aisdk 0.5.2

An open-source Rust library for building AI-powered applications, inspired by the Vercel AI SDK. It provides a robust, type-safe, and easy-to-use interface for interacting with various Large Language Models (LLMs).
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42

//! Embedding model
//! TODO: add more doc

/// Embedding request builder and related types.
#[cfg(feature = "embedding-model-request")]
pub mod request;

use crate::error::Result;
use async_trait::async_trait;

use derive_builder::Builder;
#[cfg(feature = "embedding-model-request")]
pub use request::EmbeddingModelRequest;

/// The options for embedding requests.
// pub type EmbeddingModelOptions = Vec<String>;
#[derive(Debug, Clone, Builder)]
pub struct EmbeddingModelOptions {
    /// The input text to generate embeddings for
    pub input: Vec<String>,
    /// The desired number of embeddings to generate. This allowed value depends on the
    /// model used and if the provider returns an error for invalid dimensions `embed`
    /// will propagate the error.
    pub dimensions: Option<usize>,
}

impl EmbeddingModelOptions {
    /// Returns the OpenAI Embeddings builder.
    pub fn builder() -> EmbeddingModelOptionsBuilder {
        EmbeddingModelOptionsBuilder::default()
    }
}

/// The core trait abstracting the capabilities of an embedding model.
#[async_trait]
pub trait EmbeddingModel: Clone + Send + Sync + std::fmt::Debug + 'static {
    /// Embeds text inputs into vectors of floats.
    async fn embed(&self, input: EmbeddingModelOptions) -> Result<EmbeddingModelResponse>;
}

/// The response type for embedding requests.
pub type EmbeddingModelResponse = Vec<Vec<f32>>;