agentutility(7)$ man text-embedding
/text-embedding
PRICE / CALL
$0.005
USDC · base mainnet · scheme: exact
METHOD
POST
CLUSTER
wordmintCATEGORY
uncategorized
STATUS
● live
NAME
text-embedding — embeds 1 to 100 strings into semantic vectors via venice
SYNOPSIS
POST https://x402.agentutility.ai/text-embedding
Content-Type: application/json
X-PAYMENT: <signed-transferWithAuthorization>
{ ... }↳ first call →
402 Payment Required. Sign USDCtransferWithAuthorization, retry with theX-PAYMENT header.DESCRIPTION
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.
INPUT — request schema
| property | type | description | req? |
|---|---|---|---|
| texts | array | 1 to 100 strings to embed; each up to 30,000 chars. | required |
| model | string | Tier shorthand ('default'|'fast'|'openai-compat') or full Venice embedding model name. Default 'default'. | optional |
OUTPUT — response shape
| field | type | description |
|---|---|---|
| embeddings | string | Array of float vectors, one per input string, in the same order as the input array. |
| count | string | Number of input strings that were embedded in this call. |
| dimensions | string | Vector length of each returned embedding (varies by model). |
| model | string | Full Venice model name actually used to generate the embeddings. |
| tier | string | Tier shorthand resolved for this call (default, fast, openai-compat, or the passed model name). |
| usage | string | Token usage stats from Venice for this embedding call. |
| source | string | Upstream provider that produced the vectors (Venice). |
EXAMPLES — two ways to call
EXAMPLE 1 · curl
curl -X POST https://x402.agentutility.ai/text-embedding \
-H 'Content-Type: application/json' \
-d '{ }'first response =
402 Payment Required with payment requirements; sign + retry with X-PAYMENT.EXAMPLE 2 · mcp
# Install the MCP package for this endpoint's cluster npx -y @agentutility/mcp-<cluster> # Required: EVM private key with USDC on Base export X402_PRIVATE_KEY=0x... # Then call the text-embedding tool from your MCP-aware agent.
MCP server handles payment automatically — your coding agent just calls the tool by name.
METADATA
- tags
- embeddingstext-embeddingvector-embeddingsemantic-searchwordmintgemini-embeddingbge-m3
- methods
- POST
- cluster
- wordmint
- price
- $0.005 USDC per call
ADJACENT — other endpoints in wordmint
| endpoint | description | price |
|---|---|---|
| brand-tagline | Generates brand taglines and slogans for launch pages, X bios, email copy, and product cards. | $0.005 |
| brand-tagline-generate | Generates tagline options for a brand or startup from its name, concept, audience, and tone. | $0.005 |
| card-resolve | Normalizes free-form graded card text into a canonical card object. | $0.005 |
| content-simhash | Fingerprints text with a 64-bit SimHash for near-duplicate detection, computed entirely locally. | $0.005 |
| cron-parse | Cron parser. | $0.005 |
| detect-language | Language detector / language identification. | $0.005 |
| dictionary-define | Looks up English word definitions with pronunciation, part of speech, and synonyms. | $0.005 |
| embedding-similarity | Measures how semantically similar two strings are: embeds both via Venice (default model: text-embedding-bge-m3) and returns the cosine s… | $0.005 |
SEE ALSO