//! OpenAI 兼容 Provider — 通过 /v1/chat/completions 端点实现
//!
//! 覆盖: OpenAI / GLM (open.bigmodel.cn) / DeepSeek / Claude OpenAI 兼容模式
//! 支持: 同步调用 + SSE 流式 + Function Calling / Tool Use

use std::time::Duration;

use async_trait::async_trait;
use futures::StreamExt;
use reqwest::Client;
use tracing::{debug, error, warn};

use crate::provider::{
    tool_call_id_or_fallback, CompletionRequest, CompletionResponse, LlmProvider, StreamResult,
    TokenUsage, ToolCall,
};
// ChatMessage 仅单测构造 CompletionRequest 用，避免非 test 构建的 unused import 警告。
#[cfg(test)]
use crate::provider::ChatMessage;
use crate::retry::{
    retry_with_backoff, AttemptOutcome, is_reqwest_error_retryable, is_status_retryable,
};

// 协议数据结构（OpenAiRequest / OpenAiResponse / OpenAiStreamChunk …）与 SSE 解析纯函数
// apply_openai_sse 抽离到 openai_helpers.rs，结构对齐 anthropic_helpers.rs。
// Rust impl 块不可跨文件，Provider struct + impl 仍留本文件。
use crate::openai_helpers::{
    apply_openai_sse, OpenAiMessage, OpenAiRequest, OpenAiResponse, OpenAiToolCallResp,
};

// ============================================================
// OpenAI Compat Provider
// ============================================================

/// OpenAI 兼容 LLM Provider
pub struct OpenAICompatProvider {
    client: Client,
    api_key: String,
    base_url: String,
    default_model: String,
}

impl OpenAICompatProvider {
    /// 创建 Provider
    ///
    /// - `base_url`: 如 "https://api.openai.com", "https://open.bigmodel.cn/api/paas", "https://api.deepseek.com"
    /// - `api_key`: API 密钥
    /// - `default_model`: 默认模型名称
    pub fn new(base_url: impl Into<String>, api_key: impl Into<String>, default_model: impl Into<String>) -> Self {
        // SW-260618-10: reqwest Client 构建抽 crate::build_provider_client（与 Anthropic 共用 DRY）。
        // connect_timeout/回退策略集中此处，未来改一处即可（见 lib.rs::build_provider_client）。
        let client = crate::build_provider_client();
        Self {
            client,
            api_key: api_key.into(),
            base_url: base_url.into(),
            default_model: default_model.into(),
        }
    }

    /// 构建完整 API URL
    ///
    /// 智能拼接，兼容三种 base_url 约定：
    /// - 已含完整端点（…/chat/completions）→ 直接用
    /// - 已含版本段（…/v1 …/v4 等，如 GLM 的 /api/paas/v4）→ 补 /chat/completions
    /// - 仅域名无版本（如 api.openai.com / api.deepseek.com）→ 补 /v1/chat/completions（OpenAI 约定）
    fn chat_url(&self) -> String {
        let base = self.base_url.trim_end_matches('/');
        if base.ends_with("/chat/completions") {
            return base.to_string();
        }
        if Self::ends_with_version(base) {
            return format!("{}/chat/completions", base);
        }
        format!("{}/v1/chat/completions", base)
    }

    /// base_url 是否以 `/v<数字>` 结尾（如 /v1 /v4）
    fn ends_with_version(base: &str) -> bool {
        match base.rsplit_once('/') {
            Some((_, last)) if last.starts_with('v') && last.len() > 1 => {
                last[1..].bytes().all(|b| b.is_ascii_digit())
            }
            _ => false,
        }
    }

    /// 构建 embeddings API URL（与 chat_url 同套智能拼接规则）
    fn embed_url(&self) -> String {
        let base = self.base_url.trim_end_matches('/');
        if base.ends_with("/embeddings") {
            return base.to_string();
        }
        if Self::ends_with_version(base) {
            return format!("{}/embeddings", base);
        }
        format!("{}/v1/embeddings", base)
    }

    /// 将通用请求转换为 OpenAI 格式
    fn convert_request(&self, req: CompletionRequest) -> OpenAiRequest {
        let model = if req.model.is_empty() {
            self.default_model.clone()
        } else {
            req.model
        };

        let mut messages: Vec<OpenAiMessage> = req
            .messages
            .into_iter()
            .map(|m| {
                let role = match m.role {
                    crate::provider::MessageRole::System => "system",
                    crate::provider::MessageRole::User => "user",
                    crate::provider::MessageRole::Assistant => "assistant",
                    crate::provider::MessageRole::Tool => "tool",
                };
                // 多模态 content（须在 move m.tool_calls 之前算，借用 m）。
                // 含图消息走 content 数组（text/image_url）；纯文本走字符串简写
                // （保持与现有纯文本端点零回归）。image_url 支持 data URI（base64）与 http(s) URL。
                let content = if m.has_image() {
                    let parts: Vec<serde_json::Value> = m
                        .flattened_parts()
                        .into_iter()
                        .map(|p| match p {
                            crate::provider::ContentPart::Text { text } => serde_json::json!({
                                "type": "text",
                                "text": text,
                            }),
                            crate::provider::ContentPart::Image { url, base64, media_type, alt: _ } => {
                                let final_url = match (base64, url, media_type) {
                                    (Some(b), _, Some(mt)) => {
                                        format!("data:{};base64,{}", mt, b)
                                    }
                                    (None, Some(u), _) => u,
                                    // 完整性兜底：当前 image_base64 构造器强制 media_type:Some，
                                    // image_url 构造器提供 url:Some，二者分别命中上两个分支；
                                    // 此分支仅在 parts 来源被外部直接构造且字段均缺时才可达
                                    //（如 url:None+base64:None 或 base64:Some+media_type:None）。
                                    // 退化为空串（OpenAI 对空 image_url.url 会 400），
                                    // 由调用方保证 parts 合法性，provider 层不做静默伪造。
                                    _ => String::new(),
                                };
                                serde_json::json!({
                                    "type": "image_url",
                                    "image_url": { "url": final_url },
                                })
                            }
                        })
                        .collect();
                    serde_json::Value::Array(parts)
                } else {
                    serde_json::Value::String(m.content.clone())
                };
                let tool_calls = m.tool_calls.map(|calls| {
                    calls
                        .into_iter()
                        .map(|tc| {
                            serde_json::json!({
                                "id": tc.id,
                                "type": tc.call_type,
                                "function": {
                                    "name": tc.function.name,
                                    "arguments": tc.function.arguments,
                                }
                            })
                        })
                        .collect()
                });
                OpenAiMessage {
                    role: role.to_string(),
                    content,
                    tool_call_id: m.tool_call_id,
                    tool_calls,
                    reasoning_content: m.reasoning_content,
                }
            })
            .collect();

