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vision-bridge
Advanced tools
Use GLM-4.6V-Flash to convert images to text for non-vision AI models.
Many LLMs don't support image input. Vision Bridge solves this by using GLM-4.6V-Flash (free) as a vision intermediary.
Image → GLM-4.6V-Flash → Text Description → Your Non-Vision Model
npm install -g vision-bridge
Set your ZhipuAI API key:
export ZHIPUAI_API_KEY="your-api-key"
Get API key: https://open.bigmodel.cn
# From local file
vision-bridge analyze photo.jpg
# From URL
vision-bridge analyze https://example.com/image.png
# Custom prompt
vision-bridge analyze screenshot.png -p "What UI components are in this screenshot?"
# Stream output
vision-bridge analyze doc.jpg --stream
# Enable thinking mode for complex images
vision-bridge analyze chart.png --thinking
# Save to file
vision-bridge analyze receipt.jpg -o output.txt
# Extract text from image
vision-bridge ocr document.jpg
# Save OCR result
vision-bridge ocr table.jpg -o table.md
# Generate AI art prompt from image
vision-bridge prompt artwork.jpg
# Use with any CLI model tool
vision-bridge analyze image.jpg | some-text-model --stdin
# Example workflow
vision-bridge analyze screenshot.png -o desc.txt
cat desc.txt | your-favorite-llm "Based on this UI description, generate the HTML/CSS code"
| Option | Description |
|---|---|
-p, --prompt | Custom analysis prompt |
-k, --api-key | API key (or use env var) |
-t, --thinking | Enable deep reasoning |
-s, --stream | Stream output |
-o, --output | Save to file |
import { VisionBridge } from "vision-bridge";
const bridge = new VisionBridge({ apiKey: "your-api-key" });
// Analyze image
const description = await bridge.analyze("https://example.com/image.png");
// Stream analysis
for await (const chunk of bridge.analyzeStream("photo.jpg")) {
process.stdout.write(chunk);
}
// OCR
const text = await bridge.ocr("document.jpg");
// Image to prompt
const prompt = await bridge.image2prompt("artwork.jpg");
MIT
FAQs
Use GLM-4.6V-Flash to convert images to text for non-vision AI models
The npm package vision-bridge receives a total of 637 weekly downloads. As such, vision-bridge popularity was classified as not popular.
We found that vision-bridge demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.

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