feat: integrate vectordb for note embeddings
- Added `vectordb` as a dependency in `package.json`. - Implemented `embedText` function in `src/main/ai/embeddings.ts` to handle text embeddings using GitHub Copilot OAuth token or OpenAI token. - Created `vectordb.ts` for managing LanceDB connection and embedding notes with upsert strategy. - Updated `index.ts` to initialize vector database and migrate existing notes on app ready. - Modified `router/index.ts` to fire-and-forget embedding calls on note creation and updates. - Enhanced `progress.txt` with detailed implementation notes and learnings regarding the integration.
This commit is contained in:
Roberto Musso
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package-lock.json
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"resolved": "https://registry.npmjs.org/lodash.camelcase/-/lodash.camelcase-4.3.0.tgz",
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"license": "MIT",
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"peer": true
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"version": "4.4.2",
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"resolved": "https://registry.npmjs.org/lodash.get/-/lodash.get-4.4.2.tgz",
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@@ -15952,7 +16308,6 @@
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"version": "1.1.0",
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"resolved": "https://registry.npmjs.org/math-intrinsics/-/math-intrinsics-1.1.0.tgz",
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"dev": true,
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"license": "MIT",
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"engines": {
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"node": ">= 0.4"
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@@ -16929,7 +17284,6 @@
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"version": "1.52.0",
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"resolved": "https://registry.npmjs.org/mime-db/-/mime-db-1.52.0.tgz",
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"dev": true,
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"license": "MIT",
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"engines": {
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"node": ">= 0.6"
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||||
@@ -16939,7 +17293,6 @@
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"version": "2.1.35",
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"dev": true,
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"license": "MIT",
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"dependencies": {
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"mime-db": "1.52.0"
|
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@@ -18070,6 +18423,19 @@
|
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"dev": true,
|
||||
"license": "MIT"
|
||||
},
|
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"node_modules/pad-left": {
|
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"version": "2.1.0",
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"resolved": "https://registry.npmjs.org/pad-left/-/pad-left-2.1.0.tgz",
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"license": "MIT",
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"peer": true,
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"repeat-string": "^1.5.4"
|
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"node": ">=0.10.0"
|
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}
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"node_modules/parent-module": {
|
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"version": "1.0.1",
|
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"resolved": "https://registry.npmjs.org/parent-module/-/parent-module-1.0.1.tgz",
|
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@@ -18822,6 +19188,12 @@
|
||||
"node": ">= 0.10"
|
||||
}
|
||||
},
|
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"node_modules/proxy-from-env": {
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"version": "1.1.0",
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"resolved": "https://registry.npmjs.org/proxy-from-env/-/proxy-from-env-1.1.0.tgz",
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"integrity": "sha512-D+zkORCbA9f1tdWRK0RaCR3GPv50cMxcrz4X8k5LTSUD1Dkw47mKJEZQNunItRTkWwgtaUSo1RVFRIG9ZXiFYg==",
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"license": "MIT"
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},
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"node_modules/pump": {
|
||||
"version": "3.0.3",
|
||||
"resolved": "https://registry.npmjs.org/pump/-/pump-3.0.3.tgz",
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||||
@@ -19656,6 +20028,16 @@
|
||||
"url": "https://opencollective.com/unified"
|
||||
}
|
||||
},
|
||||
"node_modules/repeat-string": {
|
||||
"version": "1.6.1",
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"resolved": "https://registry.npmjs.org/repeat-string/-/repeat-string-1.6.1.tgz",
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"integrity": "sha512-PV0dzCYDNfRi1jCDbJzpW7jNNDRuCOG/jI5ctQcGKt/clZD+YcPS3yIlWuTJMmESC8aevCFmWJy5wjAFgNqN6w==",
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"license": "MIT",
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"peer": true,
