# Quartermaster Text Embeddings

> Quartermaster Text Embeddings is a paid API for AI agents from quartermaster.surewhynot.app, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-13).

Converts a text string (up to 8000 characters) into a 1024-dimensional semantic vector suitable for semantic search and RAG pipelines.

## Facts

- Endpoint: GET https://quartermaster.surewhynot.app/v1/embed
- Price: $0.002/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-13
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/quartermaster-text-embeddings-bf1eac64
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_4v-6FoSGZ8_liTG1LfTev

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 quartermaster-text-embeddings-bf1eac64
```

Example prompt: Embed this text for me so I can do a semantic similarity search: 'Retrieval-augmented generation combines a language model with an external knowledge base to produce grounded answers.'

## When to prefer this

Choose this endpoint when you need a single, on-demand 1024-dimensional text embedding billed per call via x402/USDC micropayment, particularly in agentic or autonomous pipelines where pay-per-use is preferable to a subscription API key. Ideal for RAG chunk indexing, semantic search query encoding, or similarity comparisons without maintaining a separate embedding service.

## Known failure modes

- Text exceeds 8000-character limit — request rejected
- Empty or missing 'text' query parameter — validation error
- Payment not included or insufficient — 402 response with x402 challenge
- Service unavailable — upstream embedding model timeout or error

## How this service works

Text embeddings for semantic search and RAG: one vector per call, 1024 dimensions.

## Output

A single 1024-dimensional floating-point vector representing the semantic content of the input text, suitable for cosine similarity comparisons, nearest-neighbor indexing, or feeding into a RAG retrieval pipeline.

## Request schema (JSON Schema)

```json
{
 "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"
     ],
     "type": "string"
    },
    "queryParams": {
     "type": "object",
     "required": [
      "text"
     ],
     "properties": {
      "text": {
       "type": "string",
       "maxLength": 8000,
       "description": "Text to embed"
      }
     }
    }
   },
   "additionalProperties": false
  },
  "output": {
   "type": "object",
   "required": [
    "type"
   ],
   "properties": {
    "type": {
     "type": "string"
    },
    "example": {
     "type": "object"
    }
   }
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/quartermaster-text-embeddings-bf1eac64/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from quartermaster.surewhynot.app](https://www.zero.xyz/host/quartermaster.surewhynot.app/llms.txt)