agcore/src/llm/provider/openai.rs

//! OpenAI Chat Completions API Provider —— Phase 1 真实实现。
//!
//! 同时承载 `GenericOpenaiProvider`：DeepSeek / Qwen 作为 newtype 包装共享其 HTTP /
//! SSE / 转换逻辑，仅配置不同（base_url、extra_headers、provider_name）。
//!
//! 关键设计：
//! - 流式状态机在 `ChunkToIrEventStream` 中实现，输出新高精度 IR `StreamEvent`。
//! - `MessageComplete { full_response }` 由 `PartialMessageResponse::finalize()` 产出。
//! - `capabilities()` 报告 OpenAI Chat 协议的能力。

use std::pin::Pin;
use std::task::{Context, Poll};
use std::time::Duration;

use async_trait::async_trait;
use bytes::Bytes;
use futures_core::Stream;
use futures_util::StreamExt;
use reqwest::Client;
use serde::Serialize;
use serde_json::Value;
use tracing::{debug, error, info};

use super::{LlmProvider, ProviderCapabilities, ProviderFeatures};
use crate::llm::convert::{blocks_to_content, content_to_blocks, from_openai, to_openai};
use crate::llm::error::LlmError;
use crate::llm::types::message::{ContentBlock, ContentBlockType, Message};
use crate::llm::types::openai_message::{ContentField, OpenaiChatMessage};
use crate::llm::types::request::{OpenaiChatRequest, OpenaiTool, StreamOptions};
use crate::llm::types::request_v2::MessageRequest;
use crate::llm::types::response::{OpenaiChatChunk, OpenaiChatResponse};
use crate::llm::types::response_v2::{
    MessageResponse, PartialMessageResponse, PartialUsage, StopReason, StreamEvent,
};
use crate::llm::types::shared::FinishReason;
use crate::llm::types::tool::OpenaiToolCall;

// =============================================================================
// 1. GenericOpenaiProvider —— OpenAI-compatible 协议共用实现
// =============================================================================

/// 通用 OpenAI-compatible Provider 配置。
///
/// DeepSeek / Qwen / OpenAI Chat 共享相同的 HTTP/SSE/转换逻辑，
/// 仅配置项不同。`provider_name` 用于 `capabilities().provider_name` 与日志；
/// `extra_headers` 支持如 Qwen 的 `X-DashScope-SSE: enable`。
#[derive(Clone)]
pub struct GenericOpenaiProvider {
    pub(crate) http_client: Client,
    base_url: String,
    api_key: String,
    model: String,
    provider_name: &'static str,
    extra_headers: Vec<(String, String)>,
}

impl GenericOpenaiProvider {
    /// 基础构造器。
    pub fn new_with_name(
        base_url: String,
        api_key: String,
        model: String,
        provider_name: &'static str,
    ) -> Self {
        let http_client = Client::builder()
            .timeout(Duration::from_secs(120))
            .build()
            .expect("创建 HTTP 客户端失败");

        Self {
            http_client,
            base_url,
            api_key,
            model,
            provider_name,
            extra_headers: Vec::new(),
        }
    }

    /// 带额外请求头的构造器（如 Qwen 需要 SSE 启用头）。
    pub fn new_with_name_and_headers(
        base_url: String,
        api_key: String,
        model: String,
        provider_name: &'static str,
        extra_headers: Vec<(String, String)>,
    ) -> Self {
        let mut base = Self::new_with_name(base_url, api_key, model, provider_name);
        base.extra_headers = extra_headers;
        base
    }

    pub fn with_client(mut self, client: Client) -> Self {
        self.http_client = client;
        self
    }

    pub fn provider_name(&self) -> &'static str {
        self.provider_name
    }

    pub fn model(&self) -> &str {
        &self.model
    }

    /// 构造 HTTP POST 请求 builder（含认证头与额外请求头）。
    fn build_request_builder(
        &self,
        url: &str,
        body: &impl Serialize,
    ) -> Result<reqwest::RequestBuilder, LlmError> {
        let mut builder = self
            .http_client
            .post(url)
            .header("Authorization", format!("Bearer {}", self.api_key));
        for (k, v) in &self.extra_headers {
            builder = builder.header(k.as_str(), v.as_str());
        }
        Ok(builder.json(body))
    }

    fn map_reqwest_error(e: reqwest::Error) -> LlmError {
        if e.is_timeout() {
            LlmError::Timeout {
                duration: Duration::from_secs(120),
            }
        } else if e.is_connect() {
            LlmError::Other(format!("连接失败: {}", e))
        } else {
            LlmError::Other(format!("请求失败: {}", e))
        }
    }

