Compare commits
7 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| 8dfcd06752 | |||
| 14f6cdd313 | |||
| 73d6ee0f2a | |||
| bee4085666 | |||
| 3b5c71de7c | |||
| 8229e02a52 | |||
| a6fb8ae828 |
234
package-lock.json
generated
234
package-lock.json
generated
@@ -1,12 +1,12 @@
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|||||||
{
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||||||
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@@ -2433,9 +2473,9 @@
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@@ -2454,9 +2494,9 @@
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@@ -2475,9 +2515,9 @@
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@@ -2496,9 +2536,9 @@
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"node_modules/lightningcss-linux-arm-gnueabihf": {
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"cpu": [
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"cpu": [
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@@ -2517,9 +2557,9 @@
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}
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},
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},
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"node_modules/lightningcss-linux-arm64-gnu": {
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"node_modules/lightningcss-linux-arm64-gnu": {
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"version": "1.33.0",
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"integrity": "sha512-0nnMyoyOLRJXfbMOilaSRcLH3Jw5z9HDNGfT/gwCPgaDjnx0i8w7vBzFLFR1f6CMLKF8gVbebmkUN3fa/kQJpQ==",
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"cpu": [
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"cpu": [
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"arm64"
|
"arm64"
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],
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],
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@@ -2541,9 +2581,9 @@
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}
|
}
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},
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},
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"node_modules/lightningcss-linux-arm64-musl": {
|
"node_modules/lightningcss-linux-arm64-musl": {
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"version": "1.32.0",
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"version": "1.33.0",
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"resolved": "https://registry.npmjs.org/lightningcss-linux-arm64-musl/-/lightningcss-linux-arm64-musl-1.32.0.tgz",
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"resolved": "https://registry.npmjs.org/lightningcss-linux-arm64-musl/-/lightningcss-linux-arm64-musl-1.33.0.tgz",
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"integrity": "sha512-UpQkoenr4UJEzgVIYpI80lDFvRmPVg6oqboNHfoH4CQIfNA+HOrZ7Mo7KZP02dC6LjghPQJeBsvXhJod/wnIBg==",
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"cpu": [
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"cpu": [
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"arm64"
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],
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],
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@@ -2565,9 +2605,9 @@
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}
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}
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},
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},
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"node_modules/lightningcss-linux-x64-gnu": {
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"node_modules/lightningcss-linux-x64-gnu": {
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"version": "1.32.0",
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"version": "1.33.0",
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"resolved": "https://registry.npmjs.org/lightningcss-linux-x64-gnu/-/lightningcss-linux-x64-gnu-1.32.0.tgz",
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"resolved": "https://registry.npmjs.org/lightningcss-linux-x64-gnu/-/lightningcss-linux-x64-gnu-1.33.0.tgz",
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"integrity": "sha512-V7Qr52IhZmdKPVr+Vtw8o+WLsQJYCTd8loIfpDaMRWGUZfBOYEJeyJIkqGIDMZPwPx24pUMfwSxxI8phr/MbOA==",
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"cpu": [
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],
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@@ -2589,9 +2629,9 @@
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}
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}