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"engines": {
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"node": ">=0.10"
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}
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},
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"node_modules/require-directory": {
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||||
"version": "2.1.1",
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"resolved": "https://registry.npmjs.org/require-directory/-/require-directory-2.1.1.tgz",
|
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@@ -21189,6 +21571,16 @@
|
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"node": ">= 0.4"
|
||||
}
|
||||
},
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"node_modules/stream-read-all": {
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"version": "3.0.1",
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"resolved": "https://registry.npmjs.org/stream-read-all/-/stream-read-all-3.0.1.tgz",
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"integrity": "sha512-EWZT9XOceBPlVJRrYcykW8jyRSZYbkb/0ZK36uLEmoWVO5gxBOnntNTseNzfREsqxqdfEGQrD8SXQ3QWbBmq8A==",
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"license": "MIT",
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"peer": true,
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"engines": {
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}
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},
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"node_modules/strict-event-emitter": {
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"version": "0.5.1",
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||||
"resolved": "https://registry.npmjs.org/strict-event-emitter/-/strict-event-emitter-0.5.1.tgz",
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@@ -21476,7 +21868,6 @@
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"version": "7.2.0",
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"resolved": "https://registry.npmjs.org/supports-color/-/supports-color-7.2.0.tgz",
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"integrity": "sha512-qpCAvRl9stuOHveKsn7HncJRvv501qIacKzQlO/+Lwxc9+0q2wLyv4Dfvt80/DPn2pqOBsJdDiogXGR9+OvwRw==",
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"dev": true,
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"license": "MIT",
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"dependencies": {
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"has-flag": "^4.0.0"
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@@ -21498,6 +21889,48 @@
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"url": "https://github.com/sponsors/ljharb"
|
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}
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},
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"node_modules/table-layout": {
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"version": "3.0.2",
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"resolved": "https://registry.npmjs.org/table-layout/-/table-layout-3.0.2.tgz",
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"integrity": "sha512-rpyNZYRw+/C+dYkcQ3Pr+rLxW4CfHpXjPDnG7lYhdRoUcZTUt+KEsX+94RGp/aVp/MQU35JCITv2T/beY4m+hw==",
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"license": "MIT",
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"dependencies": {
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"@75lb/deep-merge": "^1.1.1",
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"array-back": "^6.2.2",
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"command-line-args": "^5.2.1",
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"command-line-usage": "^7.0.0",
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"stream-read-all": "^3.0.1",
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"typical": "^7.1.1",
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"wordwrapjs": "^5.1.0"
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"bin": {
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"table-layout": "bin/cli.js"
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"engines": {
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"resolved": "https://registry.npmjs.org/array-back/-/array-back-6.2.2.tgz",
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"integrity": "sha512-gUAZ7HPyb4SJczXAMUXMGAvI976JoK3qEx9v1FTmeYuJj0IBiaKttG1ydtGKdkfqWkIkouke7nG8ufGy77+Cvw==",
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"license": "MIT",
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"peer": true,
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"engines": {
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"node": ">=12.17"
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}
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},
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"node_modules/table-layout/node_modules/typical": {
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"version": "7.3.0",
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"resolved": "https://registry.npmjs.org/typical/-/typical-7.3.0.tgz",
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"integrity": "sha512-ya4mg/30vm+DOWfBg4YK3j2WD6TWtRkCbasOJr40CseYENzCUby/7rIvXA99JGsQHeNxLbnXdyLLxKSv3tauFw==",
