refactor(llm): unify LLM access layer via pi-ai

- Refactor llm/index.ts: remove chat()/chatStream() wrappers, inline
  provider info from deleted service files, add buildPiModel() for
  direct pi-ai model construction
- Migrate summary/index.ts to use pi-ai's completeSimple directly
- Migrate rag/pipeline/semantic.ts to use pi-ai's completeSimple directly

electron/main/ai/llm/index.ts

@@ -8,22 +8,19 @@ import * as path from 'path'
 import { randomUUID } from 'crypto'
 import { getAiDataDir } from '../../paths'
 import type {
-  LLMConfig,
   LLMProvider,
-  ILLMService,
   ProviderInfo,
-  ChatMessage,
-  ChatOptions,
-  ChatStreamChunk,
   AIServiceConfig,
   AIConfigStore,
 } from './types'
 import { MAX_CONFIG_COUNT } from './types'
-import { GeminiService, GEMINI_INFO } from './gemini'
-import { OpenAICompatibleService, OPENAI_COMPATIBLE_INFO } from './openai-compatible'
-import { aiLogger, extractErrorInfo, extractErrorStack } from '../logger'
+import { aiLogger } from '../logger'
 import { encryptApiKey, decryptApiKey, isEncrypted } from './crypto'
 import { t } from '../../i18n'
+import {
+  completeSimple,
+  type Model as PiModel,
+} from '@mariozechner/pi-ai'
 
 // 导出类型
 export * from './types'
@@ -110,6 +107,31 @@ const DOUBAO_INFO: ProviderInfo = {
   ],
 }
 
+/** Gemini 提供商信息 */
+const GEMINI_INFO: ProviderInfo = {
+  id: 'gemini',
+  name: 'Gemini',
+  description: 'Google Gemini 大语言模型',
+  defaultBaseUrl: 'https://generativelanguage.googleapis.com',
+  models: [
+    { id: 'gemini-3-flash-preview', name: 'Gemini 3 Flash Preview', description: '高速预览版' },
+    { id: 'gemini-3-pro-preview', name: 'Gemini 3 Pro Preview', description: '专业预览版' },
+  ],
+}
+
+/** OpenAI 兼容提供商信息 */
+const OPENAI_COMPATIBLE_INFO: ProviderInfo = {
+  id: 'openai-compatible',
+  name: 'OpenAI 兼容',
+  description: '支持任何兼容 OpenAI API 的服务（如 Ollama、LocalAI、vLLM 等）',
+  defaultBaseUrl: 'http://localhost:11434/v1',
+  models: [
+    { id: 'llama3.2', name: 'Llama 3.2', description: 'Meta Llama 3.2 模型' },
+    { id: 'qwen2.5', name: 'Qwen 2.5', description: '通义千问 2.5 模型' },
+    { id: 'deepseek-r1', name: 'DeepSeek R1', description: 'DeepSeek R1 推理模型' },
+  ],
+}
+
 // 所有支持的提供商信息
 export const PROVIDERS: ProviderInfo[] = [
   DEEPSEEK_INFO,
@@ -392,15 +414,6 @@ export function hasActiveConfig(): boolean {
   return config !== null
 }
 
-/**
- * 扩展的 LLM 配置（包含本地服务特有选项）
- */
-interface ExtendedLLMConfig extends LLMConfig {
-  disableThinking?: boolean
-  /** 标记为推理模型（如 DeepSeek-R1、QwQ 等） */
-  isReasoningModel?: boolean
-}
-
 /**
  * 不再自动补齐 Base URL，对 DeepSeek/Qwen 的格式做显式校验
  */
@@ -431,72 +444,6 @@ function validateProviderBaseUrl(provider: LLMProvider, baseUrl?: string): void
   }
 }
 
