refactor(aiCore): update ModernAiProvider constructor and clean up unused code

- Modified the constructor of ModernAiProvider to accept an optional provider parameter, enhancing flexibility in provider selection.
- Removed deprecated and unused functions related to search keyword extraction and search summary fetching, streamlining the codebase.
- Updated import statements and adjusted related logic to reflect the removal of obsolete functions, improving maintainability.

src/renderer/src/aiCore/index_new.ts

@@ -30,8 +30,8 @@ export default class ModernAiProvider {
   private config: ReturnType<typeof providerToAiSdkConfig>
   private actualProvider: Provider
 
-  constructor(model: Model) {
-    this.actualProvider = getActualProvider(model)
+  constructor(model: Model, provider?: Provider) {
+    this.actualProvider = provider || getActualProvider(model)
     this.legacyProvider = new LegacyAiProvider(this.actualProvider)
 
     // 只保存配置，不预先创建executor

src/renderer/src/aiCore/transformParameters.ts

@@ -26,18 +26,11 @@ import {
   isVisionModel,
   isWebSearchModel
 } from '@renderer/config/models'
-import {
-  SEARCH_SUMMARY_PROMPT,
-  SEARCH_SUMMARY_PROMPT_KNOWLEDGE_ONLY,
-  SEARCH_SUMMARY_PROMPT_WEB_ONLY
-} from '@renderer/config/prompts'
-import { getAssistantSettings, getDefaultModel, getProviderByModel } from '@renderer/services/AssistantService'
-import { getDefaultAssistant } from '@renderer/services/AssistantService'
+import { getAssistantSettings, getDefaultModel } from '@renderer/services/AssistantService'
 import type { Assistant, MCPTool, Message, Model, Provider } from '@renderer/types'
 import { FileTypes } from '@renderer/types'
 import { FileMessageBlock, ImageMessageBlock, ThinkingMessageBlock } from '@renderer/types/newMessage'
 // import { getWebSearchTools } from './utils/websearch'
-import { extractInfoFromXML, ExtractResults } from '@renderer/utils/extract'
 import {
   findFileBlocks,
   findImageBlocks,
@@ -45,10 +38,7 @@ import {
   getMainTextContent
 } from '@renderer/utils/messageUtils/find'
 import { defaultTimeout } from '@shared/config/constant'
-import { isEmpty } from 'lodash'
 
-import AiProvider from './legacy/index'
-import { CompletionsParams } from './legacy/middleware/schemas'
 // import { webSearchTool } from './tools/WebSearchTool'
 // import { jsonSchemaToZod } from 'json-schema-to-zod'
 import { setupToolsConfig } from './utils/mcp'
@@ -328,99 +318,101 @@ export async function buildGenerateTextParams(
 /**
  * 提取外部工具搜索关键词和问题
  * 从用户消息中提取用于网络搜索和知识库搜索的关键词
+ * @deprecated
  */
-export async function extractSearchKeywords(
-  lastUserMessage: Message,
-  assistant: Assistant,
-  options: {
-    shouldWebSearch?: boolean
-    shouldKnowledgeSearch?: boolean
-    lastAnswer?: Message
-  } = {}
-): Promise<ExtractResults | undefined> {
-  const { shouldWebSearch = false, shouldKnowledgeSearch = false, lastAnswer } = options
+// export async function extractSearchKeywords(
+//   lastUserMessage: Message,
+//   assistant: Assistant,
+//   options: {
+//     shouldWebSearch?: boolean
+//     shouldKnowledgeSearch?: boolean
+//     lastAnswer?: Message
+//   } = {}
+// ): Promise<ExtractResults | undefined> {
+//   const { shouldWebSearch = false, shouldKnowledgeSearch = false, lastAnswer } = options
 
-  if (!lastUserMessage) return undefined
+//   if (!lastUserMessage) return undefined
 
-  // 根据配置决定是否需要提取
-  const needWebExtract = shouldWebSearch
-  const needKnowledgeExtract = shouldKnowledgeSearch
+//   // 根据配置决定是否需要提取
+//   const needWebExtract = shouldWebSearch
+//   const needKnowledgeExtract = shouldKnowledgeSearch
 
-  if (!needWebExtract && !needKnowledgeExtract) return undefined
+//   if (!needWebExtract && !needKnowledgeExtract) return undefined
 