        // 保证首条 user/system(OpenAI 协议要求首条非 assistant/tool)。
        // 对齐 AnthropicCompatProvider::ensure_leading_user:上游绕过 sanitize 的调用方
        // (标题生成/知识注入/工作流 AI 节点等直构造 CompletionRequest 的路径)可能传入首条
        // assistant 的序列(会话恢复/续发/片段截取),补 user 占位保留上下文,首条合法。
        Self::ensure_leading_user(&mut messages);

        // 治 DeepSeek/OpenAI 400(三元组完整性 P0)。OpenAI 协议铁律:
        //   (a) assistant 的每个 tool_call.id 必须有后续 tool(role=tool, tool_call_id 匹配)响应,
        //       否则 "insufficient tool messages" 400(assistant 调了工具但无结果)。
        //   (b) 反之,每条 tool 消息必须紧跟一个含 tool_calls(同 tool_call_id)的 assistant,
        //       否则 "Messages with role tool must be a response to a preceding message
        //       with tool_calls" 400(tool 无配对头)。
        //
        // 旧逻辑只检查「下一条 role 是否为 tool」(粗粒度),漏两类 orphan:
        //   1) 部分 tool_call 无响应:assistant(tc=[a,b]) → tool(a)(b 丢失)→ 旧逻辑因下一条是
        //      tool 不剥 → 发出未闭合的 b → 400。修法:按 tool_call_id 精确配对,剥未闭合 id。
        //   2) orphan tool_result(tool 无前置 assistant tool_calls 配对):DB/直构造路径绕过
        //      ContextManager::sanitize_messages(标题/知识注入/工作流节点),tool 残留无头 →
        //      旧逻辑不处理 → 400。修法:剥 assistant tool_calls 时同步丢弃同 id 的 orphan
        //      tool(一致性:不留无头 result),并对独立 orphan tool(全程无配对头)直接丢弃。
        //
        // 正常三元组形如:assistant(tc=[a]) → tool(a) → assistant(tc=[b]) → tool(b),各 id 闭合,
        // 本守卫零介入。仅异常截断/恢复/直构造路径触发(防 400 兜底)。
        // view-only:仅改发送视图(本函数消费 req.messages 所有权),持久化由调用方/上层 sanitize 全量保留。
        sanitize_openai_triplets(&mut messages);

        let tools = req.tools.map(|defs| {
            defs.into_iter()
                .map(|d| serde_json::to_value(d).unwrap_or_default())
                .collect()
        });

        OpenAiRequest {
            model,
            messages,
            temperature: req.temperature,
            max_tokens: req.max_tokens,
            stream: req.stream,
            tools,
            tool_choice: req.tool_choice,
            reasoning_content: req.reasoning_content,
            // 流式请求末 chunk 带 usage（同步调用 complete 不需要）
            stream_options: if req.stream {
                Some(serde_json::json!({ "include_usage": true }))
            } else {
                None
            },
        }
    }

    /// 生成 messages 诊断摘要（每条 role + content 形态 + tool 标记），不含敏感数据。
    /// 流中途 error 时附摘要定位哪条非法（对齐 `AnthropicCompatProvider::summarize_messages`）。
    fn summarize_openai_messages(messages: &[OpenAiMessage]) -> String {
        let lines: Vec<String> = messages
            .iter()
            .enumerate()
            .map(|(i, m)| {
                let role = m.role.as_str();
                let desc = match &m.content {
                    serde_json::Value::String(s) => format!("text({}B)", s.len()),
                    serde_json::Value::Array(blocks) => {
                        let parts: Vec<String> = blocks
                            .iter()
                            .map(|b| {
                                let ty = b.get("type").and_then(|t| t.as_str()).unwrap_or("?");
                                match ty {
                                    "text" => format!(
                                        "text({}B)",
                                        b.get("text")
                                            .and_then(|t| t.as_str())
                                            .map(|s| s.len())
                                            .unwrap_or(0)
                                    ),
                                    "image_url" => "image".to_string(),
                                    _ => ty.to_string(),
                                }
                            })
                            .collect();
                        format!("[{}]", parts.join(","))
                    }
                    _ => "?".to_string(),
                };
                let tool_mark = match (&m.tool_calls, &m.tool_call_id) {
                    (Some(tcs), _) => format!(" tool_calls={}", tcs.len()),
                    (None, Some(tid)) => format!(" tool_result[tid={}]", tid),
                    (None, None) => String::new(),
                };
                format!("#{}:{} {}{}", i, role, desc, tool_mark)
            })
            .collect();
        format!("{} msgs: {}", lines.len(), lines.join(" | "))
    }

    /// 保证 messages 首条为 user/system(OpenAI 协议要求首条非 assistant/tool)。
    ///
    /// 对齐 `AnthropicCompatProvider::ensure_leading_user`。上游绕过 `ContextManager::sanitize_messages`
    /// 的调用方(标题生成/知识注入/工作流 AI 节点等直构造 CompletionRequest 的路径)可能传入首条
    /// assistant 的序列——会话恢复、续发或历史片段截取时,真正的首条 user 已被裁剪/压缩掉。
    ///
    /// **用"补"而非"砍"**:开头插一条 user 占位,保留全部上下文(砍会丢工具调用历史,多轮砍到空)。
    /// 占位 user 紧贴原首条,不破坏 user/assistant 交替;仅异常路径触发(正常首条本就是 user)。
    fn ensure_leading_user(messages: &mut Vec<OpenAiMessage>) {
        let first_role = messages.first().map(|m| m.role.as_str()).unwrap_or("");
        if first_role == "user" || first_role == "system" {
            return;
        }
        warn!(
            first_role,
            msg_count = messages.len(),
            "ensure_leading_user: 首条非 user/system,补 user 占位(保留上下文,防 OpenAI 首条 assistant/tool 非法)"
        );
        messages.insert(
            0,
            OpenAiMessage {
                role: "user".into(),
                content: serde_json::Value::String("(continued from previous context)".into()),
                tool_call_id: None,
                tool_calls: None,
                reasoning_content: None,
            },
        );
    }

    /// 解析同步响应中的工具调用。
    ///
    /// 兜底（CR-空 id）：id 空时按数组 index 生成 `gen_tool_{index}` fallback。
    /// SenseNova 等兼容缺陷 provider 发空 id，多 tool_call 同 id 致结果路由全落首个。
    /// 详见 `tool_call_id_or_fallback`。正常 provider id 非空原样透传。
    fn parse_tool_calls(calls: Vec<OpenAiToolCallResp>) -> Vec<ToolCall> {
        calls
            .into_iter()
            .enumerate()
            .map(|(i, c)| {
                let id = tool_call_id_or_fallback(&c.id, i, "gen_tool");
                ToolCall::new(id, c.function.name, c.function.arguments)
            })
            .collect()
    }
}

