AiSpace Embeddings API

x402

AiSpace Embeddings API is a paid API for AI agents from x402.aispace.bot, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-12).

Generates vector embeddings for text input using Venice.ai-powered models via a privacy-focused API

Price / call$0.01

Seller volume →—

Success—

StatusUnknown

How this service works

Text embeddings on Venice models for retrieval, RAG, and semantic search, paid per request in USDC on Base via x402, no API key. Resolve a model id from GET /api/v1/models?type=embedding.

Output

Returns a JSON object with an 'ok' boolean field indicating success; the actual embedding vector data would be returned alongside it. The response confirms the embedding was computed and returns the numerical vector representation of the input text.

Example use cases

Semantic search index building

“I need to vectorize all my product descriptions so I can do semantic similarity search — can you generate embeddings for each one using AiSpace with a text embedding model?”

RAG pipeline document encoding

“Before I can answer questions from my knowledge base, I need to embed this paragraph into a vector: 'Our refund policy allows returns within 30 days of purchase.' Can you get the embedding using AiSpace?”

Text similarity comparison

“I want to check how semantically similar two customer support queries are — can you embed both 'How do I reset my password?' and 'I forgot my login credentials' via AiSpace so I can compare their vectors?”

Technical details

Endpoint

POSThttps://x402.aispace.bot/api/v1/embeddings?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need privacy-preserving text embeddings where data does not leave the device/local context, and you want a Venice.ai-backed model accessed via x402 micropayment. Prefer this over OpenAI or Cohere embeddings if privacy is a hard requirement or if you are operating in a crypto-native/x402 payment environment.

Side effects

Missing required 'input' field returns a validation error
Invalid or unsupported model name may return an error or fallback behavior
Malformed query parameters cause request failure
Insufficient USDC balance (x402 payment failure) blocks the request
Rate limiting if too many requests are made in quick succession

Input

Schema · Request body

Field	Type	Description
input	—	String or array of strings to embed.
model	string	Venice embedding model id. See GET /api/v1/models?type=embedding (e.g. text-embedding-bge-m3, gemini-embedding-2-preview).

Output

Schema · Response

{
  "type": "json",
  "example": {
    "data": [
      {
        "index": 0,
        "object": "embedding",
        "embedding": [
          -0.05365830659866333
        ]
      }
    ],
    "model": "text-embedding-bge-m3",
    "usage": {
      "total_tokens": 3,
      "prompt_tokens": 3
    },
    "object": "list"
  }
}

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