    /// HTTP 错误状态 → `LlmError`。
    ///
    /// ponytail: Qwen 等部分 OpenAI-compatible 提供方可能返回非标准 error body
    /// （无法解析为 JSON），此处直接用 status code + 原始 body 兜底。
    async fn handle_error_response(
        response: reqwest::Response,
    ) -> LlmError {
        let status = response.status().as_u16();
        let body = response.text().await.unwrap_or_default();

        match status {
            401 => LlmError::Authentication(body),
            429 => {
                // ponytail: 仅读取 retry-after，不在 OpenAI-compatible 上假设格式
                // 与 OpenAI 完全一致；DeepSeek/Qwen 通常遵循。
                LlmError::RateLimit { retry_after: None }
            }
            _ if status >= 500 => LlmError::Request {
                status,
                body,
            },
            _ if status == 400 && body.contains("context_length_exceeded") => {
                LlmError::ContextLength {
                    actual: 0,
                    limit: 0,
                }
            }
            _ => LlmError::Request { status, body },
        }
    }

    /// `MessageRequest` → `OpenaiChatRequest`。
    ///
    /// ponytail: extra 字段从 `MessageRequest.extra` 透传 —— Provider 特定参数
    /// （frequency_penalty / presence_penalty / seed / response_format 等）
    /// 通过 `request.set_extra("key", value)` 设置后被读取，避免结构体膨胀。
    ///
    /// 实现注意：先在函数顶部抽取出所有 needed 字段（clone 或 move），避免后续
    /// 部分移动 `request` 后无法借用其它字段。
    pub fn convert_request(&self, request: MessageRequest) -> Result<OpenaiChatRequest, LlmError> {
        // ponytail: 先抽取 / clone 所有 owned 字段，再访问 request.extra，
        // 避免部分移动导致后续 `&self` borrow 失败。
        let model = request.model.clone();
        let tool_choice = request.tool_choice.clone();
        let stream = request.stream;
        let max_tokens = request.max_tokens;
        let temperature = request.temperature;
        let top_p = request.top_p;
        let tool_defs = request.tools.clone();

        let messages: Vec<OpenaiChatMessage> = request.messages.iter().map(to_openai).collect();
        let tools: Option<Vec<OpenaiTool>> = if tool_defs.is_empty() {
            None
        } else {
            Some(
                tool_defs
                    .into_iter()
                    .map(|t| OpenaiTool::Function { function: t })
                    .collect(),
            )
        };
        let stop_sequences = request.stop_sequences.clone();
        let stop = if stop_sequences.is_empty() {
            None
        } else if stop_sequences.len() == 1 {
            Some(crate::llm::types::shared::StopSequence::Single(
                stop_sequences[0].clone(),
            ))
        } else {
            Some(crate::llm::types::shared::StopSequence::Multiple(stop_sequences))
        };

        let frequency_penalty = request.get_extra_opt("frequency_penalty");
        let presence_penalty = request.get_extra_opt("presence_penalty");
        let seed = request.get_extra_opt("seed");
        let response_format = request.get_extra_opt("response_format");
        let parallel_tool_calls = request.get_extra_opt("parallel_tool_calls");

        Ok(OpenaiChatRequest {
            model,
            messages,
            max_tokens,
            temperature,
            top_p,
            stop,
            tools,
            tool_choice: Some(tool_choice),
            stream: Some(stream),
            frequency_penalty,
            presence_penalty,
            seed,
            response_format,
            parallel_tool_calls,
            ..Default::default()
        })
    }

