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Pocket Network Text Embeddings
x402
Pocket Network Text Embeddings is a paid API for AI agents from agent.pocket.network, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-02).
Converts a string or array of strings into deterministic embedding vectors using a fixed open-weight model, suitable for RAG and semantic memory pipelines.
Price / call$0.005
Success—
StatusUnknown
How this service works
Open-weight text embeddings at a fixed model and precision: deterministic vectors for RAG and memory pipelines, identical across suppliers. POST /v1/text with {inputs} (a string or array of strings) and get the embedding vectors as one JSON object. Pay per request in USDC; no account, no API key.
Output
Returns a JSON object containing one or more embedding vectors — each a fixed-length array of floating-point numbers — corresponding to each input string. The output is deterministic: identical inputs always produce identical vectors regardless of when or where the call is made.
Example use cases
RAG pipeline document ingestion
“Turn these product documentation paragraphs into embedding vectors so I can store them in Pinecone and retrieve relevant chunks when users ask questions: ['Installation guide: run npm install pocket-sdk.', 'Authentication uses API keys scoped per project.', 'Rate limits apply per tier.']”
Semantic search over user notes
“Embed this search query so I can find the most similar notes in my vector store: 'how do I configure environment variables for deployment?'”
Duplicate content detection
“Convert these two article summaries into vectors so I can check if they're semantically duplicate before publishing: 'Fed raises rates by 25 basis points amid inflation concerns.' and 'Federal Reserve increases interest rates quarter-point citing ongoing price pressures.'”
Technical details
Endpoint
POSThttps://agent.pocket.network/v1/text-embeddings?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need deterministic, reproducible embeddings from a fixed open-weight model — critical for RAG, vector stores, or memory pipelines where embedding drift would break retrieval. It requires no API key or account, making it ideal for agent-to-agent or serverless workflows that pay per request in USDC via x402. Prefer it over OpenAI or Cohere embeddings when you need supplier-agnostic, stable vectors that won't change across model updates.
Side effects
- Empty or null inputs array returns a validation error
- Inputs exceeding model token limit may be truncated or rejected
- Network timeout if Pocket Network decentralized nodes are unavailable
- Payment failure (insufficient USDC or x402 handshake issue) results in 402 response
- Malformed JSON body returns 400 bad request
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputs | — | A single string or an array of strings to embed. |
Output
Schema · Response
{
"type": "json",
"example": {
"data": {},
"portal": {
"serviceId": "text-embeddings",
"provenance": "third-party-supplier",
"schemaCheck": "passed"
}
}
}Tags
x402
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