/// 从 OpenAiMessage 的 tool_calls 数组里取每个 call 的 id(tool_calls 形如
/// [{id, type, function:{name, arguments}}, ...])。非数组 / 缺 id 的条目跳过。
fn extract_tool_call_ids(msg: &OpenAiMessage) -> Vec<String> {
    let Some(arr) = msg.tool_calls.as_ref() else {
        return Vec::new();
    };
    arr.iter()
        .filter_map(|tc| tc.get("id").and_then(|v| v.as_str()).map(|s| s.to_string()))
        .collect()
}

/// 三元组一致性自愈(view-only,发送视图):保证 OpenAI 协议 tool_call/tool_result
/// 双向闭合,防 DeepSeek/OpenAI 400。详见 [`OpenAICompatProvider::convert_request`] 调用处注释。
///
/// 两轮扫描:
/// 1) 收集 resolved_ids = 所有 tool 消息的 tool_call_id(这些 id 有 result 响应)。
/// 2) assistant(tool_calls):剥未在 resolved_ids 内的 call.id;剥空则 tool_calls=None。
///    (头被剥后,其 tool_call.id 不再进 head_ids,故 step3 会同步丢弃对应 orphan tool。)
/// 3) tool:tool_call_id 不在任何保留 assistant 头(任意 assistant 仍含此 id)→ orphan
///    tool_result,丢弃。这覆盖「头被剥后残留的 tool」与「全程无配对头的 tool」两类。
///
/// 一致性:剥 assistant tool_call → 该 id 不进 head_ids → 对应 tool 在 step3 被丢;
/// 反之剥 orphan tool 不动 assistant(若 assistant 的所有 id 都被剥则 tool_calls=None)。
/// 正常三元组(各 id 闭合)零介入。
///
/// 设计取舍:OpenAI 协议 assistant(tool_calls) 需有效函数结构,补头(像 Anthropic
/// TOOL_MISSING_PREFIX)风险高于丢弃——故选「丢弃 orphan」而非「补头」。Anthropic
/// 路径由 drop_reverse_orphans 补头自愈(保留 LLM 可见的工具结果);OpenAI 路径走丢弃,
/// 二者各自适配协议特性(Anthropic 严格交替 + 补头可行;OpenAI tool 必须紧跟 tool_calls)。
fn sanitize_openai_triplets(messages: &mut Vec<OpenAiMessage>) {
    use std::collections::HashSet;

    // step 1:resolved_ids = 所有 tool 消息提供的 tool_call_id(有 result 响应的 id)。
    let resolved_ids: HashSet<String> = messages
        .iter()
        .filter(|m| m.role == "tool")
        .filter_map(|m| m.tool_call_id.clone())
        .collect();

    let mut stripped_heads = 0u32;
    let mut total_stripped = 0u32;

    // step 2:assistant 剥未闭合 tool_call(无对应 tool result 响应)。
    for m in messages.iter_mut() {
        if m.role != "assistant" {
            continue;
        }
        let Some(calls) = m.tool_calls.as_ref() else {
            continue;
        };
        if calls.is_empty() {
            continue;
        }
        let kept: Vec<serde_json::Value> = calls
            .iter()
            .filter(|tc| {
                tc.get("id")
                    .and_then(|v| v.as_str())
                    .is_some_and(|id| resolved_ids.contains(id))
            })
            .cloned()
            .collect();
        let stripped_count = calls.len() - kept.len();
        if stripped_count == 0 {
            continue;
        }
        m.tool_calls = if kept.is_empty() { None } else { Some(kept) };
        stripped_heads += 1;
        total_stripped += stripped_count as u32;
        tracing::warn!(
            stripped_count,
            "[openai] assistant 含未闭合 tool_calls(无对应 tool result),已剥离 {} 个(防 insufficient tool messages 400)",
            stripped_count,
        );
    }

    // step 3:head_ids = step2 后仍保留在任意 assistant 头的 id(有头配对的 tool 才保留)。
    let head_ids: HashSet<String> = messages
        .iter()
        .filter(|m| m.role == "assistant")
        .flat_map(extract_tool_call_ids)
        .collect();

    let original_len = messages.len();
    let mut dropped_orphan_tools = 0u32;
    messages.retain(|m| {
        if m.role != "tool" {
            return true;
        }
        let id = match m.tool_call_id.as_deref() {
            None => {
                // 无 tool_call_id 的 tool 消息(异常数据):无法配对,丢弃(发出去必 400)。
                dropped_orphan_tools += 1;
                tracing::warn!(
                    "[openai] tool 消息缺少 tool_call_id,已丢弃(无 id 无法配对 assistant tool_calls,防 400)"
                );
                return false;
            }
            Some(id) => id,
        };
        if head_ids.contains(id) {
            // 有配对头 → 保留(正常三元组)。
            return true;
        }
        // 无配对头(id 不在任何保留 assistant 头内)→ orphan tool_result,丢弃。
        // 含两类:(a) assistant 头被 step2 剥后残留的 tool;(b) 全程无配对头的直构造/DB 残留。
        dropped_orphan_tools += 1;
        tracing::warn!(
            tool_call_id = %id,
            "[openai] orphan tool result(无配对 assistant tool_calls),已丢弃(防 'tool must be response to preceding tool_calls' 400)",
        );
        false
    });

    if stripped_heads > 0 || dropped_orphan_tools > 0 {
        tracing::warn!(
            stripped_heads,
            total_stripped,
            dropped_orphan_tools,
            before = original_len,
            after = messages.len(),
            "[openai] tool_call 三元组自愈(view-only, 持久化不受影响)"
        );
    }
}

#[async_trait]
impl LlmProvider for OpenAICompatProvider {
    /// 文本嵌入: POST /v1/embeddings(OpenAI 兼容,智谱/阿里百炼/OpenAI 通用)
    async fn embed(&self, model: &str, texts: Vec<String>) -> anyhow::Result<Vec<Vec<f32>>> {
        #[derive(serde::Deserialize)]
        struct EmbedData { embedding: Vec<f32>, index: usize }
        #[derive(serde::Deserialize)]
        struct EmbedResponse { data: Vec<EmbedData> }

        let resp = self
            .client
            .post(self.embed_url())
            .header("Authorization", format!("Bearer {}", self.api_key))
            .header("Content-Type", "application/json")
            .json(&serde_json::json!({ "model": model, "input": texts }))
            .send()
            .await?;

        if !resp.status().is_success() {
            let status = resp.status();
            let body = resp.text().await.unwrap_or_default();
            anyhow::bail!("Embedding API 错误 {}: {}", status, body);
        }

        let mut body: EmbedResponse = resp.json().await?;
        // 按 index 排序保证与输入顺序一致(API 不保证返回顺序)
        body.data.sort_by_key(|d| d.index);
        Ok(body.data.into_iter().map(|d| d.embedding).collect())
    }

    async fn complete(&self, request: CompletionRequest) -> anyhow::Result<CompletionResponse> {
        let mut req = request;
        req.stream = false;
        let openai_req = self.convert_request(req);

        debug!(model = %openai_req.model, "OpenAI 同步调用");

