← The evidence ledger
Evidence row 125694
Observed , by baseline/1.
- Kind
- listing
- About
- source_record x402_bazaar:https://x402.agentutility.ai/text-embedding
- Recorded
- Previous row
- None: the first of its kind about this subject
- Body SHA-256
- e667ffe24c297b0885f3df57ca43df6c2cb32a8410a09ab111446db6a5ff7f71
- Leaf
- 0x800f3f4bce64ae3d9a552187810924649d237e46b7820644864f13791ee73001
What was observed
{
"endpoints": [
[
"x402",
"https://x402.agentutility.ai/text-embedding"
]
],
"name": "AgentUtility.ai",
"owner_address": "0x6c0752c09e7f6fa6526fcdf40e456a159ebb5621",
"raw": {
"accepts": [
{
"amount": "5000",
"asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
"extra": {
"name": "USD Coin",
"version": "2"
},
"maxTimeoutSeconds": 300,
"network": "eip155:8453",
"payTo": "0x6c0752c09e7F6fA6526fCDf40e456a159ebB5621",
"scheme": "exact"
},
{
"amount": "5000",
"asset": "EPjFWdd5AufqSSqeM2qN1xzybapC8G4wEGGkZwyTDt1v",
"extra": {
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"network": "solana:5eykt4UsFv8P8NJdTREpY1vzqKqZKvdp",
"payTo": "FJqAJ4dXkFbrp3EEo8E9x98iaBGUQwaU8Srz5ePfUXLH",
"scheme": "exact"
}
],
"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.",
"extensions": {
"bazaar": {
"discoverable": true,
"info": {
"input": {
"body": {
"model": "default",
"texts": [
"The first sentence",
"The second sentence"
]
},
"bodyType": "json",
"method": "POST",
"type": "http"
},
"output": {
"example": {
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"dimensions": 1024,
"embeddings": [
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0.0011,
0.083,
0.071
],
[
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0.0072,
0.078,
0.062
]
],
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"properties": {
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"type": "string"
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"type": {
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"type": "object"
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"output": {
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"embeddings": {
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},
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}
},
"required": [
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}
},
"builder-code": {
"info": {
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},
"schema": {
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"additionalProperties": false,
"properties": {
"a": {
"description": "App builder code",
"pattern": "^[a-z0-9_]{1,32}$",
"type": "string"
},
"s": {
"description": "Service builder codes",
"items": {
"pattern": "^[a-z0-9_]{1,32}$",
"type": "string"
},
"type": "array"
},
"w": {
"description": "Wallet builder code",
"pattern": "^[a-z0-9_]{1,32}$",
"type": "string"
}
},
"type": "object"
}
}
},
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"lastUpdated": "2026-10-13T08:02:05.661Z",
"quality": {
"l30DaysTotalCalls": 4,
"l30DaysUniquePayers": 3,
"lastCalledAt": "2026-10-13T08:02:05.447Z"
},
"resource": "https://x402.agentutility.ai/text-embedding",
"serviceName": "AgentUtility.ai",
"tags": [
"wordmint",
"text",
"embedding"
],
"type": "http",
"x402Version": 2
},
"source": "x402_bazaar",
"summary": "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."
}What the leaf hashes
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Fold these into the leaf in order, hashing each sorted pair with keccak256, to reach the root of (296695 rows).
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- Day root
- 0xf43e8dcc3d451a4788b26be904c55e8c4c0fa75c9666796c51db38d37e2c0df8
- Chained
- 0x702d5ac508f08b0d1accf36af25822d8e5660367494edd17a642fb61e158a57a
- Published
- On Arc Testnet, block 69251483: 0x495162f8936e19f0039918feebe6c307f327cd6bf8c37219620b1970041fa9ae