New search function et drag and drop clean
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notes/research/ai/auto-tagging.md
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---
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title: "Automatic Tagging"
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date: "10-11-2025"
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last_modified: "10-11-2025:19:21"
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tags: ["research", "ai", "nlp"]
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---
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# Automatic Tagging
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## Goal
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Suggest tags based on note content.
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## Approaches
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### Rule-based
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- Keyword extraction
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- TF-IDF
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### ML-based
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- Zero-shot classification
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- Fine-tuned model
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### Hybrid
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- Combine both approaches
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## Training Data
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Use existing notes with tags as training set.
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notes/research/ai/semantic-search.md
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notes/research/ai/semantic-search.md
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---
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title: "Semantic Search Research"
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date: "10-11-2025"
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last_modified: "10-11-2025:19:21"
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tags: ["research", "ai", "search"]
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---
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# Semantic Search
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## Current Search
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Keyword-based with scoring.
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## Semantic Search
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Use embeddings for similarity:
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- OpenAI embeddings API
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- Local models (sentence-transformers)
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- Vector database (Pinecone, Weaviate)
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## Implementation
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1. Generate embeddings for all notes
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2. Store in vector DB
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3. Query with user search
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4. Return top-k similar
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## Cost Analysis
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OpenAI: /tmp/generate_notes.sh.0001 per 1K tokens
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Local: Free but slower
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