        // 指数退避重试: 包裹 send + 状态码判定。
        // 单请求 60s timeout 保持不变,重试是额外层: 3 次 × 60s 最坏 180s,
        // 由 retry_with_backoff 内部 30s 总预算主动止损。
        let label = format!("OpenAI[{}]", openai_req.model);
        retry_with_backoff(&label, move |_| {
            let client = self.client.clone();
            let url = self.chat_url();
            let api_key = self.api_key.clone();
            let openai_req = openai_req.clone();
            async move {
                // send
                let resp = client
                    .post(url)
                    .header("Authorization", format!("Bearer {}", api_key))
                    .header("Content-Type", "application/json")
                    .timeout(Duration::from_secs(60))
                    .json(&openai_req)
                    .send()
                    .await;
                let resp = match resp {
                    Ok(r) => r,
                    Err(e) => {
                        if is_reqwest_error_retryable(&e) {
                            return AttemptOutcome::Retryable(format!("请求失败(可重试): {}", e));
                        }
                        return AttemptOutcome::Fatal(format!("请求失败(不可重试): {}", e));
                    }
                };
                // 状态码判定
                if !resp.status().is_success() {
                    let status = resp.status().as_u16();
                    let body = resp.text().await.unwrap_or_default();
                    let msg = format!("LLM API 错误 {}: {}", status, body);
                    if is_status_retryable(status) {
                        warn!(%status, "OpenAI 同步调用可重试状态码");
                        return AttemptOutcome::Retryable(msg);
                    }
                    error!(%status, %body, "LLM API 调用失败(不可重试)");
                    return AttemptOutcome::Fatal(msg);
                }
                // body 解析: 解析错属 Fatal(响应已成功送达，重试也会因同样格式失败)
                let body: OpenAiResponse = match resp.json().await {
                    Ok(b) => b,
                    Err(e) => return AttemptOutcome::Fatal(format!("响应解析失败: {}", e)),
                };
                let choice = match body.choices.into_iter().next() {
                    Some(c) => c,
                    None => return AttemptOutcome::Fatal("LLM 响应无 choices".to_string()),
                };
                let text = choice.message.content.unwrap_or_default();
                let tool_calls = choice.message.tool_calls.map(Self::parse_tool_calls);
                let usage = body.usage.map(|u| TokenUsage {
                    prompt_tokens: u.prompt_tokens,
                    completion_tokens: u.completion_tokens,
                    total_tokens: u.total_tokens,
                }).unwrap_or(TokenUsage {
                    prompt_tokens: 0,
                    completion_tokens: 0,
                    total_tokens: 0,
                });
                AttemptOutcome::Ok(CompletionResponse {
                    text,
                    model: body.model,
                    usage,
                    tool_calls,
                    reasoning_content: choice.message.reasoning_content,
                })
            }
        })
        .await
    }

    async fn stream(&self, request: CompletionRequest) -> anyhow::Result<StreamResult> {
        let mut req = request;
        req.stream = true;
        let openai_req = self.convert_request(req);

        debug!(model = %openai_req.model, "OpenAI 流式调用");

        // send 阶段需 timeout 防 hang(同 Anthropic 路径)。
        // 不能用 reqwest .timeout()(会砍流式 body),改用 tokio::time::timeout 包裹 send。
        let send_future = self
            .client
            .post(self.chat_url())
            .header("Authorization", format!("Bearer {}", self.api_key))
            .header("Content-Type", "application/json")
            .json(&openai_req)
            .send();
        let resp = match tokio::time::timeout(Duration::from_secs(60), send_future).await {
            Ok(Ok(r)) => r,
            Ok(Err(e)) => {
                tracing::error!(error = %e, is_timeout = e.is_timeout(), "OpenAI 流式 send 失败");
                return Err(e.into());
            }
            Err(_elapsed) => {
                tracing::error!(url = %self.chat_url(), "OpenAI 流式 send 超时(60s 未返回响应头)");
                anyhow::bail!("流式请求超时(60秒未收到 HTTP 响应，可能服务不可达或被防火墙拦截)");
            }
        };

        if !resp.status().is_success() {
            let status = resp.status();
            let body = resp.text().await.unwrap_or_default();
            error!(%status, %body, "LLM 流式 API 调用失败");
            anyhow::bail!("LLM 流式 API 错误 {}: {}", status, body);
        }

        // 原生 SSE 解析器替代 eventsource-stream 库。
        // eventsource-stream 在 Windows 上对 Deepseek 等响应报 "error decoding response body"
        // (严格 UTF-8 + SSE 协议校验,跨 chunk 字符/不完整事件均报错且不可恢复)。
        // 原生解析器:bytes 累积 + from_utf8_lossy 宽松处理 + \n\n 分隔,容错不中断流。
        let mut last_usage: Option<TokenUsage> = None;
        // MidStream error（中转站按 OpenAI 协议在流中途发 error 帧）时附 messages 摘要定位哪条非法
        // （对齐 anthropic_compat 672）。
        let messages_summary = Self::summarize_openai_messages(&openai_req.messages);

        let sse = crate::sse_parser::SseStream::new(resp.bytes_stream());
        let stream = sse.flat_map(move |result: Result<Vec<String>, String>| {
            let mut chunks: Vec<anyhow::Result<crate::provider::StreamChunk>> = Vec::new();
            match result {
                Ok(events) => {
                    for data in events {
                        let mut chunk = apply_openai_sse(&data, &mut last_usage);
                        if let Some(err) = chunk.error.as_mut() {
                            *err = format!("{} | messages 摘要: {}", err, messages_summary);
                        }
                        chunks.push(Ok(chunk));
                    }
                }
                Err(e) => {
                    let ctx = format!("SSE 流错误: {}", e);
                    error!("{}", ctx);
                    chunks.push(Err(anyhow::anyhow!("{}", ctx)));
                }
            }
            futures::stream::iter(chunks)
        });

        Ok(Box::pin(stream))
    }

    fn name(&self) -> &str {
        &self.default_model
    }

    fn endpoint(&self) -> String {
        self.chat_url()
    }
}

// ============================================================
// 单测（不发真实 HTTP，喂构造的 SSE data 字符串序列）
// ============================================================

#[cfg(test)]
mod tests {
    use super::*;

