refactor: 解耦 Provider Adapter 继承关系
问题：AgnesAdapter extends DeepSeekAdapter 在架构上不合理。 DeepSeek、Agnes AI、Ollama 是三个完全不同的 API，不应有继承关系。 重构： BaseAdapter ├── DeepSeekAdapter (独立) ├── AgnesAdapter (独立) └── OllamaAdapter (独立) 变更： - 新增 shared/openai-format.ts — 提取 OpenAI 兼容消息/工具格式构建 - 新增 shared/sse-stream.ts — 提取 SSE 流式解析逻辑 - DeepSeekAdapter 重写为独立继承 BaseAdapter，使用共享工具 - AgnesAdapter 重写为独立继承 BaseAdapter，使用共享工具 - OllamaAdapter 无需变更（原本就独立继承） - adapters/index.ts 清理导出，移除隐式耦合
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/**
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* OpenAI 兼容 API 格式构建工具
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*
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* 将 MetonaRequest 转换为 OpenAI /chat/completions 兼容的原生请求格式。
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* DeepSeek 和 Agnes AI 共享此工具，各自 Adapter 只需处理 Provider 特有的差异参数。
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*
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* @see electron/harness/types/metona-request.ts — MetonaRequest 定义
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* @see apis/deepseek-api-docs-20260518.html
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* @see apis/agnes-ai-api-docs-20260625.html
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*/
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import type { MetonaRequest, MetonaToolDef } from '../../types';
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/**
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* 构建 OpenAI 兼容的 messages 数组
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*
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* 处理：
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* - System Prompt 拼接（静态区 + 动态区 + 安全准则）
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* - 图片 → 多模态 content 数组 [{type:"text"}, {type:"image_url"}]
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* - 工具调用历史保留（reasoning_content + tool_calls）
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* - 工具结果注入（tool_call_id + content）
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*/
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export function buildOpenAICompatibleMessages(
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request: MetonaRequest,
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): Array<Record<string, unknown>> {
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const systemContent = [
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request.systemPrompt.roleDefinition,
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request.systemPrompt.outputConstraints,
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request.systemPrompt.safetyGuidelines,
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request.systemPrompt.dynamicReminders,
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]
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.filter(Boolean)
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.join('\n\n');
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const nonSystemMessages = request.messages
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.filter((m) => m.role !== 'system')
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.map((m) => {
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const msg: Record<string, unknown> = { role: m.role };
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// === 多模态图片处理 ===
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if (m.images && m.images.length > 0) {
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const contentParts: Array<Record<string, unknown>> = [];
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if (m.content) {
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contentParts.push({ type: 'text', text: m.content });
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}
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for (const img of m.images) {
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contentParts.push({
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type: 'image_url',
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image_url: { url: img.url, detail: img.detail ?? 'auto' },
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});
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}
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msg.content = contentParts;
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} else {
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msg.content = m.content;
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}
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// === Assistant 工具调用历史 ===
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if (m.role === 'assistant' && m.toolCalls?.length) {
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msg.tool_calls = m.toolCalls.map((tc) => ({
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id: tc.id,
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type: 'function',
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function: { name: tc.name, arguments: JSON.stringify(tc.args) },
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}));
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// 推理内容必须带回上下文（否则 LLM 丢失思考链）
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if (m.reasoningContent) {
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msg.reasoning_content = m.reasoningContent;
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}
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}
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// === 工具执行结果 ===
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if (m.role === 'tool' && m.toolResult) {
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msg.tool_call_id = m.toolResult.toolCallId;
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msg.content =
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typeof m.toolResult.result === 'string'
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? m.toolResult.result
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: JSON.stringify(m.toolResult.result);
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}
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return msg;
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});
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return [{ role: 'system', content: systemContent }, ...nonSystemMessages];
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}
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/**
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* 构建 OpenAI 兼容的 tools 数组
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*/
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export function buildOpenAICompatibleTools(
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tools?: MetonaToolDef[],
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): Array<Record<string, unknown>> | undefined {
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if (!tools?.length) return undefined;
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return tools.map((t) => ({
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type: 'function',
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function: {
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name: t.name,
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description: t.description,
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parameters: t.parameters,
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},
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}));
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}
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/**
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* SSE 流式解析工具
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*
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* 解析 OpenAI 兼容的 Server-Sent Events (SSE) 流式响应，
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* 产出 MetonaStreamEvent。DeepSeek 和 Agnes AI 共享此工具。
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*
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* SSE 格式：data: {json}\n\n
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* 结束标记：data: [DONE]
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*/
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import { nanoid } from 'nanoid';
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import type { MetonaStreamEvent, MetonaTokenUsage } from '../../types';
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import { MetonaStreamEventType } from '../../types';
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/**
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* 解析 OpenAI 兼容 SSE 流式响应
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*
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* @param responseBody - fetch Response.body (ReadableStream<Uint8Array>)
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* @param requestId - 对应的请求 ID
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* @param sessionId - 会话 ID
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* @param iteration - 当前迭代轮次
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* @yields MetonaStreamEvent
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*/
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export async function* parseSSEStream(
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responseBody: ReadableStream<Uint8Array>,
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requestId: string,
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sessionId: string,
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iteration: number,
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): AsyncGenerator<MetonaStreamEvent> {
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const reader = responseBody.getReader();
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const decoder = new TextDecoder();
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let seq = 0;
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let buffer = '';
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// 工具调用缓冲区：index → { name, argsBuffer }
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const toolCallsBuffer = new Map<number, { name: string; argsBuffer: string }>();
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while (true) {
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const { done, value } = await reader.read();
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if (done) break;
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buffer += decoder.decode(value, { stream: true });
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const lines = buffer.split('\n');
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buffer = lines.pop() ?? '';
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for (const line of lines) {
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const trimmed = line.trim();
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if (!trimmed || !trimmed.startsWith('data: ')) continue;
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const data = trimmed.slice(6);
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// 流结束
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if (data === '[DONE]') {
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// 将缓冲区中未完成拼接的工具调用发送
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for (const [index, buf] of toolCallsBuffer) {
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try {
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yield {
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type: MetonaStreamEventType.TOOL_CALL_COMPLETE,
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requestId,
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sessionId,
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iteration,