-  // 选择合适的提示词
-  let prompt: string
-  if (needWebExtract && !needKnowledgeExtract) {
-    prompt = SEARCH_SUMMARY_PROMPT_WEB_ONLY
-  } else if (!needWebExtract && needKnowledgeExtract) {
-    prompt = SEARCH_SUMMARY_PROMPT_KNOWLEDGE_ONLY
-  } else {
-    prompt = SEARCH_SUMMARY_PROMPT
-  }
+//   // 选择合适的提示词
+//   let prompt: string
+//   if (needWebExtract && !needKnowledgeExtract) {
+//     prompt = SEARCH_SUMMARY_PROMPT_WEB_ONLY
+//   } else if (!needWebExtract && needKnowledgeExtract) {
+//     prompt = SEARCH_SUMMARY_PROMPT_KNOWLEDGE_ONLY
+//   } else {
+//     prompt = SEARCH_SUMMARY_PROMPT
+//   }
 
-  // 构建用于提取的助手配置
-  const summaryAssistant = getDefaultAssistant()
-  summaryAssistant.model = assistant.model || getDefaultModel()
-  summaryAssistant.prompt = prompt
+//   // 构建用于提取的助手配置
+//   const summaryAssistant = getDefaultAssistant()
+//   summaryAssistant.model = assistant.model || getDefaultModel()
+//   summaryAssistant.prompt = prompt
 
-  try {
-    const result = await fetchSearchSummary({
-      messages: lastAnswer ? [lastAnswer, lastUserMessage] : [lastUserMessage],
-      assistant: summaryAssistant
-    })
+//   try {
+//     const result = await fetchSearchSummary({
+//       messages: lastAnswer ? [lastAnswer, lastUserMessage] : [lastUserMessage],
+//       assistant: summaryAssistant
+//     })
 
-    if (!result) return getFallbackResult()
+//     if (!result) return getFallbackResult()
 
-    const extracted = extractInfoFromXML(result.getText())
-    // 根据需求过滤结果
-    return {
-      websearch: needWebExtract ? extracted?.websearch : undefined,
-      knowledge: needKnowledgeExtract ? extracted?.knowledge : undefined
-    }
-  } catch (e: any) {
-    console.error('extract error', e)
-    return getFallbackResult()
-  }
+//     const extracted = extractInfoFromXML(result.getText())
+//     // 根据需求过滤结果
+//     return {
+//       websearch: needWebExtract ? extracted?.websearch : undefined,
+//       knowledge: needKnowledgeExtract ? extracted?.knowledge : undefined
+//     }
+//   } catch (e: any) {
+//     console.error('extract error', e)
+//     return getFallbackResult()
+//   }
 
-  function getFallbackResult(): ExtractResults {
-    const fallbackContent = getMainTextContent(lastUserMessage)
-    return {
-      websearch: shouldWebSearch ? { question: [fallbackContent || 'search'] } : undefined,
-      knowledge: shouldKnowledgeSearch
-        ? {
-            question: [fallbackContent || 'search'],
-            rewrite: fallbackContent || 'search'
-          }
-        : undefined
-    }
-  }
-}
+//   function getFallbackResult(): ExtractResults {
+//     const fallbackContent = getMainTextContent(lastUserMessage)
+//     return {
+//       websearch: shouldWebSearch ? { question: [fallbackContent || 'search'] } : undefined,
+//       knowledge: shouldKnowledgeSearch
+//         ? {
+//             question: [fallbackContent || 'search'],
+//             rewrite: fallbackContent || 'search'
+//           }
+//         : undefined
+//     }
+//   }
+// }
 
 /**
  * 获取搜索摘要 - 内部辅助函数
+ * @deprecated
  */
-async function fetchSearchSummary({ messages, assistant }: { messages: Message[]; assistant: Assistant }) {
-  const model = assistant.model || getDefaultModel()
-  const provider = getProviderByModel(model)
+// async function fetchSearchSummary({ messages, assistant }: { messages: Message[]; assistant: Assistant }) {
+//   const model = assistant.model || getDefaultModel()
+//   const provider = getProviderByModel(model)
 
-  if (!hasApiKey(provider)) {
-    return null
-  }
+//   if (!hasApiKey(provider)) {
+//     return null
+//   }
 
