2380d33861
- Context Compaction System with token counting and summarization - Deterministic State Machine for flow control (no LLM decisions) - Parallel Execution Engine (up to 12 concurrent sessions) - Event-Driven Coordination via Event Bus - Agent Workspace Isolation (tools, memory, identity, files) - YAML Workflow Integration (OpenClaw/Lobster compatible) - Claude Code integration layer - Complete demo UI with real-time visualization - Comprehensive documentation and README Components: - agent-system/: Context management, token counting, subagent spawning - pipeline-system/: State machine, parallel executor, event bus, workflows - skills/: AI capabilities (LLM, ASR, TTS, VLM, image generation, etc.) - src/app/: Next.js demo application Total: ~100KB of production-ready TypeScript code
42 lines
1.0 KiB
TypeScript
Executable File
42 lines
1.0 KiB
TypeScript
Executable File
import ZAI, { VisionMessage } from 'z-ai-web-dev-sdk';
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async function main(videoUrl: string, prompt: string) {
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try {
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const zai = await ZAI.create();
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const messages: VisionMessage[] = [
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{
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role: 'assistant',
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content: [
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{ type: 'text', text: 'Output only text, no markdown.' }
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]
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},
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{
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role: 'user',
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content: [
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{ type: 'text', text: prompt },
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{ type: 'video_url', video_url: { url: videoUrl } }
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]
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}
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];
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const response = await zai.chat.completions.createVision({
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model: 'glm-4.6v',
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messages,
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thinking: { type: 'disabled' }
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});
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const reply = response.choices?.[0]?.message?.content;
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console.log('Video Understanding Result:');
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console.log(reply ?? JSON.stringify(response, null, 2));
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} catch (err: any) {
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console.error('Video understanding failed:', err?.message || err);
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}
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}
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// Example usage - analyze a video
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main(
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"https://example.com/sample-video.mp4",
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"Please analyze this video and describe the main events, actions, and key moments in chronological order."
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);
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