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},
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},
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"node_modules/lightningcss-linux-x64-musl": {
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"node_modules/lightningcss-linux-x64-musl": {
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"version": "1.32.0",
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"version": "1.33.0",
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"resolved": "https://registry.npmjs.org/lightningcss-linux-x64-musl/-/lightningcss-linux-x64-musl-1.32.0.tgz",
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"resolved": "https://registry.npmjs.org/lightningcss-linux-x64-musl/-/lightningcss-linux-x64-musl-1.33.0.tgz",
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"integrity": "sha512-bYcLp+Vb0awsiXg/80uCRezCYHNg1/l3mt0gzHnWV9XP1W5sKa5/TCdGWaR/zBM2PeF/HbsQv/j2URNOiVuxWg==",
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"integrity": "sha512-RYiYbkokw0trfKqqzfF55lginwEPrD3OJDfTuJzFs1MK6iFnDenaz1fqLLtX4ITG3OktJQXOeTaw1awrBAlZPw==",
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"cpu": [
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"cpu": [
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"x64"
|
"x64"
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],
|
],
|
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@@ -2613,9 +2653,9 @@
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}
|
}
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},
|
},
|
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"node_modules/lightningcss-win32-arm64-msvc": {
|
"node_modules/lightningcss-win32-arm64-msvc": {
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"version": "1.32.0",
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"version": "1.33.0",
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"resolved": "https://registry.npmjs.org/lightningcss-win32-arm64-msvc/-/lightningcss-win32-arm64-msvc-1.32.0.tgz",
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"resolved": "https://registry.npmjs.org/lightningcss-win32-arm64-msvc/-/lightningcss-win32-arm64-msvc-1.33.0.tgz",
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"integrity": "sha512-8SbC8BR40pS6baCM8sbtYDSwEVQd4JlFTOlaD3gWGHfThTcABnNDBda6eTZeqbofalIJhFx0qKzgHJmcPTnGdw==",
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"integrity": "sha512-1K+MPfLSFVpphzpdbfkhlWk6wBrTObBzS2T6db10PNOZgR9GoVsAWzwNyuhUYYbTp23j+4RrncfujZ4uAzXvwA==",
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"cpu": [
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"cpu": [
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"arm64"
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"arm64"
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],
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],
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@@ -2634,9 +2674,9 @@
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}
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}
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},
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},
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"node_modules/lightningcss-win32-x64-msvc": {
|
"node_modules/lightningcss-win32-x64-msvc": {
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"version": "1.32.0",
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"version": "1.33.0",
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"resolved": "https://registry.npmjs.org/lightningcss-win32-x64-msvc/-/lightningcss-win32-x64-msvc-1.32.0.tgz",
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"resolved": "https://registry.npmjs.org/lightningcss-win32-x64-msvc/-/lightningcss-win32-x64-msvc-1.33.0.tgz",
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"integrity": "sha512-Amq9B/SoZYdDi1kFrojnoqPLxYhQ4Wo5XiL8EVJrVsB8ARoC1PWW6VGtT0WKCemjy8aC+louJnjS7U18x3b06Q==",
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"integrity": "sha512-OlEICDx/Xl0FqSp4bry8zFnCvGpig3Gl4gCquvYwHuqJKEC1+n9NgDniFvqHGmMv1ZkqDJrDqKKSykTDX+ehuA==",
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"cpu": [
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"cpu": [
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"x64"
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"x64"
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],
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],
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@@ -2794,9 +2834,9 @@
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}
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}