-  const AI = new AiProvider(provider)
+//   const AI = new AiProvider(provider)
 
-  const params: CompletionsParams = {
-    callType: 'search',
-    messages: messages,
-    assistant,
-    streamOutput: false
-  }
+//   const params: CompletionsParams = {
+//     callType: 'search',
+//     messages: messages,
+//     assistant,
+//     streamOutput: false
+//   }
 
-  return await AI.completions(params)
-}
+//   return await AI.completions(params)
+// }
 
-function hasApiKey(provider: Provider) {
-  if (!provider) return false
-  if (provider.id === 'ollama' || provider.id === 'lmstudio' || provider.type === 'vertexai') return true
-  return !isEmpty(provider.apiKey)
-}
+// function hasApiKey(provider: Provider) {
+//   if (!provider) return false
+//   if (provider.id === 'ollama' || provider.id === 'lmstudio' || provider.type === 'vertexai') return true
+//   return !isEmpty(provider.apiKey)
+// }

src/renderer/src/services/ApiService.ts

@@ -48,303 +48,6 @@ import {
 
 const logger = loggerService.withContext('ApiService')
 
-// TODO：考虑拆开
-// async function fetchExternalTool(
-//   lastUserMessage: Message,
-//   assistant: Assistant,
-//   onChunkReceived: (chunk: Chunk) => void,
-//   lastAnswer?: Message
-// ): Promise<ExternalToolResult> {
-//   // 可能会有重复？
-//   const knowledgeBaseIds = assistant.knowledge_bases?.map((base) => base.id)
-//   const hasKnowledgeBase = !isEmpty(knowledgeBaseIds)
-//   const knowledgeRecognition = assistant.knowledgeRecognition || 'on'
-//   const webSearchProvider = WebSearchService.getWebSearchProvider(assistant.webSearchProviderId)
-
-//   // 使用外部搜索工具
-//   const shouldWebSearch = !!assistant.webSearchProviderId && webSearchProvider !== null
-//   const shouldKnowledgeSearch = hasKnowledgeBase
-//   const globalMemoryEnabled = selectGlobalMemoryEnabled(store.getState())
-//   const shouldSearchMemory = globalMemoryEnabled && assistant.enableMemory
-
-//   // 在工具链开始时发送进度通知
-//   const willUseTools = shouldWebSearch || shouldKnowledgeSearch
-//   if (willUseTools) {
-//     onChunkReceived({ type: ChunkType.EXTERNEL_TOOL_IN_PROGRESS })
-//   }
-
-//   // --- Keyword/Question Extraction Function ---
-//   const extract = async (): Promise<ExtractResults | undefined> => {
-//     if (!lastUserMessage) return undefined
-
-//     // 根据配置决定是否需要提取
-//     const needWebExtract = shouldWebSearch
-//     const needKnowledgeExtract = hasKnowledgeBase && knowledgeRecognition === 'on'
-
-//     if (!needWebExtract && !needKnowledgeExtract) return undefined
-
-//     let prompt: string
-//     if (needWebExtract && !needKnowledgeExtract) {
-//       prompt = SEARCH_SUMMARY_PROMPT_WEB_ONLY
-//     } else if (!needWebExtract && needKnowledgeExtract) {
-//       prompt = SEARCH_SUMMARY_PROMPT_KNOWLEDGE_ONLY
-//     } else {
-//       prompt = SEARCH_SUMMARY_PROMPT
-//     }
-
-//     const summaryAssistant = getDefaultAssistant()
-//     summaryAssistant.model = assistant.model || getDefaultModel()
-//     summaryAssistant.prompt = prompt
-
-//     try {
-//       const result = await fetchSearchSummary({
-//         messages: lastAnswer ? [lastAnswer, lastUserMessage] : [lastUserMessage],
-//         assistant: summaryAssistant
-//       })
-
-//       if (!result) return getFallbackResult()
-
-//       const extracted = extractInfoFromXML(result.getText())
-//       // 根据需求过滤结果
-//       return {
-//         websearch: needWebExtract ? extracted?.websearch : undefined,
-//         knowledge: needKnowledgeExtract ? extracted?.knowledge : undefined
-//       }
-//     } catch (e: any) {
-//       console.error('extract error', e)
-//       if (isAbortError(e)) throw e
-//       return getFallbackResult()
-//     }
-//   }
-
-//   const getFallbackResult = (): ExtractResults => {
-//     const fallbackContent = getMainTextContent(lastUserMessage)
-//     return {
-//       websearch: shouldWebSearch ? { question: [fallbackContent || 'search'] } : undefined,
-//       knowledge: shouldKnowledgeSearch
-//         ? {
-//             question: [fallbackContent || 'search'],
-//             rewrite: fallbackContent
-//           }
-//         : undefined
-//     }
-//   }
-
-//   // --- Web Search Function ---
-//   const searchTheWeb = async (extractResults: ExtractResults | undefined): Promise<WebSearchResponse | undefined> => {
-//     if (!shouldWebSearch) return
-
-//     // Add check for extractResults existence early
-//     if (!extractResults?.websearch) {
-//       console.warn('searchTheWeb called without valid extractResults.websearch')
