{"uid":"cap_DCcPPaS7F0LCGxlUye-Wc","slug":"text-embeddings-vector-api-bbf9c166","name":"text-embeddings-vector-api","description":"Text embeddings over x402 — nomic-embed-text (768-dim) on OUR OWN GPU, never forwarded to a third-party API. OpenAI-shaped data[{index, embedding}] response; batch up to 16 texts in ONE flat-price call. For RAG, semantic search, dedup and memory pipelines. No API key, no account. GET ?text= or POST {input:[...]}. Free: GET /v1/ai/embed/preview. Cryptographically signed (Ed25519) — every response is independently verifiable via /.well-known/obol-pubkey.","url":"https://obol-x402.fly.dev/v1/ai/embed","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method"],"properties":{"type":{"type":"string","const":"http"},"method":{"enum":["GET","HEAD","DELETE"],"type":"string"},"queryParams":{"type":"object","required":["text"],"properties":{"text":{"type":"string","description":"string to embed (GET ?text=… — or POST input as string[] for batch, max 16)"}}}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object"}}}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.002","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"down","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.002/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_V7Cep855-5soJB3JAiVtW","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Generates 768-dimensional text embeddings (nomic-embed-text model) on self-hosted GPU, returning OpenAI-shaped [{index, embedding}] vectors for single or batch (up to 16) inputs, cryptographically signed with Ed25519.","exampleAgentPrompt":"Embed this text for me so I can store it in my vector database: 'The quick brown fox jumps over the lazy dog' — I need a 768-dim nomic-embed-text vector, cryptographically signed.","exampleUseCases":[{"title":"RAG document ingestion pipeline","prompt":"I need to embed these 16 product descriptions into vectors so I can store them in my Pinecone index for semantic search — can you batch embed all of them in one call?"},{"title":"Semantic deduplication of user queries","prompt":"I have two user support messages and I want to check if they're semantically the same before routing — can you embed both texts so I can compute their cosine similarity?"},{"title":"Memory encoding for AI agent","prompt":"Store this conversation summary as a vector in my agent's long-term memory: 'User prefers concise answers and works in fintech.' I need the 768-dim embedding, signed so I can verify it later."}],"resultDescription":"Returns an OpenAI-shaped JSON array of objects [{index: number, embedding: number[768]}], one per input text. Each response is cryptographically signed with Ed25519 (verifiable via /.well-known/obol-pubkey). Inference runs on the provider's own GPU — nomic-embed-text model, 768 dimensions per vector.","failureModes":["Missing or empty 'text' query param on GET returns error","Batch exceeds 16 texts — rejected with error","Payment not provided or insufficient USDC — 402 Payment Required","Invalid input type or schema mismatch — 400 Bad Request","GPU inference timeout on very long inputs","Signature verification failure if pubkey endpoint is unavailable"],"whenToPreferThis":"Choose this endpoint when you need 768-dim nomic-embed-text vectors without signing up for an API key or account, want cryptographically verifiable (Ed25519-signed) embeddings, need to batch up to 16 texts in a single flat-price call ($0.002 USDC), or require embeddings computed on a self-hosted GPU with no third-party data forwarding. Prefer over OpenAI embeddings when privacy or auditability of inference is a concern, or when integrating into an x402 micropayment workflow.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T11:28:10.235Z","isFirstParty":false}