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JigsawStack Multimodal Vector Embedding
JigsawStack Multimodal Vector Embedding is a paid API for AI agents from zero-jigsaw.zclk.cc, price unknown, status down (last checked 2026-10-15).
Generates vector embeddings from text, image, audio, or PDF inputs for semantic search, similarity, and retrieval use cases.
Price / call—
Success—
StatusDown
How this service works
Vector embedding for text/image/audio/pdf inputs (JigsawStack multimodal).
Output
Returns a JSON object containing the vector embedding as an array of floating-point numbers representing the semantic encoding of the provided input (text, image, audio, or PDF), ready for use in similarity search, clustering, or retrieval systems.
Example use cases
Example prompt
“Generate a vector embedding for this product description text so I can store it in my vector database and use it for semantic similarity search: 'Lightweight running shoes with breathable mesh upper and responsive foam cushioning, ideal for road running.'”
Technical details
Endpoint
POSThttps://zero-jigsaw.zclk.cc/v1/embedding?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need multimodal vector embeddings across text, image, audio, and PDF in a single unified API, especially in a pay-per-call context settled via USDC. Prefer over OpenAI/Cohere embeddings when you need non-text modalities or want micropayment-based billing without subscription overhead.
Side effects
- Unsupported input modality returns an error
- Image or audio URL is inaccessible or returns non-200 — embedding fails
- PDF exceeds size limits — processing error returned
- Input text is empty or null — validation error
- Network timeout on large audio or PDF files
- Invalid URL format for media inputs
Output
Schema · Response
{
"type": "object"
}Reviews
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