addrbookFor publishers
← 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": {
          "feePayer": "BFK9TLC3edb13K6v4YyH3DwPb5DSUpkWvb7XnqCL9b4F"
        },
        "maxTimeoutSeconds": 300,
        "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": {
              "count": 2,
              "dimensions": 1024,
              "embeddings": [
                [
                  0.0231,
                  -0.142,
                  0.0011,
                  0.083,
                  0.071
                ],
                [
                  0.0193,
                  -0.151,
                  0.0072,
                  0.078,
                  0.062
                ]
              ],
              "model": "gemini-embedding-2-preview",
              "source": "venice",
              "tier": "default",
              "usage": {
                "prompt_tokens": 6,
                "total_tokens": 6
              }
            },
            "type": "json"
          }
        },
        "schema": {
          "$schema": "https://json-schema.org/draft/2020-12/schema",
          "properties": {
            "input": {
              "additionalProperties": false,
              "properties": {
                "body": {
                  "properties": {
                    "model": {
                      "description": "Tier shorthand ('default'|'fast'|'openai-compat') or full Venice embedding model name. Default 'default'.",
                      "type": "string"
                    },
                    "texts": {
                      "description": "1 to 100 strings to embed; each up to 30,000 chars.",
                      "items": {
                        "type": "string"
                      },
                      "type": "array"
                    }
                  },
                  "required": [
                    "texts"
                  ]
                },
                "bodyType": {
                  "enum": [
                    "json",
                    "form-data",
                    "text"
                  ],
                  "type": "string"
                },
                "method": {
                  "enum": [
                    "POST"
                  ],
                  "type": "string"
                },
                "type": {
                  "const": "http",
                  "type": "string"
                }
              },
              "required": [
                "type",
                "method",
                "bodyType",
                "body"
              ],
              "type": "object"
            },
            "output": {
              "properties": {
                "example": {
                  "properties": {
                    "count": {
                      "type": "integer"
                    },
                    "dimensions": {
                      "type": "integer"
                    },
                    "embeddings": {
                      "items": {
                        "items": {
                          "type": "number"
                        },
                        "type": "array"
                      },
                      "type": "array"
                    },
                    "model": {
                      "type": "string"
                    },
                    "source": {
                      "type": "string"
                    },
                    "tier": {
                      "type": "string"
                    },
                    "usage": {
                      "properties": {
                        "prompt_tokens": {
                          "type": "integer"
                        },
                        "total_tokens": {
                          "type": "integer"
                        }
                      },
                      "type": "object"
                    }
                  },
                  "type": "object"
                },
                "type": {
                  "type": "string"
                }
              },
              "required": [
                "type"
              ],
              "type": "object"
            }
          },
          "required": [
            "input"
          ],
          "type": "object"
        }
      },
      "builder-code": {
        "info": {
          "a": "bc_awpbwsy3"
        },
        "schema": {
          "$schema": "https://json-schema.org/draft/2020-12/schema",
          "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"
        }
      }
    },
    "iconUrl": "https://res.cloudinary.com/bdb-prod/image/upload/c_fill,ar_1:1,w_256,h_256,f_auto,q_auto/v1783070352/bazaar-icons/54f004eda341546b.png",
    "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

{"body":"e667ffe24c297b0885f3df57ca43df6c2cb32a8410a09ab111446db6a5ff7f71","id":125694,"kind":"listing","method":"baseline/1","observed_us":1793025626609398,"prev":null,"subject":"x402_bazaar:https://x402.agentutility.ai/text-embedding","subject_kind":"source_record"}

Proof

Fold these into the leaf in order, hashing each sorted pair with keccak256, to reach the root of (296695 rows).

  1. 0x800f78668fbcfd9a287a7320a4b2493496949ad8ace02521e8468bcabbb33e5a
  2. 0xdf60134f91287d4bede8d3422b5c1fce599c338700797908adeee2ce05687693
  3. 0x51ecb1456c28036c2dbd8e18481d43838a788560c89804e100df6a3c1f9c9a9d
  4. 0x90990ed17fe93e71fef2a5924861ccb46322a78bdb6d6581a2b6eaf27379334c
  5. 0xbf782446ef5d678253392f7da0b2046e10640e0183a43c9acd5932615a904e13
  6. 0xd9027a3439aaab41b0508469b751cf53468216ad575a241ea02991a1dfa1ae93
  7. 0xb3890022a1cf0b99e1297179c1074d8251c8f30a925ecc05f19fa6166ef0bea2
  8. 0x7ce231718661b4a86c9a6d35ed8c55a8bd6fc2e3a7cd92be547cbc5071262ad4
  9. 0x446341aac5252a350e04f43c2a957a932bda3f6c5cf9e71dc0f70c46c81ffbdb
  10. 0x4ac6e7b8710a19dd69c1ac61f6e5bc5b40663b9f632b7d8a1515e0c4fc86564d
  11. 0x94ff6a1838bb06ac09899310e3d37518c2cdbae79f32eb90e1b61eb995aed881
  12. 0xb9c4107c5cbaf0844324e5ba287c90e0643bb7b9e48e7877adda9372132cf0ea
  13. 0x4d2ee986a690e903126f449b4e9e62d5572b350357ab4496a83488949b131c29
  14. 0x0eff463856b0992d53ab7d8c0b97c71541413358bdd445710c1e4b6aeb4d8f78
  15. 0x7eb603d37f0f22dae1ea013e391889906ffc0139ff26e25b044bee38eec8f9b2
  16. 0x2b6e23e3624a1b66e1d2298cfbce99a93ce6b13d24a58112a71802d79c5f7a76
  17. 0xd21942ae3214eff88e08ab04884fd4b11958b51af9a0741b7668e2d07a2c56c9
  18. 0x9a9024edb5069d88a324facb310a5ce8e495214bbaa968f077bede437390194f
  19. 0x9b9ce5b74b9bab5121612ef6b64920a8d453cbf311096a423eef9702747e868a
Day root
0xf43e8dcc3d451a4788b26be904c55e8c4c0fa75c9666796c51db38d37e2c0df8
Chained
0x702d5ac508f08b0d1accf36af25822d8e5660367494edd17a642fb61e158a57a
Published
On Arc Testnet, block 69251483: 0x495162f8936e19f0039918feebe6c307f327cd6bf8c37219620b1970041fa9ae

How to check it · This row as JSON