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"license": "MIT",
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"peer": true,
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"engines": {
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"node_modules/tagged-tag": {
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"version": "1.0.0",
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"resolved": "https://registry.npmjs.org/tagged-tag/-/tagged-tag-1.0.0.tgz",
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@@ -22620,6 +23053,16 @@
|
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"node": ">=14.17"
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}
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},
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"node_modules/typical": {
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"version": "4.0.0",
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"resolved": "https://registry.npmjs.org/typical/-/typical-4.0.0.tgz",
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"integrity": "sha512-VAH4IvQ7BDFYglMd7BPRDfLgxZZX4O4TFcRDA6EN5X7erNJJq+McIEp8np9aVtxrCJ6qx4GTYVfOWNjcqwZgRw==",
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"license": "MIT",
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"peer": true,
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"engines": {
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"version": "1.1.0",
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"resolved": "https://registry.npmjs.org/unbox-primitive/-/unbox-primitive-1.1.0.tgz",
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@@ -23049,6 +23492,37 @@
|
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"node": ">= 0.8"
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}
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},
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"node_modules/vectordb": {
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"version": "0.21.2",
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"cpu": [
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"x64",
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"arm64"
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],
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"deprecated": "Use @lancedb/lancedb instead.",
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"license": "Apache-2.0",
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"os": [
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"darwin",
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"linux",
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"win32"
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],
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"@neon-rs/load": "^0.0.74",
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"axios": "^1.4.0"
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"@lancedb/vectordb-darwin-x64": "0.21.2",
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"@lancedb/vectordb-linux-arm64-gnu": "0.21.2",
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"@lancedb/vectordb-win32-x64-msvc": "0.21.2"
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||||
"version": "6.0.3",
|
||||
"resolved": "https://registry.npmjs.org/vfile/-/vfile-6.0.3.tgz",
|
||||
@@ -23405,6 +23879,16 @@
|
||||
"node": ">=0.10.0"
|
||||
}
|
||||
},
|
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"node_modules/wordwrapjs": {
|
||||
"version": "5.1.1",
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"resolved": "https://registry.npmjs.org/wordwrapjs/-/wordwrapjs-5.1.1.tgz",
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||||
"license": "MIT",
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"peer": true,
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"engines": {
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"node": ">=12.17"
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|
||||
"resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-8.1.0.tgz",
|
||||
|
||||
@@ -79,6 +79,7 @@
|
||||
"remark-gfm": "^4.0.1",
|
||||
"tailwind-merge": "^3.5.0",
|
||||
"tw-animate-css": "^1.4.0",
|
||||
"vectordb": "^0.21.2",
|
||||
"zod": "^4.3.6"
|
||||
}
|
||||
}
|
||||
|
||||
4
prd.json
4
prd.json
@@ -401,8 +401,8 @@
|
||||
"Typecheck passes"
|
||||
],
|
||||
"priority": 22,
|
||||
"passes": false,
|
||||
"notes": ""
|
||||
"passes": true,
|
||||
"notes": "Completed: vectordb npm package installed. src/main/ai/embeddings.ts reads GitHub Copilot OAuth token from ~/.copilot/config.json (falls back to stored OpenAI token) and calls OpenAIEmbeddings with Copilot base URL or standard OpenAI. src/main/db/vectordb.ts: initVectorDb() singleton, upsertNoteEmbedding() with delete-then-add strategy (table created on first upsert from schema inferred record), migrateNotesIfNeeded() embeds all SQLite notes sequentially on first launch. notes.create and notes.update made async with fire-and-forget .catch() embed calls; update re-fetches full note from SQLite to embed current title+content. initVectorDb().then(migrateNotesIfNeeded) chained in app.ready after initAI(). vectordb externalized in vite.main.config.mts. Typecheck passes (tsc --noEmit clean). Note: @github/copilot-sdk has no embeddings API; embeddings use @langchain/openai's OpenAIEmbeddings pointed at https://api.githubcopilot.com with the CLI OAuth token."
