# Video Generation Research — Qdrant Quick-Start

**Canonical data store**: Qdrant `comfyui_decisions` @ `http://10.0.0.22:6333` (64+ points)
**Embedding**: `snowflake-arctic-embed2:latest` (1024-dim, Cosine)
**Human companion**: `/home/n8n/workspace/comfy/research.md` (search topics + URLs only, no specs)

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## What Lives Where

| Store | Content | How to Query |
|-------|---------|-------------|
| **Qdrant** `comfyui_decisions` | All specs, VRAM numbers, node names, workarounds, paper abstracts, trend analysis | Vector search via Ollama embedding + POST to 10.0.0.22:6333 |
| **Qdrant** `comfyui_kb` | General knowledge (3884 points, pre-existing) | Same method |
| **research.md** | Human-readable search topics, URL quick-reference, comparison tables | grep / raw read |
| **This file** | Quick-start pointers to the above | Read once, then query Qdrant |

---

## Qdrant Query Pattern

```python
import requests, json

# 1. Get embedding
emb_resp = requests.post("http://localhost:11434/api/embed", json={
    "model": "snowflake-arctic-embed2:latest",
    "input": "YOUR SEARCH TOPIC"
})
vector = emb_resp.json()["embeddings"][0]

# 2. Search Qdrant
search_resp = requests.post(
    "http://10.0.0.22:6333/collections/comfyui_decisions/points/search",
    json={"vector": vector, "limit": 5, "with_payload": True}
)
results = search_resp.json()["result"]["points"]
```

---

## Example Search Topics

Use these as `input` strings above (from `research.md`):

- "LTX 2.3 specs, frames, VRAM, audio latent"
- "HunyuanVideo frame limits block swapping FP8"
- "Wan 2.2 low VRAM sound-to-video FreeLong"
- "Google Veo 3 native audio cloud"
- "Kling 3.0 Omni visual identity vocal tone"
- "ID-LoRA face identity lock LTX"
- "FreeLong spectral blending Wan motion reversal"
- "Segment chaining last frame to first frame"
- "LivePortrait portrait animation"
- "MuseTalk LatentSync lip-sync"
- "RIFE frame interpolation"
- "MultiTalk NeurIPS multi-person conversation"
- "StreamingT2V CVPR long video streaming"

---

## Quick Model Comparison (from Qdrant)

| Tool | Native Audio | Max Frames | VRAM | Best For |
|------|-------------|------------|------|----------|
| LTX 2.3 | YES | 361 | 12-24 GB | Local, audio-synced |
| HunyuanVideo 1.5 | NO | 129 | 20-24 GB | Best open-source quality |
| Wan 2.2 | YES (S2V) | 81 / 1025 | 5-16 GB | Low VRAM, sound-driven |
| Veo 3 | YES | API | Cloud | Highest fidelity |
| Kling 3.0 Omni | YES | API | Cloud | Character + voice |

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## Key URLs (Quick Reference)

- LTX: https://huggingface.co/Lightricks
- Hunyuan: https://github.com/Tencent-Hunyuan/HunyuanVideo
- Wan wrapper: https://github.com/kijai/ComfyUI-WanVideoWrapper
- Veo: https://deepmind.google/models/veo/
- Kling: https://kling.ai/
- LivePortrait: https://github.com/kijai/ComfyUI-LivePortraitKJ
- VHS: https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite

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*All detailed data is in Qdrant. This file is a navigation aid only.*