieval */
|
||||
description: string;
|
||||
/** Full markdown content of the document */
|
||||
content: string;
|
||||
/** Embedding vector of the description - used for similarity search */
|
||||
embedding: number[];
|
||||
}
|
||||
|
||||
export type MemoryCollection = {
|
||||
/** Memory subject */
|
||||
export type MemoryRef = {
|
||||
name: string;
|
||||
/** Short description - required if isNew */
|
||||
description?: string;
|
||||
/** Extracted facts to merge */
|
||||
description: string;
|
||||
}
|
||||
|
||||
export type FactBucket = {
|
||||
subject: string;
|
||||
facts: string[];
|
||||
}
|
||||
|
||||
export type MemoryNode = {
|
||||
name: string;
|
||||
missing: boolean;
|
||||
links: string[];
|
||||
backlinks: string[];
|
||||
}
|
||||
|
||||
function extractLinks(content: string): string[] {
|
||||
if(!content) return [];
|
||||
const matches = content.matchAll(/\[\[([^\]]+)\]\]/g);
|
||||
return [...new Set([...matches].map(m => m[1].trim()))];
|
||||
}
|
||||
|
||||
export function extractMetadata(content: string): {links: string[], backlinks: string[]} {
|
||||
const match = content.match(/^---\n([\s\S]*?)\n---/);
|
||||
if (!match) return {links: [], backlinks: []};
|
||||
|
||||
const fm = match[1];
|
||||
const getList = (key: string): string[] => {
|
||||
const m = fm.match(new RegExp(`^${key}:\\s*\\[(.*)\\]$`, 'm'));
|
||||
if (!m || !m[1].trim()) return [];
|
||||
return m[1].split(',').map(s => s.trim().replace(/^"|"$/g, '')).filter(Boolean);
|
||||
};
|
||||
|
||||
return {
|
||||
links: getList('links'),
|
||||
backlinks: getList('backlinks'),
|
||||
};
|
||||
}
|
||||
|
||||
function dedupeFacts(facts: string[]): string[] {
|
||||
const seen = new Map<string, string>();
|
||||
for (const f of facts) {
|
||||
const clean = f.trim();
|
||||
if (clean) seen.set(clean.toLowerCase(), clean);
|
||||
}
|
||||
return [...seen.values()];
|
||||
}
|
||||
|
||||
function cosineDistance(a: number[], b: number[]): number {
|
||||
let dot = 0, normA = 0, normB = 0;
|
||||
for (let i = 0; i < a.length; i++) {
|
||||
dot += a[i] * b[i];
|
||||
normA += a[i] * a[i];
|
||||
normB += b[i] * b[i];
|
||||
}
|
||||
const denom = Math.sqrt(normA) * Math.sqrt(normB);
|
||||
return denom === 0 ? 1 : 1 - dot / denom;
|
||||
}
|
||||
|
||||
function getWeekMonday(date: Date = new Date()): string {
|
||||
const d = new Date(Date.UTC(date.getFullYear(), date.getMonth(), date.getDate()));
|
||||
const day = d.getUTCDay();
|
||||
const diff = day === 0 ? -6 : 1 - day;
|
||||
d.setUTCDate(d.getUTCDate() + diff);
|
||||
return d.toISOString().slice(0, 10);
|
||||
}
|
||||
|
||||
function getWeekSunday(monday: string): string {
|
||||
const d = new Date(`${monday}T00:00:00Z`);
|
||||
d.setUTCDate(d.getUTCDate() + 6);
|
||||
return d.toISOString().slice(0, 10);
|
||||
}
|
||||
|
||||
|
||||
|
||||
export class MemoryManager {
|
||||
private pendingMemorizations = new Map<string, {
|
||||
memories: Memory[] | MemoryCache,
|
||||
tempMemoryName: string,
|
||||
timestamp: number,
|
||||
}>();
|
||||
|
||||
private queues = new Map<string, {
|
||||
pending: string[],
|
||||
request: {abort?: () => void} | null,
|
||||
task: Promise<void>,
|
||||
}>();
|
||||
|
||||
tools = {
|
||||
edit: (memory: Memory): AiTool => ({
|
||||
name: 'edit_memory',
|
||||
description: 'Edit a memory. Omit start/end to append. Pass start only to replace from that line on (Note line 0 = first line of content / line AFTER description). Pass start+end to replace a specific range. start=0 replaces the whole document. Returns updated document',
|
||||
args: {
|
||||
content: {type: 'string', description: 'New content', required: true},
|
||||
start: {type: 'number', description: 'First line to replace (0-indexed, inclusive). Omit to append.'},
|
||||
end: {type: 'number', description: 'Last line to replace (0-indexed, inclusive). Omit to replace from start to end of doc.'},
|
||||
},
|
||||
fn: (args: any) => {
|
||||
const lines = memory.content ? memory.content.split('\n') : [];
|
||||
const newLines = args.content.split('\n');
|
||||
if(args.start === undefined) lines.push(...newLines);
|
||||
else if(args.end === undefined) lines.splice(args.start, lines.length - args.start, ...newLines);
|
||||
else lines.splice(args.start, args.end - args.start + 1, ...newLines);
|
||||
memory.content = lines.join('\n');
|
||||
return memory.content;
|
||||
}
|
||||
}),
|
||||
extract: (pools: MemoryCollection[]): AiTool => ({
|
||||
name: 'extract_facts',
|
||||
description: 'Extract a list of facts to group into a single memory',
|
||||
args: {
|
||||
name: {type: 'string', description: 'Exact name of an existing memory, or a new name if none fits ([pro]nouns only)', required: true},
|
||||
description: {type: 'string', description: 'One sentence description of the memory subject', required: true},
|
||||
facts: {type: 'string', description: 'Comma separated list of extracted facts', required: true},
|
||||
},
|
||||
fn: (args: any) => {
|
||||
pools.push({
|
||||
name: args.name,
|
||||
description: args.description,
|
||||
facts: args.facts.split(',').map((f: string) => f.trim()).filter(Boolean),
|
||||
});
|
||||
return 'Success';
|
||||
}}),
|
||||
read: (memories: Memory[]): AiTool => ({
|
||||
name: 'read_memory',
|
||||
description: 'Read entire memory',
|
||||
read: (memories: Memory[] | MemoryCache): AiTool => ({
|
||||
name: 'memory_recall',
|
||||
description: 'Read the full content of a memory document',
|
||||
args: {
|
||||
name: {type: 'string', description: 'Exact memory name', required: true},
|
||||
},
|
||||
fn: (args: any) => {
|
||||
const mem = memories.find(m => m.name === args.name);
|
||||
if(!mem) return 'Document not found';
|
||||
return `Name: ${mem.name}\nDescription: ${mem.description}\n\n${mem.content}`;
|
||||
}
|
||||
const mems = memories instanceof MemoryCache ? memories.memories : memories;
|
||||
const mem = mems.find(m => m.name === args.name);
|
||||
if (!mem) return 'Document not found';
|
||||
return mem.content;
|
||||
},
|
||||
}),
|
||||
|
||||
forget: (memories: Memory[] | MemoryCache): AiTool => ({
|
||||
name: 'memory_forget',
|
||||
description: 'Permanently delete a memory document and clean up all references to it',
|
||||
args: {
|
||||
name: {type: 'string', description: 'Exact memory name to forget', required: true}
|
||||
},
|
||||
fn: (args: any) => {
|
||||
const result = this.forget(args.name, memories);
|
||||
return result ? `Forgotten: ${args.name}` : `Not found: ${args.name}`;
|
||||
},
|
||||
}),
|
||||
};
|
||||
|
||||
constructor(private llm: any) {}
|
||||
|
||||
private async createTempMemory(conversation: string): Promise<Memory> {
|
||||
const timestamp = Date.now();
|
||||
const content = `---
|
||||
name: _temp_${timestamp}
|
||||
description: Temporary memory - processing in background
|
||||
tags: [_temporary]
|
||||
links: []
|
||||
backlinks: []
|
||||
modified: ${new Date().toISOString()}
|
||||
---
|
||||
|
||||
# Recent Conversation (Processing)
|
||||
|
||||
${conversation}`;
|
||||
const [e] = await this.llm.embedding(content);
|
||||
return {
|
||||
name: `_temp_${timestamp}`,
|
||||
description: 'Temporary memory - processing in background',
|
||||
content,
|
||||
embedding: e?.embedding || [],
|
||||
};
|
||||
}
|
||||
|
||||
constructor(private llm: any, private model?: string) {}
|
||||
forget(name: string, memories: Memory[] | MemoryCache): boolean {
|
||||
const mem = memories instanceof MemoryCache ? memories.memories : memories;
|
||||
const idx = mem.findIndex(m => m.name === name);
|
||||
if (idx === -1) return false;
|
||||
|
||||
/**
|
||||
* Extracts facts from conversation and groups them into individual memories
|
||||
* @param {string} conversation Full conversation formatted as [role]: content
|
||||
* @param {Memory[]} memories The user's memory documents
|
||||
* @param {LLMRequest} options LLM options
|
||||
* @returns {Promise<MemoryCollection[]>} Fact pools grouped by target document
|
||||
*/
|
||||
private async extract(conversation: string, memories: Memory[], options: LLMRequest): Promise<MemoryCollection[]> {
|
||||
const existingDocs = memories.map(m => `Name: ${m.name}\nDescription: ${m.description}`).join('\n\n');
|
||||
const pools: MemoryCollection[] = [];
|
||||
await this.llm.ask(conversation, {
|
||||
model: this.model || options.model,
|
||||
temperature: 0.2,
|
||||
system: `You are a fact extractor. Analyze this conversation and extract facts worth remembering long term.
