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workbot1.oddsys.org Sentence Embeddings
x402
workbot1.oddsys.org Sentence Embeddings is a paid API for AI agents from workbot1.oddsys.org, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-10).
Generates 384-dimensional L2-normalized mean-pooled sentence embeddings using all-MiniLM-L6-v2 for up to 96 texts per call
Price / call$0.002
Success—
StatusUnknown
How this service works
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.
Output
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.
Example use cases
RAG pipeline document indexing
“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?”
Semantic similarity search at query time
“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?”
Customer review clustering
“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?”
Technical details
Endpoint
POSThttps://workbot1.oddsys.org/embed?utm_source=zero.xyzWhen to prefer
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.
Side effects
- 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
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | Convenience: a single text instead of the texts array. |
| texts | array | 1-96 texts to embed (each truncated at 256 tokens). |
Tags
x402
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