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},
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},
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"node_modules/mdurl": {
|
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"version": "2.0.0",
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"version": "2.1.0",
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"resolved": "https://registry.npmjs.org/mdurl/-/mdurl-2.0.0.tgz",
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"resolved": "https://registry.npmjs.org/mdurl/-/mdurl-2.1.0.tgz",
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"integrity": "sha512-Lf+9+2r+Tdp5wXDXC4PcIBjTDtq4UKjCPMQhKIuzpJNW0b96kVqSwW0bT7FhRSfmAiFYgP+SCRvdrDozfh0U5w==",
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"integrity": "sha512-1+HBaOx0zi/dQWht8rNv9MYf9qqpqL/kxI0hXImU6Y547zM6Sni8BQibt7ifgMcYtQg41ao3Ivd6cnSM86inpg==",
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"dev": true,
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"dev": true,
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"license": "MIT"
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"license": "MIT"
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},
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},
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@@ -2971,9 +3011,9 @@
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"license": "MIT"
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"license": "MIT"
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},
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"node_modules/nanoid": {
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"version": "3.3.15",
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"version": "3.3.16",
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"resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.15.tgz",
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"resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.16.tgz",
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"integrity": "sha512-y7Wygv/7mEOvxTuEQDB8StXdMRBWf1kR/tlhAzBRUFkB2jfcLOAxO/SHmOO2zgz1pVgK29/kyupn059/bCHdjA==",
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"integrity": "sha512-bzlKTyNJ7+LdGIIwy8ijFpIqEQIvafahV7eYykJ8Cvh42EdJeODoJ6gUJXpQJvej1BddH8OqTXZNE/KfbWAu8Q==",
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"dev": true,
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"dev": true,
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"funding": [
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"funding": [
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{
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{
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@@ -3092,9 +3132,9 @@
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"license": "MIT"
|
"license": "MIT"
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},
|
},
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"node_modules/openai": {
|
"node_modules/openai": {
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"version": "6.46.0",
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"version": "6.49.0",
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"resolved": "https://registry.npmjs.org/openai/-/openai-6.46.0.tgz",
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"resolved": "https://registry.npmjs.org/openai/-/openai-6.49.0.tgz",
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"integrity": "sha512-DFg6jEPT2RO+oAyXtddeUJU8zkGy1OQ1AjGzNIJUMQG03TTqvCpy9tBpQ+2VVVnvrl3E56F8GEin2JYtWpITtA==",
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"integrity": "sha512-aYCc0C6L864eR6WSYIwQGyXriw/nIyZx0ObvhzOEVuk0zoBDpynjSbrionWI7q65B5H8jJX0DXR9snEzM6bfPg==",
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"license": "Apache-2.0",
|
"license": "Apache-2.0",
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"peerDependencies": {
|
"peerDependencies": {
|
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"@aws-sdk/credential-provider-node": ">=3.972.0 <4",
|
"@aws-sdk/credential-provider-node": ">=3.972.0 <4",
|
||||||
@@ -3232,9 +3272,9 @@
|
|||||||
"license": "MIT"
|
"license": "MIT"
|
||||||
},
|
},
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||||||
"node_modules/postcss": {
|
"node_modules/postcss": {
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"version": "8.5.17",
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"version": "8.5.25",
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"resolved": "https://registry.npmjs.org/postcss/-/postcss-8.5.17.tgz",
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"resolved": "https://registry.npmjs.org/postcss/-/postcss-8.5.25.tgz",
