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用來簡要描述影響本次變動，概述即可
This commit is contained in:
lizhixuan 2025-07-22 18:52:39 +08:00
parent addd5ffdfa
commit 4b62384fc5
9 changed files with 458 additions and 60 deletions
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61
packages/aiCore/src/core/runtime/executor.ts
View File

@ -42,6 +42,15 @@ export class RuntimeExecutor<T extends ProviderId = ProviderId> {
}) })
} }
createConfigureContextPlugin() {
return definePlugin({
name: '_internal_configureContext',
configureContext: async (context: AiRequestContext) => {
context.executor = this
}
})
}
// === 高阶重载：直接使用模型 === // === 高阶重载：直接使用模型 ===
/** /**
@ -51,10 +60,6 @@ export class RuntimeExecutor<T extends ProviderId = ProviderId> {
model: LanguageModel, model: LanguageModel,
params: Omit<Parameters<typeof streamText>[0], 'model'> params: Omit<Parameters<typeof streamText>[0], 'model'>
): Promise<ReturnType<typeof streamText>> ): Promise<ReturnType<typeof streamText>>
/**
* 流式文本生成 - 使用modelId + 可选middleware（灵活用法）
*/
async streamText( async streamText(
modelId: string, modelId: string,
params: Omit<Parameters<typeof streamText>[0], 'model'>, params: Omit<Parameters<typeof streamText>[0], 'model'>,
@ -62,10 +67,6 @@ export class RuntimeExecutor<T extends ProviderId = ProviderId> {
middlewares?: LanguageModelV2Middleware[] middlewares?: LanguageModelV2Middleware[]
} }
): Promise<ReturnType<typeof streamText>> ): Promise<ReturnType<typeof streamText>>
/**
* 流式文本生成 - 内部实现（统一处理重载）
*/
async streamText( async streamText(
modelOrId: LanguageModel, modelOrId: LanguageModel,
params: Omit<Parameters<typeof streamText>[0], 'model'>, params: Omit<Parameters<typeof streamText>[0], 'model'>,
@ -73,7 +74,10 @@ export class RuntimeExecutor<T extends ProviderId = ProviderId> {
middlewares?: LanguageModelV2Middleware[] middlewares?: LanguageModelV2Middleware[]
} }
): Promise<ReturnType<typeof streamText>> { ): Promise<ReturnType<typeof streamText>> {
this.pluginEngine.use(this.createResolveModelPlugin(options?.middlewares)) this.pluginEngine.usePlugins([
this.createResolveModelPlugin(options?.middlewares),
this.createConfigureContextPlugin()
])
// 2. 执行插件处理 // 2. 执行插件处理
return this.pluginEngine.executeStreamWithPlugins( return this.pluginEngine.executeStreamWithPlugins(
@ -102,10 +106,6 @@ export class RuntimeExecutor<T extends ProviderId = ProviderId> {
model: LanguageModel, model: LanguageModel,
params: Omit<Parameters<typeof generateText>[0], 'model'> params: Omit<Parameters<typeof generateText>[0], 'model'>
): Promise<ReturnType<typeof generateText>> ): Promise<ReturnType<typeof generateText>>
/**
* 生成文本 - 使用modelId + 可选middleware
*/
async generateText( async generateText(
modelId: string, modelId: string,
params: Omit<Parameters<typeof generateText>[0], 'model'>, params: Omit<Parameters<typeof generateText>[0], 'model'>,
@ -113,10 +113,6 @@ export class RuntimeExecutor<T extends ProviderId = ProviderId> {
middlewares?: LanguageModelV2Middleware[] middlewares?: LanguageModelV2Middleware[]
} }
): Promise<ReturnType<typeof generateText>> ): Promise<ReturnType<typeof generateText>>
/**
* 生成文本 - 内部实现
*/
async generateText( async generateText(
modelOrId: LanguageModel | string, modelOrId: LanguageModel | string,
params: Omit<Parameters<typeof generateText>[0], 'model'>, params: Omit<Parameters<typeof generateText>[0], 'model'>,
@ -124,7 +120,10 @@ export class RuntimeExecutor<T extends ProviderId = ProviderId> {
middlewares?: LanguageModelV2Middleware[] middlewares?: LanguageModelV2Middleware[]
} }
): Promise<ReturnType<typeof generateText>> { ): Promise<ReturnType<typeof generateText>> {
