use octolib::{ProviderFactory, ChatCompletionParams, Message};

async fn example() -> anyhow::Result<()> {
    // Parse model and get provider
    let (provider, model) = ProviderFactory::get_provider_for_model("openai:gpt-4o")?;

    // Create messages
    let messages = vec![
        Message::user("Hello, how are you?"),
    ];

    // Create completion parameters
    let params = ChatCompletionParams::new(&messages, &model, 0.7, 1.0, 50, 1000);

    // Get completion (requires OPENAI_API_KEY environment variable)
    let response = provider.chat_completion(params).await?;
    println!("Response: {}", response.content);

    Ok(())
}

use octolib::{ProviderFactory, ChatCompletionParams, Message, StructuredOutputRequest};
use serde::{Deserialize, Serialize};

#[derive(Serialize, Deserialize, Debug)]
struct PersonInfo {
    name: String,
    age: u32,
    skills: Vec<String>,
}

async fn structured_example() -> anyhow::Result<()> {
    let (provider, model) = ProviderFactory::get_provider_for_model("openai:gpt-4o")?;

    // Check if provider supports structured output
    if !provider.supports_structured_output(&model) {
        return Err(anyhow::anyhow!("Provider does not support structured output"));
    }

    let messages = vec![
        Message::user("Tell me about a software engineer in JSON format"),
    ];

    // Request structured JSON output
    let structured_request = StructuredOutputRequest::json();
    let params = ChatCompletionParams::new(&messages, &model, 0.7, 1.0, 50, 1000)
        .with_structured_output(structured_request);

    let response = provider.chat_completion(params).await?;

    if let Some(structured) = response.structured_output {
        let person: PersonInfo = serde_json::from_value(structured)?;
        println!("Person: {:?}", person);
    }

    Ok(())
}

use octolib::embedding::{generate_embeddings, generate_embeddings_batch, InputType};

async fn embedding_example() -> anyhow::Result<()> {
    // Single embedding generation
    let embedding = generate_embeddings(
        "Hello, world!",
        "voyage",  // provider
        "voyage-3.5-lite"  // model
    ).await?;

    println!("Embedding dimension: {}", embedding.len());

    // Batch embedding generation
    let texts = vec![
        "First document".to_string(),
        "Second document".to_string(),
    ];

    let embeddings = generate_embeddings_batch(
        texts,
        "jina",  // provider
        "jina-embeddings-v4",  // model
        InputType::Document,  // input type for better embeddings
        16,  // batch size
        100_000,  // max tokens per batch
    ).await?;

    println!("Generated {} embeddings", embeddings.len());

    Ok(())
}

// Supported embedding providers:
// - Jina: jina-embeddings-v4, jina-clip-v2, etc.
// - Voyage: voyage-3.5, voyage-code-2, etc.
// - Google: gemini-embedding-001, text-embedding-005
// - OpenAI: text-embedding-3-small, text-embedding-3-large
// - FastEmbed: Local models (feature-gated)
// - HuggingFace: sentence-transformers models