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"integrity": "sha512-J7EF+8X+CzRPaJPOv9Ck2wNWJvGnnl3PcNPAdGg6GTLjyVpyQ0yATMSXRFRV01BviT/9Gwuc3rjEyJbDJG9a4w==",
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"integrity": "sha512-DTPx3RWSSnWyzLxQnlH0rJP+EW5ekl16ZU4/psbIhA0e53kJfdgaN5vKM+xP7yJtXVu+nfdVFmlgFDEKAe4Pyw==",
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"dev": true,
|
"dev": true,
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"funding": [
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"funding": [
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{
|
{
|
||||||
@@ -3252,7 +3292,7 @@
|
|||||||
],
|
],
|
||||||
"license": "MIT",
|
"license": "MIT",
|
||||||
"dependencies": {
|
"dependencies": {
|
||||||
"nanoid": "^3.3.12",
|
"nanoid": "^3.3.16",
|
||||||
"picocolors": "^1.1.1",
|
"picocolors": "^1.1.1",
|
||||||
"source-map-js": "^1.2.1"
|
"source-map-js": "^1.2.1"
|
||||||
},
|
},
|
||||||
@@ -3787,9 +3827,9 @@
|
|||||||
"license": "MIT"
|
"license": "MIT"
|
||||||
},
|
},
|
||||||
"node_modules/undici": {
|
"node_modules/undici": {
|
||||||
"version": "7.28.0",
|
"version": "7.29.0",
|
||||||
"resolved": "https://registry.npmjs.org/undici/-/undici-7.28.0.tgz",
|
"resolved": "https://registry.npmjs.org/undici/-/undici-7.29.0.tgz",
|
||||||
"integrity": "sha512-cRZYrTDwWznlnRiPjggAGxZXanty6M8RV1ff8Wm4LWXBp7/IG8v5DnOm74DtUBp9OONpK75YlPnIjQqX0dBDtA==",
|
"integrity": "sha512-IDxfleLmmbSskfWSUATiN1nfn2rDuvnMOqb5CWR92iIfojA0Ud+ulOAAEQ57LPr9rWmsreUyf5lwyao+7GNNVw==",
|
||||||
"license": "MIT",
|
"license": "MIT",
|
||||||
"engines": {
|
"engines": {
|
||||||
"node": ">=20.18.1"
|
"node": ">=20.18.1"
|
||||||
@@ -3981,16 +4021,16 @@
|
|||||||
}
|
}
|
||||||
},
|
},
|
||||||
"node_modules/vite": {
|
"node_modules/vite": {
|
||||||
"version": "8.1.4",
|
"version": "8.1.5",
|
||||||
"resolved": "https://registry.npmjs.org/vite/-/vite-8.1.4.tgz",
|
"resolved": "https://registry.npmjs.org/vite/-/vite-8.1.5.tgz",
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||||||
"integrity": "sha512-bTT9PsdWO+MQMNG9ZXIP/qM9wGh37DFxTV/sPq9cFpHr3w4jkgef032PkAL9jAqhk3Nz8NQw3O8n6/xFkqO4QQ==",
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"integrity": "sha512-7ULLwsCdYx/nRyrpiEwvqb5TFHrMVZyBt+rg/OAXT7rgj/z+DtTDyKFeLAdDkubDVDKD8jOsndmy7m55XcfUsw==",
|
||||||
"dev": true,
|
"dev": true,
|
||||||
"license": "MIT",
|
"license": "MIT",
|
||||||
"dependencies": {
|
"dependencies": {
|
||||||
"lightningcss": "^1.32.0",
|
"lightningcss": "^1.32.0",
|
||||||
"picomatch": "^4.0.5",
|
"picomatch": "^4.0.5",
|
||||||
"postcss": "^8.5.16",
|
"postcss": "^8.5.17",
|
||||||
"rolldown": "~1.1.4",
|
"rolldown": "~1.1.5",
|
||||||
"tinyglobby": "^0.2.17"
|
"tinyglobby": "^0.2.17"
|
||||||
},
|
},
|
||||||
"bin": {
|
"bin": {
|
||||||
|
|||||||
@@ -1,6 +1,6 @@
|
|||||||
{
|
{
|
||||||
"name": "@ztimson/ai-utils",
|
"name": "@ztimson/ai-utils",
|
||||||
"version": "1.2.1",
|
"version": "1.2.7",
|
||||||
"description": "AI Utility library",
|
"description": "AI Utility library",
|
||||||
"author": "Zak Timson",
|
"author": "Zak Timson",
|
||||||
"license": "MIT",
|
"license": "MIT",
|
||||||
|
|||||||
@@ -3,6 +3,8 @@ export * from './antrhopic';
|
|||||||
export * from './audio';
|
export * from './audio';
|
||||||
export * from './llm';
|
export * from './llm';
|
||||||
export * from './memory';
|
export * from './memory';
|
||||||
|
export * from './memory-cache';
|
||||||
|
export * from './memory-graph';
|
||||||
export * from './open-ai';
|
export * from './open-ai';
|
||||||
export * from './provider';
|
export * from './provider';
|
||||||
export * from './tools';
|
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 ${this.dims}`
|
||||||
|
);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
private validate(point: KDPoint<T>): void {
|
||||||
|
this.validateVector(point.vector);
|
||||||
|
}
|
||||||
|
|
||||||
|
private validateAll(points: KDPoint<T>[]): void {
|
||||||
|
for (const p of points) this.validate(p);
|
||||||
|
}
|
||||||
|
}
|
||||||
59
src/llm.ts
59
src/llm.ts
@@ -1,5 +1,6 @@
|
|||||||
import {AbortablePromise, Ai} from './ai.ts';
|
import {AbortablePromise, Ai} from './ai.ts';
|
||||||
import {Anthropic} from './antrhopic.ts';
|
import {Anthropic} from './antrhopic.ts';
|
||||||
|
import {MemoryCache} from './memory-cache.ts';
|
||||||
import {OpenAi} from './open-ai.ts';
|
import {OpenAi} from './open-ai.ts';
|
||||||
import {LLMProvider} from './provider.ts';
|
import {LLMProvider} from './provider.ts';
|
||||||
import {AiTool, AiToolArg} from './tools.ts';
|
import {AiTool, AiToolArg} from './tools.ts';
|
||||||
