新增: 初始化 DevFlow 项目仓库
Tauri 2 + Vue 3 + Vite 6 桌面应用，Rust workspace 含 13 个 crate (df-ai / df-storage / df-workflow / df-core / df-execute 等)。 核心能力：AI 聊天 agentic 循环(工具调用+人工审批)、工作流引擎、 任务/想法/项目/阶段管理、可追溯性，及配套前端组件。
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410
crates/df-ai/src/openai_compat.rs
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410
crates/df-ai/src/openai_compat.rs
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//! 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::pin::Pin;
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use async_trait::async_trait;
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use eventsource_stream::Eventsource;
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use futures::{Stream, StreamExt};
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use reqwest::Client;
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use serde::{Deserialize, Serialize};
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use tracing::{debug, error, info, warn};
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use crate::provider::{
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CompletionRequest, CompletionResponse, LlmProvider, ProviderFeatures, StreamChunk, StreamResult,
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TokenUsage, ToolCall, ToolCallDelta,
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};
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// ============================================================
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// OpenAI API 请求/响应结构体
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// ============================================================
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/// OpenAI 兼容请求体
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#[derive(Debug, Serialize)]
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struct OpenAiRequest {
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model: String,
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messages: Vec<OpenAiMessage>,
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#[serde(skip_serializing_if = "Option::is_none")]
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temperature: Option<f32>,
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#[serde(skip_serializing_if = "Option::is_none")]
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max_tokens: Option<u32>,
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stream: bool,
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#[serde(skip_serializing_if = "Option::is_none")]
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tools: Option<Vec<serde_json::Value>>,
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#[serde(skip_serializing_if = "Option::is_none")]
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tool_choice: Option<serde_json::Value>,
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}
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/// OpenAI 消息格式
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#[derive(Debug, Serialize, Deserialize)]
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struct OpenAiMessage {
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role: String,
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content: String,
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#[serde(skip_serializing_if = "Option::is_none")]
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tool_call_id: Option<String>,
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#[serde(skip_serializing_if = "Option::is_none")]
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tool_calls: Option<Vec<serde_json::Value>>,
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}
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/// OpenAI 同步响应
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#[derive(Debug, Deserialize)]
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struct OpenAiResponse {
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choices: Vec<OpenAiChoice>,
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model: String,
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usage: Option<OpenAiUsage>,
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}
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#[derive(Debug, Deserialize)]
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struct OpenAiChoice {
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message: OpenAiMessageResp,
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finish_reason: Option<String>,
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}
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#[derive(Debug, Deserialize)]
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struct OpenAiMessageResp {
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content: Option<String>,
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tool_calls: Option<Vec<OpenAiToolCallResp>>,
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}
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#[derive(Debug, Deserialize)]
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struct OpenAiToolCallResp {
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id: String,
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#[serde(rename = "type")]
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call_type: String,
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function: OpenAiFunctionResp,
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}
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#[derive(Debug, Deserialize)]
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struct OpenAiFunctionResp {
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name: String,
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arguments: String,
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}
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#[derive(Debug, Deserialize)]
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struct OpenAiUsage {
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prompt_tokens: u32,
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completion_tokens: u32,
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total_tokens: u32,
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}
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/// SSE 流式响应 chunk
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#[derive(Debug, Deserialize)]
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struct OpenAiStreamChunk {
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choices: Vec<OpenAiStreamChoice>,
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}
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#[derive(Debug, Deserialize)]
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struct OpenAiStreamChoice {
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delta: OpenAiStreamDelta,
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finish_reason: Option<String>,
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}
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#[derive(Debug, Deserialize)]
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struct OpenAiStreamDelta {
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content: Option<String>,
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tool_calls: Option<Vec<OpenAiStreamToolCall>>,
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}
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#[derive(Debug, Deserialize)]
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struct OpenAiStreamToolCall {
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index: u32,
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id: Option<String>,
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function: Option<OpenAiStreamFunction>,
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}
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#[derive(Debug, Deserialize)]
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struct OpenAiStreamFunction {
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name: Option<String>,
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arguments: Option<String>,
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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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// connect_timeout 防连接阶段无限 hang（网络静默断）；不设总 timeout——
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// reqwest 的 .timeout() 会限制整个响应 body 时长，流式长生成任务会被误砍。
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// 读取阶段的中途静默由上层 stream_llm 的 idle timeout 兜底。
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let client = Client::builder()
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.connect_timeout(std::time::Duration::from_secs(30))
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.build()
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.unwrap_or_else(|e| {
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warn!("reqwest builder 失败，降级为默认 client（无 connect_timeout）: {}", e);
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Client::new()
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});
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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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/// 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 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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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: m.content,
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tool_call_id: m.tool_call_id,
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tool_calls,
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}
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})
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.collect();
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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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}
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}
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/// 解析同步响应中的工具调用
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fn parse_tool_calls(calls: Vec<OpenAiToolCallResp>) -> Vec<ToolCall> {
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calls
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.into_iter()
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.map(|c| ToolCall::new(c.id, c.function.name, c.function.arguments))
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.collect()
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}
