Embeddings

Turn text into embedding vectors from a single API call — for semantic search, clustering, deduplication, or feeding a vector index. Backed by Amazon Bedrock Titan Text Embeddings v2. Embed a single string or a batch in one request; pricing is metered per started 10 KB of input — no per-token math, no monthly minimum.

curl -X POST https://api.relaystation.ai/v1/llm/embed \
  -H 'Authorization: Bearer rs_live_<key>' \
  -H 'Idempotency-Key: embed-20260707' \
  -H 'Content-Type: application/json' \
  -d '{ "input": "the quick brown fox", "dimensions": 1024 }'

Or on the lodestone path — no account, a signed x402 payment instead of an API key:

curl -X POST https://api.relaystation.ai/v1/llm/embed \
  -H 'X-Payment: <base64 EIP-3009 authorization>' \
  -H 'Idempotency-Key: embed-20260707' \
  -H 'Content-Type: application/json' \
  -d '{ "input": "the quick brown fox", "dimensions": 1024 }'

Request

POST /v1/llm/embed:

Field	Type	Notes
`input`	string or string[], required	one text, or a batch of up to 100 strings
`dimensions`	integer	output vector size — one of `256`, `512`, `1024` (default `1024`). A smaller vector is cheaper to store and search
`normalize`	boolean	return unit-normalized vectors (recommended for cosine similarity)

A batch embeds every string in one call — cheaper and lower-latency than N single calls.

curl -X POST https://api.relaystation.ai/v1/llm/embed \
  -H 'Authorization: Bearer rs_live_<key>' \
  -H 'Idempotency-Key: embed-batch-20260707' \
  -H 'Content-Type: application/json' \
  -d '{ "input": ["first chunk", "second chunk", "third chunk"], "dimensions": 512, "normalize": true }'

Response

{
  "embeddings": [
    { "index": 0, "vector": [0.0123, -0.0456, ...], "inputTokens": 4 }
  ],
  "model": "...",
  "dimensions": 1024
}

Each entry carries its index (matching the input order), the vector, and the inputTokens counted for that string. The chosen dimensions must match the frozen index shape if you plan to write these vectors into a vector baton — the allowlisted sizes (256 / 512 / 1024) are exactly the vector-baton dimension options.

Billing

Metered per started 10 KB of input across the whole request (a batch is billed on its combined input size). Priced per unit; see Pricing for the current rate, or send an unauthenticated POST to read the exact price from the 402 challenge. Every billable call needs an Idempotency-Key; a same-key retry returns the cached vectors without re-charging.

Where embeddings go next

Embeddings are the substrate for retrieval. Write them into a vector baton and query top-k, or let RAG embed, retrieve, and answer in one call — see Vector search & RAG.

MCP tools

Callable over MCP at https://api.relaystation.ai/mcp as embed. Same auth, same pricing as the HTTP route.