-/**
- * 创建 LLM 服务实例
- */
-export function createLLMService(config: ExtendedLLMConfig): ILLMService {
-  // 获取提供商的默认 baseUrl
-  const providerInfo = getProviderInfo(config.provider)
-  const baseUrl = config.baseUrl || providerInfo?.defaultBaseUrl
-  // 未显式指定时使用提供商的首个模型作为默认模型
-  const resolvedModel = config.model || providerInfo?.models?.[0]?.id
-  // 不自动补齐，发现不合法直接抛错给用户
-  validateProviderBaseUrl(config.provider, baseUrl)
-
-  switch (config.provider) {
-    case 'gemini':
-      return new GeminiService(config.apiKey, resolvedModel, config.baseUrl)
-    // 新增的官方API都使用 OpenAI 兼容格式
-    case 'deepseek':
-    case 'qwen':
-    case 'minimax':
-    case 'glm':
-    case 'kimi':
-    case 'doubao':
-      // DeepSeek/Qwen 走 OpenAI 兼容实现时，禁用本地思考注入
-      return new OpenAICompatibleService(
-        config.apiKey,
-        resolvedModel,
-        baseUrl,
-        config.provider === 'deepseek' || config.provider === 'qwen' ? false : undefined,
-        config.provider,
-        providerInfo?.models
-      )
-    case 'openai-compatible':
-      return new OpenAICompatibleService(
-        config.apiKey,
-        resolvedModel,
-        config.baseUrl,
-        config.disableThinking,
-        config.provider,
-        providerInfo?.models,
-        config.isReasoningModel
-      )
-    default:
-      throw new Error(`Unknown LLM provider: ${config.provider}`)
-  }
-}
-
-/**
- * 获取当前配置的 LLM 服务实例
- */
-export function getCurrentLLMService(): ILLMService | null {
-  const activeConfig = getActiveConfig()
-  if (!activeConfig) {
-    return null
-  }
-
-  return createLLMService({
-    provider: activeConfig.provider,
-    apiKey: activeConfig.apiKey,
-    model: activeConfig.model,
-    baseUrl: activeConfig.baseUrl,
-    maxTokens: activeConfig.maxTokens,
-    disableThinking: activeConfig.disableThinking,
-    isReasoningModel: activeConfig.isReasoningModel,
-  })
-}
-
 /**
  * 获取提供商信息
  */
@@ -504,159 +451,94 @@ export function getProviderInfo(provider: LLMProvider): ProviderInfo | null {
   return PROVIDERS.find((p) => p.id === provider) || null
 }
 
