{"uid":"cap_bY6oLzKIyS1DMa7jevpSS","slug":"agents-zerogpu-ai-create-embeddings-zeroclick-authenticated-zerogpu-4d296d1e","name":"Create Embeddings (ZeroClick-authenticated) — ZeroGPU","description":"OpenAI-style Embeddings endpoint. Billed on input tokens only; `zc-usage`\nreports the `prompt_tokens` from the response's `usage`.\n\nAuthenticated by verifying the `zc-signature` HMAC over the raw\nrequest bytes; the same orchestration as `/v1/embeddings` runs behind it.\n","url":"https://agents.zerogpu.ai/zeroclick/v1/embeddings?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","required":["model","input"],"properties":{"input":{"oneOf":[{"type":"string","minLength":1},{"type":"array","items":{"type":"string","minLength":1},"minItems":1}],"description":"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."},"model":{"enum":["all-minilm-l6-v2","bge-small-en-v1.5"],"type":"string","example":"all-minilm-l6-v2","description":"Embedding model to use. Embedding models are routable only on this endpoint."}}},"responseSchema":{"type":"object","properties":{"data":{"type":"array","items":{"type":"object","properties":{"index":{"type":"integer","example":0,"description":"Position of the input this vector was produced from."},"object":{"type":"string","example":"embedding"},"embedding":{"type":"array","items":{"type":"number"},"description":"The embedding vector. 384 dimensions for both models."}}}},"model":{"type":"string","example":"all-MiniLM-L6-v2","description":"The resolved model id."},"usage":{"type":"object","properties":{"total_tokens":{"type":"integer","example":18},"prompt_tokens":{"type":"integer","example":18}},"description":"Embeddings are billed on input tokens only; there are no output tokens."},"object":{"type":"string","example":"list"}},"description":"An OpenAI-compatible embedding list."},"example":null,"exampleRequest":null,"tags":[],"displayCostAmount":"unknown","displayCostAsset":"USD","priceDynamic":true,"priceHint":null,"priceStatus":"unknown_unparsed","priceSource":"registry","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"unknown","summary":"price unknown","primary":{"kind":"dynamic","protocol":"mpp","network":"tempo","amountUsd":null,"per":"call","confidence":"unknown"},"accepted":[{"kind":"dynamic","protocol":"mpp","network":"tempo","amountUsd":null,"per":"call","confidence":"unknown"}]},"paymentMethods":[{"uid":"pm_e2pVsAMQqpCzBxqDpM_Rw","protocol":"mpp","methodType":"crypto","chain":"tempo","mode":"charge","costAmount":"unknown","costPer":"request","priority":0,"asset":null,"unit":"request","depositMicros":null,"planRef":null}],"brandName":"agents.zerogpu.ai","brandSlug":"agents-zerogpu-ai-x402","brandBaseUrl":"https://agents.zerogpu.ai","brandDocsUrl":null,"whatItDoes":"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.","exampleAgentPrompt":"Embed the sentence 'The quick brown fox jumps over the lazy dog' using the all-minilm-l6-v2 model on ZeroGPU and give me back the vector.","exampleUseCases":[{"title":"RAG pipeline document ingestion","prompt":"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."},{"title":"Semantic search query encoding","prompt":"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."},{"title":"Duplicate content detection","prompt":"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."}],"resultDescription":"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).","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T17:02:26.237Z","isFirstParty":false,"canonicalSlug":"agents-zerogpu-ai-create-embeddings-zeroclick-authenticated-zerogpu-4d296d1e"}