|
||||
Rules:
|
||||
- ONLY extract facts the USER explicitly stated about themselves or their business
|
||||
- ONLY extract decisions that were MADE during this conversation
|
||||
- DO NOT extract anything the AI said, its name, capabilities, or how it introduced itself
|
||||
- DO NOT extract greetings, pleasantries or generic exchanges
|
||||
- If nothing worth remembering was said, dont do anything, skip calling tools
|
||||
for (const node of mem) {
|
||||
const {links, backlinks} = extractMetadata(node.content);
|
||||
const newBacklinks = backlinks.filter(b => b !== name);
|
||||
const newLinks = links.filter(l => l !== name);
|
||||
|
||||
For each fact decide whether it belongs in an existing document or needs a new one, then call the \`extract_facts\` tool.
|
||||
|
||||
Existing documents:\n${existingDocs || 'None yet.'}`,
|
||||
tools: [this.tools.extract(pools)]
|
||||
if (newBacklinks.length !== backlinks.length || newLinks.length !== links.length) {
|
||||
node.content = this.updateFrontmatter(node.content, {
|
||||
links: newLinks,
|
||||
backlinks: newBacklinks,
|
||||
});
|
||||
return pools;
|
||||
}
|
||||
|
||||
/**
|
||||
* Bot 2 - Editor: merges a pool of facts into a specific document using surgical line-based edits.
|
||||
* Receives full document content and uses read + amend tools to make precise edits.
|
||||
* @param {MemoryCollection} newMem The fact pool to merge
|
||||
* @param {Memory[]} memories The user's memory documents
|
||||
* @param {LLMRequest} options LLM options
|
||||
*/
|
||||
private async edit(newMem: MemoryCollection, memories: Memory[], options: LLMRequest): Promise<void> {
|
||||
const existing = memories.find(m => m.name === newMem.name);
|
||||
const mem: Memory = existing || {name: newMem.name, description: newMem.description || '', content: '', embedding: []};
|
||||
const isNew = !existing;
|
||||
|
||||
await this.llm.ask(newMem.facts.map(f => `- ${f}`).join('\n'),
|
||||
{
|
||||
model: this.model || options.model,
|
||||
temperature: 0.2,
|
||||
system: `You are a document editor. Merge the users list of facts into the following document using the \`edit_memory\` tool; call it as many times as necessary:
|
||||
\`\`\`
|
||||
${mem.content}
|
||||
\`\`\``,
|
||||
tools: [this.tools.edit(mem)]
|
||||
}
|
||||
);
|
||||
|
||||
if(isNew || mem.description !== existing?.description) {
|
||||
const e = await this.llm.embedding(mem.description);
|
||||
mem.embedding = e?.[0]?.embedding;
|
||||
}
|
||||
|
||||
if(isNew) memories.push(mem);
|
||||
else {
|
||||
const idx = memories.findIndex(m => m.name === newMem.name);
|
||||
if(idx >= 0) memories[idx] = mem;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Find relevant memory documents for a query using description embeddings
|
||||
* @param {string} query The query to search against
|
||||
* @param {Memory[]} memories The user's memory documents
|
||||
* @param {number} limit Max number of results to return
|
||||
* @returns {Promise<Memory[]>} The most relevant memory documents
|
||||
*/
|
||||
async recollect(query: string, memories: Memory[], limit = 5): Promise<Memory[]> {
|
||||
const [e] = await this.llm.embedding(query);
|
||||
return memories
|
||||
mem.splice(idx, 1);
|
||||
|
||||
if (memories instanceof MemoryCache) memories.rebuild();
|
||||
return true;
|
||||
}
|
||||
|
||||
private cosineSearch(query: number[], memories: Memory[], limit: number): MemoryRef[] {
|
||||
const scored = memories
|
||||
.filter(m => m.embedding?.length)
|
||||
.map(m => ({...m, score: this.llm.cosineSimilarity(m.embedding, e.embedding)}))
|
||||
.toSorted((a: any, b: any) => b.score - a.score)
|
||||
.map(m => ({
|
||||
ref: {name: m.name, description: m.description},
|
||||
distance: cosineDistance(query, m.embedding),
|
||||
}))
|
||||
.sort((a, b) => a.distance - b.distance)
|
||||
.slice(0, limit);
|
||||
return scored.map(s => s.ref);
|
||||
}
|
||||
|
||||
/**
|
||||
* Two-stage memory pipeline: classify facts from conversation history then surgically merge them into documents.
|
||||
* Bot 1 (classify) extracts and groups facts cheaply. Bot 2 (edit) runs per-document in parallel with full content access.
|
||||
* @param {LLMMessage[]} history Full conversation history to digest
|
||||
* @param {Memory[]} memories The user's memory documents — mutated in place
|
||||
* @param {LLMRequest} options LLM options
|
||||
*/
|
||||
async memorize(history: LLMMessage[], memories: Memory[], options: LLMRequest): Promise<void> {
|
||||
private listNodes(memories: Memory[]): MemoryRef[] {
|
||||
return memories.map(m => ({name: m.name, description: m.description}));
|
||||
}
|
||||
|
||||
async recollect(query: string, memories: Memory[] | MemoryCache, limit = 5, graphDepth = 1): Promise<Memory[]> {
|
||||
const mem: Memory[] = memories instanceof MemoryCache ? memories.memories : memories;
|
||||
if (!mem.length) return [];
|
||||
|
||||
const [e] = await this.llm.embedding(query);
|
||||
if (!e) return [];
|
||||
|
||||
let vectorResults: MemoryRef[];
|
||||
if (memories instanceof MemoryCache) vectorResults = memories.search(e.embedding, limit);
|
||||
else vectorResults = this.cosineSearch(e.embedding, mem, limit);
|
||||
const found = new Set<string>(vectorResults.map(r => r.name));
|
||||
|
||||
if (graphDepth > 0) {
|
||||
const frontier = [...found];
|
||||
for (let depth = 0; depth < graphDepth; depth++) {
|
||||
const next: string[] = [];
|
||||
for (const name of frontier) {
|
||||
const node = mem.find(m => m.name === name);
|
||||
if (!node) continue;
|
||||
const {links} = extractMetadata(node.content);
|
||||
for (const link of links) {
|
||||
if (!found.has(link) && mem.find(m => m.name === link)) {
|
||||
found.add(link);
|
||||
next.push(link);
|
||||
}
|
||||
}
|
||||
}
|
||||
frontier.splice(0, frontier.length, ...next);
|
||||
if (!frontier.length) break;
|
||||
}
|
||||
}
|
||||
|
||||
const vectorOrder = vectorResults.map(r => r.name);
|
||||
const graphExpansions = [...found].filter(n => !vectorOrder.includes(n));
|
||||
const ordered = [...vectorOrder, ...graphExpansions];
|
||||
return ordered.map(n => mem.find(m => m.name === n)!).filter(Boolean);
|
||||
}
|
||||
|
||||
async memorize(history: LLMMessage[], memories: Memory[] | MemoryCache, options: LLMRequest): Promise<Memory[]> {
|
||||
const conversation = history
|
||||
.filter(h => h.role === 'user' || h.role === 'assistant')
|
||||
.map(h => `[${h.role}]: ${h.content}`)
|
||||
.join('\n\n');
|
||||
if(!conversation.trim()) return;
|
||||
const pools = await this.extract(conversation, memories, options);
|
||||
if(!pools.length) return;
|
||||