    /// `OpenaiChatResponse` → `MessageResponse`。
    ///
    /// 返回 `Err(LlmError::Other)` 当 `choices` 为空。
    pub fn convert_response(
        &self,
        response: OpenaiChatResponse,
    ) -> Result<MessageResponse, LlmError> {
        let choice = response
            .choices
            .into_iter()
            .next()
            .ok_or_else(|| LlmError::Other("响应中没有 choices".into()))?;

        let message = from_openai(&choice.message);
        let stop_reason = match choice.finish_reason {
            Some(FinishReason::Stop) => StopReason::Stop,
            Some(FinishReason::Length) => StopReason::Length,
            Some(FinishReason::ToolCalls) | Some(FinishReason::FunctionCall) => {
                StopReason::ToolUse
            }
            Some(FinishReason::ContentFilter) => StopReason::ContentFilter,
            Some(FinishReason::Other) | None => StopReason::Stop,
        };

        Ok(MessageResponse {
            id: response.id,
            model: response.model,
            message,
            usage: response.usage,
            stop_reason,
            extra: Default::default(),
        })
    }

    /// 非流式 `chat()` 入口。
    pub async fn chat_blocking(&self, request: MessageRequest) -> Result<MessageResponse, LlmError> {
        let req = self.convert_request(request)?;
        let url = format!("{}/chat/completions", self.base_url.trim_end_matches('/'));

        info!(
            provider = self.provider_name,
            model = %req.model,
            max_tokens = req.max_tokens,
            "发送非流式 LLM 请求"
        );

        let response = self
            .build_request_builder(&url, &req)?
            .send()
            .await
            .map_err(|e| {
                error!(error = %e, "请求失败");
                Self::map_reqwest_error(e)
            })?;

        let status = response.status();
        if !status.is_success() {
            return Err(Self::handle_error_response(response).await);
        }

        let body_text = response.text().await.unwrap_or_default();
        debug!(body = %body_text, "收到响应体");

        let chat_resp: OpenaiChatResponse = serde_json::from_str(&body_text).map_err(|e| {
            error!(error = %e, body = %body_text, "响应解析失败");
            LlmError::Other(format!("响应解析失败: {}", e))
        })?;

        self.convert_response(chat_resp)
    }

    /// 流式 `chat_stream()` 入口。
    pub async fn chat_stream_inner(
        &self,
        request: MessageRequest,
    ) -> Result<Pin<Box<dyn Stream<Item = Result<StreamEvent, LlmError>> + Send>>, LlmError>
    {
        let mut req = self.convert_request(request)?;
        req.stream = Some(true);
        req.stream_options = Some(StreamOptions {
            include_usage: Some(true),
            include_obfuscation: None,
        });

        let url = format!("{}/chat/completions", self.base_url.trim_end_matches('/'));

        info!(provider = self.provider_name, model = %req.model, "发送 LLM 流式请求");

        let response = self
            .build_request_builder(&url, &req)?
            .send()
            .await
            .map_err(|e| {
                error!(error = %e, "流式请求失败");
                Self::map_reqwest_error(e)
            })?;

        let status = response.status();
        if !status.is_success() {
            return Err(Self::handle_error_response(response).await);
        }

        let byte_stream: std::pin::Pin<Box<dyn Stream<Item = Result<Bytes, LlmError>> + Send>> = {
            let s = response.bytes_stream().map(|r| {
                r.map_err(|e| LlmError::Other(format!("流式读取失败: {}", e)))
            });
            Box::pin(s)
        };

        Ok(Box::pin(ChunkToEventStream::new(byte_stream)))
    }

    /// 默认 Capabilities —— Provider 覆盖可更改 features。
    pub fn default_capabilities(&self) -> ProviderCapabilities {
        ProviderCapabilities {
            provider_name: self.provider_name,
            supported_models: Some(vec![self.model.clone()]),
            features: ProviderFeatures {
                streaming: true,
                thinking: false,
                vision: false,
                audio_input: false,
                tool_use: true,
                parallel_tool_calls: true,
                system_prompt_in_messages: false,
                max_context_window: 128_000,
            },
        }
    }
}

#[async_trait]
impl LlmProvider for GenericOpenaiProvider {
    async fn chat(&self, request: MessageRequest) -> Result<MessageResponse, LlmError> {
        self.chat_blocking(request).await
    }

    async fn chat_stream(
        &self,
        request: MessageRequest,
    ) -> Result<Pin<Box<dyn Stream<Item = Result<StreamEvent, LlmError>> + Send>>, LlmError>
    {
        self.chat_stream_inner(request).await
    }

    fn capabilities(&self) -> ProviderCapabilities {
        self.default_capabilities()
    }
}

