# ForgeMesh Text Vectorizer

> ForgeMesh Text Vectorizer 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 text strings per call into 768-dimensional numeric embedding vectors for semantic similarity, clustering, and deduplication tasks.

## Facts

- Endpoint: POST https://x402.forgemesh.io/text-vectorizer
- 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-vectorizer-c38d46c0
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_82mKdJWZ7a5Q0c65jYrE5

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-vectorizer-c38d46c0 -d '<json body>'
```

Example prompt: Turn these three sentences into 768-dimensional embedding vectors so I can compare them for semantic similarity: 'The cat sat on the mat.', 'A feline rested on the rug.', 'The stock market closed higher today.'

## When to prefer this

Choose this endpoint when you need text embeddings without setting up a separate provider account (OpenAI, Cohere, etc.) or managing API keys, and when a pay-per-call USDC model is acceptable. Ideal for one-off embedding jobs, privacy-sensitive text (no retention), or agent workflows that need embeddings as a commodity service. Best when batch sizes fit within 64 texts per call.

## Known failure modes

- More than 64 texts submitted in a single call — request rejected
- Empty texts array or missing both text and texts fields — validation error
- Individual text string exceeds model token limits — truncation or error
- Payment in USDC not provided or insufficient — 402 Payment Required response
- Network timeout for very large batches near the 64-item limit

## How this service works

Vector embeddings API: converts up to 64 pieces of text per call into 768-dimensional numeric vectors suitable for similarity comparison, no separate provider account or key needed. Pay per call in USDC, and nothing submitted is retained afterward. Use it to build semantic search indexes, cluster related documents, or de-duplicate near-identical text at scale.

## Output

Returns an array of 768-dimensional floating-point vectors, one per input text, suitable for cosine similarity comparisons, nearest-neighbor lookups, clustering algorithms, or insertion into a vector database. No input data is retained after the call.

## 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-vectorizer-c38d46c0/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)