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Replace JSON-string embeddings with native pgvector(1536) storage and add PostgreSQL full-text search (tsvector/GIN) with Reciprocal Rank Fusion for hybrid keyword + semantic ranking. Changes: - NoteEmbedding.embedding: String → vector(1536) via pgvector - NoteEmbedding: added updatedAt for reindex tracking - Note: added tsv (tsvector) with auto-update trigger for FTS - semantic-search.service: hybrid FTS + vector search with RRF fusion - embedding.service: toVectorString() for pgvector SQL literals - Removed JS-side cosine similarity loops (now DB-side via <=>) - Added HNSW index on NoteEmbedding.embedding (cosine distance) - Added GIN index on Note.tsv for FTS queries Schema migration in: prisma/migrations/20260512120000_pgvector_and_fts_search/ Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
21 lines
550 B
Markdown
21 lines
550 B
Markdown
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<img width="1200" height="475" alt="GHBanner" src="https://github.com/user-attachments/assets/0aa67016-6eaf-458a-adb2-6e31a0763ed6" />
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# Run and deploy your AI Studio app
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This contains everything you need to run your app locally.
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View your app in AI Studio: https://ai.studio/apps/b7b577c6-4d9f-44ac-8fe1-85bc3c6d6e66
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## Run Locally
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**Prerequisites:** Node.js
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1. Install dependencies:
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`npm install`
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2. Set the `GEMINI_API_KEY` in [.env.local](.env.local) to your Gemini API key
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3. Run the app:
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`npm run dev`
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