this.pluginEngine.use(this.createResolveModelPlugin(options?.middlewares)) this.pluginEngine.usePlugins([
this.createResolveModelPlugin(options?.middlewares),
this.createConfigureContextPlugin()
])
return this.pluginEngine.executeWithPlugins( return this.pluginEngine.executeWithPlugins(
'generateText', 'generateText',
@ -143,10 +142,6 @@ export class RuntimeExecutor<T extends ProviderId = ProviderId> {
model: LanguageModel, model: LanguageModel,
params: Omit<Parameters<typeof generateObject>[0], 'model'> params: Omit<Parameters<typeof generateObject>[0], 'model'>
): Promise<ReturnType<typeof generateObject>> ): Promise<ReturnType<typeof generateObject>>
/**
* 生成结构化对象 - 使用modelId + 可选middleware
*/
async generateObject( async generateObject(
modelOrId: string, modelOrId: string,
params: Omit<Parameters<typeof generateObject>[0], 'model'>, params: Omit<Parameters<typeof generateObject>[0], 'model'>,
@ -154,10 +149,6 @@ export class RuntimeExecutor<T extends ProviderId = ProviderId> {
middlewares?: LanguageModelV2Middleware[] middlewares?: LanguageModelV2Middleware[]
} }
): Promise<ReturnType<typeof generateObject>> ): Promise<ReturnType<typeof generateObject>>
/**
* 生成结构化对象 - 内部实现
*/
async generateObject( async generateObject(
modelOrId: LanguageModel | string, modelOrId: LanguageModel | string,
params: Omit<Parameters<typeof generateObject>[0], 'model'>, params: Omit<Parameters<typeof generateObject>[0], 'model'>,
@ -165,7 +156,10 @@ export class RuntimeExecutor<T extends ProviderId = ProviderId> {
middlewares?: LanguageModelV2Middleware[] middlewares?: LanguageModelV2Middleware[]
} }
): Promise<ReturnType<typeof generateObject>> { ): Promise<ReturnType<typeof generateObject>> {
this.pluginEngine.use(this.createResolveModelPlugin(options?.middlewares)) this.pluginEngine.usePlugins([
this.createResolveModelPlugin(options?.middlewares),
this.createConfigureContextPlugin()
])
return this.pluginEngine.executeWithPlugins( return this.pluginEngine.executeWithPlugins(
'generateObject', 'generateObject',
@ -184,10 +178,6 @@ export class RuntimeExecutor<T extends ProviderId = ProviderId> {
model: LanguageModel, model: LanguageModel,
params: Omit<Parameters<typeof streamObject>[0], 'model'> params: Omit<Parameters<typeof streamObject>[0], 'model'>
): Promise<ReturnType<typeof streamObject>> ): Promise<ReturnType<typeof streamObject>>
/**
* 流式生成结构化对象 - 使用modelId + 可选middleware
*/
async streamObject( async streamObject(
modelId: string, modelId: string,
params: Omit<Parameters<typeof streamObject>[0], 'model'>, params: Omit<Parameters<typeof streamObject>[0], 'model'>,
@ -195,10 +185,6 @@ export class RuntimeExecutor<T extends ProviderId = ProviderId> {
middlewares?: LanguageModelV2Middleware[] middlewares?: LanguageModelV2Middleware[]
} }
): Promise<ReturnType<typeof streamObject>> ): Promise<ReturnType<typeof streamObject>>
/**
* 流式生成结构化对象 - 内部实现
*/
async streamObject( async streamObject(
modelOrId: LanguageModel | string, modelOrId: LanguageModel | string,
params: Omit<Parameters<typeof streamObject>[0], 'model'>, params: Omit<Parameters<typeof streamObject>[0], 'model'>,
@ -206,7 +192,10 @@ export class RuntimeExecutor<T extends ProviderId = ProviderId> {
middlewares?: LanguageModelV2Middleware[] middlewares?: LanguageModelV2Middleware[]
} }
): Promise<ReturnType<typeof streamObject>> { ): Promise<ReturnType<typeof streamObject>> {
this.pluginEngine.use(this.createResolveModelPlugin(options?.middlewares)) this.pluginEngine.usePlugins([
this.createResolveModelPlugin(options?.middlewares),
this.createConfigureContextPlugin()
])
return this.pluginEngine.executeWithPlugins( return this.pluginEngine.executeWithPlugins(
'streamObject', 'streamObject',

