{
  "generated_at": "2026-04-24T15:01:35.625676+00:00",
  "slug": "bk9jpt7z-nx-link-embeddings",
  "title": "Venice.ai \u00b7 Text embeddings API",
  "url": "https://bk9jpt7z.nx.link/embeddings",
  "category": "ai",
  "summary": "Create vector embeddings from text input for semantic similarity, document retrieval, and machine learning workflows. Supports compression encodings for efficient transfer.",
  "seo": {
    "title": "Text Embeddings API | Venice.ai x402",
    "description": "Generate text embeddings via Venice.ai API. Pay-per-call vector representations for semantic search, clustering, and RAG pipelines on Base."
  },
  "use_cases": [
    "Power semantic search in document databases",
    "Generate embeddings for RAG context retrieval",
    "Cluster documents by semantic similarity",
    "Build recommendation systems based on text content"
  ],
  "ideal_buyer": "AI developers and agent builders needing reliable text embeddings for retrieval-augmented generation and semantic analysis pipelines.",
  "example_prompt": "Generate an embedding for this product description to use in a semantic search index",
  "example_request_body": {
    "input": "High-performance running shoes with carbon fiber plate and responsive foam cushioning",
    "model": "text-embedding-3-small",
    "encoding_format": "float"
  },
  "risk_notes": [],
  "pricing_sanity": {
    "flag": "normal",
    "ratio": 0.333,
    "median_category_atomic": 30000
  },
  "pricing_review_required": false,
  "pricing_decimal_suspect": false,
  "trust_tier": "indexed_external",
  "accepts": [
    {
      "scheme": "exact",
      "network": "base",
      "pay_to": "0x6c404c6aa5ddd01a9a5978f67f16a4c44174c288",
      "asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
      "max_amount_required_atomic": "10000",
      "max_timeout_seconds": 300,
      "mime_type": "application/json",
      "description": "Venice.ai API https://api.venice.ai/api/v1 - POST /api/v1/embeddings",
      "verified": false,
      "hints": {
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "type": "object",
                  "example": {
                    "data": [
                      {
                        "index": 0,
                        "object": "embedding",
                        "embedding": [
                          0.0023064255,
                          -0.009327292,
                          0.015797377
                        ]
                      }
                    ],
                    "model": "text-embedding-bge-m3",
                    "usage": {
                      "total_tokens": 8,
                      "prompt_tokens": 8
                    },
                    "object": "list"
                  },
                  "required": [
                    "data",
                    "model",
                    "object",
                    "usage"
                  ],
                  "properties": {
                    "data": {
                      "type": "array",
                      "items": {
                        "type": "object",
                        "required": [
                          "embedding",
                          "index",
                          "object"
                        ],
                        "properties": {
                          "index": {
                            "type": "integer",
                            "description": "The index of this embedding in the list"
                          },
                          "object": {
                            "enum": [
                              "embedding"
                            ],
                            "type": "string",
                            "description": "The object type, which is always \"embedding\""
                          },
                          "embedding": {
                            "type": "array",
                            "items": {
                              "type": "number"
                            },
                            "description": "The embedding vector"
                          }
                        }
                      },
                      "description": "The list of embeddings generated by the model."
                    },
                    "model": {
                      "type": "string",
                      "description": "The name of the model used to generate the embedding."
                    },
                    "usage": {
                      "type": "object",
                      "required": [
                        "prompt_tokens",
                        "total_tokens"
                      ],
                      "properties": {
                        "total_tokens": {
                          "type": "integer",
                          "description": "The total number of tokens used by the request."
                        },
                        "prompt_tokens": {
                          "type": "integer",
                          "description": "The number of tokens used by the prompt."
                        }
                      },
                      "description": "The usage information for the request."
                    },
                    "object": {
                      "enum": [
                        "list"
                      ],
                      "type": "string",
                      "description": "The object type, which is always \"list\""
                    }
                  }
                }
              }
            },
            "headers": {
              "Content-Encoding": {
                "schema": {
                  "enum": [
                    "gzip",
                    "br"
                  ],
                  "type": "string"
                },
                "description": "The encoding used to compress the response"
              }
            },
            "description": "OK"
          },
          "400": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/DetailedError"
                }
              }
            },
            "description": "Invalid request parameters"
          },
          "401": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/StandardError"
                }
              }
            },
            "description": "Authentication failed"
          },
          "402": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/StandardError"
                }
              }
            },
            "description": "Insufficient USD or Diem balance to complete request"
          },
          "415": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/StandardError"
                }
              }
            },
            "description": "Invalid request content-type"
          },
          "429": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/StandardError"
                }
              }
            },
            "description": "Rate limit exceeded"
          },
          "500": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/StandardError"
                }
              }
            },
            "description": "Inference processing failed"
          },
          "503": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/StandardError"
                }
              }
            },
            "description": "The model is at capacity. Please try again later."
          }
        },
        "parameters": [
          {
            "in": "header",
            "name": "Accept-Encoding",
            "schema": {
              "type": "string",
              "example": "gzip, br"
            },
            "required": false,
            "description": "Supported compression encodings (gzip, br)"
          }
        ],
        "requestBody": {
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/CreateEmbeddingRequestSchema"
              }
            }
          },
          "required": true
        }
      }
    }
  ],
  "origin": {
    "slug": "bk9jpt7z-nx-link",
    "host": "bk9jpt7z.nx.link",
    "url": "https://bk9jpt7z.nx.link"
  },
  "json_ld": {
    "@id": "https://x402all.com/resource/bk9jpt7z-nx-link-embeddings",
    "url": "https://x402all.com/resource/bk9jpt7z-nx-link-embeddings",
    "name": "Venice.ai \u00b7 Text embeddings API",
    "@type": "WebAPI",
    "offers": {
      "url": "https://x402all.com/resource/bk9jpt7z-nx-link-embeddings",
      "@type": "Offer",
      "price": "0.01",
      "availability": "https://schema.org/InStock",
      "priceCurrency": "USDC",
      "priceSpecification": {
        "@type": "UnitPriceSpecification",
        "price": "0.010000",
        "unitText": "call",
        "priceCurrency": "USDC"
      },
      "eligibleCustomerType": "Agent",
      "additionalProperty": [
        {
          "@type": "PropertyValue",
          "name": "paymentNetwork",
          "value": "base"
        },
        {
          "@type": "PropertyValue",
          "name": "paymentAsset",
          "value": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913"
        }
      ]
    },
    "sameAs": "https://bk9jpt7z.nx.link/embeddings",
    "@context": "https://schema.org",
    "provider": {
      "@id": "https://x402all.com/server/bk9jpt7z-nx-link",
      "url": "https://bk9jpt7z.nx.link",
      "name": "bk9jpt7z.nx.link",
      "@type": "Organization"
    },
    "identifier": "bk9jpt7z-nx-link-embeddings",
    "description": "Generate text embeddings via Venice.ai API. Pay-per-call vector representations for semantic search, clustering, and RAG pipelines on Base.",
    "potentialAction": {
      "@type": "BuyAction",
      "target": "https://axon402.com/test-buy?resource=bk9jpt7z-nx-link-embeddings",
      "description": "Test-buy this endpoint on AXON"
    },
    "applicationCategory": "ai"
  },
  "axon_deep_link": "https://axon402.com/test-buy?resource=bk9jpt7z-nx-link-embeddings"
}
