//! Unified AI client with built-in retry, token tracking, and session recording.
//!
//! Provides a single entry point for all AI calls with:
//! - Exponential backoff with jitter (max 3 retries)
//! - Retryable error classification (429/500/502/503/504)
//! - Token usage tracking (input/output)

use async_openai::Client;
use async_openai::config::OpenAIConfig;
use async_openai::types::chat::{
    ChatCompletionRequestMessage, ChatCompletionTool, ChatCompletionToolChoiceOption,
    ChatCompletionTools, CreateChatCompletionRequest, CreateChatCompletionResponse,
};
use std::time::Instant;

use crate::error::{AgentError, Result};

/// AI call metrics — increments metrics crate counters for all AI calls.
/// These are registered in observability::install_recorder() and exported
/// via both the Prometheus /metrics endpoint and the Redis metrics flusher.
#[derive(Debug, Clone, Default)]
pub struct AiMetrics;

impl AiMetrics {
    pub fn new() -> Self {
        Self
    }

    /// Record a successful AI call with token usage.
    pub fn record_success(&self, input_tokens: i64, output_tokens: i64, has_function_call: bool) {
        metrics::counter!("ai_calls_total").increment(1);
        metrics::counter!("ai_calls_success").increment(1);
        if input_tokens > 0 {
            metrics::counter!("ai_input_tokens_total").increment(input_tokens as u64);
        }
        if output_tokens > 0 {
            metrics::counter!("ai_output_tokens_total").increment(output_tokens as u64);
        }
        if has_function_call {
            metrics::counter!("ai_function_calls_total").increment(1);
        }
    }

    /// Record a failed AI call.
    pub fn record_failure(&self) {
        metrics::counter!("ai_calls_total").increment(1);
        metrics::counter!("ai_calls_failure").increment(1);
    }
}

/// Configuration for the AI client.
#[derive(Clone)]
pub struct AiClientConfig {
    pub api_key: String,
    pub base_url: Option<String>,
}

impl AiClientConfig {
    pub fn new(api_key: String) -> Self {
        Self {
            api_key,
            base_url: None,
        }
    }

    pub fn with_base_url(mut self, base_url: impl Into<String>) -> Self {
        self.base_url = Some(base_url.into());
        self
    }

    pub fn build_client(&self) -> Client<OpenAIConfig> {
        let mut config = OpenAIConfig::new().with_api_key(&self.api_key);
        if let Some(ref url) = self.base_url {
            config = config.with_api_base(url);
        }
        Client::with_config(config)
    }
}

/// Response from an AI call, including usage statistics.
#[derive(Debug, Clone)]
pub struct AiCallResponse {
    pub content: String,
    pub input_tokens: i64,
    pub output_tokens: i64,
    pub latency_ms: i64,
}

impl AiCallResponse {
    pub fn total_tokens(&self) -> i64 {
        self.input_tokens + self.output_tokens
    }
}

/// Internal state for retry tracking.
#[derive(Debug)]
struct RetryState {
    attempt: u32,
    max_retries: u32,
    max_backoff_ms: u64,
}

impl RetryState {
    fn new(max_retries: u32) -> Self {
        Self {
            attempt: 0,
            max_retries,
            max_backoff_ms: 5000,
        }
    }

    fn should_retry(&self) -> bool {
        self.attempt < self.max_retries
    }

    /// Calculate backoff duration with full jitter technique.
    /// sleep = random(0, min(cap, base * 2^attempt))
    fn backoff_duration(&self) -> std::time::Duration {
        let exp = self.attempt.min(5);
        // base = 500 * 2^exp, capped at max_backoff_ms
        let base_ms = 500u64
            .saturating_mul(2u64.pow(exp))
            .min(self.max_backoff_ms);
        // Full jitter: random value in [0, base_ms]
        let jitter = fastrand_u64(base_ms + 1) as u64;
        std::time::Duration::from_millis(jitter)
    }

    fn next(&mut self) {
        self.attempt += 1;
    }
}

/// Fast pseudo-random u64 using a simple LCG.
/// Good enough for jitter — not for cryptography.
fn fastrand_u64(n: u64) -> u64 {
    use std::sync::atomic::{AtomicU64, Ordering};
    static STATE: AtomicU64 = AtomicU64::new(0x193_667_6a_5e_7c_57);
    if n <= 1 {
        return 0;
    }
    let mut current = STATE.load(Ordering::Relaxed);
    loop {
        let new_val = current.wrapping_mul(6364136223846793005).wrapping_add(1);
        match STATE.compare_exchange_weak(current, new_val, Ordering::Relaxed, Ordering::Relaxed) {
            Ok(_) => return new_val % n,
            Err(actual) => current = actual,
        }
    }
}

/// Determine if an error is retryable.
fn is_retryable_error(err: &async_openai::error::OpenAIError) -> bool {
    use async_openai::error::OpenAIError;
    match err {
        // Network errors (DNS failure, connection refused, timeout) are always retryable
        OpenAIError::Reqwest(_) => true,
        // For API errors, check the error code string (e.g., "rate_limit_exceeded")
        OpenAIError::ApiError(api_err) => api_err.code.as_ref().map_or(false, |code| {
            matches!(
                code.as_str(),
                "rate_limit_exceeded"
                    | "internal_server_error"
                    | "service_unavailable"
                    | "gateway_timeout"
                    | "bad_gateway"
            )
        }),
        _ => false,
    }
}

