From 376020b23cfc0c48390fef65fc6b24377a4d8d29 Mon Sep 17 00:00:00 2001 From: MyPrototypeWhat Date: Thu, 14 Aug 2025 18:06:11 +0800 Subject: [PATCH] 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 | 4 +- .../src/aiCore/transformParameters.ts | 168 +++--- src/renderer/src/services/ApiService.ts | 559 +++--------------- 3 files changed, 163 insertions(+), 568 deletions(-) diff --git a/src/renderer/src/aiCore/index_new.ts b/src/renderer/src/aiCore/index_new.ts index e71123dcf4..0d1dd9fa4f 100644 --- a/src/renderer/src/aiCore/index_new.ts +++ b/src/renderer/src/aiCore/index_new.ts @@ -30,8 +30,8 @@ export default class ModernAiProvider { private config: ReturnType 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 diff --git a/src/renderer/src/aiCore/transformParameters.ts b/src/renderer/src/aiCore/transformParameters.ts index c1651cdcf0..825e941447 100644 --- a/src/renderer/src/aiCore/transformParameters.ts +++ b/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 { - const { shouldWebSearch = false, shouldKnowledgeSearch = false, lastAnswer } = options +// export async function extractSearchKeywords( +// lastUserMessage: Message, +// assistant: Assistant, +// options: { +// shouldWebSearch?: boolean +// shouldKnowledgeSearch?: boolean +// lastAnswer?: Message +// } = {} +// ): Promise { +// 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) +// } diff --git a/src/renderer/src/services/ApiService.ts b/src/renderer/src/services/ApiService.ts index 20c8d1f2d8..9a3a5b0cb8 100644 --- a/src/renderer/src/services/ApiService.ts +++ b/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 { -// // 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 => { -// 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 => { -// 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 => { -// 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 => { -// 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>(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 => 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 { - 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 { 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 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 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') }