77 lines
1.7 KiB
Markdown
77 lines
1.7 KiB
Markdown
# notesearch
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Local vector search over markdown notes using LlamaIndex + Ollama.
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Point it at an Obsidian vault (or any folder of `.md` files), build a vector index, and search by meaning — not just keywords.
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## Setup
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```bash
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cd notesearch
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uv sync
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```
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Requires Ollama running locally with an embedding model pulled:
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```bash
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ollama pull qwen3-embedding:0.6b
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```
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## Usage
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### Build the index
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```bash
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./notesearch.sh index --vault /path/to/vault
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```
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### Search
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```bash
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./notesearch.sh search "where do I get my allergy shots"
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```
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Output:
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```
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[0.87] Health/allergy.md
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Started allergy shots in March 2026. Clinic is at 123 Main St.
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[0.72] Daily/2026-03-25.md
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Went to allergy appointment today.
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```
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### Configuration
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Edit `config.json`:
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```json
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{
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"vault": "/home/lyx/Documents/obsidian-yanxin",
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"index_dir": null,
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"ollama_url": "http://localhost:11434",
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"embedding_model": "qwen3-embedding:0.6b"
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}
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```
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Values can also be set via flags or env vars. Priority: **flag > env var > config.json > fallback**.
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| Flag | Env var | Config key | Default |
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|------|---------|------------|---------|
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| `--vault` | `NOTESEARCH_VAULT` | `vault` | `/home/lyx/Documents/obsidian-yanxin` |
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| `--index-dir` | `NOTESEARCH_INDEX_DIR` | `index_dir` | `<vault>/.index/` |
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| `--ollama-url` | `NOTESEARCH_OLLAMA_URL` | `ollama_url` | `http://localhost:11434` |
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| `--embedding-model` | `NOTESEARCH_EMBEDDING_MODEL` | `embedding_model` | `qwen3-embedding:0.6b` |
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| `--top-k` | — | — | `5` |
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## Tests
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```bash
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uv run pytest
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```
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## How it works
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1. **Index**: reads all `.md` files, splits on markdown headings, embeds each chunk via Ollama, stores vectors locally
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2. **Search**: embeds your query, finds the most similar chunks, returns them with file paths and relevance scores
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