    /// 辅助：构造普通文本 delta chunk 的 SSE data
    fn text_chunk(content: &str, finish_reason: Option<&str>) -> String {
        let fr = match finish_reason {
            Some(r) => format!(", \"finish_reason\": \"{}\"", r),
            None => String::from(", \"finish_reason\": null"),
        };
        format!(
            r#"{{"choices":[{{"delta":{{"content":"{}"}}{}}}]}}"#,
            content, fr
        )
    }

    /// 辅助：构造带 usage 的 chunk（choices 为空 → usage-only 末 chunk，对应 include_usage）
    fn usage_only_chunk(prompt: u32, completion: u32) -> String {
        format!(
            r#"{{"choices":[],"usage":{{"prompt_tokens":{},"completion_tokens":{},"total_tokens":{}}}}}"#,
            prompt,
            completion,
            prompt + completion
        )
    }

    /// 辅助：构造既有 content 又带 usage 的末段 chunk（部分兼容端点会把 usage 挂到正常末 chunk 上）
    fn text_chunk_with_usage(content: &str, finish_reason: &str, prompt: u32, completion: u32) -> String {
        format!(
            r#"{{"choices":[{{"delta":{{"content":"{}"}},"finish_reason":"{}"}}],"usage":{{"prompt_tokens":{},"completion_tokens":{},"total_tokens":{}}}}}"#,
            content,
            finish_reason,
            prompt,
            completion,
            prompt + completion
        )
    }

    /// 多 chunk 文本流后，末 chunk 携带 usage（include_usage 覆盖语义）
    #[test]
    fn openai_sse_multi_chunk_with_terminal_usage() {
        let mut acc: Option<TokenUsage> = None;

        // 1) 首个文本增量，无 usage
        let c = apply_openai_sse(&text_chunk("Hello", None), &mut acc);
        assert_eq!(c.delta, "Hello");
        assert!(!c.finished);
        assert!(c.usage.is_none());
        assert!(acc.is_none(), "无 usage 的 chunk 不应改累加器");

        // 2) 第二个文本增量
        let c = apply_openai_sse(&text_chunk(" world", None), &mut acc);
        assert_eq!(c.delta, " world");
        assert!(!c.finished);
        assert!(acc.is_none());

        // 3) 末段正常 chunk 带 finish_reason=stop（仍是文本 delta，不带 usage）
        let c = apply_openai_sse(&text_chunk("", Some("stop")), &mut acc);
        assert!(c.finished);
        assert_eq!(c.delta, "");
        assert!(acc.is_none(), "此 chunk 无 usage 字段，累加器仍为 None");

        // 4) usage-only chunk（choices=[]）携带累计 usage → 覆盖累加器
        let c = apply_openai_sse(&usage_only_chunk(12, 34), &mut acc);
        assert!(!c.finished);
        assert!(c.usage.is_none(), "非 [DONE] chunk 不带出 usage");
        let acc = acc.expect("累加器应已被 usage-only chunk 覆盖写入");
        assert_eq!(acc.prompt_tokens, 12);
        assert_eq!(acc.completion_tokens, 34);
        assert_eq!(acc.total_tokens, 46);
    }

    /// usage 挂在正常末段 chunk（含 finish_reason）上，而非独立 usage-only chunk
    #[test]
    fn openai_sse_usage_on_terminal_text_chunk() {
        let mut acc: Option<TokenUsage> = None;
        let c = apply_openai_sse(&text_chunk_with_usage("", "stop", 100, 200), &mut acc);
        assert!(c.finished);
        assert!(c.usage.is_none(), "非 [DONE] 不带出 usage，仅覆盖累加器");
        let acc = acc.expect("末段 chunk 的 usage 应已覆盖累加器");
        assert_eq!(acc.prompt_tokens, 100);
        assert_eq!(acc.completion_tokens, 200);
        assert_eq!(acc.total_tokens, 300);
    }

    /// [DONE] 时 take() 带出累积 usage，且取走后累加器清空
    #[test]
    fn openai_sse_done_takes_accumulated_usage() {
        let mut acc: Option<TokenUsage> = None;
        apply_openai_sse(&text_chunk("x", None), &mut acc);
        apply_openai_sse(&usage_only_chunk(5, 7), &mut acc);

        let c = apply_openai_sse("[DONE]", &mut acc);
        assert!(c.finished);
        let u = c.usage.expect("[DONE] 应带出累积 usage");
        assert_eq!(u.prompt_tokens, 5);
        assert_eq!(u.completion_tokens, 7);
        assert_eq!(u.total_tokens, 12);
        assert!(acc.is_none(), "take() 后累加器应清空");
    }

    /// 无 usage 的流：[DONE] 时 usage 字段为 None
    #[test]
    fn openai_sse_done_without_usage() {
        let mut acc: Option<TokenUsage> = None;
        apply_openai_sse(&text_chunk("hi", None), &mut acc);
        let c = apply_openai_sse("[DONE]", &mut acc);
        assert!(c.finished);
        assert!(c.usage.is_none(), "全程无 usage 时 [DONE] usage 应为 None");
        assert!(acc.is_none());
    }

    /// 后续 usage chunk 覆盖先前 usage（多轮 / 重发场景）
    #[test]
    fn openai_sse_later_usage_overrides_earlier() {
        let mut acc: Option<TokenUsage> = None;
        apply_openai_sse(&usage_only_chunk(1, 1), &mut acc);
        apply_openai_sse(&usage_only_chunk(50, 60), &mut acc);
        let c = apply_openai_sse("[DONE]", &mut acc);
        let u = c.usage.unwrap();
        assert_eq!(u.prompt_tokens, 50, "末 usage 应覆盖前值");
        assert_eq!(u.completion_tokens, 60);
        assert_eq!(u.total_tokens, 110);
    }

    /// finish_reason=length（max_tokens 截断）按正常终止处理
    #[test]
    fn openai_sse_length_finish_reason_treated_as_finished() {
        let mut acc: Option<TokenUsage> = None;
        let c = apply_openai_sse(&text_chunk("...", Some("length")), &mut acc);
        assert!(c.finished, "length 应视为正常终止");
        assert!(acc.is_none());
    }

    /// 非法 JSON data → 返回空 chunk，不 panic、不改累加器
    #[test]
    fn openai_sse_malformed_json_yields_empty_chunk() {
        let mut acc: Option<TokenUsage> = None;
        let c = apply_openai_sse("not a json", &mut acc);
        assert_eq!(c.delta, "");
        assert!(!c.finished);
        assert!(c.usage.is_none());
        assert!(acc.is_none());
    }