13
packages/aiCore/src/index.ts
View File

@ -14,7 +14,13 @@ import type { ProviderSettingsMap } from './core/providers/types'
import { createExecutor } from './core/runtime' import { createExecutor } from './core/runtime'
// ==================== 主要用户接口 ==================== // ==================== 主要用户接口 ====================
export { createExecutor, createOpenAICompatibleExecutor } from './core/runtime' export {
createExecutor,
createOpenAICompatibleExecutor,
generateObject,
generateText,
streamText
} from './core/runtime'
// ==================== 高级API ==================== // ==================== 高级API ====================
export { createModel } from './core/models' export { createModel } from './core/models'
@ -139,11 +145,6 @@ export const AiCore = {
return createExecutor(providerId, options, plugins) return createExecutor(providerId, options, plugins)
}, },
// 创建底层客户端（高级用法）
createClient(providerId: ProviderId, options: ProviderSettingsMap[ProviderId], plugins: any[] = []) {
return createExecutor(providerId, options, plugins)
},
// 获取支持的providers // 获取支持的providers
getSupportedProviders() { getSupportedProviders() {
return factoryGetSupportedProviders() return factoryGetSupportedProviders()

10
src/renderer/src/aiCore/chunk/AiSdkToChunkAdapter.ts
View File

@ -153,11 +153,11 @@ export class AiSdkToChunkAdapter {
break break
// === 步骤相关事件 === // === 步骤相关事件 ===
case 'start': // case 'start':
this.onChunk({ // this.onChunk({
type: ChunkType.LLM_RESPONSE_CREATED // type: ChunkType.LLM_RESPONSE_CREATED
}) // })
break // break
// TODO: 需要区分接口开始和步骤开始 // TODO: 需要区分接口开始和步骤开始
// case 'start-step': // case 'start-step':
// this.onChunk({ // this.onChunk({

4
src/renderer/src/aiCore/index_new.ts
View File

@ -21,7 +21,7 @@ import { createPromptToolUsePlugin, webSearchPlugin } from '@cherrystudio/ai-cor
import { isDedicatedImageGenerationModel } from '@renderer/config/models' import { isDedicatedImageGenerationModel } from '@renderer/config/models'
import { createVertexProvider, isVertexAIConfigured, isVertexProvider } from '@renderer/hooks/useVertexAI' import { createVertexProvider, isVertexAIConfigured, isVertexProvider } from '@renderer/hooks/useVertexAI'
import { getProviderByModel } from '@renderer/services/AssistantService' import { getProviderByModel } from '@renderer/services/AssistantService'
import type { GenerateImageParams, Model, Provider } from '@renderer/types' import type { Assistant, GenerateImageParams, Model, Provider } from '@renderer/types'
import { formatApiHost } from '@renderer/utils/api' import { formatApiHost } from '@renderer/utils/api'
import { cloneDeep } from 'lodash' import { cloneDeep } from 'lodash'
@ -30,6 +30,7 @@ import LegacyAiProvider from './index'
import { AiSdkMiddlewareConfig, buildAiSdkMiddlewares } from './middleware/aisdk/AiSdkMiddlewareBuilder' import { AiSdkMiddlewareConfig, buildAiSdkMiddlewares } from './middleware/aisdk/AiSdkMiddlewareBuilder'
import { CompletionsResult } from './middleware/schemas' import { CompletionsResult } from './middleware/schemas'
import reasoningTimePlugin from './plugins/reasoningTimePlugin' import reasoningTimePlugin from './plugins/reasoningTimePlugin'
import { searchOrchestrationPlugin } from './plugins/searchOrchestrationPlugin'
import { createAihubmixProvider } from './provider/aihubmix' import { createAihubmixProvider } from './provider/aihubmix'
import { getAiSdkProviderId } from './provider/factory' import { getAiSdkProviderId } from './provider/factory'
@ -162,6 +163,7 @@ export default class ModernAiProvider {
// 内置了默认搜索参数，如果改的话可以传config进去 // 内置了默认搜索参数，如果改的话可以传config进去
plugins.push(webSearchPlugin()) plugins.push(webSearchPlugin())
} }
plugins.push(searchOrchestrationPlugin(middlewareConfig.assistant as Assistant))
// 2. 推理模型时添加推理插件 // 2. 推理模型时添加推理插件
if (middlewareConfig.enableReasoning) { if (middlewareConfig.enableReasoning) {