@@ -55,7 +56,7 @@ export type LLMRequest = {
|
|||||||
/** Compress old messages in the chat to free up context */
|
/** Compress old messages in the chat to free up context */
|
||||||
compress?: {max: number; min: number};
|
compress?: {max: number; min: number};
|
||||||
/** User's memory documents - RAG injected automatically each turn */
|
/** User's memory documents - RAG injected automatically each turn */
|
||||||
memory?: Memory[];
|
memory?: Memory[] | MemoryCache;
|
||||||
/** Model to use for memory operations */
|
/** Model to use for memory operations */
|
||||||
memoryModel?: string;
|
memoryModel?: string;
|
||||||
/** Skill documents the AI can browse and read on demand */
|
/** Skill documents the AI can browse and read on demand */
|
||||||
@@ -143,7 +144,7 @@ class LLM {
|
|||||||
return {
|
return {
|
||||||
prompt: `You have access to the following skill documents, use \`read_skill\` to access them:\n${list}`,
|
prompt: `You have access to the following skill documents, use \`read_skill\` to access them:\n${list}`,
|
||||||
tools: [{
|
tools: [{
|
||||||
name: 'read_skill',
|
name: 'skill_read',
|
||||||
description: 'Read the full content of a skill/knowledge document',
|
description: 'Read the full content of a skill/knowledge document',
|
||||||
args: {
|
args: {
|
||||||
name: {type: 'string', description: 'Exact skill name', required: true}
|
name: {type: 'string', description: 'Exact skill name', required: true}
|
||||||
@@ -167,8 +168,14 @@ class LLM {
|
|||||||
}
|
}
|
||||||
const m = options.model || this.defaultModel;
|
const m = options.model || this.defaultModel;
|
||||||
if(!this.models[m]) throw new Error(`Model does not exist: ${m}`);
|
if(!this.models[m]) throw new Error(`Model does not exist: ${m}`);
|
||||||
let abort = () => {};
|
let request: AbortablePromise<string> | null = null;
|
||||||
return Object.assign(new Promise<string>(async res => {
|
let aborted = false;
|
||||||
|
const abort = () => {
|
||||||
|
aborted = true;
|
||||||
|
request?.abort?.();
|
||||||
|
};
|
||||||
|
|
||||||
|
const promise = (async () => {
|
||||||
let tools: AiTool[] = options.tools || this.ai.options.llm?.tools || [];
|
let tools: AiTool[] = options.tools || this.ai.options.llm?.tools || [];
|
||||||
const prompts: string[] = [];
|
const prompts: string[] = [];
|
||||||
let history = options.history || [];
|
let history = options.history || [];
|
||||||
@@ -190,23 +197,29 @@ class LLM {
|
|||||||
}
|
}
|
||||||
|
|
||||||
// Memory
|
// Memory
|
||||||
if(options.memory) {
|
if (options.memory) {
|
||||||
const relevant = await this.memoryManager.recollect(message, options.memory, 1);
|
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:
|
prompts.unshift(`You have access to the following memory files:
|
||||||
${options.memory.map(m => `- ${m.name}: ${m.description}`).join('\n')}
|
${mems.map(m => `- ${m.name}: ${m.description}`).join('\n')}
|
||||||
${relevant.length ? `
|
${relevant.length ? `
|
||||||
The closest memory has been added primitively:
|
Relevant memories have been preloaded:
|
||||||
\`\`\`
|
${relevant.map(r => `
|
||||||
Name: ${relevant[0].name}
|
**${r.name}**
|
||||||
Description: ${relevant[0].description}
|
${r.description}
|
||||||
${relevant[0].content}
|
${r.content}
|
||||||
\`\`\`
|
`).join('\n---\n')}
|
||||||
`: ''}`.trim());
|
` : ''}`.trim());
|
||||||
tools.push(this.memoryManager.tools.read(<Memory[]>options.memory));
|
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 || '');
|
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
|
// Trim memory injections from history
|
||||||
if(options.memory) {
|
if(options.memory) {
|
||||||
@@ -215,21 +228,23 @@ ${relevant[0].content}
|
|||||||
|
|
||||||
// Auto-memorize before compressing
|
// Auto-memorize before compressing
|
||||||
if(options.compress && this.estimateTokens(history) >= options.compress.max) {
|
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);
|
const compressed = await this.compressHistory(history, options.compress.max, options.compress.min, options);
|
||||||
if(options.history) options.history.splice(0, options.history.length, ...compressed);
|
if(options.history) options.history.splice(0, options.history.length, ...compressed);
|
||||||
}
|
}
|
||||||
|
|
||||||
return res(resp);
|
return resp;
|
||||||
}), {abort});
|
})();
|
||||||
|
|
||||||
|
return Object.assign(promise, {abort});
|
||||||
}
|
}
|
||||||
|
|
||||||
/**
|
/**
|
||||||
* Digest full conversation history into memory documents.
|
* Digest full conversation history into memory documents.
|
||||||
* Call on session end to persist the conversation.
|
* Call on session end to persist the conversation.
