# workbot1.oddsys.org Sentence Embeddings

> 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

## Facts

- Endpoint: POST https://workbot1.oddsys.org/embed?utm_source=zero.xyz
- Price: $0.002/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-10
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/workbot1-oddsys-org-sentence-embeddings-f85e5604
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_4mxaOiP-1hjSuE3WnaAid

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability workbot1-oddsys-org-sentence-embeddings-f85e5604 -d '<json body>'
```

Example prompt: 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?

## When to prefer this

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.

## Known failure modes

- 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

## 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.

## Request schema (JSON Schema)

```json
{
 "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)."
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/workbot1-oddsys-org-sentence-embeddings-f85e5604/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from workbot1.oddsys.org](https://www.zero.xyz/host/workbot1.oddsys.org/llms.txt)