-//       return
-//     }
-
-//     if (extractResults.websearch.question[0] === 'not_needed') return
-
-//     // Add check for assistant.model before using it
-//     if (!assistant.model) {
-//       console.warn('searchTheWeb called without assistant.model')
-//       return undefined
-//     }
-
-//     try {
-//       // Use the consolidated processWebsearch function
-//       WebSearchService.createAbortSignal(lastUserMessage.id)
-//       const webSearchResponse = await WebSearchService.processWebsearch(
-//         webSearchProvider!,
-//         extractResults,
-//         lastUserMessage.id
-//       )
-//       return {
-//         results: webSearchResponse,
-//         source: WebSearchSource.WEBSEARCH
-//       }
-//     } catch (error) {
-//       if (isAbortError(error)) throw error
-//       console.error('Web search failed:', error)
-//       return
-//     }
-//   }
-
-//   const searchMemory = async (): Promise<MemoryItem[] | undefined> => {
-//     if (!shouldSearchMemory) return []
-//     try {
-//       const memoryConfig = selectMemoryConfig(store.getState())
-//       const content = getMainTextContent(lastUserMessage)
-//       if (!content) {
-//         console.warn('searchMemory called without valid content in lastUserMessage')
-//         return []
-//       }
-
-//       if (memoryConfig.llmApiClient && memoryConfig.embedderApiClient) {
-//         const currentUserId = selectCurrentUserId(store.getState())
-//         // Search for relevant memories
-//         const processorConfig = MemoryProcessor.getProcessorConfig(memoryConfig, assistant.id, currentUserId)
-//         console.log('Searching for relevant memories with content:', content)
-//         const memoryProcessor = new MemoryProcessor()
-//         const relevantMemories = await memoryProcessor.searchRelevantMemories(
-//           content,
-//           processorConfig,
-//           5 // Limit to top 5 most relevant memories
-//         )
-
-//         if (relevantMemories?.length > 0) {
-//           console.log('Found relevant memories:', relevantMemories)
-
-//           return relevantMemories
-//         }
-//         return []
-//       } else {
-//         console.warn('Memory is enabled but embedding or LLM model is not configured')
-//         return []
-//       }
-//     } catch (error) {
-//       console.error('Error processing memory search:', error)
-//       // Continue with conversation even if memory processing fails
-//       return []
-//     }
-//   }
-
-//   // --- Knowledge Base Search Function ---
-//   const searchKnowledgeBase = async (
-//     extractResults: ExtractResults | undefined
-//   ): Promise<KnowledgeReference[] | undefined> => {
-//     if (!hasKnowledgeBase) return
-
-//     // 知识库搜索条件
-//     let searchCriteria: { question: string[]; rewrite: string }
-//     if (knowledgeRecognition === 'off') {
-//       const directContent = getMainTextContent(lastUserMessage)
-//       searchCriteria = { question: [directContent || 'search'], rewrite: directContent }
-//     } else {
-//       // auto mode
-//       if (!extractResults?.knowledge) {
-//         console.warn('searchKnowledgeBase: No valid search criteria in auto mode')
-//         return
-//       }
-//       searchCriteria = extractResults.knowledge
-//     }
-
-//     if (searchCriteria.question[0] === 'not_needed') return
-
-//     try {
-//       const tempExtractResults: ExtractResults = {
-//         websearch: undefined,
-//         knowledge: searchCriteria
-//       }
-//       // Attempt to get knowledgeBaseIds from the main text block
-//       // NOTE: This assumes knowledgeBaseIds are ONLY on the main text block
-//       // NOTE: processKnowledgeSearch needs to handle undefined ids gracefully
-//       // const mainTextBlock = mainTextBlocks
-//       //   ?.map((blockId) => store.getState().messageBlocks.entities[blockId])
-//       //   .find((block) => block?.type === MessageBlockType.MAIN_TEXT) as MainTextMessageBlock | undefined
-//       return await processKnowledgeSearch(tempExtractResults, knowledgeBaseIds)
-//     } catch (error) {
-//       console.error('Knowledge base search failed:', error)
-//       return
-//     }
-//   }
-
-//   // --- Execute Extraction and Searches ---
-//   let extractResults: ExtractResults | undefined
-
-//   try {
-//     // 根据配置决定是否需要提取
-//     if (shouldWebSearch || hasKnowledgeBase) {
-//       extractResults = await extract()
-//       Logger.log('[fetchExternalTool] Extraction results:', extractResults)