|
||||
},
|
||||
{
|
||||
"id": "US-023",
|
||||
|
||||
26
progress.txt
26
progress.txt
@@ -493,3 +493,29 @@
|
||||
- Short-circuit optimizations that skip LLM nodes are only safe for providers where the SDK has no internal state to initialize (OpenAI, Anthropic)
|
||||
- If you want to restore the short-circuit as an OpenAI/Anthropic optimization, gate it: `if (TOOL_CALLING_PROVIDERS.has(getActiveProviderName()) && state.chatContext.type === 'project')`
|
||||
---
|
||||
|
||||
## [2026-02-24] - US-022
|
||||
- What was implemented:
|
||||
- Installed `vectordb` (LanceDB Node.js binding v0.21.2) as a project dependency
|
||||
- Created `src/main/ai/embeddings.ts`: reads GitHub Copilot OAuth token from `~/.copilot/config.json` (via `copilot_tokens` map), falls back to stored OpenAI token via `getToken('openai')`. Uses `@langchain/openai` `OpenAIEmbeddings` with `baseURL: 'https://api.githubcopilot.com'` for Copilot path, or standard OpenAI API for fallback. Exposes `embedText(text): Promise<number[]>`
|
||||
- Created `src/main/db/vectordb.ts`: LanceDB singleton (`initVectorDb()` / `getConn()`), `upsertNoteEmbedding(noteId, projectId, content)` with delete-then-add upsert strategy (first call auto-creates table with schema inferred from first record), `migrateNotesIfNeeded()` that checks table existence on startup and bulk-embeds all SQLite notes sequentially with per-note error isolation
|
||||
- Modified `src/main/router/index.ts`: imported `upsertNoteEmbedding`, made `notes.create` and `notes.update` async, added fire-and-forget embedding calls with `.catch(console.error)` in both handlers; `notes.update` re-fetches the full note from SQLite after the write to embed current title+content
|
||||
- Modified `src/main/index.ts`: imported `initVectorDb` + `migrateNotesIfNeeded`, added `initVectorDb().then(() => migrateNotesIfNeeded()).catch(...)` chain in `app.on('ready')`
|
||||
- Modified `vite.main.config.mts`: added `'vectordb'` to the `external` array so ViteRollup doesn't try to bundle the NAPI-RS binary
|
||||
- Files changed:
|
||||
- `package.json` (vectordb dependency added)
|
||||
- `vite.main.config.mts`
|
||||
- `src/main/index.ts`
|
||||
- `src/main/router/index.ts`
|
||||
- `src/main/ai/embeddings.ts` (new)
|
||||
- `src/main/db/vectordb.ts` (new)
|
||||
- **Learnings for future iterations:**
|
||||
- `@github/copilot-sdk` has **no embeddings API** — it is a pure chat/session SDK. The `CopilotClient` type definitions contain zero mention of "embedding". Do not assume any LLM provider SDK supports embeddings
|
||||
- GitHub Copilot CLI stores OAuth tokens in `~/.copilot/config.json` under `copilot_tokens["{host}:{login}"]`. The token format is `gho_*` (GitHub OAuth). These tokens work with the GitHub Copilot REST API (`https://api.githubcopilot.com`) which is OpenAI-compatible — including embeddings
|
||||
- `@langchain/openai`'s `OpenAIEmbeddings` accepts a `configuration.baseURL` option that makes it work against any OpenAI-compatible endpoint
|
||||
- `vectordb` (v0.21.2): deprecated but functional. The new package name is `@lancedb/lancedb`. `vectordb` requires at least one data record for `createTable()` (cannot create an empty table — schema is inferred from the first record). Use delete-then-add for upsert since there's no native upsert API at this version
|
||||
- When using dynamic `import('@langchain/openai')`, TypeScript cannot infer the exact return type of `embedDocuments()` — it resolves to `{}` instead of `number[][]`. Fix: cast explicitly `as number[][]`
|
||||
- tRPC mutation handlers support both sync and async functions transparently — making a mutation `async` does not break the renderer-side interface
|
||||
- `notes.update` allows partial field updates (title or content can be omitted). Always re-fetch the full note from SQLite after the update write to get the correct combined text for embedding
|
||||
- `vectordb`'s `table.delete(where)` accepts a raw SQL WHERE clause string. UUID v4 IDs are safe to interpolate directly (only `[0-9a-f-]` characters)
|
||||
---
|
||||
|
||||
73
src/main/ai/embeddings.ts
Normal file
73
src/main/ai/embeddings.ts
Normal file
@@ -0,0 +1,73 @@
|
||||
import fs from 'node:fs';
|
||||
import os from 'node:os';
|
||||
import path from 'node:path';
|
||||
import { getToken } from './token';
|
||||
|
||||
interface CopilotConfig {
|
||||
copilot_tokens?: Record<string, string>;
|
||||
}
|
||||
|
||||
/**
|
||||
* Read the GitHub Copilot OAuth token from the CLI config file.
