# ForgeMesh Semantic Vector Generation

> ForgeMesh Semantic Vector Generation is a paid API for AI agents from x402.forgemesh.io, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-16).

Converts batches of up to 64 text strings into 768-dimensional dense embeddings for similarity search, clustering, or RAG retrieval — no external API key required.

## Facts

- Endpoint: POST https://x402.forgemesh.io/semantic-search-vectors
- Price: $0.002/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-16
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/forgemesh-semantic-vector-generation-f1204576
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_KUp0tNSCi6KNp1CgbGI6E

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-semantic-vector-generation-f1204576 -d '<json body>'
```

Example prompt: Turn these three product descriptions into 768-dimensional semantic vectors so I can store them in my Pinecone index and do similarity search: 'wireless noise-cancelling headphones', 'bluetooth over-ear headphones with ANC', 'wired studio monitor headphones'.

## When to prefer this

Choose this endpoint when you need text embeddings without an OpenAI, Cohere, or similar subscription — pay per call at $0.002 USDC. Ideal for agents building RAG pipelines, populating vector databases, or performing similarity scoring on the fly. Best when batching up to 64 texts at once to maximize cost efficiency.

## Known failure modes

- More than 64 texts submitted in a single request — exceeds batch limit
- Empty texts array or missing both text and texts fields — returns validation error
- Individual text too long — may be truncated or rejected
- Payment not included or insufficient — returns 402 Payment Required
- Network timeout for large batches of long documents

## How this service works

Semantic vector generation: turns batches of text (up to 64 per request) into dense 768-dimension embeddings ready to feed into a vector database or similarity search index. Runs without any external API key or subscription, pay only for the call. Well suited for building RAG retrieval layers, clustering related content, or scoring how similar two passages are.

## Output

Returns an array of 768-dimensional dense float vectors, one per input text, ready to insert into a vector database or use for cosine/dot-product similarity computation. Each vector captures semantic meaning of the corresponding input text.

## 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-semantic-vector-generation-f1204576/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)