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scopeapi.dev Text Embeddings
x402
scopeapi.dev Text Embeddings is a paid API for AI agents from scopeapi.dev, paid per call via x402, $0.003/call, status unknown (last checked 2026-09-14).
Converts one or more text strings into high-dimensional vector embeddings using a 1536-dimension model
Price / call$0.003
Success—
StatusUnknown
How this service works
Text Embeddings
Output
Returns a JSON object containing the model name (text-embedding-3-small), the number of dimensions (1536), token usage statistics (prompt tokens and total tokens), and an array of embedding objects each with an index and a floating-point vector array representing the semantic content of the input text.
Example use cases
Semantic search index population
“I need to embed these 10 product descriptions so I can store them in my Pinecone vector database and do semantic similarity search: ['Wireless noise-cancelling headphones', 'Bluetooth earbuds with mic', 'Over-ear studio monitor headphones', ...]”
RAG document chunking pipeline
“Take this paragraph and turn it into a vector embedding so I can add it to my retrieval-augmented generation knowledge base: 'Climate change refers to long-term shifts in global temperatures and weather patterns.'”
Duplicate content detection
“Generate embeddings for both of these sentences so I can compare their cosine similarity and check if they mean the same thing: 'How do I reset my password?' and 'What steps do I take to change my login credentials?'”
Technical details
Endpoint
POSThttps://scopeapi.dev/embeddingsWhen to prefer
Choose this endpoint when you need fast, affordable text vectorization via a pay-per-call x402 micropayment model without managing API keys. It is ideal for agents that need to embed text on-demand within a pipeline, store vectors in a database, or perform semantic similarity at $0.003 per call. Prefer this over raw OpenAI API access when operating in an agentic marketplace context where x402 payments are already wired in.
Side effects
- Input array exceeds 16 items (maxItems constraint violated)
- Empty input array (minItems:1 not satisfied)
- Malformed JSON body causing parse error
- Token limit exceeded for very long input strings
- Payment failure via x402 protocol returning 402 status
- Network timeout for large batch requests
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"model": "text-embedding-3-small",
"usage": {
"totalTokens": 6,
"promptTokens": 6
},
"endpoint": "/embeddings",
"dimensions": 1536,
"embeddings": [
{
"index": 0,
"embedding": [
0.0123,
-0.0456,
0.0789
]
}
]
}
}Tags
x402
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