|
||||
* Stored at ~/.copilot/config.json under copilot_tokens["{host}:{login}"].
|
||||
* Returns the first available token, or null if unavailable.
|
||||
*/
|
||||
function readCopilotToken(): string | null {
|
||||
try {
|
||||
const raw = fs.readFileSync(
|
||||
path.join(os.homedir(), '.copilot', 'config.json'),
|
||||
'utf-8',
|
||||
);
|
||||
const cfg = JSON.parse(raw) as CopilotConfig;
|
||||
const vals = Object.values(cfg.copilot_tokens ?? {});
|
||||
return vals[0] ?? null;
|
||||
} catch {
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Embed a single text string using the best available credentials.
|
||||
*
|
||||
* Priority:
|
||||
* 1. GitHub Copilot CLI token → OpenAI-compatible embeddings endpoint at
|
||||
* https://api.githubcopilot.com
|
||||
* 2. Stored OpenAI token → standard OpenAI embeddings API
|
||||
*
|
||||
* Throws if no credentials are available or the API call fails.
|
||||
* Callers must .catch() this and handle the error without rejecting
|
||||
* the surrounding tRPC mutation.
|
||||
*/
|
||||
export async function embedText(text: string): Promise<number[]> {
|
||||
const { OpenAIEmbeddings } = await import('@langchain/openai');
|
||||
|
||||
const copilotToken = readCopilotToken();
|
||||
|
||||
let embeddingsInstance;
|
||||
if (copilotToken) {
|
||||
embeddingsInstance = new OpenAIEmbeddings({
|
||||
apiKey: copilotToken,
|
||||
model: 'text-embedding-3-small',
|
||||
configuration: { baseURL: 'https://api.githubcopilot.com' },
|
||||
});
|
||||
} else {
|
||||
const openaiToken = await getToken('openai');
|
||||
if (!openaiToken) {
|
||||
throw new Error(
|
||||
'[Embeddings] No credentials available. Authenticate with Copilot CLI or add an OpenAI token in Settings.',
|
||||
);
|
||||
}
|
||||
embeddingsInstance = new OpenAIEmbeddings({
|
||||
apiKey: openaiToken,
|
||||
model: 'text-embedding-3-small',
|
||||
});
|
||||
}
|
||||
|
||||
// embedDocuments returns number[][] — cast explicitly to satisfy strict TS
|
||||
const results = (await embeddingsInstance.embedDocuments([text])) as number[][];
|
||||
const vector = results[0] as number[] | undefined;
|
||||
if (!vector || vector.length === 0) {
|
||||
throw new Error('[Embeddings] Empty vector returned from embedding API');
|
||||
}
|
||||
return vector;
|
||||
}
|
||||
113
src/main/db/vectordb.ts
Normal file
113
src/main/db/vectordb.ts
Normal file
@@ -0,0 +1,113 @@
|
||||
import * as lancedb from 'vectordb';
|
||||
import { app } from 'electron';
|
||||
import path from 'node:path';
|
||||
import { getDb } from './index';
|
||||
import { notes } from './schema';
|
||||
import { embedText } from '../ai/embeddings';
|
||||
|
||||
interface NoteRecord {
|
||||
id: string;
|
||||
/** Empty string when the note has no project (Arrow string fields don't cleanly handle null) */
|
||||
projectId: string;
|
||||
content: string;
|
||||
vector: number[];
|
||||
}
|
||||
|
||||
let conn: lancedb.Connection | null = null;
|
||||
|
||||
/**
|
||||
* Initialize the LanceDB connection. Must be called before any other
|
||||
* function in this module. Vector data is stored at userData/vectors/.
|
||||
*/
|
||||
export async function initVectorDb(): Promise<void> {
|
||||
const vectorPath = path.join(app.getPath('userData'), 'vectors');
|
||||
conn = await lancedb.connect(vectorPath);
|
||||
console.log('[VectorDB] Connected at:', vectorPath);
|
||||
}
|
||||
|
||||
function getConn(): lancedb.Connection {
|
||||
if (!conn) throw new Error('[VectorDB] Not initialized. Call initVectorDb() first.');
|
||||
return conn;
|
||||
}
|
||||
|
||||
/**
|
||||
* Embed note content and upsert the record into the LanceDB 'notes' table.
