lxy
57d6a2d066
治 5 个对话停止/工具失败根因:sanitize 三元组按 id 配对治 400;G2 探索熔断从结果空 改签名重复判定(治误停正常探索);handshake 删越权强杀活 loop(generating 归 guard 单源); 空 tool_call id 兜底 gen_<index>(治 SenseNova 工具结果路由错位)。
1246 lines
54 KiB
Rust
1246 lines
54 KiB
Rust
//! OpenAI 兼容 Provider — 通过 /v1/chat/completions 端点实现
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//!
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//! 覆盖: OpenAI / GLM (open.bigmodel.cn) / DeepSeek / Claude OpenAI 兼容模式
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//! 支持: 同步调用 + SSE 流式 + Function Calling / Tool Use
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use std::time::Duration;
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use async_trait::async_trait;
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use futures::StreamExt;
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use reqwest::Client;
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use tracing::{debug, error, warn};
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use crate::provider::{
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tool_call_id_or_fallback, CompletionRequest, CompletionResponse, LlmProvider, StreamResult,
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TokenUsage, ToolCall,
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};
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// ChatMessage 仅单测构造 CompletionRequest 用，避免非 test 构建的 unused import 警告。
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#[cfg(test)]
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use crate::provider::ChatMessage;
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use crate::retry::{
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retry_with_backoff, AttemptOutcome, is_reqwest_error_retryable, is_status_retryable,
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};
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// 协议数据结构（OpenAiRequest / OpenAiResponse / OpenAiStreamChunk …）与 SSE 解析纯函数
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// apply_openai_sse 抽离到 openai_helpers.rs，结构对齐 anthropic_helpers.rs。
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// Rust impl 块不可跨文件，Provider struct + impl 仍留本文件。
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use crate::openai_helpers::{
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apply_openai_sse, OpenAiMessage, OpenAiRequest, OpenAiResponse, OpenAiToolCallResp,
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};
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// ============================================================
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// OpenAI Compat Provider
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// ============================================================
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/// OpenAI 兼容 LLM Provider
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pub struct OpenAICompatProvider {
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client: Client,
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api_key: String,
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base_url: String,
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default_model: String,
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}
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impl OpenAICompatProvider {
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/// 创建 Provider
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///
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/// - `base_url`: 如 "https://api.openai.com", "https://open.bigmodel.cn/api/paas", "https://api.deepseek.com"
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/// - `api_key`: API 密钥
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/// - `default_model`: 默认模型名称
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pub fn new(base_url: impl Into<String>, api_key: impl Into<String>, default_model: impl Into<String>) -> Self {
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// SW-260618-10: reqwest Client 构建抽 crate::build_provider_client（与 Anthropic 共用 DRY）。
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// connect_timeout/回退策略集中此处，未来改一处即可（见 lib.rs::build_provider_client）。
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let client = crate::build_provider_client();
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Self {
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client,
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api_key: api_key.into(),
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base_url: base_url.into(),
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default_model: default_model.into(),
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}
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}
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/// 构建完整 API URL
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///
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/// 智能拼接，兼容三种 base_url 约定：
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/// - 已含完整端点（…/chat/completions）→ 直接用
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/// - 已含版本段（…/v1 …/v4 等，如 GLM 的 /api/paas/v4）→ 补 /chat/completions
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/// - 仅域名无版本（如 api.openai.com / api.deepseek.com）→ 补 /v1/chat/completions（OpenAI 约定）
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fn chat_url(&self) -> String {
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let base = self.base_url.trim_end_matches('/');
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if base.ends_with("/chat/completions") {
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return base.to_string();
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}
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if Self::ends_with_version(base) {
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return format!("{}/chat/completions", base);
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}
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format!("{}/v1/chat/completions", base)
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}
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/// base_url 是否以 `/v<数字>` 结尾（如 /v1 /v4）
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fn ends_with_version(base: &str) -> bool {
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match base.rsplit_once('/') {
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Some((_, last)) if last.starts_with('v') && last.len() > 1 => {
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last[1..].bytes().all(|b| b.is_ascii_digit())
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}
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_ => false,
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}
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}
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/// 构建 embeddings API URL（与 chat_url 同套智能拼接规则）
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fn embed_url(&self) -> String {
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let base = self.base_url.trim_end_matches('/');
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if base.ends_with("/embeddings") {
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return base.to_string();
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}
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if Self::ends_with_version(base) {
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return format!("{}/embeddings", base);
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}
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format!("{}/v1/embeddings", base)
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}
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/// 将通用请求转换为 OpenAI 格式
