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AgentUtility Text Embedding API
x402
AgentUtility Text Embedding API is a paid API for AI agents from x402.agentutility.ai, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-14).
Converts 1–100 text strings into semantic embedding vectors using Venice-hosted models (Gemini, BGE-M3, or OpenAI-compatible), paid per call via x402/USDC.
Price / call$0.005
Success—
StatusUnknown
How this service works
Embeds 1 to 100 strings into semantic vectors via Venice. Tier shorthand: 'default' → gemini-embedding-2-preview (newest, recommended), 'fast' → text-embedding-bge-m3, 'openai-compat' → text-embedding-3-small. You can also pass a full Venice embedding model name. Returns a list of vectors aligned with input order. Use it for text embedding, vector embedding, Venice embeddings, Gemini embeddings, or BGE-M3.
Output
Returns an ordered list of float vectors, one per input string, aligned positionally with the input array. Each vector encodes the semantic meaning of its corresponding string in the embedding space of the chosen model (Gemini embedding-2-preview, BGE-M3, or text-embedding-3-small).
Example use cases
RAG pipeline document indexing
“Take these 20 chunks from my technical documentation and convert them all into embedding vectors using the default Gemini model so I can index them in Pinecone for semantic search.”
Semantic similarity scoring
“Embed both of these customer support tickets into vectors with the fast BGE-M3 tier so I can compute their cosine similarity and check if they're about the same issue.”
OpenAI-compatible vector migration
“I need to embed this list of 50 product titles using the openai-compat tier so the vectors will be dimensionally compatible with my existing OpenAI embeddings already in Weaviate.”
Technical details
Endpoint
POSThttps://x402.agentutility.ai/text-embeddingWhen to prefer
Choose this endpoint when you need on-demand text embeddings from Venice-hosted models (especially Gemini embedding-2-preview or BGE-M3) paid per call with USDC via x402, without managing API keys or subscriptions. Prefer it for agentic workflows where payment is automated, batch sizes are under 100, and you want model-tier flexibility (newest Gemini, fast BGE-M3, or OpenAI-compatible dimensions).
Side effects
- Input exceeds 100 strings — batch size limit exceeded
- Payment failure via x402/USDC — insufficient balance or payment not confirmed
- Invalid or unsupported model name passed — falls back to error or default
- Empty string or null input in batch — may cause partial or full failure
- Rate limiting if too many calls in short succession
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"tier": "default",
"count": 2,
"model": "gemini-embedding-2-preview",
"usage": {
"total_tokens": 6,
"prompt_tokens": 6
},
"source": "venice",
"dimensions": 1024,
"embeddings": [
[
0.0231,
-0.142,
0.0011,
0.083,
0.071
],
[
0.0193,
-0.151,
0.0072,
0.078,
0.062
]
]
}
}Tags
x402
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