|
||||
*
|
||||
* Upsert strategy: delete-then-add.
|
||||
* table.delete(where) is a no-op when no rows match, so this is safe for
|
||||
* both first-time inserts and subsequent updates.
|
||||
*
|
||||
* On the very first call when the table does not yet exist, createTable
|
||||
* infers the Arrow schema from the initial record.
|
||||
*
|
||||
* Throws on error — callers fire-and-forget via .catch().
|
||||
*/
|
||||
export async function upsertNoteEmbedding(
|
||||
noteId: string,
|
||||
projectId: string | null,
|
||||
content: string,
|
||||
): Promise<void> {
|
||||
const c = getConn();
|
||||
const vector = await embedText(content);
|
||||
|
||||
const record: NoteRecord = {
|
||||
id: noteId,
|
||||
projectId: projectId ?? '',
|
||||
content,
|
||||
vector,
|
||||
};
|
||||
|
||||
const tableNames = await c.tableNames();
|
||||
|
||||
if (!tableNames.includes('notes')) {
|
||||
// First embedding: createTable infers the Arrow schema from this record.
|
||||
// The vector dimension (1536 for text-embedding-3-small) is baked in here.
|
||||
await c.createTable('notes', [record]);
|
||||
console.log('[VectorDB] Created notes table');
|
||||
return;
|
||||
}
|
||||
|
||||
const table = await c.openTable<NoteRecord>('notes');
|
||||
// Note IDs are UUID v4 — only [0-9a-f-] chars, no SQL injection risk.
|
||||
await table.delete(`id = '${noteId}'`);
|
||||
await table.add([record]);
|
||||
}
|
||||
|
||||
/**
|
||||
* On first startup, check if the LanceDB 'notes' table exists.
|
||||
* If not, embed all existing SQLite notes and populate LanceDB.
|
||||
*
|
||||
* Per-note errors are caught and logged; a single failure does not
|
||||
* abort the remaining notes.
|
||||
*/
|
||||
export async function migrateNotesIfNeeded(): Promise<void> {
|
||||
const c = getConn();
|
||||
const tableNames = await c.tableNames();
|
||||
|
||||
if (tableNames.includes('notes')) {
|
||||
console.log('[VectorDB] Notes table exists, skipping migration');
|
||||
return;
|
||||
}
|
||||
|
||||
const allNotes = getDb().select().from(notes).all();
|
||||
|
||||
if (allNotes.length === 0) {
|
||||
console.log('[VectorDB] No existing notes to migrate');
|
||||
return;
|
||||
}
|
||||
|
||||
console.log(`[VectorDB] Migrating ${allNotes.length} notes...`);
|
||||
let successCount = 0;
|
||||
|
||||
for (const note of allNotes) {
|
||||
try {
|
||||
const embeddingText = `${note.title}\n\n${note.content}`;
|
||||
await upsertNoteEmbedding(note.id, note.projectId ?? null, embeddingText);
|
||||
successCount++;
|
||||
} catch (err) {
|
||||
console.error(`[VectorDB] Failed to embed note ${note.id} during migration:`, err);
|
||||
}
|
||||
}
|
||||
|
||||
console.log(`[VectorDB] Migration complete: ${successCount}/${allNotes.length} notes embedded`);
|
||||
}
|
||||
@@ -5,6 +5,7 @@ import { initDb } from './db';
|
||||
import { appRouter } from './router';
|
||||
import { createIPCHandler } from './ipc';
|
||||
import { initAI } from './ai/provider';
|
||||
import { initVectorDb, migrateNotesIfNeeded } from './db/vectordb';
|
||||
// Import to trigger provider registration before initAI() runs
|
||||
import './ai/copilot';
|
||||
|
||||
@@ -54,6 +55,10 @@ app.on('ready', () => {
|
||||
createIPCHandler({ router: appRouter, windows: [win] });
|
||||
// AI init is best-effort — never block window creation
