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mistralai-client-rs/examples/embeddings_async.rs
Sienna Meridian Satterwhite 79bc40bb15
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Update to latest Mistral AI API (v1.0.0)
- Replace closed Model enum with flexible string-based Model type
  with constructor methods for all current models (Mistral Large 3,
  Small 4, Magistral, Codestral, Devstral, Pixtral, Voxtral, etc.)
- Add new API endpoints: FIM completions, Files, Fine-tuning, Batch
  jobs, OCR, Audio transcription, Moderations/Classifications, and
  Agent completions (sync + async for all)
- Add new chat fields: frequency_penalty, presence_penalty, stop,
  n, parallel_tool_calls, reasoning_effort, min_tokens, json_schema
  response format
- Add embedding fields: output_dimension, output_dtype
- Tool parameters now accept raw JSON Schema (serde_json::Value)
  instead of limited enum types
- Add tool call IDs and Required tool choice variant
- Add DELETE HTTP method support and multipart file upload
- Bump thiserror to v2, add reqwest multipart feature
- Remove strum dependency (no longer needed)
- Update all tests and examples for new API
2026-03-20 17:16:26 +00:00

21 lines
623 B
Rust

use mistralai_client::v1::{client::Client, constants::Model};
#[tokio::main]
async fn main() {
// This example suppose you have set the `MISTRAL_API_KEY` environment variable.
let client: Client = Client::new(None, None, None, None).unwrap();
let model = Model::mistral_embed();
let input = vec!["Embed this sentence.", "As well as this one."]
.iter()
.map(|s| s.to_string())
.collect();
let options = None;
let response = client
.embeddings_async(model, input, options)
.await
.unwrap();
println!("First Embedding: {:?}", response.data[0]);
}