# notesearch

Local vector search over markdown notes using LlamaIndex + Ollama.

Point it at an Obsidian vault (or any folder of `.md` files), build a vector index, and search by meaning — not just keywords.

## Setup

```bash
cd ~/.openclaw/workspace/skills/notesearch
uv sync
```

Requires Ollama running locally with an embedding model pulled:

```bash
ollama pull qwen3-embedding:0.6b
```

## Usage

### Build the index

```bash
./notesearch.sh index --vault /path/to/vault
```

### Search

```bash
./notesearch.sh search "where do I get my allergy shots"
```

Output:

```
[0.87] Health/allergy.md
Started allergy shots in March 2026. Clinic is at 123 Main St.

[0.72] Daily/2026-03-25.md
Went to allergy appointment today.
```

### Configuration

Edit `config.json`:

```json
{
  "vault": "/home/lyx/Documents/obsidian-yanxin",
  "index_dir": null,
  "ollama_url": "http://localhost:11434",
  "embedding_model": "qwen3-embedding:0.6b"
}
```

Values can also be set via flags or env vars. Priority: **flag > env var > config.json > fallback**.

| Flag | Env var | Config key | Default |
|------|---------|------------|---------|
| `--vault` | `NOTESEARCH_VAULT` | `vault` | `/home/lyx/Documents/obsidian-yanxin` |
| `--index-dir` | `NOTESEARCH_INDEX_DIR` | `index_dir` | `<vault>/.index/` |
| `--ollama-url` | `NOTESEARCH_OLLAMA_URL` | `ollama_url` | `http://localhost:11434` |
| `--embedding-model` | `NOTESEARCH_EMBEDDING_MODEL` | `embedding_model` | `qwen3-embedding:0.6b` |
| `--top-k` | — | — | `5` |

## Tests

```bash
uv run pytest
```

## How it works

1. **Index**: reads all `.md` files, splits on markdown headings, embeds each chunk via Ollama, stores vectors locally
2. **Search**: embeds your query, finds the most similar chunks, returns them with file paths and relevance scores