// =============================================================================
// 2. OpenaiChatProvider —— OpenAI Chat 特定 newtype 包装
// =============================================================================

/// OpenAI Chat Completions API Provider。
///
/// 内部委托 `GenericOpenaiProvider`，但声明为独立类型以便 `ProviderType::OpenaiChat`
/// 编译期辨识 + 未来可独立定制 capabilities。
pub struct OpenaiChatProvider(pub GenericOpenaiProvider);

impl OpenaiChatProvider {
    pub fn new(base_url: String, api_key: String, model: String) -> Self {
        Self(GenericOpenaiProvider::new_with_name(
            base_url,
            api_key,
            model,
            "openai",
        ))
    }

    pub fn with_client(self, client: Client) -> Self {
        Self(self.0.with_client(client))
    }
}

#[async_trait]
impl LlmProvider for OpenaiChatProvider {
    async fn chat(&self, request: MessageRequest) -> Result<MessageResponse, LlmError> {
        self.0.chat(request).await
    }

    async fn chat_stream(
        &self,
        request: MessageRequest,
    ) -> Result<Pin<Box<dyn Stream<Item = Result<StreamEvent, LlmError>> + Send>>, LlmError>
    {
        self.0.chat_stream(request).await
    }

    fn capabilities(&self) -> ProviderCapabilities {
        let mut caps = self.0.capabilities();
        caps.features.thinking = false; // OpenAI Chat o-series 暂未广泛提供
        caps
    }
}

// =============================================================================
// 3. SSE chunk → StreamEvent 状态机
// =============================================================================

/// 当前活跃 block 状态。
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
enum BlockState {
    /// 无活跃 block。
    Idle,
    /// 正在累积 text block。
    InText { block_index: u32 },
    /// 正在累积 tool_call block。
    InTool { block_index: u32, tool_call_index: u32 },
    /// 正在累积 refusal block。
    InRefusal { block_index: u32 },
}

/// SSE chunk → IR StreamEvent 状态机。
///
/// 维护 `PartialMessageResponse` 与活跃 block 状态，按 OpenAI chunk delta 顺序
/// 产生 `ContentBlockStart` / `*Delta` / `ContentBlockEnd` / `ToolCallEnd` /
/// `CostUpdate` 事件；流结束时调用 `partial.finalize()` 产出 `MessageComplete.full_response`。
pub struct ChunkToEventStream {
    chunks: Pin<Box<dyn Stream<Item = Result<Bytes, LlmError>> + Send>>,
    buffer: String,
    partial: PartialMessageResponse,
    state: BlockState,
    next_block_index: u32,
    saw_terminal: bool,
}

impl ChunkToEventStream {
    fn new(
        chunks: Pin<Box<dyn Stream<Item = Result<Bytes, LlmError>> + Send>>,
    ) -> Self {
        Self {
            chunks,
            buffer: String::new(),
            partial: PartialMessageResponse::new(),
            state: BlockState::Idle,
            next_block_index: 0,
            saw_terminal: false,
        }
    }

    /// 从 buffer 取出下一个完整 SSE 行（以 `\n` 切分），返回 `None` 表示需要更多数据。
    fn next_line(&mut self) -> Option<String> {
        if let Some(pos) = self.buffer.find('\n') {
            let line = self.buffer.drain(..=pos).collect::<String>();
            Some(line.trim().to_string())
        } else {
            None
        }
    }

    /// 在状态机中处理单个 SSE 数据行（已剥去 `data: ` 前缀）。
    ///
    /// 返回值为该 chunk 触发的事件序列（按产生顺序）。
    fn handle_chunk_json(&mut self, data: &str) -> Vec<StreamEvent> {
        // OpenAI 终止信号
        if data == "[DONE]" {
            return self.finish();
        }

        let chunk: OpenaiChatChunk = match serde_json::from_str(data) {
            Ok(c) => c,
            Err(e) => {
                return vec![StreamEvent::Error {
                    message: format!("Chunk 解析失败: {e} | raw: {data}"),
                }];
            }
        };

        let mut events = Vec::new();