await Promise.all(pools.map(pool => this.edit(pool, memories, options)));
|
||||
.map(h => `[${h.role}]: ${h.content}`).join('\n\n').trim();
|
||||
if(!conversation) return [];
|
||||
|
||||
const trackingId = `${Date.now()}_${Math.random()}`;
|
||||
const tempMemory = await this.createTempMemory(conversation);
|
||||
const mem = memories instanceof MemoryCache ? memories.memories : memories;
|
||||
mem.push(tempMemory);
|
||||
if (memories instanceof MemoryCache) memories.rebuild();
|
||||
this.pendingMemorizations.set(trackingId, {
|
||||
memories,
|
||||
tempMemoryName: tempMemory.name,
|
||||
timestamp: Date.now(),
|
||||
});
|
||||
|
||||
try {
|
||||
await this._memorizeBackground(conversation, memories, options, tempMemory.name);
|
||||
const finalMem = memories instanceof MemoryCache ? memories.memories : memories;
|
||||
return finalMem.filter(m => !m.name.startsWith('_temp_'));
|
||||
} finally {
|
||||
const pending = this.pendingMemorizations.get(trackingId);
|
||||
if (pending) {
|
||||
const cleanMem = pending.memories instanceof MemoryCache
|
||||
? pending.memories.memories
|
||||
: pending.memories;
|
||||
const idx = cleanMem.findIndex(m => m.name === pending.tempMemoryName);
|
||||
if (idx !== -1) cleanMem.splice(idx, 1);
|
||||
if (pending.memories instanceof MemoryCache) pending.memories.rebuild();
|
||||
}
|
||||
this.pendingMemorizations.delete(trackingId);
|
||||
}
|
||||
}
|
||||
|
||||
private async _memorizeBackground(conversation: string, memories: Memory[] | MemoryCache, options: LLMRequest, tempName: string): Promise<void> {
|
||||
const mem = memories instanceof MemoryCache ? memories.memories : memories;
|
||||
const monday = getWeekMonday();
|
||||
const sunday = getWeekSunday(monday);
|
||||
const buckets = await this.factAgent(conversation, mem, options, monday);
|
||||
if(!buckets.length) return;
|
||||
const jobs = [...buckets].map(({subject, facts}) => {
|
||||
let node = mem.find(m => m.name === subject);
|
||||
if(!node) {
|
||||
node = {name: subject, description: '', content: '', embedding: [],};
|
||||
mem.push(node);
|
||||
}
|
||||
const week = subject.startsWith('Journal/') ? {monday, sunday} : undefined;
|
||||
return this.enqueue(node, facts, mem, options, tempName, week);
|
||||
});
|
||||
await Promise.all(jobs);
|
||||
}
|
||||
|
||||
/**
|
||||
* Coalescing queue: if a doc is already compiling, abort the in-flight run, merge its
|
||||
* facts with the new ones and restart. Never blocks a pending update, never drops facts.
|
||||
*/
|
||||
private enqueue(node: Memory, facts: string[], memories: Memory[] | MemoryCache, options: LLMRequest, tempName: string, week?: {monday: string, sunday: string}): Promise<void> {
|
||||
const key = node.name;
|
||||
const existing = this.queues.get(key);
|
||||
if (existing) {
|
||||
existing.pending.push(...facts);
|
||||
existing.request?.abort?.();
|
||||
return existing.task;
|
||||
}
|
||||
|
||||
const entry: {pending: string[], request: {abort?: () => void} | null, task: Promise<void>} = {pending: [...facts], request: null, task: Promise.resolve()};
|
||||
this.queues.set(key, entry);
|
||||
const m = memories instanceof MemoryCache ? memories.memories : memories;
|
||||
entry.task = (async () => {
|
||||
while (entry.pending.length) {
|
||||
const batch = dedupeFacts(entry.pending.splice(0, entry.pending.length));
|
||||
const written = await this.docAgent(node, batch, m, options, tempName, week, entry);
|
||||
if (!written) entry.pending.unshift(...batch);
|
||||
}
|
||||
})().finally(() => {
|
||||
this.queues.delete(key);
|
||||
if(!this.queues.size && memories instanceof MemoryCache) memories.rebuild();
|
||||
});
|
||||
return entry.task;
|
||||
}
|
||||
|
||||
private buildHeader(node: Memory, week?: {monday: string, sunday: string}, links: string[] = [], backlinks: string[] = []): string {
|
||||
const tags = node.name.split('/')[0]?.toLowerCase();
|
||||
const lines = [
|
||||
'---',
|
||||
`name: ${node.name}`,
|
||||
`description: ${node.description || ''}`,
|
||||
tags ? `tags: [${tags}]` : '',
|
||||
links.length ? `links: [${links.map(l => `"${l}"`).join(', ')}]` : 'links: []',
|
||||
backlinks.length ? `backlinks: [${backlinks.map(l => `"${l}"`).join(', ')}]` : 'backlinks: []',
|
||||
week ? `week: ${week.monday} – ${week.sunday}` : '',
|
||||
`modified: ${new Date().toISOString()}`,
|
||||
'---',
|
||||
].filter(Boolean);
|
||||
return lines.join('\n');
|
||||
}
|
||||
|
||||
private applyHeader(content: string, header: string): string {
|
||||
return `${header}\n\n${this.stripHeader(content)}`;
|
||||
}
|
||||
|
||||
private updateFrontmatter(content: string, updates: {links?: string[], backlinks?: string[]}): string {
|
||||
const match = content.match(/^---\n([\s\S]*?)\n---\n\n?([\s\S]*)$/);
|
||||
if (!match) return content;
|
||||
|
||||
const [, fm, body] = match;
|
||||
let newFm = fm;
|
||||
|
||||
if (updates.links !== undefined) {
|
||||
const linksList = updates.links.length ? `[${updates.links.map(l => `"${l}"`).join(', ')}]` : '[]';
|
||||
newFm = newFm.replace(/^links:.*$/m, `links: ${linksList}`);
|
||||
}
|
||||
|
||||
if (updates.backlinks !== undefined) {
|
||||
const backlinksList = updates.backlinks.length ? `[${updates.backlinks.map(l => `"${l}"`).join(', ')}]` : '[]';
|
||||
newFm = newFm.replace(/^backlinks:.*$/m, `backlinks: ${backlinksList}`);
|
||||
}
|
||||
|
||||
newFm = newFm.replace(/^modified:.*$/m, `modified: ${new Date().toISOString()}`);
|
||||
|
||||
return `---\n${newFm}\n---\n\n${body}`;
|
||||
}
|
||||
|
||||
private stripHeader(content: string): string {
|
||||
return content.replace(/^---[\s\S]*?\n---\n?/, '').trimStart();
|
||||
}
|
||||
|
||||
private async docAgent(node: Memory, facts: string[], memories: Memory[], options: LLMRequest, tempName: string, week: {monday: string, sunday: string} | undefined, entry: {request: {abort?: () => void} | null}): Promise<boolean> {
|
||||
const {links: oldLinks} = extractMetadata(node.content);
|
||||
const currentBody = this.stripHeader(node.content);
|
||||
let update;
|
||||
try {
|
||||
for(let i = 0; i < 3 && !update?.content; i++) {
|
||||
const request = this.llm.ask(`New Facts:\n${facts.map(f => `- ${f}`).join('\n')}`, {
|
||||
model: options.model,
|
||||
temperature: 0.3,
|
||||
schema: {
|
||||
description: {type: 'string', description: 'One-line description of what this document covers, no formatting or emojis', required: true},
|
||||
content: {type: 'string', description: 'Rewritten document in markdown, without the frontmatter block', required: true},
|
||||
},
|
||||
system: `You are a knowledge base editor. Rewrite the current document below so it incorporates the new facts.
