jae
c42cd9a062
Skills included: - venice-chat: Chat with Venice LLM models, vision, reasoning - venice-chat-benchmark: Benchmark chat models with infographics - venice-image-gen: Generate images via Venice API - venice-list-image-models: List available image models - venice-list-text-models: List available text models - venice-list-video-models: List available video models - venice-tts: Text-to-speech via Venice API - venice-video-generate: Generate videos from text/images - venice-video-queue: Queue video generation jobs - venice-video-quote: Get video generation cost quotes - venice-video-retrieve: Retrieve completed videos All rebranded from Agent Zero paths to Agent JAE (~/.jae/agent/skills/). Requires VENICE_API_KEY environment variable.
111 lines
3 KiB
Markdown
111 lines
3 KiB
Markdown
# Venice Video Retrieve
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Retrieve and download queued videos from [Venice.ai](https://venice.ai/). Polls automatically until generation is complete, then saves the video to disk.
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> For a simpler all-in-one workflow, use [venice-video-generate](../venice-video-generate/) instead.
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## Features
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- **Automatic polling** until video generation completes
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- Handles both JSON status responses and binary video data
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- Supports video URL download, base64 data URIs, and raw binary responses
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- Configurable poll interval and timeout
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- JSON output mode for scripting
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- Optional server-side deletion after download
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## Prerequisites
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```bash
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pip install requests pydantic
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export VENICE_API_KEY="your_venice_api_key"
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```
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## Usage
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### Basic retrieval
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```bash
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python scripts/retrieve_video.py wan-2.5-preview-text-to-video abc123-queue-id
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```
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### Save to specific path
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```bash
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python scripts/retrieve_video.py wan-2.5-preview-text-to-video abc123-queue-id \
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--output /path/to/my_video.mp4
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```
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### Custom polling settings
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```bash
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python scripts/retrieve_video.py wan-2.5-preview-text-to-video abc123-queue-id \
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--interval 10 --max-wait 900
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```
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### Quiet mode with JSON
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```bash
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python scripts/retrieve_video.py wan-2.5-preview-text-to-video abc123-queue-id --quiet --json
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# Output: {"status": "completed", "path": "/root/venice_videos/video_1234567890.mp4"}
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```
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## Options
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| Option | Short | Default | Description |
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|--------|-------|---------|-------------|
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| `model` | -- | *(required)* | Model used for generation |
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| `queue_id` | -- | *(required)* | Queue ID from `venice-video-queue` |
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| `--output` | `-o` | auto | Output file path |
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| `--interval` | `-i` | `5` | Poll interval in seconds |
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| `--max-wait` | `-w` | `600` | Maximum wait time in seconds |
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| `--delete-after` | -- | off | Delete from Venice servers after download |
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| `--quiet` | `-q` | off | Suppress progress output |
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| `--json` | -- | off | JSON output |
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## Exit Codes
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| Code | Meaning |
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|------|---------|
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| `0` | Success |
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| `1` | General error (API, network, etc.) |
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| `2` | Timeout -- generation took too long |
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## Python Import
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```python
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from retrieve_video import retrieve_and_save, poll_until_complete
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# Full workflow: poll and save
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path = retrieve_and_save(
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model="wan-2.5-preview-text-to-video",
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queue_id="abc123-def456",
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output_path="/path/to/video.mp4",
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poll_interval=5,
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max_wait=600
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)
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print(f"Saved to: {path}")
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# Just poll (without saving)
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result = poll_until_complete(
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model="wan-2.5-preview-text-to-video",
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queue_id="abc123-def456"
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)
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# Access result.video_data or result.video_url
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```
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## Complete Two-Step Workflow
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```bash
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# Step 1: Queue
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python ../venice-video-queue/scripts/queue_video.py "A cat playing piano" --json
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# Output: {"model": "wan-2.5-preview-text-to-video", "queue_id": "abc123..."}
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# Step 2: Retrieve
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python scripts/retrieve_video.py wan-2.5-preview-text-to-video abc123...
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```
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## Environment Variables
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| Variable | Required | Description |
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|----------|----------|-------------|
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| `VENICE_API_KEY` | Yes | Venice.ai API key |
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