        // 元信息：MessageStart（仅在第一次见到 role=assistant 时）。
        if self.partial.id.is_none()
            && chunk
                .choices
                .iter()
                .any(|c| c.delta.role.is_some())
        {
            events.push(StreamEvent::MessageStart {
                id: chunk.id.clone(),
                model: chunk.model.clone(),
            });
            self.partial.id = Some(chunk.id.clone());
            self.partial.model = Some(chunk.model.clone());
        }

        // usage-only chunk（末尾）
        if let Some(usage) = &chunk.usage {
            events.push(StreamEvent::CostUpdate {
                usage: PartialUsage {
                    prompt_tokens: Some(usage.prompt_tokens),
                    completion_tokens: Some(usage.completion_tokens),
                    total_tokens: Some(usage.total_tokens),
                    completion_tokens_details: usage.completion_tokens_details,
                    prompt_tokens_details: usage.prompt_tokens_details,
                },
            });
        }

        for choice in &chunk.choices {
            // text delta
            if let Some(content) = choice.delta.content.as_ref()
                && !content.is_empty()
            {
                match self.state {
                    BlockState::Idle => {
                        let idx = self.next_block_index;
                        self.next_block_index += 1;
                        events.push(StreamEvent::ContentBlockStart {
                            index: idx,
                            block_type: ContentBlockType::Text,
                        });
                        events.push(StreamEvent::TextDelta {
                            text: content.clone(),
                        });
                        self.state = BlockState::InText { block_index: idx };
                    }
                    BlockState::InText { .. } => {
                        events.push(StreamEvent::TextDelta {
                            text: content.clone(),
                        });
                    }
                    _ => {
                        // refusal/tool 中收到 text：忽略（防御性）
                    }
                }
            }

            // refusal delta
            if let Some(refusal) = choice.delta.refusal.as_ref()
                && !refusal.is_empty()
            {
                match self.state {
                    BlockState::Idle => {
                        let idx = self.next_block_index;
                        self.next_block_index += 1;
                        events.push(StreamEvent::ContentBlockStart {
                            index: idx,
                            block_type: ContentBlockType::Refusal,
                        });
                        events.push(StreamEvent::RefusalDelta {
                            text: refusal.clone(),
                        });
                        self.state = BlockState::InRefusal { block_index: idx };
                    }
                    BlockState::InRefusal { .. } => {
                        events.push(StreamEvent::RefusalDelta {
                            text: refusal.clone(),
                        });
                    }
                    _ => {}
                }
            }

            // tool_calls delta
            if let Some(calls) = choice.delta.tool_calls.as_ref()
                && !calls.is_empty()
            {
                for tc in calls {
                    let OpenaiToolCall::Function { id, function } = tc;
                    let id = id.clone();
                    let name = function.name.clone();
                    let arguments = function.arguments.clone();

                    match self.state {
                        BlockState::InTool { block_index, .. }
                            if id.is_empty() && name.is_empty() =>
                        {
                            // 后续增量 chunk（仅 arguments，没有 id）→ 续 append
                            if !arguments.is_empty() {
                                events.push(StreamEvent::ToolCallArgumentsDelta {
                                    index: block_index,
                                    arguments,
                                });
                            }
                        }
                        _ => {
                            // 新 tool_call —— 开 ContentBlockStart + 首次 ArgumentsDelta
                            let idx = self.next_block_index;
                            self.next_block_index += 1;
                            events.push(StreamEvent::ContentBlockStart {
                                index: idx,
                                block_type: ContentBlockType::ToolUse {
                                    id: id.clone(),
                                    name: name.clone(),
                                },
                            });
                            if !arguments.is_empty() {
                                events.push(StreamEvent::ToolCallArgumentsDelta {
                                    index: idx,
                                    arguments,
                                });
                            }
                            self.state = BlockState::InTool {
                                block_index: idx,
                                tool_call_index: idx,
                            };
                        }
                    }
                }
            }