+// ==================== pi-ai Model 构建 ====================
+
 /**
- * 验证 API Key
+ * 将 AIServiceConfig 转换为 pi-ai Model 对象
+ */
+export function buildPiModel(
+  config: AIServiceConfig
+): PiModel<'openai-completions'> | PiModel<'google-generative-ai'> {
+  const providerInfo = getProviderInfo(config.provider)
+  const baseUrl = config.baseUrl || providerInfo?.defaultBaseUrl || ''
+  const modelId = config.model || providerInfo?.models?.[0]?.id || ''
+
+  validateProviderBaseUrl(config.provider, baseUrl)
+
+  if (config.provider === 'gemini') {
+    return {
+      id: modelId,
+      name: modelId,
+      api: 'google-generative-ai',
+      provider: 'google',
+      baseUrl,
+      reasoning: false,
+      input: ['text'],
+      cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
+      contextWindow: 1048576,
+      maxTokens: config.maxTokens ?? 8192,
+    }
+  }
+
+  return {
+    id: modelId,
+    name: modelId,
+    api: 'openai-completions',
+    provider: config.provider,
+    baseUrl,
+    reasoning: config.isReasoningModel ?? false,
+    input: ['text'],
+    cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
+    contextWindow: 128000,
+    maxTokens: config.maxTokens ?? 4096,
+    compat: config.disableThinking ? { thinkingFormat: 'qwen' } : undefined,
+  }
+}
+
+/**
+ * 验证 API Key（基于 pi-ai completeSimple）
+ * 发送一个最小请求来验证 API Key 是否有效
  */
 export async function validateApiKey(
   provider: LLMProvider,
-  apiKey: string
+  apiKey: string,
+  baseUrl?: string,
+  model?: string
 ): Promise<{ success: boolean; error?: string }> {
-  const service = createLLMService({ provider, apiKey })
-  return service.validateApiKey()
-}
-
-/**
- * 发送聊天请求（使用当前配置）
- * 返回完整的 ChatResponse 对象，包含 finishReason 和 tool_calls
- */
-export async function chat(
-  messages: ChatMessage[],
-  options?: ChatOptions
-): Promise<{ content: string; finishReason: string; tool_calls?: import('./types').ToolCall[] }> {
-  const activeConfig = getActiveConfig()
-
-  aiLogger.info('LLM', 'Starting non-streaming chat request', {
-    messagesCount: messages.length,
-    firstMessageRole: messages[0]?.role,
-    firstMessageLength: messages[0]?.content?.length,
-    config: activeConfig
-      ? {
-          name: activeConfig.name,
-          provider: activeConfig.provider,
-          model: activeConfig.model,
-          baseUrl: activeConfig.baseUrl,
-        }
-      : null,
-    options,
-  })
-
-  const service = getCurrentLLMService()
-  if (!service) {
-    aiLogger.error('LLM', 'Service not configured')
-    throw new Error(t('llm.notConfigured'))
-  }
-
   try {
-    const response = await service.chat(messages, options)
-    aiLogger.info('LLM', 'Non-streaming request succeeded', {
-      contentLength: response.content?.length,
-      finishReason: response.finishReason,
-      usage: response.usage,
-    })
-    return response
-  } catch (error) {
-    // 配置信息
-    const configStr = activeConfig
-      ? `${activeConfig.name} (${activeConfig.provider}/${activeConfig.model}) baseUrl=${activeConfig.baseUrl || '默认'}`
-      : '未配置'
-    // 错误信息
-    const errorInfo = extractErrorInfo(error)
-    const errorStr = `${errorInfo.name || 'Error'}: ${errorInfo.message}`
-
-    aiLogger.error('LLM', `Non-streaming request failed | config: ${configStr}`)
-    aiLogger.error('LLM', `Error: ${errorStr}`)
-
-    // 堆栈单独一行
-    const stack = extractErrorStack(error)
-    if (stack) {
-      aiLogger.error('LLM', `Stack:\n${stack}`)
+    const providerInfo = getProviderInfo(provider)
+    const config: AIServiceConfig = {
+      id: 'validate-temp',
+      name: 'validate-temp',
+      provider,
+      apiKey,
+      baseUrl,
+      model: model || providerInfo?.models?.[0]?.id,
+      createdAt: 0,
+      updatedAt: 0,
     }
-    throw error
-  }
-}
+    const piModel = buildPiModel(config)
 
-/**
- * 发送聊天请求（流式，使用当前配置）
- */
-export async function* chatStream(messages: ChatMessage[], options?: ChatOptions): AsyncGenerator<ChatStreamChunk> {
-  const activeConfig = getActiveConfig()
+    const abortController = new AbortController()
+    const timeout = setTimeout(() => abortController.abort(), 15000)
 
