# AGISHub Text Embedding

> AGISHub Text Embedding is a paid API for AI agents from api.agishub.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-14).

Converts a text string into a numeric embedding vector for use in semantic search, RAG pipelines, and similarity matching, with multilingual support.

## Facts

- Endpoint: POST https://api.agishub.com/paid/embed
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-14
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/agishub-text-embedding-86554e4d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_U1fUtmHtPp66nM-WCEac9

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 agishub-text-embedding-86554e4d -d '<json body>'
```

Example prompt: Can you embed this sentence into a vector for me so I can use it in my semantic search index: 'The quick brown fox jumps over the lazy dog'?

## When to prefer this

Choose this endpoint when you need a lightweight, pay-per-call embedding API with multilingual support and no subscription required. Ideal for agents that need on-demand vectorization without managing API keys for large embedding providers like OpenAI or Cohere.

## Known failure modes

- Empty or missing 'text' query parameter returns a validation error
- Text too long for the model's context window may be truncated or rejected
- Network timeout for very long inputs
- Payment failure via x402 protocol if USDC balance is insufficient
- Malformed request returns 400 Bad Request

## How this service works

Turn text into a numeric embedding vector for semantic search, RAG and similarity. Multilingual.

## Output

A JSON object containing a numeric embedding vector (array of floats) representing the semantic content of the input text, suitable for use in vector databases, similarity calculations, or RAG retrieval pipelines.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "required": [
  "input",
  "output"
 ],
 "properties": {
  "input": {
   "type": "object",
   "required": [
    "type",
    "method",
    "bodyType",
    "body"
   ],
   "properties": {
    "body": {
     "type": "object",
     "required": [
      "text"
     ],
     "properties": {
      "text": {
       "type": "string",
       "minLength": 1,
       "description": "Text to embed into a numeric vector for semantic search / RAG."
      }
     },
     "additionalProperties": false
    },
    "type": {
     "enum": [
      "http"
     ],
     "type": "string"
    },
    "method": {
     "enum": [
      "POST"
     ],
     "type": "string"
    },
    "bodyType": {
     "enum": [
      "json"
     ],
     "type": "string"
    }
   },
   "additionalProperties": false
  },
  "output": {
   "type": "object",
   "required": [
    "type",
    "example"
   ],
   "properties": {
    "type": {
     "enum": [
      "json"
     ],
     "type": "string"
    },
    "example": {
     "type": "object"
    }
   },
   "additionalProperties": false
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/agishub-text-embedding-86554e4d/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from api.agishub.com](https://www.zero.xyz/host/api.agishub.com/llms.txt)