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fn convert_request(&self, req: CompletionRequest) -> OpenAiRequest {
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let model = if req.model.is_empty() {
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self.default_model.clone()
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} else {
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req.model
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};
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let mut messages: Vec<OpenAiMessage> = req
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.messages
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.into_iter()
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.map(|m| {
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let role = match m.role {
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crate::provider::MessageRole::System => "system",
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crate::provider::MessageRole::User => "user",
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crate::provider::MessageRole::Assistant => "assistant",
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crate::provider::MessageRole::Tool => "tool",
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};
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// 多模态 content（须在 move m.tool_calls 之前算，借用 m）。
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// 含图消息走 content 数组（text/image_url）；纯文本走字符串简写
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// （保持与现有纯文本端点零回归）。image_url 支持 data URI（base64）与 http(s) URL。
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let content = if m.has_image() {
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let parts: Vec<serde_json::Value> = m
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.flattened_parts()
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.into_iter()
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.map(|p| match p {
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crate::provider::ContentPart::Text { text } => serde_json::json!({
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"type": "text",
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"text": text,
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}),
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crate::provider::ContentPart::Image { url, base64, media_type, alt: _ } => {
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let final_url = match (base64, url, media_type) {
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(Some(b), _, Some(mt)) => {
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format!("data:{};base64,{}", mt, b)
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}
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(None, Some(u), _) => u,
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// 完整性兜底：当前 image_base64 构造器强制 media_type:Some，
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// image_url 构造器提供 url:Some，二者分别命中上两个分支；
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// 此分支仅在 parts 来源被外部直接构造且字段均缺时才可达
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//（如 url:None+base64:None 或 base64:Some+media_type:None）。
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// 退化为空串（OpenAI 对空 image_url.url 会 400），
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// 由调用方保证 parts 合法性，provider 层不做静默伪造。
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_ => String::new(),
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};
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serde_json::json!({
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"type": "image_url",
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"image_url": { "url": final_url },
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})
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}
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})
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.collect();
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serde_json::Value::Array(parts)
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} else {
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serde_json::Value::String(m.content.clone())
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};
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let tool_calls = m.tool_calls.map(|calls| {
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calls
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.into_iter()
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.map(|tc| {
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serde_json::json!({
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"id": tc.id,
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"type": tc.call_type,
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"function": {
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"name": tc.function.name,
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"arguments": tc.function.arguments,
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}
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})
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})
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.collect()
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});
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OpenAiMessage {
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role: role.to_string(),
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content,
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tool_call_id: m.tool_call_id,
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tool_calls,
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reasoning_content: m.reasoning_content,
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}
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})
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.collect();
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// 保证首条 user/system(OpenAI 协议要求首条非 assistant/tool)。
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// 对齐 AnthropicCompatProvider::ensure_leading_user:上游绕过 sanitize 的调用方
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// (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
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// 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。
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Self::ensure_leading_user(&mut messages);
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// 治 DeepSeek/OpenAI 400(三元组完整性 P0)。OpenAI 协议铁律:
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// (a) assistant 的每个 tool_call.id 必须有后续 tool(role=tool, tool_call_id 匹配)响应,
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// 否则 "insufficient tool messages" 400(assistant 调了工具但无结果)。
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// (b) 反之,每条 tool 消息必须紧跟一个含 tool_calls(同 tool_call_id)的 assistant,
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// 否则 "Messages with role tool must be a response to a preceding message
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// with tool_calls" 400(tool 无配对头)。
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//
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// 旧逻辑只检查「下一条 role 是否为 tool」(粗粒度),漏两类 orphan:
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// 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
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// 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。
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// 2) orphan tool_result(tool 无前置 assistant tool_calls 配对):DB/直构造路径绕过
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// ContextManager::sanitize_messages(标题/知识注入/工作流节点),tool 残留无头 →
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// 旧逻辑不处理 → 400。修法:剥 assistant tool_calls 时同步丢弃同 id 的 orphan
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// 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。