4
src/renderer/src/aiCore/middleware/aisdk/AiSdkMiddlewareBuilder.ts
View File

@ -3,7 +3,7 @@ import {
LanguageModelV2Middleware, LanguageModelV2Middleware,
simulateStreamingMiddleware simulateStreamingMiddleware
} from '@cherrystudio/ai-core' } from '@cherrystudio/ai-core'
import type { BaseTool, Model, Provider } from '@renderer/types' import type { Assistant, BaseTool, Model, Provider } from '@renderer/types'
import type { Chunk } from '@renderer/types/chunk' import type { Chunk } from '@renderer/types/chunk'
/** /**
@ -19,6 +19,8 @@ export interface AiSdkMiddlewareConfig {
enableTool?: boolean enableTool?: boolean
enableWebSearch?: boolean enableWebSearch?: boolean
mcpTools?: BaseTool[] mcpTools?: BaseTool[]
// TODO assistant
assistant?: Assistant
} }
/** /**

376
src/renderer/src/aiCore/plugins/searchOrchestrationPlugin.ts Normal file
View File

@ -0,0 +1,376 @@
/**
* 搜索编排插件
*
* 功能：
* 1. onRequestStart: 智能意图识别 - 分析是否需要网络搜索、知识库搜索、记忆搜索
* 2. transformParams: 根据意图分析结果动态添加对应的工具
* 3. onRequestEnd: 自动记忆存储
*/
import type { AiRequestContext } from '@cherrystudio/ai-core'
import { definePlugin } from '@cherrystudio/ai-core'
import { RuntimeExecutor } from '@cherrystudio/ai-core/core/runtime/executor'
// import { generateObject } from '@cherrystudio/ai-core'
import {
SEARCH_SUMMARY_PROMPT,
SEARCH_SUMMARY_PROMPT_KNOWLEDGE_ONLY,
SEARCH_SUMMARY_PROMPT_WEB_ONLY
} from '@renderer/config/prompts'
import { getDefaultModel, getProviderByModel } from '@renderer/services/AssistantService'
import store from '@renderer/store'
import { selectCurrentUserId, selectGlobalMemoryEnabled, selectMemoryConfig } from '@renderer/store/memory'
import type { Assistant, Message } from '@renderer/types'
import { getMainTextContent } from '@renderer/utils/messageUtils/find'
import { isEmpty } from 'lodash'
import { z } from 'zod'
import { MemoryProcessor } from '../../services/MemoryProcessor'
import { memorySearchTool } from '../tools/MemorySearchTool'
import { webSearchTool } from '../tools/WebSearchTool'
// const getMessageContent = (message: Message) => {
// if (typeof message.content === 'string') return message.content
// return message.content.reduce((acc, part) => {
// if (part.type === 'text') {
// return acc + part.text + '\n'
// }
// return acc
// }, '')
// }
// === Schema Definitions ===
const WebSearchSchema = z.object({
question: z
.array(z.string())
.describe('Search queries for web search. Use "not_needed" if no web search is required.'),
links: z.array(z.string()).optional().describe('Specific URLs to search or summarize if mentioned in the query.')
})
const KnowledgeSearchSchema = z.object({
question: z
.array(z.string())
.describe('Search queries for knowledge base. Use "not_needed" if no knowledge search is required.'),
rewrite: z
.string()
.describe('Rewritten query with alternative phrasing while preserving original intent and meaning.')
})
const SearchIntentAnalysisSchema = z.object({
websearch: WebSearchSchema.optional().describe('Web search intent analysis results.'),
knowledge: KnowledgeSearchSchema.optional().describe('Knowledge base search intent analysis results.')
})
type SearchIntentResult = z.infer<typeof SearchIntentAnalysisSchema>
/**
* 🧠 意图分析函数 - 使用结构化输出重构
*/
async function analyzeSearchIntent(
lastUserMessage: Message,
assistant: Assistant,
options: {
shouldWebSearch?: boolean
shouldKnowledgeSearch?: boolean
shouldMemorySearch?: boolean
lastAnswer?: Message
context?:
| AiRequestContext
| {
executor: RuntimeExecutor
}
} = {}
): Promise<SearchIntentResult | undefined> {
