# ForgeMesh Text Embeddings API

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

Converts up to 64 texts per call into 768-dimensional embedding vectors for semantic search, clustering, deduplication, and RAG pipelines, billed per call via USDC over x402.

## Facts

- Endpoint: POST https://x402.forgemesh.io/text-embeddings
- 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/forgemesh-text-embeddings-api-b51d1da4
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_JfMNlRf5PLMYrFqYTjhbw

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 forgemesh-text-embeddings-api-b51d1da4 -d '<json body>'
```

Example prompt: Can you embed these 10 product descriptions into 768-dimensional vectors so I can index them for semantic search: 'wireless noise-cancelling headphones', 'ergonomic office chair', 'standing desk converter', 'USB-C hub', 'mechanical keyboard', 'monitor arm', 'webcam 4K', 'desk lamp LED', 'cable management box', 'laptop stand'?

## When to prefer this

Choose this endpoint when you need text embeddings without registering for or managing an API key, want to pay per call in USDC rather than a monthly subscription, need a 768-dimension vector space suitable for standard RAG and semantic search pipelines, or want assurance that inputs are not stored. Prefer this over OpenAI or Cohere embeddings when keyless, pay-as-you-go crypto billing is required.

## Known failure modes

- More than 64 texts in a single call — API likely rejects or truncates the batch
- Empty texts array or missing both 'text' and 'texts' fields — validation error
- Payment failure via x402 (insufficient USDC balance or bad payment header) — 402 response
- Input text too long for the underlying model — may truncate or error
- Network timeout on large batches processed on their own hardware

## How this service works

Text embeddings API without an API key: up to 64 texts per call, 768-dimension embedding vectors computed on our own hardware. For semantic search, clustering, dedup, and RAG. Pay per call with USDC over x402; inputs never stored.

## Output

An array of 768-dimensional float vectors, one per input text, suitable for cosine similarity, nearest-neighbor search, clustering, or as input features to downstream ML models.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "text": {
   "type": "string",
   "description": "single text alternative to texts[]"
  },
  "texts": {
   "type": "array",
   "items": {
    "type": "string"
   },
   "description": "1-64 texts to embed"
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json",
 "example": {
  "model": "nomic-embed-text",
  "dimensions": 768,
  "embeddings": [
   [
    0.011,
    -0.02
   ],
   [
    0.03,
    0.004
   ]
  ]
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/forgemesh-text-embeddings-api-b51d1da4/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from x402.forgemesh.io](https://www.zero.xyz/host/x402.forgemesh.io/llms.txt)