Add comprehensive test suite for OpenAI audio and chat response parsing and provider implementation

- Add test module for OpenAISpeechRequest serialization: cover required fields, optional field skipping, response_format/speed/instructions handling, all TTS models (tts-1/tts-1-hd/gpt-4o-mini-tts), all voices (alloy/ash/ballad/coral/echo/fable/onyx/nova/sage/shimmer), all response formats (mp3/opus/aac/flac/wav/pcm), speed range (0.25-4.0), unicode input, and long input
- Add test module for OpenAI

Author: gitctrlx

machi/examples/llm_ollama_embedding.rs

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+//! Embedding example using Ollama.
+//!
+//! ```bash
+//! ollama pull nomic-embed-text
+//! cargo run --example llm_ollama_embedding
+//! ```
+
+#![allow(clippy::print_stdout)]
+
+use machi::embedding::{EmbeddingProvider, EmbeddingRequest};
+use machi::prelude::*;
+
+#[tokio::main]
+async fn main() -> Result<()> {
+    let client = Ollama::with_defaults()?;
+
+    // Single text embedding
+    let request = EmbeddingRequest::new("nomic-embed-text", vec!["Hello, world!".to_owned()]);
+
+    let response = client.embed(&request).await?;
+    println!("Single embedding:");
+    println!("  Dimension: {}", response.embeddings[0].vector.len());
+    println!(
+        "  First 5 values: {:?}",
+        &response.embeddings[0].vector[..5]
+    );
+
+    // Batch embeddings for similarity comparison
+    let texts = vec![
+        "The cat sat on the mat.".to_owned(),
+        "A feline rested on the rug.".to_owned(),
+        "The stock market crashed today.".to_owned(),
+    ];
+
+    let request = EmbeddingRequest::new("nomic-embed-text", texts);
+    let response = client.embed(&request).await?;
+
+    println!("\nBatch embeddings ({} texts):", response.embeddings.len());
+
+    // Calculate cosine similarity between embeddings
+    let sim_0_1 = cosine_similarity(
+        &response.embeddings[0].vector,
+        &response.embeddings[1].vector,
+    );
+    let sim_0_2 = cosine_similarity(
+        &response.embeddings[0].vector,
+        &response.embeddings[2].vector,
+    );
+
+    println!("  Similarity (cat/feline sentences): {sim_0_1:.4}");
+    println!("  Similarity (cat/stock sentences):  {sim_0_2:.4}");
+
+    if let Some(tokens) = response.total_tokens {
+        println!("\nTotal tokens used: {tokens}");
+    }
+
+    Ok(())
+}
+
+/// Calculate cosine similarity between two vectors.
+fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
+    let dot: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
+    let norm_a: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
+    let norm_b: f32 = b.iter().map(|x| x * x).sum::<f32>().sqrt();
+    dot / (norm_a * norm_b)
+}

machi/examples/llm_openai_chat.rs

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+//! Basic chat example using OpenAI.
+//!
+//! ```bash
+//! export OPENAI_API_KEY=sk-...
+//! cargo run --example llm_openai_chat
+//! ```
+
+#![allow(clippy::print_stdout)]
+
+use machi::prelude::*;
+
+#[tokio::main]
+async fn main() -> Result<()> {
+    let client = OpenAI::from_env()?;
+
+    let request = ChatRequest::new("gpt-4o-mini")
+        .system("You are a helpful assistant.")
+        .user("What is the capital of France?");
+
+    let response = client.chat(&request).await?;
+    println!("{}", response.text().unwrap_or_default());
+
+    Ok(())
+}