|
||||||
*/
|
*/
|
||||||
async updateMemory(history: LLMMessage[], memories: Memory[], options: LLMRequest = {}): Promise<void> {
|
async updateMemory(history: LLMMessage[], memories: Memory[] | MemoryCache, options: LLMRequest = {}): Promise<Memory[]> {
|
||||||
await this.memoryManager.memorize(history, memories, {model: this.defaultModel, ...options});
|
return this.memoryManager.memorize(history, memories, {model: this.defaultModel, ...options});
|
||||||
}
|
}
|
||||||
|
|
||||||
/**
|
/**
|
||||||
|
|||||||
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();
|
||||||
|
}
|
||||||
585
src/memory.ts
585
src/memory.ts
@@ -1,177 +1,484 @@
|
|||||||
// memory.ts
|
|
||||||
import {LLMRequest, LLMMessage} from './llm.ts';
|
import {LLMRequest, LLMMessage} from './llm.ts';
|
||||||
|
import {MemoryCache} from './memory-cache.ts';
|
||||||
import {AiTool} from './tools.ts';
|
import {AiTool} from './tools.ts';
|
||||||
|
|
||||||
/** Background information the AI will be fed as a knowledge document */
|
|
||||||
export type Memory = {
|
export type Memory = {
|
||||||
/** Memory subject */
|
|
||||||
name: string;
|
name: string;
|
||||||
/** Short description of what this document contains - used for RAG retrieval */
|
|
||||||
description: string;
|
description: string;
|
||||||
/** Full markdown content of the document */
|
|
||||||
content: string;
|
content: string;
|
||||||
/** Embedding vector of the description - used for similarity search */
|
|
||||||
embedding: number[];
|
embedding: number[];
|
||||||
}
|
}
|
||||||
|
|
||||||
export type MemoryCollection = {
|
export type MemoryRef = {
|
||||||
/** Memory subject */
|
|
||||||
name: string;
|
name: string;
|
||||||
/** Short description - required if isNew */
|
description: string;
|
||||||
description?: string;
|
}
|
||||||
/** Extracted facts to merge */
|
|
||||||
|
export type FactBucket = {
|
||||||
|
subject: string;
|
||||||
facts: 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 {
|
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 = {
|
tools = {
|
||||||
edit: (memory: Memory): AiTool => ({
|
read: (memories: Memory[] | MemoryCache): AiTool => ({
|
||||||
name: 'edit_memory',
|
name: 'memory_recall',
|
||||||
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',
|
description: 'Read the full content of a memory 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',
|
|
||||||
args: {
|
args: {
|
||||||
name: {type: 'string', description: 'Exact memory name', required: true},
|
name: {type: 'string', description: 'Exact memory name', required: true},
|
||||||
},
|
},
|
||||||
fn: (args: any) => {
|
fn: (args: any) => {
|
||||||
const mem = memories.find(m => m.name === args.name);
|
const mems = memories instanceof MemoryCache ? memories.memories : memories;
|
||||||
if(!mem) return 'Document not found';
|
const mem = mems.find(m => m.name === args.name);
|
||||||
return `Name: ${mem.name}\nDescription: ${mem.description}\n\n${mem.content}`;
|
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;
|
||||||
|
|
||||||
/**
|
for (const node of mem) {
|
||||||
* Extracts facts from conversation and groups them into individual memories
|
const {links, backlinks} = extractMetadata(node.content);
|
||||||
* @param {string} conversation Full conversation formatted as [role]: content
|
const newBacklinks = backlinks.filter(b => b !== name);
|
||||||
* @param {Memory[]} memories The user's memory documents
|
const newLinks = links.filter(l => l !== name);
|
||||||
* @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: ${m.description}`).join('\n\n');
|
|
||||||
const pools: MemoryCollection[] = [];
|
|
||||||
await this.llm.ask(conversation, {
|
|
||||||
model: this.model || options.model,
|
|
||||||
temperature: 0.2,
|
|
||||||
system: `You are a fact extractor. Analyze this conversation and extract facts worth remembering long term.
|
|
||||||
Rules:
|
|
||||||
- ONLY extract facts the USER explicitly stated about themselves or their business
|
|
||||||
- ONLY extract decisions that were MADE during this conversation
|
|
||||||
- DO NOT extract anything the AI said, its name, capabilities, or how it introduced itself
|
|
||||||
- DO NOT extract greetings, pleasantries or generic exchanges
|
|
||||||
- If nothing worth remembering was said, dont do anything, skip calling tools
|
|
||||||
|
|
||||||
For each fact decide whether it belongs in an existing document or needs a new one, then call the \`extract_facts\` tool.
