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Create Embeddings (ZeroClick-authenticated) — ZeroGPU
Create Embeddings (ZeroClick-authenticated) — ZeroGPU is a paid API for AI agents from agents.zerogpu.ai, paid per call via MPP, price unknown, status unknown (last checked 2026-10-16).
Converts one or more text strings into 384-dimensional embedding vectors using lightweight sentence-transformer models (all-MiniLM-L6-v2 or BGE-small-en-v1.5), authenticated via HMAC signature.
Price / call—
Success—
StatusUnknown
How this service works
OpenAI-style Embeddings endpoint. Billed on input tokens only; `zc-usage` reports the `prompt_tokens` from the response's `usage`. Authenticated by verifying the `zc-signature` HMAC over the raw request bytes; the same orchestration as `/v1/embeddings` runs behind it.
Output
Returns an OpenAI-compatible embedding list object containing: an array of embedding objects each with its index, object type, and a 384-dimensional float vector; the resolved model ID; and a usage object reporting prompt_tokens and total_tokens (billed on input tokens only, no output tokens).
Example use cases
RAG pipeline document ingestion
“Take these 10 product description strings and generate an embedding vector for each one using the all-minilm-l6-v2 model so I can store them in my Pinecone index.”
Semantic search query encoding
“Convert my user's search query 'affordable noise-cancelling headphones for travel' into a 384-dimension embedding vector using bge-small-en-v1.5 so I can find the closest matches in my vector store.”
Duplicate content detection
“Embed both of these two article paragraphs with all-minilm-l6-v2 and return the vectors so I can calculate their cosine similarity and check whether they're near-duplicates.”
Technical details
Endpoint
POSThttps://agents.zerogpu.ai/zeroclick/v1/embeddings?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, low-cost text embeddings using compact sentence-transformer models (all-MiniLM-L6-v2 or BGE-small-en-v1.5) with OpenAI-compatible output format and HMAC-based ZeroClick authentication. Prefer it over the standard /v1/embeddings endpoint when operating in a ZeroClick orchestration context. Ideal for high-volume embedding workloads (RAG, semantic search, clustering) where small 384-dimension models provide sufficient quality at minimal token cost.
Side effects
- Invalid or missing zc-signature HMAC — authentication failure, request rejected
- Unsupported model name outside the enum [all-minilm-l6-v2, bge-small-en-v1.5] — validation error
- Empty input string or empty array — schema validation error (minLength/minItems violated)
- Input text truncated silently at model's maximum token length without error
- Insufficient credits or payment failure — 402 Payment Required
- Rate limiting or overload on ZeroGPU inference network — 429 or 503 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | — | Text to embed. A single string, or an array of strings (one vector per element). Inputs are truncated at the model's max token length. |
| modelrequired | string | Embedding model to use. Embedding models are routable only on this endpoint. |
Output
Schema · Response
| Field | Type | Description |
|---|---|---|
| data | array | |
| model | string | The resolved model id. |
| usage | object | Embeddings are billed on input tokens only; there are no output tokens. |
| object | string |
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