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}
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#[async_trait]
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impl LlmProvider for OpenAICompatProvider {
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async fn complete(&self, request: CompletionRequest) -> anyhow::Result<CompletionResponse> {
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let mut req = request;
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req.stream = false;
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let openai_req = self.convert_request(req);
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debug!(model = %openai_req.model, "OpenAI 同步调用");
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let resp = self
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.client
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.post(self.chat_url())
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.header("Authorization", format!("Bearer {}", self.api_key))
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.header("Content-Type", "application/json")
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.json(&openai_req)
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.send()
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.await?;
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if !resp.status().is_success() {
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let status = resp.status();
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let body = resp.text().await.unwrap_or_default();
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error!(%status, %body, "LLM API 调用失败");
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anyhow::bail!("LLM API 错误 {}: {}", status, body);
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}
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let body: OpenAiResponse = resp.json().await?;
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let choice = body
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.choices
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.into_iter()
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.next()
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.ok_or_else(|| anyhow::anyhow!("LLM 响应无 choices"))?;
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let text = choice.message.content.unwrap_or_default();
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let tool_calls = choice.message.tool_calls.map(Self::parse_tool_calls);
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let usage = body.usage.map(|u| TokenUsage {
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prompt_tokens: u.prompt_tokens,
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completion_tokens: u.completion_tokens,
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total_tokens: u.total_tokens,
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}).unwrap_or(TokenUsage {
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prompt_tokens: 0,
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completion_tokens: 0,
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total_tokens: 0,
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});
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Ok(CompletionResponse {
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text,
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model: body.model,
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usage,
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tool_calls,
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})
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}
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async fn stream(&self, request: CompletionRequest) -> anyhow::Result<StreamResult> {
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let mut req = request;
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req.stream = true;
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let openai_req = self.convert_request(req);
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debug!(model = %openai_req.model, "OpenAI 流式调用");
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let resp = self
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.client
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.post(self.chat_url())
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.header("Authorization", format!("Bearer {}", self.api_key))
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.header("Content-Type", "application/json")
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.json(&openai_req)
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.send()
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.await?;
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if !resp.status().is_success() {
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let status = resp.status();
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let body = resp.text().await.unwrap_or_default();
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error!(%status, %body, "LLM 流式 API 调用失败");
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anyhow::bail!("LLM 流式 API 错误 {}: {}", status, body);
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}
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let stream = resp
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.bytes_stream()
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.eventsource()
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.map(move |event| {
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match event {
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Ok(event) => {
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// OpenAI 发送 "data: [DONE]" 表示流结束
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if event.data == "[DONE]" {
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return Ok(StreamChunk {
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delta: String::new(),
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finished: true,
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tool_calls: None,
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});
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}
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match serde_json::from_str::<OpenAiStreamChunk>(&event.data) {
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Ok(chunk) => {
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if let Some(choice) = chunk.choices.into_iter().next() {
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let delta_text = choice.delta.content.unwrap_or_default();
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// "length" = max_tokens 截断，属正常终止（非断连），纳入 finished
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let finished = choice.finish_reason.as_deref() == Some("stop")
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|| choice.finish_reason.as_deref() == Some("tool_calls")
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|| choice.finish_reason.as_deref() == Some("length");
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let tool_calls = choice.delta.tool_calls.map(|tcs| {
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tcs.into_iter()
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.map(|tc| ToolCallDelta {
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index: tc.index,
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id: tc.id,
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function_name: tc.function.as_ref().and_then(|f| f.name.clone()),
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function_arguments: tc.function.and_then(|f| f.arguments),
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})
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.collect()
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});
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Ok(StreamChunk {
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delta: delta_text,
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finished,
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tool_calls,
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})
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} else {
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Ok(StreamChunk {
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delta: String::new(),
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finished: false,
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tool_calls: None,
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})
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}
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}
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Err(e) => {
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debug!("SSE 数据解析失败: {} — data: {}", e, event.data);
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Ok(StreamChunk {
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delta: String::new(),
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finished: false,
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tool_calls: None,
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})
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}
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}
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}
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Err(e) => {
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error!("SSE 流错误: {}", e);
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Err(anyhow::anyhow!("SSE 流错误: {}", e))
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}
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}
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});
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Ok(Box::pin(stream))
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}
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fn name(&self) -> &str {
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&self.default_model
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}
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fn supported_features(&self) -> ProviderFeatures {
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ProviderFeatures {
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streaming: true,
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function_calling: true,
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vision: false,
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}
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}
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}
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Block a user