|
if (newBacklinks.length !== backlinks.length || newLinks.length !== links.length) {
|
||||||
|
node.content = this.updateFrontmatter(node.content, {
|
||||||
Existing documents:\n${existingDocs || 'None yet.'}`,
|
links: newLinks,
|
||||||
tools: [this.tools.extract(pools)]
|
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;
|
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
/**
|
mem.splice(idx, 1);
|
||||||
* Find relevant memory documents for a query using description embeddings
|
|
||||||
* @param {string} query The query to search against
|
if (memories instanceof MemoryCache) memories.rebuild();
|
||||||
* @param {Memory[]} memories The user's memory documents
|
return true;
|
||||||
* @param {number} limit Max number of results to return
|
}
|
||||||
* @returns {Promise<Memory[]>} The most relevant memory documents
|
|
||||||
*/
|
private cosineSearch(query: number[], memories: Memory[], limit: number): MemoryRef[] {
|
||||||
async recollect(query: string, memories: Memory[], limit = 5): Promise<Memory[]> {
|
const scored = memories
|
||||||
const [e] = await this.llm.embedding(query);
|
|
||||||
return memories
|
|
||||||
.filter(m => m.embedding?.length)
|
.filter(m => m.embedding?.length)
|
||||||
.map(m => ({...m, score: this.llm.cosineSimilarity(m.embedding, e.embedding)}))
|
.map(m => ({
|
||||||
.toSorted((a: any, b: any) => b.score - a.score)
|
ref: {name: m.name, description: m.description},
|
||||||
|
distance: cosineDistance(query, m.embedding),
|
||||||
|
}))
|
||||||
|
.sort((a, b) => a.distance - b.distance)
|
||||||
.slice(0, limit);
|
.slice(0, limit);
|
||||||
|
return scored.map(s => s.ref);
|
||||||
}
|
}
|
||||||
|
|
||||||
/**
|
private listNodes(memories: Memory[]): MemoryRef[] {
|
||||||
* Two-stage memory pipeline: classify facts from conversation history then surgically merge them into documents.
|
return memories.map(m => ({name: m.name, description: m.description}));
|
||||||
* 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
|
async recollect(query: string, memories: Memory[] | MemoryCache, limit = 5, graphDepth = 1): Promise<Memory[]> {
|
||||||
* @param {LLMRequest} options LLM options
|
const mem: Memory[] = memories instanceof MemoryCache ? memories.memories : memories;
|
||||||
*/
|
if (!mem.length) return [];
|
||||||
async memorize(history: LLMMessage[], memories: Memory[], options: LLMRequest): Promise<void> {
|
|
||||||
|
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
|
const conversation = history
|
||||||
.filter(h => h.role === 'user' || h.role === 'assistant')
|
.filter(h => h.role === 'user' || h.role === 'assistant')
|
||||||
.map(h => `[${h.role}]: ${h.content}`)
|
.map(h => `[${h.role}]: ${h.content}`).join('\n\n').trim();
|
||||||
.join('\n\n');
|
if(!conversation) return [];
|
||||||
if(!conversation.trim()) return;
|
|
||||||
const pools = await this.extract(conversation, memories, options);
|
const trackingId = `${Date.now()}_${Math.random()}`;
|
||||||
if(!pools.length) return;
|
const tempMemory = await this.createTempMemory(conversation);
|
||||||
await Promise.all(pools.map(pool => this.edit(pool, memories, options)));
|
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 = update.description;
|
||||||
|
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 "Personal/..." (e.g., Personal/Info, Personal/Todos)
|
||||||
|
- 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}));
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|||||||
@@ -1,5 +1,5 @@
|
|||||||
import {AbortablePromise} from './ai.ts';
|
import {AbortablePromise} from './ai.ts';
|
||||||
import {LLMMessage, LLMRequest} from './llm.ts';
|
import {LLMRequest} from './llm.ts';
|
||||||
|
|
||||||
export abstract class LLMProvider {
|
export abstract class LLMProvider {
|
||||||
abstract ask(message: string, options: LLMRequest): AbortablePromise<string>;
