Generates a completion
Generates a completion from endpoint . Your endpoint ID can be found in the
API tab of your endpoint or in the URL.
Parameters
Paths
Baseplate API key. Needs to be in the format “Bearer &BASEPLATE_API_KEY”
Body
A json object with keys matching the variables of your deployed template
A prompt for Anthropic, ada, babbage, curie, and davinci models. One of
messages or prompt is required.
An array of the chat history, for gpt-3.5 and gpt-4 models. One of messages or
prompt is required.
If true, response will be sent back as Server Sent Events. A [DONE] message is
sent at the end of the completion and the search results (if applicable) are
sent after the [DONE]. See below for example.
A user string to use for OpenAI’s API.
🟢 200: OK
🟠 401: Unauthorized
{
"id": "cmpl-6htWn45Ch0G37ZZFI48zt29k5atNe",
"object": "text_completion",
"created": 1675919573,
"model": "text-davinci-003",
"choices": [
{
"text": "\n\nThis Jacket is the ultimate in shoe fashion. It's sleek, stylish",
"index": 0,
"logprobs": null,
"finish_reason": "length",
"usage_id": "89b572c6-f0c9-40b5-bdb4-dc5cb6a99144"
}
],
"usage": {
"prompt_tokens": 23,
"completion_tokens": 16,
"total_tokens": 39
},
"search_results": [
{
"data": {
"text": "Example text."
},
"embedding": "Example text.",
"image_paths": null,
"confidence": 0.84870479,
"metadata": {
"documentId": "007c8ab6-4972-4457-9a1b-244fdefb22bc",
"rowId": 571724,
"url": "/5274962e-04c3-4a23-b3d5-1d7e1ea6a230/text.pdf"
},
"variable": "context",
"last_accessed": "2023-05-03T00:03:40.525+00:00",
"query": "example query"
}
],
"prompt_cost": 0.00046,
"completion_cost": 0.00032,
"baseplate_model_name": "text-davinci-003"
}
Steaming Example (Next.js 13)
Heres an example if you are using NextJS 13/React:
Backend (/api/completions/chat/route.ts):
import {
createParser,
ParsedEvent,
ReconnectInterval,
} from "eventsource-parser";
export async function POST(request: Request) {
const body = await request.json();
const encoder = new TextEncoder();
const decoder = new TextDecoder();
const res = await fetch("https://app.baseplate.ai/api/endpoints/${endpoint-id}/completions", {
method: "POST",
headers: {
"Content-Type": "application/json",
Authorization: `Bearer ${process.env.BASEPLATE_API_KEY}`,
},
body: JSON.stringify({
messages: body.messages,
stream: true,
}),
});
const stream = new ReadableStream({
async start(controller) {
// callback
function onParse(event: ParsedEvent | ReconnectInterval) {
if (event.type === "event") {
const data = event.data;
// https://beta.openai.com/docs/api-reference/completions/create#completions/create-stream
if (data === "[DONE]") {
controller.close();
return;
}
try {
const json = JSON.parse(data);
const text =
json.choices[0].delta?.content || json.choices[0].text || "";
const queue = encoder.encode(text);
controller.enqueue(queue);
} catch (e) {
// maybe parse error
controller.error(e);
}
}
}
// stream response (SSE) from OpenAI may be fragmented into multiple chunks
// this ensures we properly read chunks and invoke an event for each SSE event stream
const parser = createParser(onParse);
// https://web.dev/streams/#asynchronous-iteration
for await (const chunk of res.body as any) {
parser.feed(decoder.decode(chunk));
}
},
});
return new Response(stream);
}
try {
res = await fetch(`/api/completions/chat`, {
method: "POST",
headers: {
"Content-Type": "application/json",
},
body: JSON.stringify({
messages: [{role: "user", content: "Hello"}],
}),
}).catch((err) => {
throw err;
});
} catch (e: any) {
console.error(e);
return;
}
setGenerating(false);
if (!res.ok) {
const error = await res.json();
console.error(`Error generating: ${error.message}`);
return;
}
const stream = res.body;
if (!stream) {
return;
}
const reader = stream.getReader();
const decoder = new TextDecoder();
let done = false;
while (!done) {
const { value, done: doneReading } = await reader.read();
done = doneReading;
const chunkValue = decoder.decode(value);
console.log(chunkValue);
}
