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openai/text-embedding-3-largeHigher-quality vectors for search and retrieval
OpenAI's text-embedding-3-large turns text into a higher-dimensional vector than Embedding Small, for semantic search, retrieval and clustering where recall quality matters more than raw cost. Output is a fixed-size vector, not generated tokens, so there is no output charge. Not a chat model — no streaming, no tool calls, no prose.
Billed on input tokens only; the returned vector costs nothing extra
Yes. Top up your Tchavi account with Orange Money, MTN MoMo, M-Pesa or another mobile money operator. No bank card is needed. Credits land as soon as the payment is confirmed and are deducted as you use them.
Yes. Keep your current OpenAI SDK, just change the base URL to Tchavi's (shown in the API tab) and use your Tchavi key. The rest of your code stays the same.
import Tchavi from '@tchavi/sdk';
const client = new Tchavi({ apiKey: 'API_KEY' });
const response = await client.chat.completions.create({
model: 'text-embedding-3-large',
messages: [
{
"role": "user",
"content": "Hello!"
}
],
});
console.log(response.choices[0].message.content);
console.log('Credits used:', response.tchavi.credits_used);Every parameter this model accepts on the API. Check the SDK for full typings.
modelRequiredID of the embedding model (e.g. `text-embedding-3-large`).
inputRequiredText to embed. Pass an array to batch multiple strings in one request.
encoding_formatfloatFormat to return embeddings in.
dimensionsShorten the output dimensions (supported models only).
userEnd-user identifier passed to the upstream provider for abuse / moderation tracking.
Estimated over 30 days, for a typical message: ~500 input + ~150 output tokens, billed to the thousandth of a credit.