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BGE-M3 Multilingual Text Embedding and Semantic Ranking API
x402
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.
Price / call$0.0005
Success—
StatusUnknown
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.
Example use cases
Semantic search over knowledge base
“I have a list of 20 FAQ answers and a user's question 'how do I reset my password' — embed all of them and tell me which FAQ answer is most relevant to the question.”
Multilingual document similarity ranking
“Here are five product descriptions in mixed English and Spanish — embed them all and rank them by similarity to the query 'wireless noise-cancelling headphones'.”
Vectorize texts for storage in vector DB
“Take these 10 customer review sentences and generate BGE-M3 embeddings for each one so I can store the vectors in my Pinecone index — I want the raw 1024-dimensional vectors back, not just the rankings.”
Technical details
Endpoint
GEThttps://ai-policy-kit.moneymaker-ai.workers.dev/api/ai/embed?utm_source=zero.xyzWhen to prefer
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.
Side effects
- 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
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"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
}
}Tags
x402
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