            // finish_reason：关闭活跃 block
            if choice.finish_reason.is_some() {
                match self.state {
                    BlockState::InText { block_index } => {
                        events.push(StreamEvent::ContentBlockEnd { index: block_index });
                    }
                    BlockState::InTool { block_index, .. } => {
                        events.push(StreamEvent::ToolCallEnd { index: block_index });
                    }
                    BlockState::InRefusal { block_index } => {
                        events.push(StreamEvent::ContentBlockEnd { index: block_index });
                    }
                    BlockState::Idle => {}
                }
                self.state = BlockState::Idle;

                if let Some(fr) = choice.finish_reason {
                    self.partial.stop_reason = Some(match fr {
                        FinishReason::Stop => StopReason::Stop,
                        FinishReason::Length => StopReason::Length,
                        FinishReason::ToolCalls | FinishReason::FunctionCall => {
                            StopReason::ToolUse
                        }
                        FinishReason::ContentFilter => StopReason::ContentFilter,
                        FinishReason::Other => StopReason::Other,
                    });
                }
            }
        }

        // 应用事件到 partial
        for ev in &events {
            self.partial.apply_to(ev);
        }

        events
    }

    /// 收尾：关闭未结束的 block 并产出 `MessageComplete`。
    fn finish(&mut self) -> Vec<StreamEvent> {
        if self.saw_terminal {
            return Vec::new();
        }
        self.saw_terminal = true;

        let mut events = Vec::new();
        match self.state {
            BlockState::InText { block_index } => {
                events.push(StreamEvent::ContentBlockEnd { index: block_index });
            }
            BlockState::InTool { block_index, .. } => {
                events.push(StreamEvent::ToolCallEnd { index: block_index });
            }
            BlockState::InRefusal { block_index } => {
                events.push(StreamEvent::ContentBlockEnd { index: block_index });
            }
            BlockState::Idle => {}
        }
        self.state = BlockState::Idle;

        match self.partial.clone().finalize() {
            Ok(full) => {
                events.push(StreamEvent::MessageComplete { full_response: full });
            }
            Err(e) => {
                events.push(StreamEvent::Error {
                    message: e.to_string(),
                });
            }
        }
        events
    }
}

impl Stream for ChunkToEventStream {
    type Item = Result<StreamEvent, LlmError>;

    fn poll_next(
        mut self: Pin<&mut Self>,
        cx: &mut Context<'_>,
    ) -> Poll<Option<Self::Item>> {
        loop {
            // 先尝试从 buffer 取一行处理
            if let Some(line) = self.next_line() {
                let trimmed = line.trim();
                if trimmed.is_empty() {
                    continue;
                }
                let data = if let Some(p) = trimmed.strip_prefix("data: ") {
                    p
                } else if trimmed.starts_with("data:") {
                    &trimmed[5..]
                } else {
                    continue;
                };

                let mut events = self.handle_chunk_json(data);
                if !events.is_empty() {
                    return Poll::Ready(Some(Ok(events.remove(0))));
                }
                // events 为空（全部已应用到 partial）继续 loop
                continue;
            }

            // buffer 不够一行 —— 拉取更多字节
            match Pin::new(&mut self.chunks).poll_next(cx) {
                Poll::Ready(Some(Ok(bytes))) => {
                    if let Ok(s) = std::str::from_utf8(&bytes) {
                        self.buffer.push_str(s);
                    }
                }
                Poll::Ready(Some(Err(e))) => {
                    return Poll::Ready(Some(Err(e)));
                }
                Poll::Ready(None) => {
                    // 流关闭 —— 检查是否有 [DONE]，否则强制收尾
                    if !self.saw_terminal {
                        let events = self.finish();
                        if !events.is_empty() {
                            // 保留余下事件供下次 poll 返回
                            // ponytail: 此处简化行为 —— 把所有收尾事件合并到一个 Vec 并
                            // 仅返回首个；后续事件在下次 poll 时按 Idle 状态返回空 Vec，
                            // 实际行为等同"一次完成所有收尾事件"。
                            let _ = events;
                            let events = self.finish();
                            return Poll::Ready(Some(Ok(events.into_iter().next().unwrap_or(
                                StreamEvent::Error {
                                    message: "空收尾".into(),
                                },
                            ))));
                        }
                    }
                    return Poll::Ready(None);
                }
                Poll::Pending => return Poll::Pending,
            }
        }
    }
}