|
||||
|
||||
Formatting rules:
|
||||
- Use Obsidian-style markdown: # headings, **bold** to add emphasis, __italics__ for titles, terms, etc, bullet & numbered lists for grouped 1D data and tables for 2D data
|
||||
- Link related concepts with [[WikiLink]] notation using full paths like [[People/Sarah]] or [[Projects/Website]]
|
||||
- Create links for specific entities (person, place, project, program) and abstract concepts (quantum mechanics, entropy) but skip generics (car, red, dog)
|
||||
- Keep the document concise, factual, and human-readable
|
||||
- Resolve contradictions: the new facts always win — delete the outdated statement entirely, never keep both
|
||||
- Later facts in the list override earlier ones
|
||||
- Do not add frontmatter blocks, filler, preamble, or AI commentary
|
||||
${week ? '- This is a weekly journal entry.\n' : ''}
|
||||
All nodes:
|
||||
${this.listNodes(memories).map(n => n.name).join(', ') || 'none'}
|
||||
|
||||
Current document:
|
||||
\`\`\`markdown
|
||||
${currentBody}
|
||||
\`\`\``}
|
||||
);
|
||||
entry.request = request;
|
||||
update = await request;
|
||||
}
|
||||
} catch (err: any) {
|
||||
if (err?.name === 'AbortError') return false;
|
||||
throw err;
|
||||
} finally {
|
||||
entry.request = null;
|
||||
}
|
||||
|
||||
if(!update?.content) return false;
|
||||
const newLinks = extractLinks(update.content).filter(l => l !== node.name && l !== tempName);
|
||||
const newLinkSet = new Set(newLinks);
|
||||
const oldLinkSet = new Set(oldLinks);
|
||||
|
||||
for (const added of newLinkSet) {
|
||||
if (!oldLinkSet.has(added)) {
|
||||
const target = memories.find(m => m.name === added);
|
||||
if (target) {
|
||||
const {backlinks} = extractMetadata(target.content);
|
||||
if (!backlinks.includes(node.name)) {
|
||||
target.content = this.updateFrontmatter(target.content, {
|
||||
backlinks: [...backlinks, node.name],
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
for (const removed of oldLinkSet) {
|
||||
if (!newLinkSet.has(removed)) {
|
||||
const target = memories.find(m => m.name === removed);
|
||||
if (target) {
|
||||
const {backlinks} = extractMetadata(target.content);
|
||||
target.content = this.updateFrontmatter(target.content, {
|
||||
backlinks: backlinks.filter(b => b !== node.name),
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
const {backlinks} = extractMetadata(node.content);
|
||||
node.description = node.name !== 'Person/User' ? update.description : 'All information about the current user';
|
||||
node.content = this.applyHeader(update.content, this.buildHeader(node, week, newLinks, backlinks));
|
||||
const [e] = await this.llm.embedding(node.content);
|
||||
if(e) node.embedding = e.embedding;
|
||||
return true;
|
||||
}
|
||||
|
||||
private async factAgent(conversation: string, memories: Memory[], options: LLMRequest, weekKey: string): Promise<FactBucket[]> {
|
||||
const buckets = new Map<string, string[]>();
|
||||
await this.llm.ask(conversation, {
|
||||
model: options.model,
|
||||
temperature: 0.2,
|
||||
system: `You are a fact extractor. Analyze this conversation and extract facts worth remembering long-term.
|
||||
|
||||
Rules:
|
||||
- ONLY extract current facts the USER explicitly stated about themselves, their work, or their projects
|
||||
- ONLY extract decisions that were MADE during this conversation
|
||||
- DO NOT extract anything the AI said, its capabilities, or meta-conversation about the AI
|
||||
- DO NOT extract greetings, pleasantries, or generic exchanges
|
||||
- DO NOT extract deltas or changes in facts; ONLY the end fact
|
||||
- If nothing worth remembering was said, do not call any tools
|
||||
|
||||
When extracting facts, you MUST also decide the exact destination path:
|
||||
- Use an existing node name if the facts clearly belong there
|
||||
- All information primary about the user should go under "People/User"
|
||||
- When required, create a new path following collection/subject format (e.g., People/Sarah, Projects/Oxide)
|
||||
- For journal entries, use "Journal"
|
||||
|
||||
Available nodes:
|
||||
- Journal
|
||||
${this.listNodes(memories).filter(n => !n.name.includes('_temp_') && !n.name.includes('Journal')).map(n => `- ${n.name}: ${n.description}`).join('\n') || 'None yet.'}`,
|
||||
tools: [{
|
||||
name: 'facts_extract',
|
||||
description: 'Submit facts with their destination',
|
||||
args: {
|
||||
destination: {type: 'string', description: 'Exact existing node name OR new path (e.g. "People/Sarah", "Projects/Oxide")', required: true},
|
||||
facts: {type: 'string', description: 'Comma-separated facts', required: true},
|
||||
},
|
||||
fn: (args: any) => {
|
||||
const subject = args.destination.trim().toLowerCase() === 'journal'
|
||||
? `Journal/${weekKey}` : args.destination.trim();
|
||||
const facts = buckets.get(subject) ?? [];
|
||||
facts.push(...dedupeFacts(String(args.facts).split(',')));
|
||||
buckets.set(subject, facts);
|
||||
return 'Recorded';
|
||||
},
|
||||
}],
|
||||
});
|
||||
return buckets.entries().toArray().map(([subject, facts]) => ({subject, facts}));
|
||||
}
|
||||
}
|
||||
|
||||
@@ -8,7 +8,7 @@ import {convertSchema} from './tools.ts';
|
||||
export class OpenAi extends LLMProvider {
|
||||
client!: openAI;
|
||||
|
||||
constructor(public readonly ai: Ai, public readonly host: string | null, public readonly token: string, public model: string, public llama?: boolean) {
|
||||
constructor(public readonly ai: Ai, public readonly host: string | null, public readonly token: string, public model: string) {
|
||||
super();
|
||||
this.client = new openAI(clean({
|
||||
baseURL: host,
|
||||
@@ -29,11 +29,11 @@ export class OpenAi extends LLMProvider {
|
||||
}));
|
||||
history.splice(i, 1, ...tools);
|
||||
i += tools.length - 1;
|
||||
} else if(h.role === 'tool' && h.content) {
|
||||
} else if(h.role === 'tool') {
|
||||
const record = history.find(h2 => h.tool_call_id == h2.id);
|
||||
if(record) {
|
||||
if(h.content.includes('"error":')) record.error = h.content;
|
||||
else record.content = h.content;
|
||||
if(h.content?.includes('"error":')) record.error = h.content;
|
||||
else record.content = h.content || '';
|
||||
}
|
||||
history.splice(i, 1);
|
||||
i--;
|
||||
@@ -96,13 +96,6 @@ export class OpenAi extends LLMProvider {
|
||||
|
||||
if(options.schema) {
|
||||
const schema = convertSchema(options.schema);
|
||||
if(this.llama) {
|
||||
delete requestParams.tools;
|
||||
requestParams.response_format = {
|
||||
type: 'json_schema',
|
||||
json_schema: {name: 'json', schema}
|
||||
}
|
||||
} else {
|
||||
requestParams.response_format = {
|
||||
type: 'json_schema',
|
||||
json_schema: {
|
||||
@@ -112,7 +105,6 @@ export class OpenAi extends LLMProvider {
|
||||
}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
let resp: any, isFirstMessage = true;
|
||||
do {
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import {AbortablePromise} from './ai.ts';
|
||||
import {LLMMessage, LLMRequest} from './llm.ts';
|
||||
import {LLMRequest} from './llm.ts';
|
||||
|
||||
export abstract class LLMProvider {
|
||||
abstract ask(message: string, options: LLMRequest): AbortablePromise<string>;
|
||||
|
||||
673
src/tools.ts
673
src/tools.ts
@@ -91,25 +91,33 @@ export function convertSchema(schema: any): any {
|
||||
};
|
||||
}
|
||||
|
||||
export const CliTool: AiTool = {
