New search function et drag and drop clean

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title: "Automatic Tagging"
date: "10-11-2025"
last_modified: "10-11-2025:19:21"
tags: ["research", "ai", "nlp"]
---
# Automatic Tagging
## Goal
Suggest tags based on note content.
## Approaches
### Rule-based
- Keyword extraction
- TF-IDF
### ML-based
- Zero-shot classification
- Fine-tuned model
### Hybrid
- Combine both approaches
## Training Data
Use existing notes with tags as training set.

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title: "Semantic Search Research"
date: "10-11-2025"
last_modified: "10-11-2025:19:21"
tags: ["research", "ai", "search"]
---
# Semantic Search
## Current Search
Keyword-based with scoring.
## Semantic Search
Use embeddings for similarity:
- OpenAI embeddings API
- Local models (sentence-transformers)
- Vector database (Pinecone, Weaviate)
## Implementation
1. Generate embeddings for all notes
2. Store in vector DB
3. Query with user search
4. Return top-k similar
## Cost Analysis
OpenAI: /tmp/generate_notes.sh.0001 per 1K tokens
Local: Free but slower