-  aiLogger.info('LLM', 'Starting streaming chat request', {
-    messagesCount: messages.length,
-    firstMessageRole: messages[0]?.role,
-    firstMessageLength: messages[0]?.content?.length,
-    config: activeConfig
-      ? {
-          name: activeConfig.name,
-          provider: activeConfig.provider,
-          model: activeConfig.model,
-          baseUrl: activeConfig.baseUrl,
-        }
-      : null,
-  })
-
-  const service = getCurrentLLMService()
-  if (!service) {
-    aiLogger.error('LLM', 'Service not configured (streaming)')
-    throw new Error(t('llm.notConfigured'))
-  }
-
-  let chunkCount = 0
-  let totalContent = ''
-
-  let receivedFinish = false
-  let contentChunkCount = 0
-
-  try {
-    for await (const chunk of service.chatStream(messages, options)) {
-      chunkCount++
-      totalContent += chunk.content
-
-      // 追踪内容 chunk
-      if (chunk.content) {
-        contentChunkCount++
-      }
-
-      yield chunk
-
-      if (chunk.isFinished) {
-        receivedFinish = true
-        aiLogger.info('LLM', 'Streaming request completed', {
-          chunkCount,
-          contentChunkCount,
-          totalContentLength: totalContent.length,
-          finishReason: chunk.finishReason,
-        })
-      }
-    }
-
-    // 如果循环正常结束但没有收到 isFinished 的 chunk，记录警告
-    if (chunkCount > 0 && !receivedFinish) {
-      aiLogger.warn('LLM', 'Stream loop ended without completion signal', {
-        chunkCount,
-        totalContentLength: totalContent.length,
+    try {
+      await completeSimple(piModel, {
+        messages: [{ role: 'user', content: 'Hi', timestamp: Date.now() }],
+      }, {
+        apiKey,
+        maxTokens: 1,
+        signal: abortController.signal,
       })
+      return { success: true }
+    } finally {
+      clearTimeout(timeout)
     }
   } catch (error) {
-    // 配置信息
-    const configStr = activeConfig
-      ? `${activeConfig.name} (${activeConfig.provider}/${activeConfig.model}) baseUrl=${activeConfig.baseUrl || '默认'}`
-      : '未配置'
-    // 错误信息
-    const errorInfo = extractErrorInfo(error)
-    const errorStr = `${errorInfo.name || 'Error'}: ${errorInfo.message}`
-
-    aiLogger.error(
-      'LLM',
-      `Stream request failed | config: ${configStr} | received: ${chunkCount} chunks/${totalContent.length} chars`
-    )
-    aiLogger.error('LLM', `Error: ${errorStr}`)
-
-    // 堆栈单独一行
-    const stack = extractErrorStack(error)
-    if (stack) {
-      aiLogger.error('LLM', `Stack:\n${stack}`)
+    const message = error instanceof Error ? error.message : String(error)
+    if (message.includes('aborted') || message.includes('AbortError')) {
+      return { success: false, error: 'Request timed out (15s)' }
     }
-    throw error
+    return { success: false, error: message }
   }
 }

electron/main/ai/rag/pipeline/semantic.ts

@@ -10,7 +10,8 @@ import { getEmbeddingService } from '../embedding'
 import { getVectorStore } from '../store'
 import { getSessionChunks } from '../chunking'
 import { loadRAGConfig } from '../config'
-import { chat } from '../../llm'
+import { completeSimple, type TextContent as PiTextContent } from '@mariozechner/pi-ai'
+import { getActiveConfig, buildPiModel } from '../../llm'
 import { aiLogger as logger } from '../../logger'
 
 /**
@@ -33,21 +34,28 @@ const QUERY_REWRITE_PROMPT = `你是一个查询优化专家。请将用户的
  */
 async function rewriteQuery(query: string, abortSignal?: AbortSignal): Promise<string> {
   try {
+    const activeConfig = getActiveConfig()
+    if (!activeConfig) return query
+
+    const piModel = buildPiModel(activeConfig)
     const prompt = QUERY_REWRITE_PROMPT.replace('{query}', query)
 
-    const response = await chat(
-      [
-        { role: 'system', content: '你是一个查询优化专家，专门将用户问题改写为更适合语义检索的形式。' },
-        { role: 'user', content: prompt },
-      ],
-      {
-        temperature: 0.3,
-        maxTokens: 200,
-        abortSignal,
-      }
-    )
+    const result = await completeSimple(piModel, {
+      systemPrompt: '你是一个查询优化专家，专门将用户问题改写为更适合语义检索的形式。',
+      messages: [{ role: 'user', content: prompt, timestamp: Date.now() }],
+    }, {
+      apiKey: activeConfig.apiKey,
+      temperature: 0.3,
+      maxTokens: 200,
+      signal: abortSignal,
+    })
+
+    const rewritten = result.content
+      .filter((item): item is PiTextContent => item.type === 'text')
+      .map((item) => item.text)
+      .join('')
+      .trim()
 