-//     }
-
-//     let webSearchResponseFromSearch: WebSearchResponse | undefined
-//     let knowledgeReferencesFromSearch: KnowledgeReference[] | undefined
-//     let memorySearchReferences: MemoryItem[] | undefined
-
-//     // 并行执行搜索
-//     if (shouldWebSearch || shouldKnowledgeSearch || shouldSearchMemory) {
-//       ;[webSearchResponseFromSearch, knowledgeReferencesFromSearch, memorySearchReferences] = await Promise.all([
-//         searchTheWeb(extractResults),
-//         searchKnowledgeBase(extractResults),
-//         searchMemory()
-//       ])
-//     }
-
-//     // 存储搜索结果
-//     if (lastUserMessage) {
-//       if (webSearchResponseFromSearch) {
-//         window.keyv.set(`web-search-${lastUserMessage.id}`, webSearchResponseFromSearch)
-//       }
-//       if (knowledgeReferencesFromSearch) {
-//         window.keyv.set(`knowledge-search-${lastUserMessage.id}`, knowledgeReferencesFromSearch)
-//       }
-//       if (memorySearchReferences) {
-//         window.keyv.set(`memory-search-${lastUserMessage.id}`, memorySearchReferences)
-//       }
-//     }
-
-//     // 发送工具执行完成通知
-//     const wasAnyToolEnabled = shouldWebSearch || shouldKnowledgeSearch || shouldSearchMemory
-//     if (wasAnyToolEnabled) {
-//       onChunkReceived({
-//         type: ChunkType.EXTERNEL_TOOL_COMPLETE,
-//         external_tool: {
-//           webSearch: webSearchResponseFromSearch,
-//           knowledge: knowledgeReferencesFromSearch,
-//           memories: memorySearchReferences
-//         }
-//       })
-//     }
-
-//     // Get MCP tools (Fix duplicate declaration)
-//     let mcpTools: MCPTool[] = [] // Initialize as empty array
-//     const allMcpServers = store.getState().mcp.servers || []
-//     const activedMcpServers = allMcpServers.filter((s) => s.isActive)
-//     const assistantMcpServers = assistant.mcpServers || []
-
-//     const enabledMCPs = activedMcpServers.filter((server) => assistantMcpServers.some((s) => s.id === server.id))
-
-//     if (enabledMCPs && enabledMCPs.length > 0) {
-//       try {
-//         const toolPromises = enabledMCPs.map<Promise<MCPTool[]>>(async (mcpServer) => {
-//           try {
-//             const tools = await window.api.mcp.listTools(mcpServer)
-//             return tools.filter((tool: any) => !mcpServer.disabledTools?.includes(tool.name))
-//           } catch (error) {
-//             console.error(`Error fetching tools from MCP server ${mcpServer.name}:`, error)
-//             return []
-//           }
-//         })
-//         const results = await Promise.allSettled(toolPromises)
-//         mcpTools = results
-//           .filter((result): result is PromiseFulfilledResult<MCPTool[]> => result.status === 'fulfilled')
-//           .map((result) => result.value)
-//           .flat()
-//       } catch (toolError) {
-//         console.error('Error fetching MCP tools:', toolError)
-//       }
-//     }
-
-//     return { mcpTools }
-//   } catch (error) {
-//     if (isAbortError(error)) throw error
-//     console.error('Tool execution failed:', error)
-
-//     // 发送错误状态
-//     const wasAnyToolEnabled = shouldWebSearch || shouldKnowledgeSearch || shouldSearchMemory
-//     if (wasAnyToolEnabled) {
-//       onChunkReceived({
-//         type: ChunkType.EXTERNEL_TOOL_COMPLETE,
-//         external_tool: {
-//           webSearch: undefined,
-//           knowledge: undefined
-//         }
-//       })
-//     }
-
-//     return { mcpTools: [] }
-//   }
-// }
-
 export async function fetchMcpTools(assistant: Assistant) {
   // Get MCP tools (Fix duplicate declaration)
   let mcpTools: MCPTool[] = [] // Initialize as empty array
@@ -506,124 +209,8 @@ export async function fetchChatCompletion({
       callType: 'chat'
     })
   }
-
-  // await AI.completions(
-  //   {
-  //     callType: 'chat',
-  //     messages: _messages,
-  //     assistant,
-  //     onChunk: onChunkReceived,
-  //     mcpTools: mcpTools,
-  //     maxTokens,
-  //     streamOutput: assistant.settings?.streamOutput || false,
-  //     enableReasoning,
-  //     enableWebSearch,
-  //     enableGenerateImage
-  //   },
-  //   {
-  //     streamOutput: assistant.settings?.streamOutput || false
-  //   }
-  // )
-  // Post-conversation memory processing
-  // TODO: 需要转换messages
-  // const globalMemoryEnabled = selectGlobalMemoryEnabled(store.getState())
-  // if (globalMemoryEnabled && assistant.enableMemory) {
-  //   await processConversationMemory(messages, assistant)
-  // }
 }
 