/// Global AI metrics shared across all AI client calls.
static AI_METRICS: std::sync::OnceLock<AiMetrics> = std::sync::OnceLock::new();

fn ai_metrics() -> &'static AiMetrics {
    AI_METRICS.get_or_init(AiMetrics::new)
}

/// Call the AI model with automatic retry.
pub async fn call_with_retry(
    messages: &[ChatCompletionRequestMessage],
    model: &str,
    config: &AiClientConfig,
    max_retries: Option<u32>,
) -> Result<AiCallResponse> {
    let client = config.build_client();
    let mut state = RetryState::new(max_retries.unwrap_or(3));

    loop {
        let start = Instant::now();

        let req = CreateChatCompletionRequest {
            model: model.to_string(),
            messages: messages.to_vec(),
            ..Default::default()
        };

        let result = client.chat().create(req).await;

        match result {
            Ok(response) => {
                let latency_ms = start.elapsed().as_millis() as i64;
                let (input_tokens, output_tokens) = extract_usage(&response);

                // Check if response contains a tool call
                let has_function_call = response
                    .choices
                    .first()
                    .and_then(|c| c.finish_reason.as_ref())
                    .map_or(false, |r| *r == async_openai::types::chat::FinishReason::ToolCalls);
                ai_metrics().record_success(input_tokens, output_tokens, has_function_call);

                return Ok(AiCallResponse {
                    content: extract_content(&response),
                    input_tokens,
                    output_tokens,
                    latency_ms,
                });
            }
            Err(err) => {
                if state.should_retry() && is_retryable_error(&err) {
                    let duration = state.backoff_duration();
                    tracing::warn!(
                        attempt = state.attempt + 1,
                        max_retries = state.max_retries,
                        backoff_ms = duration.as_millis() as u64,
                        model = %model,
                        error = %err.to_string(),
                        "ai_call_retry"
                    );
                    tokio::time::sleep(duration).await;
                    state.next();
                    continue;
                }
                ai_metrics().record_failure();
                return Err(AgentError::OpenAi(err.to_string()));
            }
        }
    }
}

/// Call with custom parameters (temperature, max_tokens, optional tools, optional tool_choice).
///
/// When `tool_choice` is `None` and tools are present, the default is `Auto`.
/// Pass `Some(ChatCompletionToolChoiceOption::None)` to force the model to respond
/// with text only (e.g. when you want JSON-in-text for ReAct parsing).
pub async fn call_with_params(
    messages: &[ChatCompletionRequestMessage],
    model: &str,
    config: &AiClientConfig,
    temperature: f32,
    max_tokens: u32,
    max_retries: Option<u32>,
    tools: Option<&[ChatCompletionTool]>,
    tool_choice: Option<ChatCompletionToolChoiceOption>,
) -> Result<AiCallResponse> {
    let client = config.build_client();
    let mut state = RetryState::new(max_retries.unwrap_or(3));

    loop {
        let start = Instant::now();

        let req = CreateChatCompletionRequest {
            model: model.to_string(),
            messages: messages.to_vec(),
            temperature: Some(temperature),
            max_completion_tokens: Some(max_tokens),
            tools: tools.map(|ts| {
                ts.iter()
                    .map(|t| ChatCompletionTools::Function(t.clone()))
                    .collect()
            }),
            tool_choice: tool_choice.clone(),
            ..Default::default()
        };

        let result = client.chat().create(req).await;

        match result {
            Ok(response) => {
                let latency_ms = start.elapsed().as_millis() as i64;
                let (input_tokens, output_tokens) = extract_usage(&response);

                // Check if response contains a tool call
                let has_function_call = response
                    .choices
                    .first()
                    .and_then(|c| c.finish_reason.as_ref())
                    .map_or(false, |r| *r == async_openai::types::chat::FinishReason::ToolCalls);
                ai_metrics().record_success(input_tokens, output_tokens, has_function_call);

                return Ok(AiCallResponse {
                    content: extract_content(&response),
                    input_tokens,
                    output_tokens,
                    latency_ms,
                });
            }
            Err(err) => {
                if state.should_retry() && is_retryable_error(&err) {
                    let duration = state.backoff_duration();
                    tracing::warn!(
                        attempt = state.attempt + 1,
                        max_retries = state.max_retries,
                        backoff_ms = duration.as_millis() as u64,
                        model = %model,
                        error = %err.to_string(),
                        "ai_call_retry"
                    );
                    tokio::time::sleep(duration).await;
                    state.next();
                    continue;
                }
                ai_metrics().record_failure();
                return Err(AgentError::OpenAi(err.to_string()));
            }
        }
    }
}

/// Extract text content from a chat completion response.
fn extract_content(response: &CreateChatCompletionResponse) -> String {
    response
        .choices
        .first()
        .and_then(|c| c.message.content.clone())
        .unwrap_or_default()
}

/// Extract usage (input/output tokens) from a response.
fn extract_usage(response: &CreateChatCompletionResponse) -> (i64, i64) {
    response
        .usage
        .as_ref()
        .map(|u| {
            (
                i64::try_from(u.prompt_tokens).unwrap_or(0),
                i64::try_from(u.completion_tokens).unwrap_or(0),
            )
        })
        .unwrap_or((0, 0))
}