# AGISHub Text Embedding API

> AGISHub Text Embedding API is a paid API for AI agents from api.agishub.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-15).

Converts input text into a numeric embedding vector suitable for semantic search, retrieval-augmented generation (RAG), and similarity comparison, with multilingual support.

## Facts

- Endpoint: GET https://api.agishub.com/v1/embed
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/agishub-text-embedding-api-1ddc2e9a
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_gSYhpgSE_9mpCx3WouAU_

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-api-1ddc2e9a
```

Example prompt: Embed this text into a numeric vector so I can do a semantic similarity search: 'What are the best practices for microservices architecture?'

## When to prefer this

Choose this endpoint when you need a simple, pay-per-call multilingual text embedding with no API key setup, particularly well-suited for agents that need on-demand vectorization without managing embedding model infrastructure. Ideal for RAG pipelines, semantic search, and similarity tasks where cost predictability ($0.01/call) matters.

## Known failure modes

- Empty or missing 'text' query parameter returns a validation error
- Text too long may exceed model context window and return an error
- Non-UTF-8 or malformed text may cause processing failures
- Service unavailability or rate limiting returns a 4xx/5xx response
- Payment not completed (x402) results in access denial

## How this service works

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

## Output

Returns a JSON object containing the embedding result — a numeric vector representation of the input text, usable for cosine similarity, nearest-neighbor search, and RAG retrieval pipelines. The response includes the embedded representation under the 'result' key.

## 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",
    "queryParams"
   ],
   "properties": {
    "type": {
     "enum": [
      "http"
     ],
     "type": "string"
    },
    "method": {
     "enum": [
      "GET"
     ],
     "type": "string"
    },
    "queryParams": {
     "type": "object",
     "required": [
      "text"
     ],
     "properties": {
      "text": {
       "type": "string",
       "minLength": 1,
       "description": "Text to embed into a numeric vector for semantic search / RAG."
      }
     },
     "additionalProperties": false
    }
   },
   "additionalProperties": false
  },
  "output": {
   "type": "object",
   "required": [
    "type",
    "example"
   ],
   "properties": {
    "type": {
     "enum": [
      "json"
     ],
     "type": "string"
    },
    "example": {
     "type": "object"
    }
   },
   "additionalProperties": false
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json",
 "example": {
  "result": "Turn text into a numeric embedding vector for semantic search, RAG and similarity. Multilingual."
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/agishub-text-embedding-api-1ddc2e9a/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)