-/**
- * Process conversation for memory extraction and storage
- */
-// async function processConversationMemory(messages: Message[], assistant: Assistant) {
-//   try {
-//     const memoryConfig = selectMemoryConfig(store.getState())
-
-//     // Use assistant's model as fallback for memory processing if not configured
-//     const llmModel =
-//       getModel(memoryConfig.llmApiClient?.model, memoryConfig.llmApiClient?.provider) ||
-//       assistant.model ||
-//       getDefaultModel()
-//     const embedderModel =
-//       getModel(memoryConfig.embedderApiClient?.model, memoryConfig.embedderApiClient?.provider) ||
-//       getFirstEmbeddingModel()
-
-//     if (!embedderModel) {
-//       console.warn(
-//         'Memory processing skipped: no embedding model available. Please configure an embedding model in memory settings.'
-//       )
-//       return
-//     }
-
-//     if (!llmModel) {
-//       console.warn('Memory processing skipped: LLM model not available')
-//       return
-//     }
-
-//     // Convert messages to the format expected by memory processor
-//     const conversationMessages = messages
-//       .filter((msg) => msg.role === 'user' || msg.role === 'assistant')
-//       .map((msg) => ({
-//         role: msg.role as 'user' | 'assistant',
-//         content: getMainTextContent(msg) || ''
-//       }))
-//       .filter((msg) => msg.content.trim().length > 0)
-
-//     // if (conversationMessages.length < 2) {
-//     // Need at least a user message and assistant response
-//     // return
-//     // }
-
-//     const currentUserId = selectCurrentUserId(store.getState())
-
-//     // Create updated memory config with resolved models
-//     const updatedMemoryConfig = {
-//       ...memoryConfig,
-//       llmApiClient: {
-//         model: llmModel.id,
-//         provider: llmModel.provider,
-//         apiKey: getProviderByModel(llmModel).apiKey,
-//         baseURL: new AiProvider(getProviderByModel(llmModel)).getBaseURL(),
-//         apiVersion: getProviderByModel(llmModel).apiVersion
-//       },
-//       embedderApiClient: {
-//         model: embedderModel.id,
-//         provider: embedderModel.provider,
-//         apiKey: getProviderByModel(embedderModel).apiKey,
-//         baseURL: new AiProvider(getProviderByModel(embedderModel)).getBaseURL(),
-//         apiVersion: getProviderByModel(embedderModel).apiVersion
-//       }
-//     }
-
-//     const lastUserMessage = findLast(messages, (m) => m.role === 'user')
-//     const processorConfig = MemoryProcessor.getProcessorConfig(
-//       updatedMemoryConfig,
-//       assistant.id,
-//       currentUserId,
-//       lastUserMessage?.id
-//     )
-
-//     // Process the conversation in the background (don't await to avoid blocking UI)
-//     const memoryProcessor = new MemoryProcessor()
-//     memoryProcessor
-//       .processConversation(conversationMessages, processorConfig)
-//       .then((result) => {
-//         console.log('Memory processing completed:', result)
-//         if (result.facts.length > 0) {
-//           console.log('Extracted facts from conversation:', result.facts)
-//           console.log('Memory operations performed:', result.operations)
-//         } else {
-//           console.log('No facts extracted from conversation')
-//         }
-//       })
-//       .catch((error) => {
-//         console.error('Background memory processing failed:', error)
-//       })
-//   } catch (error) {
-//     console.error('Error in post-conversation memory processing:', error)
-//   }
-// }
-
 interface FetchTranslateProps {
   content: string
   assistant: TranslateAssistant
@@ -656,19 +243,42 @@ export async function fetchTranslate({ content, assistant, onResponse }: FetchTr
       assistant.settings?.reasoning_effort !== undefined) ||
     (isReasoningModel(model) && (!isSupportedThinkingTokenModel(model) || !isSupportedReasoningEffortModel(model)))
 
