# agent402.tools Text Embedding (Large, 3072-dim)

> agent402.tools Text Embedding (Large, 3072-dim) is a paid API for AI agents from agent402.tools, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-13).

Generates a 3072-dimensional text embedding vector using OpenAI text-embedding-3-large, ideal for semantic search, RAG, and clustering — no API key needed, pay-per-call via x402.

## Facts

- Endpoint: POST https://agent402.tools/api/embed-large
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-13
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/agent402-tools-text-embedding-large-3072-dim-44085611
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_rf2etg0RPHC3Z0GycPOcB

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 agent402-tools-text-embedding-large-3072-dim-44085611 -d '<json body>'
```

Example prompt: Can you generate a high-accuracy text embedding for this paragraph so I can index it in my vector database for semantic search? The text is: 'Large language models have transformed the way we interact with software, enabling natural conversation interfaces across many domains.'

## When to prefer this

Choose this endpoint when you need the highest-quality embeddings from OpenAI's text-embedding-3-large model (3072 dimensions) and either lack an OpenAI API key or prefer a simple pay-per-call model via x402. Prefer over the small variant when accuracy is critical — e.g., for RAG systems where retrieval quality matters, or clustering tasks requiring fine-grained semantic distinctions. The $0.01 USDC per call pricing is straightforward for low-to-medium volume usage.

## Known failure modes

- Text exceeds 32,000 character limit — request rejected with error
- Payment via x402 fails or is insufficient — request blocked
- Empty or missing text field — returns validation error
- Network timeout for very long texts near the character limit
- OpenAI upstream API unavailability — service temporarily unavailable

## How this service works

Generate a text embedding vector using OpenAI text-embedding-3-large (3072 dimensions). Higher accuracy than the small model. Ideal for semantic search, RAG, and clustering. No API key needed; pay per call via x402. Text capped at 32k chars.

## Output

A 3072-dimensional floating-point vector representing the semantic content of the input text, produced by OpenAI's text-embedding-3-large model. The vector can be used directly in cosine similarity comparisons, vector database indexing, clustering, or RAG retrieval pipelines.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "text": {
   "type": "string",
   "description": "Text to embed (max 32,000 chars)"
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json",
 "example": {
  "model": "text-embedding-3-large",
  "usage": {
   "total_tokens": 12
  },
  "provider": "openai",
  "embedding": [
   0.0023,
   -0.0091,
   0.0152
  ],
  "dimensions": 3072
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/agent402-tools-text-embedding-large-3072-dim-44085611/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from agent402.tools](https://www.zero.xyz/host/agent402.tools/llms.txt)