Embeddings API

curl https://embeddings.conare.ai/api/v1/embeddings \
  -H "Authorization: Bearer $CONARE_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input_type": "document",
    "input": [
      "User prefers smaller islands, wants to avoid crowds.",
      "ASA 104 certified, comfortable with bareboat charters."
    ]
  }'

{
  "success": true,
  "model": "...",
  "dims": 1024,
  "embeddings": [[0.0132, -0.0087, "..."], ["..."]],
  "usage": { "totalChars": 98, "approxTokens": 25 }
}

Semantics

Stateless. Nothing is stored, and this is the one route with no endUserId — no end user is involved.
Limits. Up to 96 inputs per call, each at most 8,192 characters.
Vector role. input_type: "document" (the default) creates vectors to store. input_type: "query" creates vectors that search them and applies the same current UTC [asked YYYY-MM-DD] conditioning as managed Conare search. DR2E5 is asymmetric: never search with a document vector or store a query vector.
Deterministic ordering. Vectors are unit-normalized and returned in input order.
Auth. Use the same sk-conare-... platform key as ConareDB in Authorization: Bearer sk-conare-....

Search ConareDB with one key

Use the same server-side sk-conare-... key for both calls. Embed indexed text as document, then embed search text as query and pass that 1024-dimensional vector to the ConareDB vector branch:

{ "input_type": "document", "input": ["Refund policy: 30 days."] }

{ "input_type": "query", "input": ["How long can I request a refund?"] }

The API owns the exact passage: and dated query: prefixes. Send raw text; adding either prefix yourself creates the wrong input.

Model pinning

model is a pin, not a selector. The endpoint serves exactly one model, reported in every response. Pinning any other name returns 400 model_not_served — vectors from a different model are never silently substituted. Pin the model whenever your vectors must stay compatible with an existing index; an unpinned call always gets the currently served model.

{ "input": ["..."], "model": "the-model-name-from-a-previous-response" }

Metering

Usage is metered as embed tokens (~4 characters per token) and appears in your platform usage view. Full request/response schema: API Reference.

​Semantics

​Search ConareDB with one key

​Model pinning

​Metering

Semantics

Search ConareDB with one key

Model pinning

Metering