    /// tool_calls 增量解析
    #[test]
    fn openai_sse_tool_call_delta() {
        let mut acc: Option<TokenUsage> = None;
        let data = r#"{"choices":[{"delta":{"tool_calls":[{"index":0,"id":"call_1","function":{"name":"get_weather","arguments":"{\"q\":"}}]}}]}"#;
        let c = apply_openai_sse(data, &mut acc);
        assert!(acc.is_none());
        let tcs = c.tool_calls.expect("应有 tool_calls 增量");
        assert_eq!(tcs.len(), 1);
        assert_eq!(tcs[0].index, 0);
        assert_eq!(tcs[0].id.as_deref(), Some("call_1"));
        assert_eq!(tcs[0].function_name.as_deref(), Some("get_weather"));
        assert_eq!(tcs[0].function_arguments.as_deref(), Some("{\"q\":"));
        assert!(!c.finished);
    }

    /// 流中途 error 事件 → error 为 Some(msg)，finished=false（避免残缺被当正常完成入库），不污染 usage 累加
    #[test]
    fn openai_sse_midstream_error_event() {
        let mut acc: Option<TokenUsage> = None;
        // 先累积一段 usage，验证 error 分支不污染累加器
        apply_openai_sse(&usage_only_chunk(10, 20), &mut acc);

        let data = r#"{"choices":[],"error":{"message":"context length exceeded","type":"invalid_request_error"}}"#;
        let c = apply_openai_sse(data, &mut acc);
        assert!(!c.finished, "error 帧不应走 finished 完成路径");
        assert_eq!(c.delta, "");
        assert!(c.tool_calls.is_none());
        assert!(c.usage.is_none(), "error 帧不应带出 usage");
        let err = c.error.expect("error 帧应映射为 Some(msg)");
        assert_eq!(err, "context length exceeded");
        // 累加器保持原值（未被覆盖/清空）
        let acc = acc.expect("累加器应保留先前 usage 不受 error 影响");
        assert_eq!(acc.prompt_tokens, 10);
        assert_eq!(acc.completion_tokens, 20);
    }

    /// error 无 message 字段 → 兜底 "stream error" 字符串
    #[test]
    fn openai_sse_midstream_error_without_message_falls_back() {
        let mut acc: Option<TokenUsage> = None;
        // error 形态异常（只有 type，无 message）
        let data = r#"{"choices":[],"error":{"type":"server_error"}}"#;
        let c = apply_openai_sse(data, &mut acc);
        assert!(!c.finished);
        assert_eq!(c.error.as_deref(), Some("stream error"), "无 message 字段应兜底");
    }

    // ---------- 多模态 convert_request ----------

    /// 含图消息 → content 数组（text + image_url data URI）；纯文本 → 字符串简写
    #[test]
    fn openai_convert_multimodal_content() {
        let provider = OpenAICompatProvider::new("https://api.openai.com", "k", "gpt-4o");
        let req = CompletionRequest {
            model: "gpt-4o".into(),
            messages: vec![ChatMessage::user_parts(
                "看图",
                vec![
                    crate::provider::ContentPart::image_base64("image/png", "iVBOR"),
                    crate::provider::ContentPart::image_url("https://x/a.png"),
                ],
            )],
            temperature: None,
            max_tokens: None,
            stream: false,
            tools: None,
            tool_choice: None,
            reasoning_content: None,
        };
        let out = provider.convert_request(req);
        let msg = &out.messages[0];
        // content 是数组：[text "看图", image_url(data URI), image_url(http url)]
        let arr = msg.content.as_array().expect("含图 → content 数组");
        assert_eq!(arr.len(), 3);
        assert_eq!(arr[0]["type"], "text");
        assert_eq!(arr[0]["text"], "看图");
        assert_eq!(arr[1]["type"], "image_url");
        assert_eq!(
            arr[1]["image_url"]["url"],
            "data:image/png;base64,iVBOR"
        );
        assert_eq!(arr[2]["image_url"]["url"], "https://x/a.png");
    }

    /// 纯文本消息 → content 仍是字符串简写（无图不数组化，对齐纯文本端点兼容）
    #[test]
    fn openai_convert_text_only_remains_string() {
        let provider = OpenAICompatProvider::new("https://api.openai.com", "k", "gpt-4o");
        let req = CompletionRequest {
            model: "gpt-4o".into(),
            messages: vec![ChatMessage::user("hello")],
            temperature: None,
            max_tokens: None,
            stream: false,
            tools: None,
            tool_choice: None,
            reasoning_content: None,
        };
        let out = provider.convert_request(req);
        let msg = &out.messages[0];
        assert_eq!(msg.content, serde_json::Value::String("hello".into()));
    }

    // ---------- ensure_leading_user（首条非 user/system → 补 user 占位,OpenAI 对称 Anthropic）----------

    /// 首条 assistant → 补 user 占位(对齐 Anthropic)。上游绕过 sanitize 的
    /// 调用方(title/knowledge_inject/工作流节点)可能传入首条 assistant 序列,补占位保留上下文。
    #[test]
    fn openai_ensure_leading_user_first_assistant_gets_placeholder() {
        let provider = OpenAICompatProvider::new("https://api.openai.com", "k", "gpt-4o");
        let req = CompletionRequest {
            model: "gpt-4o".into(),
            messages: vec![
                ChatMessage::assistant("我来帮你"),
                ChatMessage::user("继续"),
            ],
            temperature: None,
            max_tokens: None,
            stream: false,
            tools: None,
            tool_choice: None,
            reasoning_content: None,
        };
        let out = provider.convert_request(req);
        assert_eq!(out.messages.len(), 3, "占位 + 原 2 条");
        assert_eq!(out.messages[0].role.as_str(), "user", "首条应为 user(补占位)");
        assert_eq!(out.messages[1].role.as_str(), "assistant");
        assert_eq!(out.messages[2].role.as_str(), "user");
    }

    /// 正常序列(user 开头)不补占位——零回归。
    #[test]
    fn openai_ensure_leading_user_normal_unchanged() {
        let provider = OpenAICompatProvider::new("https://api.openai.com", "k", "gpt-4o");
        let req = CompletionRequest {
            model: "gpt-4o".into(),
            messages: vec![
                ChatMessage::user("hello"),
                ChatMessage::assistant("hi"),
            ],
            temperature: None,
            max_tokens: None,
            stream: false,
            tools: None,
            tool_choice: None,
            reasoning_content: None,
        };
        let out = provider.convert_request(req);
        assert_eq!(out.messages.len(), 2, "正常序列不补占位");
        assert_eq!(out.messages[0].role.as_str(), "user");
    }

    // ---------- 三元组一致性自愈(P0:治 DeepSeek/OpenAI 400) ----------

    /// 辅助:取 assistant 消息的 tool_call id 列表(发出去的形态)。
    fn openai_tool_call_ids(m: &OpenAiMessage) -> Vec<String> {
        m.tool_calls
            .as_ref()
            .map(|arr| {
                arr.iter()
                    .filter_map(|tc| tc.get("id").and_then(|v| v.as_str()).map(String::from))
                    .collect()
            })
            .unwrap_or_default()
    }