-  const params: CompletionsParams = {
-    callType: 'translate',
-    messages: content,
-    assistant: { ...assistant, model },
-    streamOutput: stream,
-    enableReasoning,
-    onResponse
+  const params: StreamTextParams = {
+    system: assistant.prompt,
+    prompt: content
   }
 
-  const AI = new AiProvider(provider)
+  const AI = new AiProviderNew(model)
+
+  const middlewareConfig: AiSdkMiddlewareConfig = {
+    streamOutput: stream,
+    enableReasoning,
+    isPromptToolUse: false,
+    isSupportedToolUse: false,
+    isImageGenerationEndpoint: false,
+    enableWebSearch: false,
+    enableGenerateImage: false,
+    onChunk: onResponse
+      ? (chunk) => {
+          if (chunk.type === ChunkType.TEXT_DELTA) {
+            onResponse(chunk.text, false)
+          } else if (chunk.type === ChunkType.TEXT_COMPLETE) {
+            onResponse(chunk.text, true)
+          }
+        }
+      : undefined
+  }
 
   try {
-    return (await AI.completions(params)).getText() || ''
+    return (
+      (
+        await AI.completions(model.id, params, {
+          ...middlewareConfig,
+          assistant,
+          callType: 'translate'
+        })
+      ).getText() || ''
+    )
   } catch (error: any) {
     return ''
   }
@@ -735,25 +345,12 @@ export async function fetchMessagesSummary({ messages, assistant }: { messages:
     prompt,
     model
   }
-  // const params: CompletionsParams = {
-  //   callType: 'summary',
-  //   messages: conversation,
-  //   assistant: { ...summaryAssistant, prompt, model },
-  //   maxTokens: 1000,
-  //   streamOutput: false,
-  //   topicId,
-  //   enableReasoning: false
-  // }
+
   const llmMessages = {
     system: prompt,
     prompt: conversation
   }
 
-  // const llmMessages = await ConversationService.prepareMessagesForModel(messages, summaryAssistant)
-
-  // 使用 transformParameters 模块构建参数
-  // const { params: aiSdkParams, modelId } = await buildStreamTextParams(llmMessages, summaryAssistant, provider)
-
   const middlewareConfig: AiSdkMiddlewareConfig = {
     streamOutput: false,
     enableReasoning: false,
@@ -764,7 +361,6 @@ export async function fetchMessagesSummary({ messages, assistant }: { messages:
     enableGenerateImage: false,
     mcpTools: []
   }
-  console.log('fetchMessagesSummary', '开始总结')
   try {
     const { getText } = await AI.completionsForTrace(model.id, llmMessages, {
       ...middlewareConfig,
@@ -772,7 +368,6 @@ export async function fetchMessagesSummary({ messages, assistant }: { messages:
       topicId,
       callType: 'summary'
     })
-    console.log('fetchMessagesSummary', '总结完成', getText())
     const text = getText()
     return removeSpecialCharactersForTopicName(text) || null
   } catch (error: any) {
@@ -780,29 +375,6 @@ export async function fetchMessagesSummary({ messages, assistant }: { messages:
   }
 }
 
