{"uid":"cap_rf2etg0RPHC3Z0GycPOcB","slug":"agent402-tools-text-embedding-large-3072-dim-44085611","name":"agent402.tools Text Embedding (Large, 3072-dim)","description":"Generate a text embedding vector using OpenAI text-embedding-3-large (3072 dimensions). Higher accuracy than the small model. Ideal for semantic search, RAG, and clustering. No API key needed; pay per call via x402. Text capped at 32k chars.","url":"https://agent402.tools/api/embed-large","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"type":"string","description":"Text to embed (max 32,000 chars)"}}},"responseSchema":{"type":"json","example":{"model":"text-embedding-3-large","usage":{"total_tokens":12},"provider":"openai","embedding":[0.0023,-0.0091,0.0152],"dimensions":3072}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","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":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_FNgM1u8HlylV-fH23Mbmm","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Generates a 3072-dimensional text embedding vector using OpenAI text-embedding-3-large, ideal for semantic search, RAG, and clustering — no API key needed, pay-per-call via x402.","exampleAgentPrompt":"Can you generate a high-accuracy text embedding for this paragraph so I can index it in my vector database for semantic search? The text is: 'Large language models have transformed the way we interact with software, enabling natural conversation interfaces across many domains.'","exampleUseCases":null,"resultDescription":"A 3072-dimensional floating-point vector representing the semantic content of the input text, produced by OpenAI's text-embedding-3-large model. The vector can be used directly in cosine similarity comparisons, vector database indexing, clustering, or RAG retrieval pipelines.","failureModes":["Text exceeds 32,000 character limit — request rejected with error","Payment via x402 fails or is insufficient — request blocked","Empty or missing text field — returns validation error","Network timeout for very long texts near the character limit","OpenAI upstream API unavailability — service temporarily unavailable"],"whenToPreferThis":"Choose this endpoint when you need the highest-quality embeddings from OpenAI's text-embedding-3-large model (3072 dimensions) and either lack an OpenAI API key or prefer a simple pay-per-call model via x402. Prefer over the small variant when accuracy is critical — e.g., for RAG systems where retrieval quality matters, or clustering tasks requiring fine-grained semantic distinctions. The $0.01 USDC per call pricing is straightforward for low-to-medium volume usage.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-13T18:53:14.306Z","isFirstParty":false}