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seq: seq++,
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timestamp: Date.now(),
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toolCall: {
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id: `tc_${nanoid(8)}`,
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name: buf.name,
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args: buf.argsBuffer ? JSON.parse(buf.argsBuffer) : {},
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iteration,
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timestamp: Date.now(),
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},
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};
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} catch {
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// JSON 解析失败，跳过
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}
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}
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toolCallsBuffer.clear();
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yield {
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type: MetonaStreamEventType.DONE,
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requestId,
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sessionId,
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iteration,
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seq: seq++,
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timestamp: Date.now(),
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};
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return;
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}
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try {
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const chunk = JSON.parse(data);
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const delta = chunk.choices?.[0]?.delta;
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// 文本内容增量
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if (delta?.content) {
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yield {
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type: MetonaStreamEventType.TEXT_DELTA,
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requestId,
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sessionId,
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iteration,
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seq: seq++,
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timestamp: Date.now(),
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delta: delta.content,
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};
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}
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// 推理内容增量（Thinking 模式）
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if (delta?.reasoning_content) {
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yield {
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type: MetonaStreamEventType.REASONING_DELTA,
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requestId,
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sessionId,
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iteration,
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seq: seq++,
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timestamp: Date.now(),
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delta: delta.reasoning_content,
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};
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}
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// 工具调用增量 — 缓冲拼接
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if (delta?.tool_calls) {
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for (const tc of delta.tool_calls) {
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const idx = tc.index ?? 0;
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if (!toolCallsBuffer.has(idx)) {
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toolCallsBuffer.set(idx, { name: tc.function?.name ?? '', argsBuffer: '' });
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}
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const buf = toolCallsBuffer.get(idx)!;
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if (tc.function?.name) buf.name = tc.function.name;
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if (tc.function?.arguments) buf.argsBuffer += tc.function.arguments;
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yield {
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type: MetonaStreamEventType.TOOL_CALL_DELTA,
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requestId,
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sessionId,
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iteration,
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seq: seq++,
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timestamp: Date.now(),
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toolCallDelta: {
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index: idx,
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name: tc.function?.name,
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argsDelta: tc.function?.arguments,
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},
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};
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}
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}
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// Token 使用统计
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if (chunk.usage) {
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const usage: MetonaTokenUsage = {
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inputTokens: chunk.usage.prompt_tokens ?? 0,
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outputTokens: chunk.usage.completion_tokens ?? 0,
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totalTokens: chunk.usage.total_tokens ?? 0,
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reasoningTokens: chunk.usage.completion_tokens_details?.reasoning_tokens,
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cacheHitTokens: chunk.usage.prompt_cache_hit_tokens,
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cacheMissTokens: chunk.usage.prompt_cache_miss_tokens,
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};
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yield {
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type: MetonaStreamEventType.USAGE,
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requestId,
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sessionId,
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iteration,
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seq: seq++,
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timestamp: Date.now(),
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usage,
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};
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}
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} catch {
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// 跳过解析失败的行
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}
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}
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}
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}
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/**
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* 解析 OpenAI 兼容的非流式 JSON 响应 → MetonaResponse
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*/
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export function parseOpenAICompatibleResponse(
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data: Record<string, unknown>,
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requestId: string,
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provider: string,
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defaultModel: string,
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): {
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content: string;
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reasoningContent?: string;
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toolCalls?: Array<{ id: string; name: string; args: Record<string, unknown>; iteration: number; timestamp: number }>;
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finishReason: string;
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usage: MetonaTokenUsage;
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} {
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const choice = (data.choices as Array<Record<string, unknown>>)?.[0];
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const message = choice?.message as Record<string, unknown> | undefined;
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const usage = data.usage as Record<string, unknown> | undefined;
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const rawToolCalls = message?.tool_calls as Array<Record<string, unknown>> | undefined;
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return {
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content: (message?.content as string) ?? '',
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reasoningContent: message?.reasoning_content as string | undefined,
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toolCalls: rawToolCalls?.map((tc) => {
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const fn = tc.function as Record<string, unknown>;
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return {
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id: tc.id as string,
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name: fn.name as string,
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args: JSON.parse(fn.arguments as string),
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iteration: 0,
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timestamp: Date.now(),
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};
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}),
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finishReason: mapOpenAIFinishReason(choice?.finish_reason as string),
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usage: {
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inputTokens: (usage?.prompt_tokens as number) ?? 0,
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outputTokens: (usage?.completion_tokens as number) ?? 0,
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totalTokens: (usage?.total_tokens as number) ?? 0,
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reasoningTokens: (usage?.completion_tokens_details as Record<string, unknown>)?.reasoning_tokens as number | undefined,
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cacheHitTokens: usage?.prompt_cache_hit_tokens as number | undefined,
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cacheMissTokens: usage?.prompt_cache_miss_tokens as number | undefined,
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},
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};
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}
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function mapOpenAIFinishReason(reason: string): string {
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switch (reason) {
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case 'stop': return 'stop';
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case 'length': return 'length';
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case 'tool_calls': return 'tool_calls';
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case 'content_filter': return 'content_filter';
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default: return 'stop';
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}
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}
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