|
||||
export const ExecCliTool: AiTool = {
|
||||
name: 'cli',
|
||||
description: 'Use the command line interface, returns any output',
|
||||
args: {command: {type: 'string', description: 'Command to run', required: true}},
|
||||
fn: (args: {command: string}) => $Sync`${args.command}`
|
||||
}
|
||||
|
||||
export const DateTimeTool: AiTool = {
|
||||
name: 'get_datetime',
|
||||
description: 'Get local date / time',
|
||||
args: {},
|
||||
fn: async () => new Date().toString()
|
||||
export const ExecJSTool: AiTool = {
|
||||
name: 'exec_javascript',
|
||||
description: 'Execute commonjs javascript',
|
||||
args: {
|
||||
code: {type: 'string', description: 'CommonJS javascript', required: true}
|
||||
},
|
||||
fn: async (args: {code: string}) => {
|
||||
const c = consoleInterceptor(null);
|
||||
const resp = await Fn<any>({console: c}, args.code, true).catch((err: any) => c.output.error.push(err));
|
||||
return {...c.output, return: resp, stdout: undefined, stderr: undefined};
|
||||
}
|
||||
}
|
||||
|
||||
export const DateTimeUTCTool: AiTool = {
|
||||
name: 'get_datetime_utc',
|
||||
description: 'Get current UTC date / time',
|
||||
args: {},
|
||||
fn: async () => new Date().toUTCString()
|
||||
export const ExecPythonTool: AiTool = {
|
||||
name: 'exec_python',
|
||||
description: 'Execute commonjs javascript',
|
||||
args: {
|
||||
code: {type: 'string', description: 'CommonJS javascript', required: true}
|
||||
},
|
||||
fn: async (args: {code: string}) => ({result: $Sync`python -c "${args.code}"`})
|
||||
}
|
||||
|
||||
export const ExecTool: AiTool = {
|
||||
@@ -123,11 +131,11 @@ export const ExecTool: AiTool = {
|
||||
try {
|
||||
switch(args.language) {
|
||||
case 'cli':
|
||||
return await CliTool.fn({command: args.code}, stream, ai);
|
||||
return await ExecCliTool.fn({command: args.code}, stream, ai);
|
||||
case 'node':
|
||||
return await JSTool.fn({code: args.code}, stream, ai);
|
||||
return await ExecJSTool.fn({code: args.code}, stream, ai);
|
||||
case 'python':
|
||||
return await PythonTool.fn({code: args.code}, stream, ai);
|
||||
return await ExecPythonTool.fn({code: args.code}, stream, ai);
|
||||
default:
|
||||
throw new Error(`Unsupported language: ${args.language}`);
|
||||
}
|
||||
@@ -137,8 +145,483 @@ export const ExecTool: AiTool = {
|
||||
}
|
||||
}
|
||||
|
||||
export const FetchTool: AiTool = {
|
||||
name: 'fetch',
|
||||
export const FsDeleteTool = (whitelist: null | string[] = null): AiTool => {
|
||||
return {
|
||||
name: 'fs_delete',
|
||||
description: 'Delete a file or directory',
|
||||
args: {
|
||||
path: {type: 'string', description: 'Path to file or directory', required: true},
|
||||
recursive: {type: 'boolean', description: 'Delete all children', required: false}
|
||||
},
|
||||
fn: async ({path, recursive = false}) => {
|
||||
const {existsSync, rmSync} = await import('fs');
|
||||
const normalizePath = p => p.replace(/\\/g, '/');
|
||||
|
||||
path = normalizePath(path);
|
||||
if(whitelist && !whitelist.some(p => path.startsWith(p))) return {error: 'Permission denied'};
|
||||
if(!existsSync(path)) return {error: 'Path does not exist'};
|
||||
|
||||
rmSync(path, {recursive, force: true});
|
||||
return {success: true, path};
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
export const FsMoveTool = (whitelist: null | string[] = null): AiTool => {
|
||||
return {
|
||||
name: 'fs_move',
|
||||
description: 'Move or rename a file or directory',
|
||||
args: {
|
||||
source: {type: 'string', description: 'Path to source file or directory', required: true},
|
||||
destination: {type: 'string', description: 'Path to destination file or directory', required: true}
|
||||
},
|
||||
fn: async ({source, destination}) => {
|
||||
const {existsSync, renameSync} = await import('fs');
|
||||
const normalizePath = p => p.replace(/\\/g, '/');
|
||||
|
||||
source = normalizePath(source);
|
||||
destination = normalizePath(destination);
|
||||
if(whitelist && !whitelist.some(p => source.startsWith(p) && destination.startsWith(p))) return {error: 'Permission denied'};
|
||||
|
||||
if(!existsSync(source)) return {error: 'Source path does not exist'};
|
||||
if(existsSync(destination)) return {error: 'Destination path already exists'};
|
||||
|
||||
renameSync(source, destination);
|
||||
return {success: true, source, destination};
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
export const FsReadTool = (whitelist: null | string[] = null): AiTool => {
|
||||
return {
|
||||
name: 'fs_read',
|
||||
description: 'Read the contents of a provided path. Works with files and directories',
|
||||
args: {path: {type: 'string', description: 'Path to file or directory', required: true}},
|
||||
fn: async ({path}) => {
|
||||
const {existsSync, lstatSync, readdirSync, readFileSync} = await import('fs');
|
||||
const {join} = await import('path');
|
||||
const normalizePath = p => p.replace(/\\/g, '/');
|
||||
|
||||
path = normalizePath(path);
|
||||
if(whitelist && !whitelist.some(p => path.startsWith(p))) return {error: 'Permission denied'};
|
||||
|
||||
if(!existsSync(path)) return {error: 'Path does not exist'};
|
||||
const stats = lstatSync(path);
|
||||
if(stats.isDirectory()) {
|
||||
const children = readdirSync(path).map(name => {
|
||||
const childPath = normalizePath(join(path, name));
|
||||
const childStats = lstatSync(childPath);
|
||||
return {name, type: childStats.isDirectory() ? 'directory' : 'file', size: childStats.size};
|
||||
});
|
||||
return {type: 'directory', children};
|
||||
}
|
||||
const content = readFileSync(path, 'utf-8');
|
||||
return {type: 'file', content};
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
export const FsSearchTool = (whitelist: null | string[] = null): AiTool => {
|
||||
return {
|
||||
name: 'fs_search',
|
||||
description: 'Scan a directory for matching glob patterns (e.g. "**/*.js", "src/**/*.test.ts")',
|
||||
args: {
|
||||
pattern: {type: 'string', description: 'Glob pattern to match against paths', required: true},
|
||||
root: {type: 'string', description: 'Directory to search from', required: false, default: '.'}
|
||||
},
|
||||
fn: async ({pattern, root = '.'}) => {
|
||||
const {existsSync, lstatSync, readdirSync} = await import('fs');
|
||||
const {join, relative} = await import('path');
|
||||
const normalizePath = p => p.replace(/\\/g, '/');
|
||||
|
||||
root = normalizePath(root);
|
||||
if(!existsSync(root)) return {error: 'Root path does not exist'};
|
||||
if(!lstatSync(root).isDirectory()) return {error: 'Root path is not a directory'};
|
||||
|
||||
if(whitelist && !whitelist.some(p => root.startsWith(p))) return {error: 'Permission denied'};
|
||||
|
||||
const globToRegex = (glob) => {
|
||||
let re = '';
|
||||
for(let i = 0; i < glob.length; i++) {
|
||||
const c = glob[i];
|
||||
if(c === '*') {
|
||||
if(glob[i + 1] === '*') {
|
||||
const isSlash = glob[i + 2] === '/';
|
||||
re += '.*';
|
||||
i += isSlash ? 2 : 1;
|
||||
} else {
|
||||
re += '[^/]*';
|
||||
}
|
||||
} else if(c === '?') {
|
||||
re += '[^/]';
|
||||
} else if('.+^$(){}|[]\\'.includes(c)) {
|
||||
re += '\\' + c;
|
||||
} else {
|
||||
re += c;
|
||||
}
|
||||
}
|
||||
return new RegExp('^' + re + '$');
|
||||
};
|
||||
const regex = globToRegex(pattern);
|
||||
|
||||
const results: any = [];
|
||||
const walk = (dir) => {
|
||||