|
||||
initAI().catch((err) => console.error('[AI] Init failed:', err));
|
||||
// Vector DB init + migration is best-effort — runs after window is shown
|
||||
initVectorDb()
|
||||
.then(() => migrateNotesIfNeeded())
|
||||
.catch((err) => console.error('[VectorDB] Init or migration failed:', err));
|
||||
});
|
||||
|
||||
// Quit when all windows are closed, except on macOS. There, it's common
|
||||
|
||||
@@ -7,6 +7,7 @@ import { clients, projects, tasks, checkpoints, notes, taskComments } from '../d
|
||||
import { getStore } from '../store';
|
||||
import { saveTokenAndInit, hasActiveToken } from '../ai/provider';
|
||||
import { orchestrate } from '../ai/orchestrator';
|
||||
import { upsertNoteEmbedding } from '../db/vectordb';
|
||||
import type { TRPCContext } from '../ipc';
|
||||
|
||||
const t = initTRPC.context<TRPCContext>().create();
|
||||
@@ -406,7 +407,7 @@ const notesRouter = router({
|
||||
|
||||
create: publicProcedure
|
||||
.input(z.object({ title: z.string(), content: z.string(), projectId: z.string().optional() }))
|
||||
.mutation(({ input }) => {
|
||||
.mutation(async ({ input }) => {
|
||||
const id = crypto.randomUUID();
|
||||
const now = Date.now();
|
||||
getDb().insert(notes).values({
|
||||
@@ -417,18 +418,37 @@ const notesRouter = router({
|
||||
createdAt: now,
|
||||
updatedAt: now,
|
||||
}).run();
|
||||
// Fire-and-forget: embed the note. Errors are logged, never thrown.
|
||||
upsertNoteEmbedding(id, input.projectId ?? null, `${input.title}\n\n${input.content}`)
|
||||
.catch((err) => console.error('[VectorDB] Failed to embed note on create:', err));
|
||||
return { id };
|
||||
}),
|
||||
|
||||
update: publicProcedure
|
||||
.input(z.object({ id: z.string(), title: z.string().optional(), content: z.string().optional() }))
|
||||
.mutation(({ input }) => {
|
||||
.mutation(async ({ input }) => {
|
||||
const set: Partial<{ title: string; content: string; updatedAt: number }> = {};
|
||||
if (input.title !== undefined) set.title = input.title;
|
||||
if (input.content !== undefined) set.content = input.content;
|
||||
// Always update updatedAt
|
||||
set.updatedAt = Date.now();
|
||||
getDb().update(notes).set(set).where(eq(notes.id, input.id)).run();
|
||||
|
||||
// Re-embed if searchable text fields changed.
|
||||
// Re-fetch from SQLite so the embedding reflects the full current note
|
||||
// (the update may have changed only one of title or content).
|
||||
if (input.title !== undefined || input.content !== undefined) {
|
||||
const updated = getDb()
|
||||
.select({ id: notes.id, projectId: notes.projectId, title: notes.title, content: notes.content })
|
||||
.from(notes)
|
||||
.where(eq(notes.id, input.id))
|
||||
.all()[0];
|
||||
if (updated) {
|
||||
upsertNoteEmbedding(updated.id, updated.projectId ?? null, `${updated.title}\n\n${updated.content}`)
|
||||
.catch((err) => console.error('[VectorDB] Failed to embed note on update:', err));
|
||||
}
|
||||
}
|
||||
|
||||
return null;
|
||||
}),
|
||||
|
||||
|
||||
@@ -17,6 +17,7 @@ export default defineConfig({
|
||||
'@langchain/anthropic',
|
||||
'@langchain/langgraph-checkpoint',
|
||||
'@langchain/langgraph-sdk',
|
||||
'vectordb',
|
||||
],
|
||||
output: {
|
||||
entryFileNames: 'main.js',
|
||||
|
||||
Reference in New Issue
Block a user