{"uid":"cap_4mxaOiP-1hjSuE3WnaAid","slug":"workbot1-oddsys-org-sentence-embeddings-f85e5604","name":"workbot1.oddsys.org Sentence Embeddings","description":"Generate sentence embeddings (all-MiniLM-L6-v2, 384-dim, mean-pooled + L2-normalized) for up to 96 texts per call. Drop-in compatible with any all-MiniLM-L6-v2 index. Pairs with /chunk for a complete RAG pipeline.","url":"https://workbot1.oddsys.org/embed?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"type":"string","description":"Convenience: a single text instead of the texts array."},"texts":{"type":"array","items":{"type":"string"},"description":"1-96 texts to embed (each truncated at 256 tokens)."}}},"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":"unknown","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_kOIasv801ViHp2n0oVlMX","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 384-dimensional L2-normalized mean-pooled sentence embeddings using all-MiniLM-L6-v2 for up to 96 texts per call","exampleAgentPrompt":"I need to embed these 50 product descriptions as 384-dimensional all-MiniLM-L6-v2 vectors so I can index them in my vector store for semantic search — can you run them all through in one batch?","exampleUseCases":[{"title":"RAG pipeline document indexing","prompt":"I have 80 chunked passages from a technical manual and I need to embed all of them as all-MiniLM-L6-v2 vectors in one shot so I can load them into my Pinecone index — can you embed the whole batch at once?"},{"title":"Semantic similarity search at query time","prompt":"When a user types a search query, I want to turn it into a 384-dim embedding so I can find the closest matching entries in my vector database — can you embed the query text for me?"},{"title":"Customer review clustering","prompt":"I have 60 short customer reviews and I want to cluster them by topic — can you generate sentence embeddings for all of them in a single call so I can run k-means on the resulting vectors?"}],"resultDescription":"An array of 384-dimensional float vectors, one per input text, mean-pooled from the all-MiniLM-L6-v2 model and L2-normalized, ready for direct use in cosine similarity comparisons or insertion into any all-MiniLM-L6-v2-compatible vector index.","failureModes":["Batch exceeds 96 texts — request rejected with an error indicating the limit","Empty input array — returns an error or empty result","Individual text is too long for the model's token limit — may be silently truncated or return an error","Payment not included or insufficient USDC — 402 Payment Required response","Network timeout for very large batches near the 96-text limit"],"whenToPreferThis":"Choose this endpoint when you need all-MiniLM-L6-v2 compatible 384-dim embeddings with drop-in compatibility for existing vector indexes, especially as part of a RAG pipeline on workbot1.oddsys.org where you can also use the /chunk endpoint for splitting and the reranker for scoring. Ideal for batching up to 96 texts in a single inexpensive $0.002 call rather than making per-text requests to a hosted embedding API.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-10T00:48:03.525Z","isFirstParty":false,"canonicalSlug":"workbot1-oddsys-org-sentence-embeddings-f85e5604"}