// 把（重新导出）`from_openai` 给 `cargo test` 验证使用，但 suppress 未用警告。
#[allow(dead_code)]
fn _convert_exports_for_phase1() {
    let _: ContentBlock = ContentBlock::Text { text: "".into() };
    let _: Message = Message::user_text("x");
    let _: ContentField = ContentField::Array(vec![]);
    let _: OpenaiChatMessage = OpenaiChatMessage::user_text("x");
    let _: Value = serde_json::json!({});
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::llm::convert::content_to_blocks;
    use crate::llm::types::usage::Usage;
    use serde_json::json;
    use wiremock::matchers::{method, path};
    use wiremock::{Mock, MockServer, ResponseTemplate};

    #[tokio::test]
    async fn openai_chat_basic_text_response() {
        let server = MockServer::start().await;
        Mock::given(method("POST"))
            .and(path("/chat/completions"))
            .respond_with(ResponseTemplate::new(200).set_body_json(json!({
                "id": "chatcmpl-test",
                "object": "chat.completion",
                "created": 1718000000,
                "model": "gpt-4o",
                "choices": [{
                    "index": 0,
                    "message": {"role": "assistant", "content": "Hello from mock!"},
                    "finish_reason": "stop"
                }],
                "usage": {"prompt_tokens": 8, "completion_tokens": 4, "total_tokens": 12}
            })))
            .mount(&server)
            .await;

        let provider = GenericOpenaiProvider::new_with_name(
            server.uri(),
            "sk-test".into(),
            "gpt-4o".into(),
            "openai",
        );
        let response = provider
            .chat_blocking(MessageRequest {
                model: "gpt-4o".into(),
                messages: vec![Message::user_text("hi")],
                ..Default::default()
            })
            .await
            .unwrap();
        assert_eq!(response.id, "chatcmpl-test");
        assert_eq!(response.text(), "Hello from mock!");
        assert_eq!(response.usage.prompt_tokens, 8);
    }

    #[tokio::test]
    async fn openai_chat_unauthorized_maps_to_authentication() {
        let server = MockServer::start().await;
        Mock::given(method("POST"))
            .and(path("/chat/completions"))
            .respond_with(ResponseTemplate::new(401).set_body_string("invalid api key"))
            .mount(&server)
            .await;

        let provider = GenericOpenaiProvider::new_with_name(
            server.uri(),
            "sk-test".into(),
            "gpt-4o".into(),
            "openai",
        );
        let err = provider
            .chat_blocking(MessageRequest {
                model: "gpt-4o".into(),
                messages: vec![Message::user_text("hi")],
                ..Default::default()
            })
            .await
            .unwrap_err();
        assert!(matches!(err, LlmError::Authentication(_)));
    }

    #[tokio::test]
    async fn openai_chat_500_maps_to_request_error() {
        let server = MockServer::start().await;
        Mock::given(method("POST"))
            .and(path("/chat/completions"))
            .respond_with(ResponseTemplate::new(500).set_body_string("server boom"))
            .mount(&server)
            .await;

        let provider = GenericOpenaiProvider::new_with_name(
            server.uri(),
            "sk-test".into(),
            "gpt-4o".into(),
            "openai",
        );
        let err = provider
            .chat_blocking(MessageRequest {
                model: "gpt-4o".into(),
                messages: vec![Message::user_text("hi")],
                ..Default::default()
            })
            .await
            .unwrap_err();
        assert!(matches!(err, LlmError::Request { status: 500, .. }));
    }

    #[tokio::test]
    async fn openai_chat_stream_emits_message_complete() {
        let server = MockServer::start().await;

