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JigsawStack Multimodal Vector Embedding
JigsawStack Multimodal Vector Embedding is a paid API for AI agents from jigsawstack.withzero.ai, paid per call via MPP, $0.001/call, status unknown (last checked 2026-09-15).
Generates vector embeddings from text, image, audio, or PDF inputs for use in semantic search, similarity, and retrieval applications.
Price / call$0.001
Success—
StatusUnknown
How this service works
Vector embedding for text/image/audio/pdf inputs (JigsawStack multimodal).
Output
Returns a JSON object containing the vector embedding as a float array representing the semantic meaning of the input, usable for similarity search, clustering, or storage in a vector database.
Example use cases
Example prompt
“Generate a vector embedding for this text using JigsawStack's multimodal embedding API: 'The quick brown fox jumps over the lazy dog' — I need the float array to store in my vector database for semantic search.”
Technical details
Endpoint
POSThttps://jigsawstack.withzero.ai/v1/embeddingWhen to prefer
Choose this endpoint when you need multimodal embedding support in a single API — especially when your pipeline needs to embed text, images, audio, and PDFs using a consistent vector space. Prefer this over text-only embedding APIs when your data is mixed-modality. Ideal for RAG pipelines, semantic search indexes, and similarity matching tasks that span content types.
Side effects
- Unsupported input type — only text, image URL, audio, and PDF are accepted
- Input too large — exceeds size limits for the given modality
- Invalid or inaccessible URL for image/audio/PDF inputs
- Malformed request body — missing required input field
- Payment failure — insufficient USDC balance for the $0.001 per-call fee
Input
{
"text": "Artificial intelligence and machine learning are transforming modern technology."
}Schema · Request body
| Field | Type | Description |
|---|---|---|
| textrequired | string | The text to generate a vector embedding for. |
Output
{
"_usage": {
"input_tokens": 18,
"total_tokens": 3519,
"output_tokens": 481,
"inference_time_tokens": 3020
},
"chunks": [
"Artificial intelligence and machine learning are transforming modern technology."
],
"log_id": "68c320d2-a8ee-4b62-a31d-1a83b9a4f788",
"success": true,
"embeddings": [
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]
}Schema · Response
| Field | Type | Description |
|---|---|---|
| _usagerequired | object | |
| chunksrequired | array | |
| log_idrequired | string | |
| successrequired | boolean | |
| embeddingsrequired | array |
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