|
abstract ask(message: string, options: LLMRequest): AbortablePromise<string>;
|
||||||
|
|||||||
25
src/tools.ts
25
src/tools.ts
@@ -100,16 +100,11 @@ export const CliTool: AiTool = {
|
|||||||
|
|
||||||
export const DateTimeTool: AiTool = {
|
export const DateTimeTool: AiTool = {
|
||||||
name: 'get_datetime',
|
name: 'get_datetime',
|
||||||
description: 'Get local date / time',
|
description: 'Get local/UTC date/time',
|
||||||
args: {},
|
args: {
|
||||||
fn: async () => new Date().toString()
|
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 DateTimeUTCTool: AiTool = {
|
|
||||||
name: 'get_datetime_utc',
|
|
||||||
description: 'Get current UTC date / time',
|
|
||||||
args: {},
|
|
||||||
fn: async () => new Date().toUTCString()
|
|
||||||
}
|
}
|
||||||
|
|
||||||
export const ExecTool: AiTool = {
|
export const ExecTool: AiTool = {
|
||||||
@@ -138,7 +133,7 @@ export const ExecTool: AiTool = {
|
|||||||
}
|
}
|
||||||
|
|
||||||
export const FetchTool: AiTool = {
|
export const FetchTool: AiTool = {
|
||||||
name: 'fetch',
|
name: 'net_fetch',
|
||||||
description: 'Make HTTP request to URL',
|
description: 'Make HTTP request to URL',
|
||||||
args: {
|
args: {
|
||||||
url: {type: 'string', description: 'URL to fetch', required: true},
|
url: {type: 'string', description: 'URL to fetch', required: true},
|
||||||
@@ -168,7 +163,7 @@ export const JSTool: AiTool = {
|
|||||||
}
|
}
|
||||||
|
|
||||||
export const PythonTool: AiTool = {
|
export const PythonTool: AiTool = {
|
||||||
name: 'exec_javascript',
|
name: 'exec_python',
|
||||||
description: 'Execute commonjs javascript',
|
description: 'Execute commonjs javascript',
|
||||||
args: {
|
args: {
|
||||||
code: {type: 'string', description: 'CommonJS javascript', required: true}
|
code: {type: 'string', description: 'CommonJS javascript', required: true}
|
||||||
@@ -177,7 +172,7 @@ export const PythonTool: AiTool = {
|
|||||||
}
|
}
|
||||||
|
|
||||||
export const ReadWebpageTool: AiTool = {
|
export const ReadWebpageTool: AiTool = {
|
||||||
name: 'read_webpage',
|
name: 'net_read',
|
||||||
description: 'Extract clean content from webpages, or convert media/documents to accessible formats',
|
description: 'Extract clean content from webpages, or convert media/documents to accessible formats',
|
||||||
args: {
|
args: {
|
||||||
url: {type: 'string', description: 'URL to read', required: true},
|
url: {type: 'string', description: 'URL to read', required: true},
|
||||||
@@ -281,7 +276,7 @@ export const ReadWebpageTool: AiTool = {
|
|||||||
};
|
};
|
||||||
|
|
||||||
export const WebSearchTool: AiTool = {
|
export const WebSearchTool: AiTool = {
|
||||||
name: 'web_search',
|
name: 'net_search',
|
||||||
description: 'Use duckduckgo (anonymous) to find find relevant online resources. Returns a list of URLs that works great with the `read_webpage` tool',
|
description: 'Use duckduckgo (anonymous) to find find relevant online resources. Returns a list of URLs that works great with the `read_webpage` tool',
|
||||||
args: {
|
args: {
|
||||||
query: {type: 'string', description: 'Search string', required: true},
|
query: {type: 'string', description: 'Search string', required: true},
|
||||||
@@ -307,7 +302,7 @@ export const WebSearchTool: AiTool = {
|
|||||||
}
|
}
|
||||||
|
|
||||||
export const WikipediaTool: AiTool = {
|
export const WikipediaTool: AiTool = {
|
||||||
name: 'wikipedia_search',
|
name: 'get_wikipedia',
|
||||||
description: 'Search Wikipedia for matching articles',
|
description: 'Search Wikipedia for matching articles',
|
||||||
args: {
|
args: {
|
||||||
query: {type: 'string', description: 'Search term or article title', required: true},
|
query: {type: 'string', description: 'Search term or article title', required: true},
|
||||||
|
|||||||
Reference in New Issue
Block a user