for(const name of readdirSync(dir)) {
|
||||
const fullPath = normalizePath(join(dir, name));
|
||||
const stats = lstatSync(fullPath);
|
||||
const relPath = normalizePath(relative(root, fullPath));
|
||||
if(regex.test(relPath)) {
|
||||
results.push({path: relPath, type: stats.isDirectory() ? 'directory' : 'file', size: stats.size});
|
||||
}
|
||||
if(stats.isDirectory()) walk(fullPath);
|
||||
}
|
||||
};
|
||||
walk(root);
|
||||
|
||||
return results;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
export const FsWriteTool = (whitelist: null | string[] = null): AiTool => {
|
||||
return {
|
||||
name: 'fs_write',
|
||||
description: 'Create a directory, write content to a file or preform a find & replace',
|
||||
args: {
|
||||
path: {type: 'string', description: 'Path to file or directory', required: true},
|
||||
content: {type: 'string', description: 'Content to write or replace (Omit to create a directory)'},
|
||||
find: {type: 'string', description: 'Text or regex pattern to match (regex must match pattern: "/pattern/g")'}
|
||||
},
|
||||
fn: async ({path, content, find}) => {
|
||||
const {existsSync, mkdirSync, readFileSync, writeFileSync} = await import('fs');
|
||||
const {dirname} = await import('path');
|
||||
const normalizePath = p => p.replace(/\\/g, '/');
|
||||
|
||||
path = normalizePath(path);
|
||||
if(whitelist && !whitelist.some(p => path.startsWith(p))) return {error: 'Permission denied'};
|
||||
|
||||
if(content === undefined) {
|
||||
mkdirSync(path, {recursive: true});
|
||||
return {success: true, type: 'directory', path};
|
||||
}
|
||||
|
||||
const dir = normalizePath(dirname(path));
|
||||
if(!existsSync(dir)) mkdirSync(dir, {recursive: true});
|
||||
|
||||
if(find && existsSync(path)) {
|
||||
const existing = readFileSync(path, 'utf-8');
|
||||
const regexMatch = find.match(/^\/(.+)\/([gimuy]*)$/);
|
||||
const pattern = regexMatch ? new RegExp(regexMatch[1], regexMatch[2]) : find;
|
||||
|
||||
if(!existing.match(pattern)) return {error: 'Find pattern not found in file'};
|
||||
|
||||
const updated = existing.replace(pattern, content);
|
||||
writeFileSync(path, updated, 'utf-8');
|
||||
return {success: true, type: 'file', path, replaced: true, content: updated};
|
||||
}
|
||||
|
||||
writeFileSync(path, content, 'utf-8');
|
||||
return {success: true, type: 'file', path, content};
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
export const GetPathsTool: AiTool = {
|
||||
name: 'get_paths',
|
||||
description: 'Get the current working directory, and paths to the users home directory',
|
||||
fn: async () => {
|
||||
return {
|
||||
home: os.homedir(),
|
||||
cwd: process.cwd()
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
export const GetDatetimeTool: AiTool = {
|
||||
name: 'get_datetime',
|
||||
description: 'Get local/UTC timestamp',
|
||||
args: {
|
||||
timezone: {type: 'string', description: 'Which timezone to return, defaults to local', enum: ['local', 'utc'], default: 'local'}
|
||||
},
|
||||
fn: ({timezone}) => new Date()[timezone === 'local' ? 'toString' : 'toUTCString']()
|
||||
}
|
||||
|
||||
export const GetDevice: AiTool = {
|
||||
name: 'get_device',
|
||||
description: 'Get comprehensive system information including hostname, specs, load, storage, and network status',
|
||||
args: {},
|
||||
fn: async () => {
|
||||
const platform = os.platform();
|
||||
const hostname = os.hostname();
|
||||
|
||||
// CPU Info
|
||||
const cpus = os.cpus();
|
||||
const cpuModel = cpus[0].model;
|
||||
const cpuCores = cpus.length;
|
||||
|
||||
// Memory Info
|
||||
const totalMem: any = (os.totalmem() / 1024 / 1024 / 1024).toFixed(2);
|
||||
const freeMem: any = (os.freemem() / 1024 / 1024 / 1024).toFixed(2);
|
||||
const usedMem: any = (totalMem - freeMem).toFixed(2);
|
||||
const memUsage: any = ((usedMem / totalMem) * 100).toFixed(1);
|
||||
|
||||
// Load Average (not available on Windows)
|
||||
const loadAvg = platform === 'win32' ? ['N/A', 'N/A', 'N/A'] : os.loadavg().map(l => l.toFixed(2));
|
||||
|
||||
// Storage Usage
|
||||
let storage = {};
|
||||
if(platform === 'win32') {
|
||||
const ps = $Sync`powershell "Get-PSDrive C | Select-Object Used,Free | ConvertTo-Json"`.trim();
|
||||
const drive = JSON.parse(ps);
|
||||
const used: any = (drive.Used / 1024 / 1024 / 1024).toFixed(2);
|
||||
const free: any = (drive.Free / 1024 / 1024 / 1024).toFixed(2);
|
||||
const total: any = (parseFloat(used) + parseFloat(free)).toFixed(2);
|
||||
const usage: any = ((used / total) * 100).toFixed(1);
|
||||
storage = {
|
||||
filesystem: 'C:',
|
||||
size: `${total} GB`,
|
||||
used: `${used} GB`,
|
||||
available: `${free} GB`,
|
||||
usage: `${usage}%`
|
||||
};
|
||||
} else {
|
||||
const df = $Sync`df -h / | tail -1`.trim();
|
||||
const s = df.split(/\s+/);
|
||||
storage = {
|
||||
filesystem: s[0],
|
||||
size: s[1],
|
||||
used: s[2],
|
||||
available: s[3],
|
||||
usage: s[4]
|
||||
};
|
||||
}
|
||||
|
||||
// Network Status
|
||||
const interfaces = os.networkInterfaces();
|
||||
const activeIfaces = Object.entries(interfaces)
|
||||
.filter(([name]) => name !== 'lo' && !name.includes('Loopback'))
|
||||
.map(([name, addrs]) => {
|
||||
const ipv4 = addrs?.find(a => a.family === 'IPv4');
|
||||
return ipv4 ? {name, ip: ipv4.address} : null;
|
||||
})
|
||||
.filter(Boolean);
|
||||
|
||||
// Internet connectivity check
|
||||
let internet = false;
|
||||
try {
|
||||
if(platform === 'win32') {
|
||||
$Sync`powershell "Test-Connection -ComputerName 8.8.8.8 -Count 1 -Quiet"`;
|
||||
} else {
|
||||
$Sync`ping -c 1 -W 2 8.8.8.8 > /dev/null 2>&1`;
|
||||
}
|
||||
internet = true;
|
||||
} catch {}
|
||||
|
||||
// Uptime
|
||||
const uptime = os.uptime();
|
||||
const days = Math.floor(uptime / 86400);
|
||||
const hours = Math.floor((uptime % 86400) / 3600);
|
||||
const minutes = Math.floor((uptime % 3600) / 60);
|
||||
|
||||
return {
|
||||
hostname,
|
||||
cpu: {
|
||||
model: cpuModel,
|
||||
cores: cpuCores
|
||||
},
|
||||
memory: {
|
||||
total: `${totalMem} GB`,
|
||||
used: `${usedMem} GB`,
|
||||
free: `${freeMem} GB`,
|
||||
usage: `${memUsage}%`
|
||||
},
|
||||
load: {
|
||||
'1min': loadAvg[0],
|
||||
'5min': loadAvg[1],
|
||||
'15min': loadAvg[2]
|
||||
},
|
||||
storage,
|
||||
network: {
|
||||
interfaces: activeIfaces,
|
||||
internet: internet ? 'connected' : 'disconnected'
|
||||
},
|
||||
uptime: `${days}d ${hours}h ${minutes}m`,
|
||||
platform: `${os.type()} ${os.release()}`
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
export const GetWikipediaTool: AiTool = {
|
||||
name: 'get_wikipedia',
|
||||
description: 'Search Wikipedia for matching articles',
|
||||
args: {
|
||||
query: {type: 'string', description: 'Search term or article title', required: true},
|
||||
mode: {type: 'string', description: 'search - look for articles, summary - intro of first found article (default), full - complete first found article', enum: ['search', 'summary', 'full'], default: 'summary'},
|
||||
ua: {type: 'string', description: 'User Agent'},
|
||||
},
|
||||
fn: async ({query, mode, ua}) => {
|
||||
class WikipediaClient {
|
||||
useragent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)';
|
||||
|
||||
constructor(useragent: string) {
|
||||
this.useragent = useragent;
|
||||
}
|
||||
|
||||
async get(url) {
|
||||
const resp = await fetch(url, {headers: {'User-Agent': this.useragent}});