-    const rewritten = response.content.trim()
     return rewritten || query
   } catch (error) {
     logger.warn('[Semantic Pipeline] Query rewrite failed, using original query:', error)

electron/main/ai/summary/index.ts

@@ -8,11 +8,41 @@
  */
 
 import Database from 'better-sqlite3'
-import { chat } from '../llm'
+import { completeSimple, type TextContent as PiTextContent } from '@mariozechner/pi-ai'
+import { getActiveConfig, buildPiModel } from '../llm'
 import { getDbPath, openDatabase } from '../../database/core'
 import { aiLogger } from '../logger'
 import { t } from '../../i18n'
 
+/** 调用 LLM 生成文本（直接使用 pi-ai completeSimple） */
+async function llmComplete(
+  systemPrompt: string,
+  userPrompt: string,
+  options?: { temperature?: number; maxTokens?: number }
+): Promise<string> {
+  const activeConfig = getActiveConfig()
+  if (!activeConfig) {
+    throw new Error(t('llm.notConfigured'))
+  }
+
+  const piModel = buildPiModel(activeConfig)
+  const now = Date.now()
+
+  const result = await completeSimple(piModel, {
+    systemPrompt,
+    messages: [{ role: 'user', content: userPrompt, timestamp: now }],
+  }, {
+    apiKey: activeConfig.apiKey,
+    temperature: options?.temperature,
+    maxTokens: options?.maxTokens,
+  })
+
+  return result.content
+    .filter((item): item is PiTextContent => item.type === 'text')
+    .map((item) => item.text)
+    .join('')
+}
+
 /** 最小消息数阈值（少于此数量不生成摘要） */
 const MIN_MESSAGE_COUNT = 3
 
@@ -408,17 +438,12 @@ export async function generateSessionSummary(
  * 直接生成摘要（适用于短会话）
  */
 async function generateDirectSummary(content: string, lengthLimit: number, locale: string): Promise<string> {
-  const response = await chat(
-    [
-      { role: 'system', content: t('summary.systemPromptDirect') },
-      { role: 'user', content: buildSummaryPrompt(content, lengthLimit, locale) },
-    ],
-    {
-      temperature: 0.3,
-      maxTokens: 300,
-    }
+  const result = await llmComplete(
+    t('summary.systemPromptDirect'),
+    buildSummaryPrompt(content, lengthLimit, locale),
+    { temperature: 0.3, maxTokens: 300 }
   )
-  return response.content.trim()
+  return result.trim()
 }
 
 /**
@@ -437,17 +462,12 @@ async function generateMapReduceSummary(
 
   for (let i = 0; i < segments.length; i++) {
     const segmentContent = formatMessages(segments[i])
-    const response = await chat(
-      [
-        { role: 'system', content: t('summary.systemPromptDirect') },
-        { role: 'user', content: buildSubSummaryPrompt(segmentContent, locale) },
-      ],
-      {
-        temperature: 0.3,
-        maxTokens: 100,
-      }
+    const result = await llmComplete(
+      t('summary.systemPromptDirect'),
+      buildSubSummaryPrompt(segmentContent, locale),
+      { temperature: 0.3, maxTokens: 100 }
     )
-    subSummaries.push(response.content.trim())
+    subSummaries.push(result.trim())
   }
 
   // 2. Reduce：合并子摘要
@@ -455,18 +475,13 @@ async function generateMapReduceSummary(
     return subSummaries[0]
   }
 
-  const mergeResponse = await chat(
-    [
-      { role: 'system', content: t('summary.systemPromptMerge') },
-      { role: 'user', content: buildMergePrompt(subSummaries, lengthLimit, locale) },
-    ],
-    {
-      temperature: 0.3,
-      maxTokens: 300,
-    }
+  const mergeResult = await llmComplete(
+    t('summary.systemPromptMerge'),
+    buildMergePrompt(subSummaries, lengthLimit, locale),
+    { temperature: 0.3, maxTokens: 300 }
   )
 
-  return mergeResponse.content.trim()
+  return mergeResult.trim()
 }
 
 /**