machi/examples/llm_openai_embedding.rs

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+//! Embedding example using OpenAI.
+//!
+//! ```bash
+//! export OPENAI_API_KEY=sk-...
+//! cargo run --example llm_openai_embedding
+//! ```
+
+#![allow(clippy::print_stdout)]
+
+use machi::embedding::{EmbeddingProvider, EmbeddingRequest};
+use machi::prelude::*;
+
+#[tokio::main]
+async fn main() -> Result<()> {
+    let client = OpenAI::from_env()?;
+
+    // Single text embedding
+    let request = EmbeddingRequest::new("text-embedding-3-small", vec!["Hello, world!".to_owned()]);
+
+    let response = client.embed(&request).await?;
+    println!("Single embedding:");
+    println!("  Dimension: {}", response.embeddings[0].vector.len());
+    println!(
+        "  First 5 values: {:?}",
+        &response.embeddings[0].vector[..5]
+    );
+
+    // Batch embeddings for similarity comparison
+    let texts = vec![
+        "The cat sat on the mat.".to_owned(),
+        "A feline rested on the rug.".to_owned(),
+        "The stock market crashed today.".to_owned(),
+    ];
+
+    let request = EmbeddingRequest::new("text-embedding-3-small", texts);
+    let response = client.embed(&request).await?;
+
+    println!("\nBatch embeddings ({} texts):", response.embeddings.len());
+
+    // Calculate cosine similarity between embeddings
+    let sim_0_1 = cosine_similarity(
+        &response.embeddings[0].vector,
+        &response.embeddings[1].vector,
+    );
+    let sim_0_2 = cosine_similarity(
+        &response.embeddings[0].vector,
+        &response.embeddings[2].vector,
+    );
+
+    println!("  Similarity (cat/feline sentences): {sim_0_1:.4}");
+    println!("  Similarity (cat/stock sentences):  {sim_0_2:.4}");
+
+    if let Some(usage) = &response.usage {
+        println!("\nTokens used: {}", usage.total_tokens);
+    }
+
+    Ok(())
+}
+
+/// Calculate cosine similarity between two vectors.
+fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
+    let dot: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
+    let norm_a: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
+    let norm_b: f32 = b.iter().map(|x| x * x).sum::<f32>().sqrt();
+    dot / (norm_a * norm_b)
+}

machi/examples/llm_openai_stream.rs

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+//! Streaming chat example using OpenAI.
+//!
+//! ```bash
+//! export OPENAI_API_KEY=sk-...
+//! cargo run --example llm_openai_stream
+//! ```
+
+#![allow(clippy::print_stdout)]
+
+use futures::StreamExt;
+use machi::prelude::*;
+use std::io::{Write, stdout};
+
+#[tokio::main]
+async fn main() -> Result<()> {
+    let client = OpenAI::from_env()?;
+
+    let request = ChatRequest::new("gpt-4o-mini").user("Write a haiku about Rust.");
+
+    let mut stream = client.chat_stream(&request).await?;
+
+    while let Some(chunk) = stream.next().await {
+        if let StreamChunk::Text(text) = chunk? {
+            print!("{text}");
+            stdout().flush()?;
+        }
+    }
+    println!();
+
+    Ok(())
+}

machi/examples/llm_openai_stt.rs

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+//! Speech-to-Text example using OpenAI.
+//!
+//! ```bash
+//! export OPENAI_API_KEY=sk-...
+//! cargo run --example llm_openai_stt
+//! ```
+
+#![allow(clippy::print_stdout)]
+
+use machi::audio::{AudioFormat, SpeechToTextProvider, TranscriptionRequest};
+use machi::prelude::*;
+use std::fs;
+
+#[tokio::main]
+async fn main() -> Result<()> {
+    let client = OpenAI::from_env()?;
+
+    // Read audio file
+    let audio_data = fs::read("machi/examples/data/en-us-natural-speech.mp3")?;
+
+    // Transcribe
+    let request = TranscriptionRequest::new("whisper-1", audio_data).format(AudioFormat::Mp3);
+    let response = client.transcribe(&request).await?;
+
+    println!("\nTranscription:");
+    println!("{}", response.text);
+
+    if let Some(lang) = &response.language {
+        println!("\nDetected language: {lang}");
+    }
+    if let Some(duration) = response.duration {
+        println!("Duration: {duration:.2}s");
+    }
+
+    Ok(())
+}

machi/examples/llm_openai_tts.rs

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+//! Text-to-Speech example using OpenAI.
+//!
+//! ```bash
+//! export OPENAI_API_KEY=sk-...
+//! cargo run --example llm_openai_tts
+//! ```
+
+#![allow(clippy::print_stdout)]
+
+use machi::audio::{SpeechRequest, TextToSpeechProvider};
+use machi::prelude::*;
+use std::fs;
+
+#[tokio::main]
+async fn main() -> Result<()> {
+    let client = OpenAI::from_env()?;
+
+    let request = SpeechRequest::new(
+        "tts-1",
+        "Hello! This is a test of OpenAI's text-to-speech API.",
+        "nova",
+    );
+
+    println!("\nGenerating speech...");
+    let response = client.speech(&request).await?;
+
+    let output_path = "output.mp3";
+    fs::write(output_path, &response.audio)?;
+    println!(
+        "Audio saved to: {output_path} ({} bytes)",
+        response.audio.len()
+    );
+
+    Ok(())
+}