# NetIntel Text Embeddings

> NetIntel Text Embeddings is a paid API for AI agents from netintel.dev, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-15).

Converts input text into a vector embedding using text-embedding-3-small, returning a float array suitable for semantic search, similarity, or downstream ML tasks.

## Facts

- Endpoint: POST https://netintel.dev/v1/embeddings
- Price: $0.005/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/netintel-text-embeddings-284ecc15
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap__k5CkK7GZxOdjw-gMlaZ6

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 netintel-text-embeddings-284ecc15 -d '<json body>'
```

Example prompt: Can you generate a text embedding vector for the sentence 'The quick brown fox jumps over the lazy dog' so I can store it in my vector database for semantic search?

## When to prefer this

Choose this endpoint when you need pay-per-call text embeddings with no signup, no API key, and automatic no-charge on failures — ideal for agents that need on-demand embeddings without committing to a subscription, or when building RAG pipelines, semantic search, or clustering workflows that fire infrequently or unpredictably.

## Known failure modes

- Empty or missing input text — input validation rejection, no charge
- Text exceeds token limit — validation error returned, no charge
- Upstream model provider failure — server error returned, no charge
- Malformed JSON request body — 400-level error, no charge
- Network timeout or infrastructure error — no charge per NetIntel policy

## How this service works

OpenAI-compatible text embeddings API — standard /v1/embeddings request shape: input as a string or a batch of up to 128 strings (64000 chars total on text-embedding-3-small, the default; 24000 on text-embedding-3-large). Flat $0.005 per call in USDC via x402, no OpenAI account or API key. Returns float vectors for RAG, semantic search, clustering, and dedup.

## Output

Returns a JSON object containing a list of embedding objects, each with an index, object type, and a float array (the embedding vector); also includes the model name ('text-embedding-3-small') and token usage counts (prompt_tokens, total_tokens).

## 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",
    "bodyType",
    "body"
   ],
   "properties": {
    "body": {
     "type": "object",
     "required": [
      "input"
     ],
     "properties": {
      "input": {
       "type": "string",
       "description": "Text to embed. Also accepts an array of up to 128 strings (64000 chars total)."
      },
      "model": {
       "type": "string",
       "description": "text-embedding-3-small (default) or text-embedding-3-large"
      },
      "dimensions": {
       "type": "number",
       "description": "Optional output vector size (truncation)."
      }
     }
    },
    "type": {
     "type": "string",
     "const": "http"
    },
    "method": {
     "enum": [
      "POST"
     ],
     "type": "string"
    },
    "bodyType": {
     "enum": [
      "json",
      "form-data",
      "text"
     ],
     "type": "string"
    }
   },
   "additionalProperties": false
  },
  "output": {
   "type": "object",
   "required": [
    "type"
   ],
   "properties": {
    "type": {
     "type": "string"
    },
    "example": {
     "type": "object",
     "properties": {
      "data": {
       "type": "array",
       "description": "[{object:'embedding', index, embedding:[…]}]"
      },
      "model": {
       "type": "string"
      },
      "usage": {
       "type": "object",
       "description": "prompt_tokens, total_tokens (input-only)"
      },
      "object": {
       "type": "string",
       "description": "Always 'list'"
      }
     }
    }
   }
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json",
 "example": {
  "data": [
   {
    "index": 0,
    "object": "embedding",
    "embedding": [
     0.0102,
     -0.0083,
     0.0264
    ]
   }
  ],
  "model": "text-embedding-3-small",
  "usage": {
   "total_tokens": 4,
   "prompt_tokens": 4
  },
  "object": "list"
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/netintel-text-embeddings-284ecc15/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from netintel.dev](https://www.zero.xyz/host/netintel.dev/llms.txt)