    /// 正常三元组(各 id 闭合)零介入:assistant(tc=[a]) → tool(a) → assistant(tc=[b]) → tool(b)。
    /// 约束铁律:不破正常三元组。
    #[test]
    fn openai_sanitize_keeps_closed_triplets() {
        let provider = OpenAICompatProvider::new("https://api.deepseek.com", "k", "deepseek-chat");
        let req = CompletionRequest {
            model: "deepseek-chat".into(),
            messages: vec![
                ChatMessage::user("查天气"),
                ChatMessage::assistant_with_tools(
                    "调用中",
                    vec![ToolCall::new("call_a", "get_weather", "{}")],
                ),
                ChatMessage::tool_result("call_a", "晴"),
                ChatMessage::assistant_with_tools(
                    "再查",
                    vec![ToolCall::new("call_b", "get_weather", "{}")],
                ),
                ChatMessage::tool_result("call_b", "雨"),
            ],
            temperature: None,
            max_tokens: None,
            stream: false,
            tools: None,
            tool_choice: None,
            reasoning_content: None,
        };
        let out = provider.convert_request(req);
        // 5 条全保留(正常三元组不剥不丢)。
        assert_eq!(out.messages.len(), 5, "正常三元组零介入,不应剥/丢任何消息");
        // 两个 assistant 头的 tool_calls 完整保留。
        let heads: Vec<&OpenAiMessage> = out
            .messages
            .iter()
            .filter(|m| m.role == "assistant")
            .collect();
        assert_eq!(openai_tool_call_ids(heads[0]), vec!["call_a".to_string()]);
        assert_eq!(openai_tool_call_ids(heads[1]), vec!["call_b".to_string()]);
    }

    /// 末尾 assistant tool_calls 无 result(残末尾)→ 剥离 tool_calls(保留 assistant 文本)。
    /// 防 "insufficient tool messages" 400。
    #[test]
    fn openai_sanitize_strips_tail_unresolved_tool_calls() {
        let provider = OpenAICompatProvider::new("https://api.deepseek.com", "k", "deepseek-chat");
        let req = CompletionRequest {
            model: "deepseek-chat".into(),
            messages: vec![
                ChatMessage::user("查天气"),
                ChatMessage::assistant_with_tools(
                    "调工具但 result 还没回来",
                    vec![ToolCall::new("call_x", "get_weather", "{}")],
                ),
            ],
            temperature: None,
            max_tokens: None,
            stream: false,
            tools: None,
            tool_choice: None,
            reasoning_content: None,
        };
        let out = provider.convert_request(req);
        // assistant 保留(content 不丢),但 tool_calls 被剥。
        let asst = out
            .messages
            .iter()
            .find(|m| m.role == "assistant")
            .expect("assistant 应保留");
        assert!(
            asst.tool_calls.is_none(),
            "未闭合 tool_calls 应被剥离"
        );
    }

    /// orphan tool_result(无配对 assistant tool_calls 头)→ 丢弃。
    /// 直构造/DB 残留路径绕过 ContextManager::sanitize_messages 时由本守卫兜底。
    /// 防 "Messages with role tool must be a response to a preceding message with tool_calls" 400。
    #[test]
    fn openai_sanitize_drops_orphan_tool_result_no_head() {
        let provider = OpenAICompatProvider::new("https://api.deepseek.com", "k", "deepseek-chat");
        let req = CompletionRequest {
            model: "deepseek-chat".into(),
            messages: vec![
                ChatMessage::user("问"),
                // 无头的 orphan tool_result(头被裁剪/丢失)。
                ChatMessage::tool_result("orphan_id", "结果"),
                ChatMessage::assistant("回复"),
            ],
            temperature: None,
            max_tokens: None,
            stream: false,
            tools: None,
            tool_choice: None,
            reasoning_content: None,
        };
        let out = provider.convert_request(req);
        // orphan tool 被丢弃,剩 user + assistant。
        let tools: Vec<&OpenAiMessage> = out
            .messages
            .iter()
            .filter(|m| m.role == "tool")
            .collect();
        assert!(tools.is_empty(), "无配对头的 orphan tool_result 应丢弃, 实际 {:?}", tools);
        assert_eq!(out.messages.len(), 2, "应剩 user + assistant");
    }

    /// assistant tool_calls 剥离后,对应 orphan tool_result 同步丢弃(一致性)。
    /// 场景:assistant(tc=[a,b]) → tool(a)(b 的 result 丢失)。旧逻辑因下一条是 tool
    /// 不剥 → 发出未闭合 b → 400。新逻辑按 id 精确配对:剥 b(保留 a),tool(a) 保留。
    #[test]
    fn openai_sanitize_partial_triplet_strips_unresolved_id() {
        let provider = OpenAICompatProvider::new("https://api.deepseek.com", "k", "deepseek-chat");
        let req = CompletionRequest {
            model: "deepseek-chat".into(),
            messages: vec![
                ChatMessage::user("问"),
                ChatMessage::assistant_with_tools(
                    "调两工具",
                    vec![
                        ToolCall::new("call_a", "tool_a", "{}"),
                        ToolCall::new("call_b", "tool_b", "{}"),
                    ],
                ),
                // 只回了 call_a,call_b 的 result 丢失。
                ChatMessage::tool_result("call_a", "a 结果"),
            ],
            temperature: None,
            max_tokens: None,
            stream: false,
            tools: None,
            tool_choice: None,
            reasoning_content: None,
        };
        let out = provider.convert_request(req);
        let asst = out
            .messages
            .iter()
            .find(|m| m.role == "assistant")
            .expect("assistant 应保留");
        // 只保留 call_a(已闭合),剥 call_b(未闭合)。
        assert_eq!(
            openai_tool_call_ids(asst),
            vec!["call_a".to_string()],
            "部分闭合头应只留已闭合 call_a, 剥未闭合 call_b"
        );
        // tool(call_a) 保留(有配对头)。
        let tools: Vec<&OpenAiMessage> = out
            .messages
            .iter()
            .filter(|m| m.role == "tool")
            .collect();
        assert_eq!(tools.len(), 1, "call_a 的 tool_result 应保留");
    }

