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::{ProviderFactory, ChatCompletionParams, Message, FunctionDefinition, ToolCall};
use serde_json::json;

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

    // Define available tools/functions
    let tools = vec![
        FunctionDefinition {
            name: "get_weather".to_string(),
            description: "Get the current weather for a location".to_string(),
            parameters: json!({
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "Temperature unit"
                    }
                },
                "required": ["location"]
            }),
            cache_control: None,
        },
        FunctionDefinition {
            name: "calculate".to_string(),
            description: "Perform a mathematical calculation".to_string(),
            parameters: json!({
                "type": "object",
                "properties": {
                    "expression": {
                        "type": "string",
                        "description": "Mathematical expression to evaluate"
                    }
                },
                "required": ["expression"]
            }),
            cache_control: None,
        },
    ];

    let mut messages = vec![
        Message::user("What's the weather in Tokyo and calculate 15 * 23?"),
    ];

    // Initial request with tools
    let params = ChatCompletionParams::new(&messages, &model, 0.7, 1.0, 50, 1000)
        .with_tools(tools.clone());

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

    // Check if model wants to call tools
    if let Some(tool_calls) = response.tool_calls {
        println!("Model requested {} tool calls", tool_calls.len());

        // Add assistant's response with tool calls to conversation
        let mut assistant_msg = Message::assistant(&response.content);
        assistant_msg.tool_calls = Some(serde_json::to_value(&tool_calls)?);
        messages.push(assistant_msg);

        // Execute each tool call and add results
        for tool_call in tool_calls {
            println!("Calling tool: {} with args: {}", tool_call.name, tool_call.arguments);

            // Execute the tool (your implementation)
            let result = match tool_call.name.as_str() {
                "get_weather" => {
                    let location = tool_call.arguments["location"].as_str().unwrap_or("Unknown");
                    json!({
                        "location": location,
                        "temperature": 22,
                        "unit": "celsius",
                        "condition": "sunny"
                    })
                }
                "calculate" => {
                    let expr = tool_call.arguments["expression"].as_str().unwrap_or("0");
                    // Simple calculation (in real app, use proper eval)
                    json!({
                        "expression": expr,
                        "result": 345  // 15 * 23
                    })
                }
                _ => json!({"error": "Unknown tool"}),
            };

            // Add tool result to conversation
            messages.push(Message::tool(
                &serde_json::to_string(&result)?,
                &tool_call.id,
                &tool_call.name,
            ));
        }

        // Get final response with tool results
        let params = ChatCompletionParams::new(&messages, &model, 0.7, 1.0, 50, 1000)
            .with_tools(tools);

        let final_response = provider.chat_completion(params).await?;
        println!("Final response: {}", final_response.content);
    } else {
        println!("Direct response: {}", response.content);
    }

    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