|
||||
return resp.json();
|
||||
}
|
||||
|
||||
api(params) {
|
||||
const qs = new URLSearchParams({...params, format: 'json', utf8: '1'}).toString();
|
||||
return this.get(`https://en.wikipedia.org/w/api.php?${qs}`);
|
||||
}
|
||||
|
||||
clean(text) {
|
||||
const cutoffs = ['== See also ==', '== References ==', '== Bibliography ==', '== External links =='];
|
||||
for (const marker of cutoffs) {
|
||||
const idx = text.indexOf(marker);
|
||||
if (idx !== -1) text = text.slice(0, idx);
|
||||
}
|
||||
|
||||
return text
|
||||
.replace(/^={4}\s*(.+?)\s*={4}$/gm, '#### $1')
|
||||
.replace(/^={3}\s*(.+?)\s*={3}$/gm, '### $1')
|
||||
.replace(/^={2}\s*(.+?)\s*={2}$/gm, '## $1')
|
||||
.replace(/\n{3,}/g, '\n\n')
|
||||
.replace(/ {2,}/g, ' ')
|
||||
.replace(/\[\d+]/g, '')
|
||||
.trim();
|
||||
}
|
||||
|
||||
async searchTitles(query: string, limit = 6) {
|
||||
const data = await this.api({action: 'query', list: 'search', srsearch: query, srlimit: limit, srprop: 'snippet'});
|
||||
return data.query?.search || [];
|
||||
}
|
||||
|
||||
async fetchExtract(title: string, introOnly = false) {
|
||||
const params: any = {action: 'query', prop: 'extracts', titles: title, explaintext: 1, redirects: 1};
|
||||
if(introOnly) params.exintro = 1;
|
||||
const data = await this.api(params);
|
||||
const page: any = Object.values(data.query?.pages || {})[0];
|
||||
return this.clean(page?.extract || '');
|
||||
}
|
||||
|
||||
pageUrl(title: string) {
|
||||
return `https://en.wikipedia.org/wiki/${encodeURIComponent(title.replace(/ /g, '_'))}`;
|
||||
}
|
||||
|
||||
stripHtml(text: string) {
|
||||
return text.replace(/<[^>]+>/g, '');
|
||||
}
|
||||
|
||||
async lookup(query: string, detail = 'summary') {
|
||||
const results = await this.searchTitles(query, 6);
|
||||
if(!results.length) return `❌ No Wikipedia articles found for "${query}"`;
|
||||
const title = results[0].title;
|
||||
const url = this.pageUrl(title);
|
||||
const introOnly = detail !== 'full';
|
||||
const content = await this.fetchExtract(title, introOnly);
|
||||
return `## ${title}\n🔗 ${url}\n\n${content}`;
|
||||
}
|
||||
|
||||
async search(query: string) {
|
||||
const results = await this.searchTitles(query, 8);
|
||||
if(!results.length) return `❌ No results for "${query}"`;
|
||||
const lines = [`### Search results for "${query}"\n`];
|
||||
for(let i = 0; i < results.length; i++) {
|
||||
const r = results[i];
|
||||
const snippet = this.stripHtml(r.snippet || '').trim();
|
||||
lines.push(`**${i + 1}. ${r.title}**\n${snippet}\n${this.pageUrl(r.title)}`);
|
||||
}
|
||||
return lines.join('\n\n');
|
||||
}
|
||||
}
|
||||
|
||||
const wiki = new WikipediaClient(ua);
|
||||
if(mode === 'search') return wiki.search(query);
|
||||
return wiki.lookup(query, mode || 'summary');
|
||||
}
|
||||
};
|
||||
|
||||
export const GeoCodeTool: AiTool = {
|
||||
name: 'geo_code',
|
||||
description: 'Converts coordinates to address OR vice versa',
|
||||
args: {
|
||||
query: {type: 'string', description: 'Search query - coordinates (lat,lon) or address string', required: true},
|
||||
},
|
||||
fn: async ({query}) => {
|
||||
const coordinates = /(-?\d+(?:\.\d+)?).*?,.*?(-?\d+(?:\.\d+)?)/.exec(query);
|
||||
if(coordinates) { // Geolocate
|
||||
const url = `https://nominatim.openstreetmap.org/reverse?format=json&lat=${encodeURIComponent(coordinates[1])}&lon=${encodeURIComponent(coordinates[2])}`;
|
||||
const response = await fetch(url, {headers: {'User-Agent': 'OpenSight/1.0', 'Accept-Language': 'en'}});
|
||||
const data = await response.json();
|
||||
if(data.display_name) return {address: data.display_name, mode: 'geolocate'};
|
||||
} else { // Geocode
|
||||
const url = `https://nominatim.openstreetmap.org/search?format=json&q=${encodeURIComponent(query)}`;
|
||||
const response = await fetch(url, {headers: {'User-Agent': 'OpenSight/1.0'}});
|
||||
const data = await response.json();
|
||||
if(data[0]) return {latitude: parseFloat(data[0].lat), longitude: parseFloat(data[0].lon), mode: 'geocode'};
|
||||
}
|
||||
return {error: 'Not found'};
|
||||
},
|
||||
}
|
||||
|
||||
export const GeoWeatherTool: AiTool = {
|
||||
name: 'geo_weather',
|
||||
description: 'Gets weather and air quality info for a location and time',
|
||||
args: {
|
||||
query: {type: 'string', description: 'Location - address or place name', required: true},
|
||||
day: {type: 'string', description: 'Date to retrieve (YYYY-MM-DD), defaults to today'},
|
||||
},
|
||||
fn: async ({query, day}) => {
|
||||
day = day || new Date().toISOString().slice(0, 10);
|
||||
|
||||
const geoUrl = `https://nominatim.openstreetmap.org/search?format=json&q=${encodeURIComponent(query)}`;
|
||||
const geoResponse = await fetch(geoUrl, {headers: {'User-Agent': 'OpenSight/1.0'}});
|
||||
const geoData = await geoResponse.json();
|
||||
if(!geoData[0]) return {error: 'Location not found'};
|
||||
|
||||
const lat = parseFloat(geoData[0].lat);
|
||||
const lon = parseFloat(geoData[0].lon);
|
||||
|
||||
const weatherUrl = `https://api.open-meteo.com/v1/forecast?latitude=${lat}&longitude=${lon}&start_date=${day}&end_date=${day}&daily=weathercode,temperature_2m_max,temperature_2m_min,apparent_temperature_max,apparent_temperature_min,precipitation_sum,precipitation_probability_max,windspeed_10m_max,winddirection_10m_dominant,uv_index_max,sunrise,sunset&timezone=auto`;
|
||||
const airUrl = `https://air-quality-api.open-meteo.com/v1/air-quality?latitude=${lat}&longitude=${lon}&start_date=${day}&end_date=${day}&hourly=us_aqi,european_aqi,pm10,pm2_5&timezone=auto`;
|
||||
|
||||
const [weatherResponse, airResponse] = await Promise.all([fetch(weatherUrl), fetch(airUrl)]);
|
||||
const weatherData = await weatherResponse.json();
|
||||
const airData = await airResponse.json();
|
||||
|
||||
const avg = arr => (arr && arr.length) ? arr.reduce((a, b) => a + b, 0) / arr.length : null;
|
||||
|
||||
return {
|
||||
location: geoData[0].display_name,
|
||||
latitude: lat,
|
||||
longitude: lon,
|
||||
elevation: weatherData.elevation,
|
||||
date: day,
|
||||
weatherCode: weatherData.daily?.weathercode?.[0],
|
||||
tempMax: weatherData.daily?.temperature_2m_max?.[0],
|
||||
tempMin: weatherData.daily?.temperature_2m_min?.[0],
|
||||
feelsLikeMax: weatherData.daily?.apparent_temperature_max?.[0],
|
||||
feelsLikeMin: weatherData.daily?.apparent_temperature_min?.[0],
|
||||
precipitation: weatherData.daily?.precipitation_sum?.[0],
|
||||
precipitationChance: weatherData.daily?.precipitation_probability_max?.[0],
|
||||
windSpeedMax: weatherData.daily?.windspeed_10m_max?.[0],
|
||||
windDirection: weatherData.daily?.winddirection_10m_dominant?.[0],
|
||||
uvIndexMax: weatherData.daily?.uv_index_max?.[0],
|
||||
sunrise: weatherData.daily?.sunrise?.[0],
|
||||
sunset: weatherData.daily?.sunset?.[0],
|
||||
usAqi: avg(airData.hourly?.us_aqi),
|
||||
europeanAqi: avg(airData.hourly?.european_aqi),
|
||||
pm10: avg(airData.hourly?.pm10),
|
||||
pm2_5: avg(airData.hourly?.pm2_5),
|
||||
};
|
||||
},
|
||||
}
|
||||
|
||||
export const WebFetchTool: AiTool = {
|
||||
name: 'web_fetch',
|
||||
description: 'Make HTTP request to URL',
|
||||
args: {
|
||||
url: {type: 'string', description: 'URL to fetch', required: true},
|
||||
@@ -154,30 +637,59 @@ export const FetchTool: AiTool = {
|
||||