const { shouldWebSearch = false, shouldKnowledgeSearch = false, lastAnswer, context } = options
if (!lastUserMessage) return undefined
// 根据配置决定是否需要提取
const needWebExtract = shouldWebSearch
const needKnowledgeExtract = shouldKnowledgeSearch
if (!needWebExtract && !needKnowledgeExtract) return undefined
// 选择合适的提示词和schema
let prompt: string
let schema: z.Schema
if (needWebExtract && !needKnowledgeExtract) {
prompt = SEARCH_SUMMARY_PROMPT_WEB_ONLY
schema = z.object({ websearch: WebSearchSchema })
} else if (!needWebExtract && needKnowledgeExtract) {
prompt = SEARCH_SUMMARY_PROMPT_KNOWLEDGE_ONLY
schema = z.object({ knowledge: KnowledgeSearchSchema })
} else {
prompt = SEARCH_SUMMARY_PROMPT
schema = SearchIntentAnalysisSchema
}
// 构建消息上下文
const messages = lastAnswer ? [lastAnswer, lastUserMessage] : [lastUserMessage]
console.log('messagesmessagesmessagesmessagesmessagesmessagesmessages', messages)
// 格式化消息为提示词期望的格式
// const chatHistory =
// messages.length > 1
// ? messages
// .slice(0, -1)
// .map((msg) => `${msg.role}: ${getMainTextContent(msg)}`)
// .join('\n')
// : ''
// const question = getMainTextContent(lastUserMessage) || ''
// // 使用模板替换变量
// const formattedPrompt = prompt.replace('{chat_history}', chatHistory).replace('{question}', question)
// 获取模型和provider信息
const model = assistant.model || getDefaultModel()
const provider = getProviderByModel(model)
if (!provider || isEmpty(provider.apiKey)) {
console.error('Provider not found or missing API key')
return getFallbackResult()
}
try {
const result = await context?.executor?.generateObject(model.id, { schema, prompt })
console.log('result', context)
const parsedResult = result?.object as SearchIntentResult
// 根据需求过滤结果
return {
websearch: needWebExtract ? parsedResult?.websearch : undefined,
knowledge: needKnowledgeExtract ? parsedResult?.knowledge : undefined
}
} catch (e: any) {
console.error('analyze search intent error', e)
return getFallbackResult()
}
function getFallbackResult(): SearchIntentResult {
const fallbackContent = getMainTextContent(lastUserMessage)
return {
websearch: shouldWebSearch ? { question: [fallbackContent || 'search'] } : undefined,
knowledge: shouldKnowledgeSearch
? {
question: [fallbackContent || 'search'],
rewrite: fallbackContent || 'search'
}
: undefined
}
}
}
/**
* 🧠 记忆存储函数 - 基于注释代码中的 processConversationMemory
*/
async function storeConversationMemory(messages: Message[], assistant: Assistant): Promise<void> {
const globalMemoryEnabled = selectGlobalMemoryEnabled(store.getState())
if (!globalMemoryEnabled || !assistant.enableMemory) {
console.log('Memory storage is disabled')
return
}
try {
const memoryConfig = selectMemoryConfig(store.getState())
// 转换消息为记忆处理器期望的格式
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) {
console.log('Need at least a user message and assistant response for memory processing')
return
}
const currentUserId = selectCurrentUserId(store.getState())
const lastUserMessage = messages.findLast((m) => m.role === 'user')
const processorConfig = MemoryProcessor.getProcessorConfig(
memoryConfig,
assistant.id,
currentUserId,
lastUserMessage?.id
)
console.log('Processing conversation memory...', { messageCount: conversationMessages.length })
// 后台处理对话记忆（不阻塞 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 conversation memory processing:', error)
// 不抛出错误，避免影响主流程
}
}
/**
* 🎯 搜索编排插件
*/
export const searchOrchestrationPlugin = (assistant: Assistant) => {
// 存储意图分析结果
const intentAnalysisResults: { [requestId: string]: SearchIntentResult } = {}
const userMessages: { [requestId: string]: Message } = {}
