# BGE-M3 Multilingual Text Embedding and Semantic Ranking API

> BGE-M3 Multilingual Text Embedding and Semantic Ranking API is a paid API for AI agents from ai-policy-kit.moneymaker-ai.workers.dev, paid per call via x402, $0.0005/call, status unknown (last checked 2026-10-30).

Generates dense 1024-dimensional multilingual embeddings using BGE-M3 and optionally ranks input texts by cosine similarity against a query.

## Facts

- Endpoint: GET https://ai-policy-kit.moneymaker-ai.workers.dev/api/ai/embed?utm_source=zero.xyz
- Price: $0.0005/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-30
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/bge-m3-multilingual-text-embedding-and-semantic-ranking-api-f4fa4e4c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_0ZrcDVYWcHHt_GaI4b7JB

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 bge-m3-multilingual-text-embedding-and-semantic-ranking-api-f4fa4e4c
```

Example prompt: I have these three passages — 'USDC is a dollar stablecoin', 'Base is an Ethereum L2', and 'The cat sat on the mat' — can you embed them and rank them by how relevant they are to the query 'what is crypto'?

## When to prefer this

Choose this endpoint when you need multilingual semantic embeddings using the BGE-M3 model specifically, or when you want server-side cosine similarity ranking computed for you without managing a vector database. It is ideal for lightweight, stateless embedding tasks where you want both vectors and ranking in a single API call at very low cost.

## Known failure modes

- Missing required 'texts' query parameter returns an error
- More than 32 strings submitted exceeds the input limit
- Empty string array causes a processing error
- Network timeout on very large batch inputs
- Malformed query parameter format returns a 400 error

## How this service works

Generate an AI_POLICY.md and a pull request template with an AI-disclosure section for your open-source repo. Free, runs in your browser.

## Output

Returns a JSON object with: BGE-M3 dense embeddings (1024-dimensional float arrays) for each input text, an optional ranked list of texts with cosine similarity scores if a query was provided, the model name, total count, and vector dimensions. Normalized flag and neuron cost estimate are also included.

## 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"
   ],
   "properties": {
    "type": {
     "type": "string",
     "const": "http"
    },
    "method": {
     "enum": [
      "GET"
     ],
     "type": "string"
    },
    "queryParams": {
     "type": "object",
     "required": [
      "texts"
     ],
     "properties": {
      "query": {
       "type": "string",
       "description": "optional; rank texts against it"
      },
      "texts": {
       "type": "array",
       "items": {
        "type": "string"
       },
       "description": "1-32 strings (or text= for one)"
      },
      "include_vectors": {
       "enum": [
        "true",
        "false"
       ],
       "type": "string"
      }
     }
    }
   },
   "additionalProperties": false
  },
  "output": {
   "type": "object",
   "required": [
    "type"
   ],
   "properties": {
    "type": {
     "type": "string"
    },
    "example": {
     "type": "object"
    }
   }
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json",
 "example": {
  "note": "bge-m3 dense vectors (multilingual). With query=, ranking is cosine similarity computed by us. Vectors from other models are not comparable.",
  "count": 3,
  "model": "@cf/baai/bge-m3",
  "ranking": [
   {
    "index": 0,
    "score": 0.64282,
    "preview": "USDC is a dollar stablecoin."
   },
   {
    "index": 2,
    "score": 0.59885,
    "preview": "Base is an Ethereum L2."
   },
   {
    "index": 1,
    "score": 0.32354,
    "preview": "The cat sat on the mat."
   }
  ],
  "dimensions": 1024,
  "embeddings": [
   [
    -0.029053,
    0.002089,
    -0.061401,
    0.005913,
    "…1020 more"
   ],
   [
    -0.011223,
    0.025696,
    -0.084412,
    -0.002304,
    "…1020 more"
   ],
   [
    0.009056,
    0.014679,
    -0.021378,
    0.038177,
    "…1020 more"
   ]
  ],
  "normalized": false,
  "neurons_estimated": 0.54
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/bge-m3-multilingual-text-embedding-and-semantic-ranking-api-f4fa4e4c/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from ai-policy-kit.moneymaker-ai.workers.dev](https://www.zero.xyz/host/ai-policy-kit.moneymaker-ai.workers.dev/llms.txt)