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//
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// 正常三元组形如:assistant(tc=[a]) → tool(a) → assistant(tc=[b]) → tool(b),各 id 闭合,
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// 本守卫零介入。仅异常截断/恢复/直构造路径触发(防 400 兜底)。
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// 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。
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sanitize_openai_triplets(&mut messages);
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let tools = req.tools.map(|defs| {
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defs.into_iter()
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.map(|d| serde_json::to_value(d).unwrap_or_default())
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.collect()
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});
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OpenAiRequest {
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model,
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messages,
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temperature: req.temperature,
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max_tokens: req.max_tokens,
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stream: req.stream,
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tools,
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tool_choice: req.tool_choice,
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reasoning_content: req.reasoning_content,
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// 流式请求末 chunk 带 usage（同步调用 complete 不需要）
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stream_options: if req.stream {
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Some(serde_json::json!({ "include_usage": true }))
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} else {
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None
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},
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}
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}
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/// 生成 messages 诊断摘要（每条 role + content 形态 + tool 标记），不含敏感数据。
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/// 流中途 error 时附摘要定位哪条非法（对齐 `AnthropicCompatProvider::summarize_messages`）。
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fn summarize_openai_messages(messages: &[OpenAiMessage]) -> String {
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let lines: Vec<String> = messages
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.iter()
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.enumerate()
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.map(|(i, m)| {
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let role = m.role.as_str();
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let desc = match &m.content {
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serde_json::Value::String(s) => format!("text({}B)", s.len()),
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serde_json::Value::Array(blocks) => {
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let parts: Vec<String> = blocks
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.iter()
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.map(|b| {
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let ty = b.get("type").and_then(|t| t.as_str()).unwrap_or("?");
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match ty {
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"text" => format!(
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"text({}B)",
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b.get("text")
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.and_then(|t| t.as_str())
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.map(|s| s.len())
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.unwrap_or(0)
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),
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"image_url" => "image".to_string(),
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_ => ty.to_string(),
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}
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})
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.collect();
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format!("[{}]", parts.join(","))
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}
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_ => "?".to_string(),
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};
|
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let tool_mark = match (&m.tool_calls, &m.tool_call_id) {
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(Some(tcs), _) => format!(" tool_calls={}", tcs.len()),
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(None, Some(tid)) => format!(" tool_result[tid={}]", tid),
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(None, None) => String::new(),
|
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};
|
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format!("#{}:{} {}{}", i, role, desc, tool_mark)
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})
|
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.collect();
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format!("{} msgs: {}", lines.len(), lines.join(" | "))
|
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}
|
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|
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/// 保证 messages 首条为 user/system(OpenAI 协议要求首条非 assistant/tool)。
|
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///
|
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/// 对齐 `AnthropicCompatProvider::ensure_leading_user`。上游绕过 `ContextManager::sanitize_messages`
|
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/// 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
|
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/// 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。
|
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///
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/// **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)。
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/// 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)。
|
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fn ensure_leading_user(messages: &mut Vec<OpenAiMessage>) {
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let first_role = messages.first().map(|m| m.role.as_str()).unwrap_or("");
|
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if first_role == "user" || first_role == "system" {
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return;
|
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}
|
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warn!(
|
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first_role,
|
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msg_count = messages.len(),
|
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"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)"
|
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);
|
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messages.insert(
|
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0,
|
||
OpenAiMessage {
|
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role: "user".into(),
|
||
content: serde_json::Value::String("(continued from previous context)".into()),
|
||
tool_call_id: None,
|
||
tool_calls: None,
|
||
reasoning_content: None,
|
||
},
|
||
);
|
||
}
|
||
|
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/// 解析同步响应中的工具调用。
|
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///
|
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/// 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");
|
||
}
|
||
}
|