Module request 

OpenAI Embeddings API Request Module

This module provides the functionality to build and send requests to the OpenAI Embeddings API. It offers a builder pattern for constructing embedding requests, allowing you to convert text into numerical vector representations that capture semantic meaning.

Key Features

• Builder Pattern: Fluent API for constructing embedding requests
• Single & Batch Input: Support for single text or multiple texts at once
• Encoding Formats: Support for float and base64 output formats
• Error Handling: Robust error management and validation

Quick Start

use openai_tools::embedding::request::Embedding;
use openai_tools::common::models::EmbeddingModel;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Initialize the embedding client
    let mut embedding = Embedding::new()?;

    // Generate embedding for a single text
    let response = embedding
        .model(EmbeddingModel::TextEmbedding3Small)
        .input_text("Hello, world!")
        .embed()
        .await?;

    let vector = response.data[0].embedding.as_1d().unwrap();
    println!("Embedding dimension: {}", vector.len());
    Ok(())
}

Batch Processing

use openai_tools::embedding::request::Embedding;
use openai_tools::common::models::EmbeddingModel;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut embedding = Embedding::new()?;

    // Embed multiple texts in a single request
    let texts = vec!["First text", "Second text", "Third text"];

    let response = embedding
        .model(EmbeddingModel::TextEmbedding3Small)
        .input_text_array(texts)
        .embed()
        .await?;

    for data in &response.data {
        println!("Index {}: {} dimensions",
                 data.index,
                 data.embedding.as_1d().unwrap().len());
    }
    Ok(())
}

Structs

Embedding

Main struct for building and sending embedding requests to the OpenAI API.