Search API Quick Start Guide

```python { "results": [ { "message_id": "...", "conversation_id": "...", "content": "...", "author": "user", "similarity": 0.85, "coordinates": {"x": 5, "y": 2, "z": 1, "t": 0.5}, "ring_position": 180.0, "depth_category": "shallow", "timestamp": 1234567890, "content_length": 150, "source": "conversations_fixed" } ], "analysis": { "similarity_stats": {...}, "depth_stats": {...}, "ring_stats": {...}, "author_distribution": {...}, "depth_distribution": {...} }, "query": "machine learning", "total_found": 25, "sources

Full HTML reader

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Extracted abstract or opening context

1. **Use semantic mode** for best accuracy 2. **Use fast mode** for good balance of speed/accuracy 3. **Use instant mode** for fastest results (lower accuracy) 4. **Model is cached** - first load is slow, subsequent searches are fast 5. **Batch operations** - use `cosine_similarity_batch` for multiple comparisons

Promotion decision

What has to happen next

Attach run IDs, datasets, metrics, and reproduction commands.

Why this is not always a full paper yet

Corpus pages are public-safe readers for discovered workspace artifacts. They are not automatically final papers. A corpus item becomes a polished paper only after the editable source, evidence checkpoints, references, figures, render path, and release status are attached through the paper schema.