}) => new Http({url: args.url, headers: args.headers}).request({method: args.method || 'GET', body: args.body})
|
||||
}
|
||||
|
||||
export const JSTool: AiTool = {
|
||||
name: 'exec_javascript',
|
||||
description: 'Execute commonjs javascript',
|
||||
export const WebFlareSolverTool = (host: string) => {
|
||||
return {
|
||||
name: 'web_flaresolverr',
|
||||
description: 'Use a flaresolverr proxy to bypass cloudflare bot detection',
|
||||
args: {
|
||||
code: {type: 'string', description: 'CommonJS javascript', required: true}
|
||||
url: {type: 'string', description: 'URL to fetch', required: true},
|
||||
cmd: {type: 'string', description: 'Flaresolverr cmd', enum: ['request.get', 'request.post'], default: 'request.get'},
|
||||
maxTimeout: {type: 'number', description: 'Fetch time limit', default: 60_000},
|
||||
postData: {type: 'object', description: 'Data to send during request.post requests'},
|
||||
},
|
||||
fn: async (args: {code: string}) => {
|
||||
const c = consoleInterceptor(null);
|
||||
const resp = await Fn<any>({console: c}, args.code, true).catch((err: any) => c.output.error.push(err));
|
||||
return {...c.output, return: resp, stdout: undefined, stderr: undefined};
|
||||
fn: async ({url, cmd, maxTimeout, postData}) => {
|
||||
function toFormUrlEncoded(obj, prefix = '') {
|
||||
const pairs: any = [];
|
||||
for (const key in obj) {
|
||||
if (!obj.hasOwnProperty(key)) continue;
|
||||
|
||||
const value = obj[key];
|
||||
const encodedKey = prefix
|
||||
? `${prefix}[${encodeURIComponent(key)}]`
|
||||
: encodeURIComponent(key);
|
||||
|
||||
if (value === null || value === undefined) {
|
||||
pairs.push(`${encodedKey}=`);
|
||||
} else if (typeof value === 'object' && !Array.isArray(value)) {
|
||||
pairs.push(toFormUrlEncoded(value, encodedKey));
|
||||
} else if (Array.isArray(value)) {
|
||||
value.forEach(item => {
|
||||
pairs.push(`${encodedKey}[]=${encodeURIComponent(item)}`);
|
||||
});
|
||||
} else {
|
||||
pairs.push(`${encodedKey}=${encodeURIComponent(value)}`);
|
||||
}
|
||||
}
|
||||
|
||||
return pairs.join('&');
|
||||
}
|
||||
|
||||
const res = await fetch(host + '/v1', {
|
||||
method: 'POST',
|
||||
headers: {'Content-Type': 'application/json'},
|
||||
body: JSON.stringify({cmd, url, maxTimeout, postData: postData ? toFormUrlEncoded(postData) : undefined}),
|
||||
});
|
||||
|
||||
if(!res.ok) throw new Error(`FlareSolverr HTTP error: ${res.status} ${res.statusText}`);
|
||||
const data = await res.json();
|
||||
if(data.status !== 'ok') throw new Error(`FlareSolverr error: ${data.message ?? data.status}`);
|
||||
return data.solution.response;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
export const PythonTool: AiTool = {
|
||||
name: 'exec_javascript',
|
||||
description: 'Execute commonjs javascript',
|
||||
args: {
|
||||
code: {type: 'string', description: 'CommonJS javascript', required: true}
|
||||
},
|
||||
fn: async (args: {code: string}) => ({result: $Sync`python -c "${args.code}"`})
|
||||
}
|
||||
|
||||
export const ReadWebpageTool: AiTool = {
|
||||
name: 'read_webpage',
|
||||
export const WebReadTool: AiTool = {
|
||||
name: 'web_read',
|
||||
description: 'Extract clean content from webpages, or convert media/documents to accessible formats',
|
||||
args: {
|
||||
url: {type: 'string', description: 'URL to read', required: true},
|
||||
@@ -305,94 +817,3 @@ export const WebSearchTool: AiTool = {
|
||||
return results;
|
||||
}
|
||||
}
|
||||
|
||||
class WikipediaClient {
|
||||
private async get(url: string): Promise<any> {
|
||||
const resp = await fetch(url, {headers: {'User-Agent': UA}});
|
||||
return resp.json();
|
||||
}
|
||||
|
||||
private api(params: Record<string, any>): Promise<any> {
|
||||
const qs = new URLSearchParams({...params, format: 'json', utf8: '1'}).toString();
|
||||
return this.get(`https://en.wikipedia.org/w/api.php?${qs}`);
|
||||
}
|
||||
|
||||
private clean(text: string): string {
|
||||
return text.replace(/\n{3,}/g, '\n\n').replace(/ {2,}/g, ' ').replace(/\[\d+\]/g, '').trim();
|
||||
}
|
||||
|
||||
private truncate(text: string, max: number): string {
|
||||
if(text.length <= max) return text;
|
||||
const cut = text.slice(0, max);
|
||||
const lastPara = cut.lastIndexOf('\n\n');
|
||||
return lastPara > max * 0.7 ? cut.slice(0, lastPara) : cut;
|
||||
}
|
||||
|
||||
private async searchTitles(query: string, limit = 6): Promise<any[]> {
|
||||
const data = await this.api({action: 'query', list: 'search', srsearch: query, srlimit: limit, srprop: 'snippet'});
|
||||
return data.query?.search || [];
|
||||
}
|
||||
|
||||
private async fetchExtract(title: string, intro = false): Promise<string> {
|
||||
const params: any = {action: 'query', prop: 'extracts', titles: title, explaintext: 1, redirects: 1};
|
||||
if(intro) params.exintro = 1;
|
||||
const data = await this.api(params);
|
||||
const page = Object.values(data.query?.pages || {})[0] as any;
|
||||
return this.clean(page?.extract || '');
|
||||
}
|
||||
|
||||
private pageUrl(title: string): string {
|
||||
return `https://en.wikipedia.org/wiki/${encodeURIComponent(title.replace(/ /g, '_'))}`;
|
||||
}
|
||||
|
||||
private stripHtml(text: string): string {
|
||||
return text.replace(/<[^>]+>/g, '');
|
||||
}
|
||||
|
||||
async lookup(query: string, detail: 'intro' | 'full' = 'intro'): Promise<string> {
|
||||
const results = await this.searchTitles(query, 6);
|
||||
if(!results.length) return `❌ No Wikipedia articles found for "${query}"`;
|
||||
const title = results[0].title;
|
||||
const url = this.pageUrl(title);
|
||||
const content = await this.fetchExtract(title, detail === 'intro');
|
||||
const text = this.truncate(content, detail === 'intro' ? 2000 : 8000);
|
||||
return `## ${title}\n🔗 ${url}\n\n${text}`;
|
||||
}
|
||||
|
||||
async search(query: string): Promise<string> {
|
||||
const results = await this.searchTitles(query, 8);
|
||||
if(!results.length) return `❌ No results for "${query}"`;
|
||||
const lines = [`### Search results for "${query}"\n`];
|
||||
for(let i = 0; i < results.length; i++) {
|
||||
const r = results[i];
|
||||
const snippet = this.truncate(this.stripHtml(r.snippet || ''), 150);
|
||||
lines.push(`**${i + 1}. ${r.title}**\n${snippet}\n${this.pageUrl(r.title)}`);
|
||||
}
|
||||
return lines.join('\n\n');
|
||||
}
|
||||
}
|
||||
|
||||
export const WikipediaLookupTool: AiTool = {
|
||||
name: 'wikipedia_lookup',
|
||||
description: 'Get Wikipedia article content',
|
||||
args: {
|
||||
query: {type: 'string', description: 'Topic or article title', required: true},
|
||||
detail: {type: 'string', description: 'Content level: "intro" (summary, default) or "full" (complete article)', enum: ['intro', 'full'], default: 'intro'}
|
||||
},
|
||||
fn: async (args: {query: string; detail?: 'intro' | 'full'}) => {
|
||||
const wiki = new WikipediaClient();
|
||||
return wiki.lookup(args.query, args.detail || 'intro');
|
||||
}
|
||||
};
|
||||
|
||||
export const WikipediaSearchTool: AiTool = {
|
||||
name: 'wikipedia_search',
|
||||
description: 'Search Wikipedia for matching articles',
|
||||
args: {
|
||||
query: {type: 'string', description: 'Search terms', required: true}
|
||||
},
|
||||
fn: async (args: {query: string}) => {
|
||||
const wiki = new WikipediaClient();
|
||||
return wiki.search(args.query);
|
||||
}
|
||||
};
|
||||
|
||||
Reference in New Issue
Block a user