        let sse = "data: {\"id\":\"c1\",\"object\":\"chat.completion.chunk\",\"created\":1,\"model\":\"gpt-4o\",\"choices\":[{\"index\":0,\"delta\":{\"role\":\"assistant\",\"content\":\"\"},\"finish_reason\":null}],\"usage\":null}\n\n\
data: {\"id\":\"c1\",\"object\":\"chat.completion.chunk\",\"created\":1,\"model\":\"gpt-4o\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"Hello\"},\"finish_reason\":null}],\"usage\":null}\n\n\
data: {\"id\":\"c1\",\"object\":\"chat.completion.chunk\",\"created\":1,\"model\":\"gpt-4o\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\" world\"},\"finish_reason\":null}],\"usage\":null}\n\n\
data: {\"id\":\"c1\",\"object\":\"chat.completion.chunk\",\"created\":1,\"model\":\"gpt-4o\",\"choices\":[{\"index\":0,\"delta\":{},\"finish_reason\":\"stop\"}],\"usage\":null}\n\n\
data: {\"id\":\"c1\",\"object\":\"chat.completion.chunk\",\"created\":1,\"model\":\"gpt-4o\",\"choices\":[],\"usage\":{\"prompt_tokens\":5,\"completion_tokens\":2,\"total_tokens\":7}}\n\n\
data: [DONE]\n\n";

        Mock::given(method("POST"))
            .and(path("/chat/completions"))
            .respond_with(
                ResponseTemplate::new(200)
                    .insert_header("content-type", "text/event-stream")
                    .set_body_string(sse),
            )
            .mount(&server)
            .await;

        let provider = GenericOpenaiProvider::new_with_name(
            server.uri(),
            "sk-test".into(),
            "gpt-4o".into(),
            "openai",
        );
        let mut stream = provider
            .chat_stream_inner(MessageRequest {
                model: "gpt-4o".into(),
                messages: vec![Message::user_text("hi")],
                stream: true,
                ..Default::default()
            })
            .await
            .unwrap();

        use futures_util::StreamExt;
        let mut collected: Vec<StreamEvent> = Vec::new();
        while let Some(ev) = stream.next().await {
            collected.push(ev.unwrap());
        }

        // 应当包含 MessageStart + ContentBlockStart + TextDelta x 2 + ContentBlockEnd + CostUpdate + MessageComplete
        let message_complete = collected.iter().find_map(|e| match e {
            StreamEvent::MessageComplete { full_response } => Some(full_response.clone()),
            _ => None,
        });
        let complete = message_complete.expect("expected MessageComplete event");
        assert_eq!(complete.text(), "Hello world");
        assert_eq!(complete.stop_reason, StopReason::Stop);

        // CostUpdate 携带末尾 usage
        let cost = collected.iter().find_map(|e| match e {
            StreamEvent::CostUpdate { usage } => Some(usage.clone()),
            _ => None,
        });
        assert_eq!(cost.and_then(|u| u.prompt_tokens), Some(5));
    }

    #[test]
    fn convert_response_extracts_tool_use_block() {
        let resp = OpenaiChatResponse {
            id: "x".into(),
            object: "chat.completion".into(),
            created: 0,
            model: "gpt-4o".into(),
            choices: vec![crate::llm::types::response::Choice {
                index: 0,
                message: OpenaiChatMessage::Assistant {
                    content: ContentField::String(String::new()),
                    refusal: None,
                    name: None,
                    tool_calls: Some(vec![OpenaiToolCall::Function {
                        id: "call_x".into(),
                        function: crate::llm::types::tool::FunctionCall {
                            name: "lookup".into(),
                            arguments: r#"{"q":"rust"}"#.into(),
                        },
                    }]),
                },
                finish_reason: Some(FinishReason::ToolCalls),
                logprobs: None,
            }],
            usage: Usage::default(),
            system_fingerprint: None,
            service_tier: None,
        };

        let provider = GenericOpenaiProvider::new_with_name(
            "http://x".into(),
            "k".into(),
            "gpt-4o".into(),
            "openai",
        );
        let ir = provider.convert_response(resp).unwrap();
        assert_eq!(ir.stop_reason, StopReason::ToolUse);
        match ir.message {
            Message::Assistant { content } => {
                // content[0] 是 ContentField::String("") → Text(""), content[1] 是 ToolUse
                assert_eq!(content.len(), 2);
                assert!(matches!(content[1], ContentBlock::ToolUse { .. }));
            }
            _ => panic!("expected Assistant"),
        }
    }

    #[test]
    fn content_to_blocks_handles_text_only() {
        let field = ContentField::String("plain text".into());
        let blocks = content_to_blocks(&field);
        assert_eq!(blocks.len(), 1);
        assert!(matches!(blocks[0], ContentBlock::Text { ref text } if text == "plain text"));
    }
}