# Semantic Search

  Search video content with natural language — "robot picking up cup" returns
  the exact 2-second clips, with playback URLs and scores. Videos are split
  into HLS chunks, embedded with CLIP, and indexed in Qdrant.

## Pipeline

```
Upload video → Lambda splits into 2s HLS chunks
    → dreamlake vectorize (CLIP + LLaVA per chunk)
    → Qdrant index
    → Query: text → CLIP embed → nearest neighbor
```

## Vectorize

```bash
dreamlake vectorize --episode robotics@alice:run-042
dreamlake vectorize --bindr "front-camera" --project robotics@alice
dreamlake vectorize --dataset "training-v1" --project robotics@alice
```

Add `--zaku-url http://host:9000` for distributed processing via Zaku task queue.

Per chunk: CLIP ViT-L/14 (768d image embedding) + LLaVA 13B (caption → CLIP text embedding).

## Search

```
GET /namespaces/:ns/projects/:project/semantic-search?q=robot+picking+up+cup
```

| Param | Description |
|-------|-------------|
| `q` | Natural language query |
| `episode` | Scope to episode |
| `bindr` | Scope to bindr |
| `limit` | Max results (default 10) |
| `using` | `image` (default) or `caption` |

Returns matched 2s clips with playback URLs and scores.

## Performance

| Step | Time |
|------|------|
| Vectorize per chunk | ~14.5s (CLIP + LLaVA) |
| Search query | ~50ms |
| Storage per chunk | ~6KB |
| 1 hour video | 1,800 points, ~11MB in Qdrant |

## Next steps

    Load the matched clips in Python and turn them into tensors.

    The search endpoints, plus everything else the server exposes.