Inference
Two models, named for what they are for rather than what they are. Generation reads pictures as well as words. Nothing you send is stored; only the token count is.
Every call below goes to the address in your
SAASIE_INFERENCE_URL
environment variable, with
Authorization: Bearer <token>. Read both from
the environment — they are injected, and hardcoding either is how
an app breaks when the platform moves.
See getting the credential.
POST
/v1/inference/embed
Embed text as vectors
One string or a batch. Vectors come back in the order they were sent, which is what makes a batch usable — pair them with your own ids by position.
Request body
| Field | Type | |
|---|---|---|
model |
string | Which logical model to use. Only saasie/embed embeds. saasie/fast · saasie/embed, defaults to saasie/embed |
input* |
string | string[] | One string or many. Many is not just convenience — a batch is one round trip and one cold start. |
200 — One vector per input
| Field | Type | |
|---|---|---|
embeddings* |
number[][] | One vector per input, in the order they were sent. |
model* |
string | — |
dimensions* |
number | Length of each vector. Read it rather than hard-coding it: it changes if the model behind saasie/embed does. |
usage* |
object | — |
remainingToday* |
number | — |
When it does not work
400 | Too large a batch, or a generation model |
401 | Missing or invalid service token |
403 | The Saasie is stopped |
429 | The daily embedding cap is spent, or this batch would not fit |
502 | The inference provider failed |
POST
/v1/inference/generate
Generate text, from words and pictures
One prompt or a conversation, one answer. Attach pictures with images and ask about them in the prompt. Not streamed: the answer arrives whole, which keeps the daily cap checkable before the work is done rather than after.
Request body
| Field | Type | |
|---|---|---|
model |
string | Which logical model to use. Only saasie/fast generates. saasie/fast · saasie/embed, defaults to saasie/fast |
prompt |
string | A single instruction. Shorthand for one user message. length 1–100000 |
messages |
object[] | A conversation, oldest first. Use instead of prompt. up to 64 items |
images |
string[] | Pictures to look at, alongside prompt. With messages, put them on the turn they belong to instead. Ask /models whether the model reads them. |
maxTokens |
integer | Ceiling on the answer's length. Capped by the platform; left out, the platform's default. 1–9007199254740991 |
temperature |
number | 0–2, defaults to 0.7 |
200 — The completion and what it cost
| Field | Type | |
|---|---|---|
text* |
string | — |
model* |
string | The logical model that served it. |
finishReason* |
string | length means the answer hit maxTokens and is cut off — ask again with a larger one. stop · length · other |
usage* |
object | — |
remainingToday* |
number | Generation tokens left before the daily cap. |
When it does not work
400 | Neither or both of prompt and messages, an embedding model, or an image that is too large, the wrong format, a link, or sent to a model with no eyes |
401 | Missing or invalid service token |
403 | The Saasie is stopped |
429 | The daily token cap is spent, or this request would not fit |
502 | The inference provider failed |
GET
/v1/inference/models
What this Saasie can call, and what is left today
The names are logical and stable. Which model actually serves one is the platform's to change — that is what makes it possible to improve them without breaking your app.
200 — The catalogue
| Field | Type | |
|---|---|---|
models* |
object[] | — |
When it does not work
401 | Missing or invalid service token |
Generated from
openapi.json, which the
service renders from its own routes. * marks
a required field. Every error body is
{ "error": { "code", "message", "details"? } }.