    /// 全未闭合三元组:assistant(tc=[a]) 但全程无 tool(a) → 剥 tool_calls,
    /// 且不残留任何 orphan tool(本就无 tool 消息)。
    #[test]
    fn openai_sanitize_fully_unresolved_strips_all() {
        let provider = OpenAICompatProvider::new("https://api.deepseek.com", "k", "deepseek-chat");
        let req = CompletionRequest {
            model: "deepseek-chat".into(),
            messages: vec![
                ChatMessage::user("问"),
                ChatMessage::assistant_with_tools(
                    "调工具无结果",
                    vec![
                        ToolCall::new("call_y", "tool_y", "{}"),
                        ToolCall::new("call_z", "tool_z", "{}"),
                    ],
                ),
                ChatMessage::assistant("纯文本续"),
            ],
            temperature: None,
            max_tokens: None,
            stream: false,
            tools: None,
            tool_choice: None,
            reasoning_content: None,
        };
        let out = provider.convert_request(req);
        let heads: Vec<&OpenAiMessage> = out
            .messages
            .iter()
            .filter(|m| m.role == "assistant")
            .collect();
        // 第一个 assistant(原含 tool_calls)应被剥空;第二个纯文本不变。
        assert!(
            heads[0].tool_calls.is_none(),
            "全未闭合 tool_calls 应全部剥离"
        );
        assert!(heads[1].tool_calls.is_none(), "纯文本 assistant 无 tool_calls");
    }

    /// 无 tool_call_id 的 tool 消息(异常数据)→ 丢弃(发出去必 400)。
    #[test]
    fn openai_sanitize_drops_tool_without_call_id() {
        let provider = OpenAICompatProvider::new("https://api.deepseek.com", "k", "deepseek-chat");
        let mut bad_tool = ChatMessage::tool_result("temp", "结果");
        bad_tool.tool_call_id = None; // 异常:无 id
        let req = CompletionRequest {
            model: "deepseek-chat".into(),
            messages: vec![ChatMessage::user("问"), bad_tool],
            temperature: None,
            max_tokens: None,
            stream: false,
            tools: None,
            tool_choice: None,
            reasoning_content: None,
        };
        let out = provider.convert_request(req);
        let tools: Vec<&OpenAiMessage> = out
            .messages
            .iter()
            .filter(|m| m.role == "tool")
            .collect();
        assert!(
            tools.is_empty(),
            "无 tool_call_id 的 tool 消息应丢弃, 实际 {:?}", tools
        );
    }

    /// CR-空 id：parse_tool_calls 对空 id 按 index 生成 gen_tool_{i} fallback，非空原样。
    /// 根因：SenseNova 等兼容缺陷 provider 发空 tool_call.id，多 tool_call 同 id（空串）
    /// 致 audit/mod.rs:203 seen_ids 去重只留首个 → 所有工具结果路由到首个。
    #[test]
    fn openai_parse_tool_calls_empty_id_fallback_unique() {
        let calls = vec![
            OpenAiToolCallResp {
                id: String::new(),
                call_type: "function".into(),
                function: OpenAiFunctionResp { name: "list_dir".into(), arguments: r#"{"path":"docs"}"#.into() },
            },
            OpenAiToolCallResp {
                id: String::new(),
                call_type: "function".into(),
                function: OpenAiFunctionResp { name: "list_dir".into(), arguments: r#"{"path":"crates"}"#.into() },
            },
            OpenAiToolCallResp {
                id: "call_abc123".into(),
                call_type: "function".into(),
                function: OpenAiFunctionResp { name: "read_file".into(), arguments: r#"{"path":"根"}"#.into() },
            },
        ];
        let parsed = OpenAICompatProvider::parse_tool_calls(calls);
        assert_eq!(parsed.len(), 3);
        // 空 id → fallback（按 index），保证唯一
        assert_eq!(parsed[0].id, "gen_tool_0");
        assert_eq!(parsed[1].id, "gen_tool_1");
        // 非空 id 原样透传
        assert_eq!(parsed[2].id, "call_abc123");
        // name/args 透传无损
        assert_eq!(parsed[0].function.name, "list_dir");
        assert_eq!(parsed[1].function.arguments, r#"{"path":"crates"}"#);
        // 关键：所有 id 互异（去重后不丢工具）
        let mut ids: Vec<&str> = parsed.iter().map(|c| c.id.as_str()).collect();
        ids.sort();
        let unique: Vec<&str> = {
            let mut u = ids.clone();
            u.dedup();
            u
        };
        assert_eq!(ids.len(), unique.len(), "id 应全部唯一，实际 {:?}", ids);
    }

    /// CR-空 id 流式：SSE chunk 携带 `"id":""`（SenseNova 兼容缺陷）→ ToolCallDelta.id
    /// 转为 `gen_stream_{index}` fallback（非 None），保证下游 accumulate_tool_calls 写入
    /// draft.id 非空。chunk 完全无 id 字段（None）保持 None（OpenAI 协议：仅首 chunk 有 id，
    /// 后续 chunk 无 id 不应覆盖首 chunk 权威 id），由 agentic 转换点兜底。
    #[test]
    fn openai_stream_chunk_empty_id_fallback() {
        let mut acc: Option<TokenUsage> = None;
        // chunk 1: tool_call index=0, id="" → fallback gen_stream_0
        let data1 = r#"{"choices":[{"delta":{"tool_calls":[{"index":0,"id":"","type":"function","function":{"name":"list_dir","arguments":"{\"path\":\"docs\"}"}}]}}]}"#;
        let c1 = apply_openai_sse(data1, &mut acc);
        let tc1 = c1.tool_calls.as_ref().expect("应有 tool_calls").first().unwrap();
        assert_eq!(tc1.index, 0);
        assert_eq!(tc1.id.as_deref(), Some("gen_stream_0"), "空 id 应转 fallback");

        // chunk 2: tool_call index=1, id="" → fallback gen_stream_1（与 index=0 不同，唯一）
        let data2 = r#"{"choices":[{"delta":{"tool_calls":[{"index":1,"id":"","type":"function","function":{"name":"read_file","arguments":""}}]}}]}"#;
        let c2 = apply_openai_sse(data2, &mut acc);
        let tc2 = c2.tool_calls.as_ref().expect("应有 tool_calls").first().unwrap();
        assert_eq!(tc2.id.as_deref(), Some("gen_stream_1"), "不同 index fallback 应不同");

        // chunk 3: tool_call index=0, 无 id 字段（None）→ 保持 None（不覆盖首 chunk）
        let data3 = r#"{"choices":[{"delta":{"tool_calls":[{"index":0,"function":{"arguments":"更多参数"}}]}}]}"#;
        let c3 = apply_openai_sse(data3, &mut acc);
        let tc3 = c3.tool_calls.as_ref().expect("应有 tool_calls").first().unwrap();
        assert!(tc3.id.is_none(), "无 id 字段 chunk 应保持 None，不覆盖首 chunk 权威 id");

        // chunk 4: tool_call 非空 id → 原样透传
        let data4 = r#"{"choices":[{"delta":{"tool_calls":[{"index":2,"id":"call_xyz","type":"function","function":{"name":"write"}}]}}]}"#;
        let c4 = apply_openai_sse(data4, &mut acc);
        let tc4 = c4.tool_calls.as_ref().expect("应有 tool_calls").first().unwrap();
        assert_eq!(tc4.id.as_deref(), Some("call_xyz"), "非空 id 原样透传");
    }
}