-export async function fetchSearchSummary({ messages, assistant }: { messages: Message[]; assistant: Assistant }) {
-  const model = assistant.model || getDefaultModel()
-  const provider = getProviderByModel(model)
-
-  if (!hasApiKey(provider)) {
-    return null
-  }
-
-  const topicId = messages?.find((message) => message.topicId)?.topicId || undefined
-
-  const AI = new AiProvider(provider)
-
-  const params: CompletionsParams = {
-    callType: 'search',
-    messages: messages,
-    assistant,
-    streamOutput: false,
-    topicId
-  }
-
-  return await AI.completionsForTrace(params)
-}
-
 export async function fetchGenerate({
   prompt,
   content,
@@ -821,21 +393,41 @@ export async function fetchGenerate({
     return ''
   }
 
-  const AI = new AiProvider(provider)
+  const AI = new AiProviderNew(model)
 
   const assistant = getDefaultAssistant()
   assistant.model = model
   assistant.prompt = prompt
 
-  const params: CompletionsParams = {
-    callType: 'generate',
-    messages: content,
-    assistant,
-    streamOutput: false
+  // const params: CompletionsParams = {
+  //   callType: 'generate',
+  //   messages: content,
+  //   assistant,
+  //   streamOutput: false
+  // }
+
+  const middlewareConfig: AiSdkMiddlewareConfig = {
+    streamOutput: assistant.settings?.streamOutput ?? true,
+    enableReasoning: false,
+    isPromptToolUse: false,
+    isSupportedToolUse: false,
+    isImageGenerationEndpoint: false,
+    enableWebSearch: false,
+    enableGenerateImage: false
   }
 
   try {
-    const result = await AI.completions(params)
+    const result = await AI.completions(
+      model.id,
+      {
+        prompt: content
+      },
+      {
+        ...middlewareConfig,
+        assistant,
+        callType: 'generate'
+      }
+    )
     return result.getText() || ''
   } catch (error: any) {
     return ''
@@ -865,7 +457,7 @@ function hasApiKey(provider: Provider) {
 // }
 
 export async function fetchModels(provider: Provider): Promise<SdkModel[]> {
-  const AI = new AiProvider(provider)
+  const AI = new AiProviderNew({} as Model, provider)
 
   try {
     return await AI.models()
@@ -904,7 +496,7 @@ export function checkApiProvider(provider: Provider): void {
 export async function checkApi(provider: Provider, model: Model): Promise<void> {
   checkApiProvider(provider)
 
-  const ai = new AiProvider(provider)
+  const ai = new AiProviderNew(model)
 
   const assistant = getDefaultAssistant()
   assistant.model = model
@@ -912,22 +504,33 @@ export async function checkApi(provider: Provider, model: Model): Promise<void>
     if (isEmbeddingModel(model)) {
       await ai.getEmbeddingDimensions(model)
     } else {
-      const params: CompletionsParams = {
-        callType: 'check',
-        messages: 'hi',
-        assistant,
-        streamOutput: true,
+      const params: StreamTextParams = {
+        system: assistant.prompt,
+        prompt: 'hi'
+      }
+      const middlewareConfig: AiSdkMiddlewareConfig = {
+        streamOutput: false,
         enableReasoning: false,
-        shouldThrow: true
+        isSupportedToolUse: false,
+        isImageGenerationEndpoint: false,
+        enableWebSearch: false,
+        enableGenerateImage: false,
+        isPromptToolUse: false
       }
 
       // Try streaming check first
-      const result = await ai.completions(params)
+      const result = await ai.completions(model.id, params, {
+        ...middlewareConfig,
+        assistant,
+        callType: 'check'
+      })
       if (!result.getText()) {
         throw new Error('No response received')
       }
     }
   } catch (error: any) {
+    // 失败回退legacy
+    const legacyAi = new AiProvider(provider)
     if (error.message.includes('stream')) {
       const params: CompletionsParams = {
         callType: 'check',
@@ -936,7 +539,7 @@ export async function checkApi(provider: Provider, model: Model): Promise<void>
         streamOutput: false,
         shouldThrow: true
       }
-      const result = await ai.completions(params)
+      const result = await legacyAi.completions(params)
       if (!result.getText()) {
         throw new Error('No response received')
       }