console.log('searchOrchestrationPlugin', assistant)
return definePlugin({
name: 'search-orchestration',
enforce: 'pre', // 确保在其他插件之前执行
/**
* 🔍 Step 1: 意图识别阶段
*/
onRequestStart: async (context: AiRequestContext) => {
console.log('🧠 [SearchOrchestration] Starting intent analysis...', context.requestId)
try {
// 从参数中提取信息
const messages = context.originalParams.messages
if (!messages || messages.length === 0) {
console.log('🧠 [SearchOrchestration] No messages found, skipping analysis')
return
}
const lastUserMessage = messages[messages.length - 1]
const lastAssistantMessage = messages.length >= 2 ? messages[messages.length - 2] : undefined
// 存储用户消息用于后续记忆存储
userMessages[context.requestId] = lastUserMessage
// 判断是否需要各种搜索
const knowledgeBaseIds = assistant.knowledge_bases?.map((base) => base.id)
const hasKnowledgeBase = !isEmpty(knowledgeBaseIds)
const knowledgeRecognition = assistant.knowledgeRecognition || 'on'
const globalMemoryEnabled = selectGlobalMemoryEnabled(store.getState())
const shouldWebSearch = !!assistant.webSearchProviderId
const shouldKnowledgeSearch = hasKnowledgeBase && knowledgeRecognition === 'on'
const shouldMemorySearch = globalMemoryEnabled && assistant.enableMemory
console.log('🧠 [SearchOrchestration] Search capabilities:', {
shouldWebSearch,
shouldKnowledgeSearch,
shouldMemorySearch
})
// 执行意图分析
if (shouldWebSearch || shouldKnowledgeSearch) {
const analysisResult = await analyzeSearchIntent(lastUserMessage, assistant, {
shouldWebSearch,
shouldKnowledgeSearch,
shouldMemorySearch,
lastAnswer: lastAssistantMessage,
context
})
if (analysisResult) {
intentAnalysisResults[context.requestId] = analysisResult
console.log('🧠 [SearchOrchestration] Intent analysis completed:', analysisResult)
}
}
} catch (error) {
console.error('🧠 [SearchOrchestration] Intent analysis failed:', error)
// 不抛出错误，让流程继续
}
},
/**
* 🔧 Step 2: 工具配置阶段
*/
transformParams: async (params: any, context: AiRequestContext) => {
console.log('🔧 [SearchOrchestration] Configuring tools based on intent...', context.requestId)
try {
const analysisResult = intentAnalysisResults[context.requestId]
console.log('analysisResult', analysisResult)
if (!analysisResult || !assistant) {
console.log('🔧 [SearchOrchestration] No analysis result or assistant, skipping tool configuration')
return params
}
// 确保 tools 对象存在
if (!params.tools) {
params.tools = {}
}
// 🌐 网络搜索工具配置
if (analysisResult.websearch && assistant.webSearchProviderId) {
const needsSearch = analysisResult.websearch.question && analysisResult.websearch.question[0] !== 'not_needed'
if (needsSearch) {
console.log('🌐 [SearchOrchestration] Adding web search tool')
params.tools['builtin_web_search'] = webSearchTool(assistant.webSearchProviderId)
}
}
// 📚 知识库搜索工具配置
if (analysisResult.knowledge) {
const needsKnowledgeSearch =
analysisResult.knowledge.question && analysisResult.knowledge.question[0] !== 'not_needed'
if (needsKnowledgeSearch) {
console.log('📚 [SearchOrchestration] Adding knowledge search tool')
// TODO: 添加知识库搜索工具
// params.tools['builtin_knowledge_search'] = knowledgeSearchTool(assistant.knowledge_bases)
}
}
// 🧠 记忆搜索工具配置
const globalMemoryEnabled = selectGlobalMemoryEnabled(store.getState())
if (globalMemoryEnabled && assistant.enableMemory) {
console.log('🧠 [SearchOrchestration] Adding memory search tool')
params.tools['builtin_memory_search'] = memorySearchTool()
}
console.log('🔧 [SearchOrchestration] Tools configured:', Object.keys(params.tools))
return params
} catch (error) {
console.error('🔧 [SearchOrchestration] Tool configuration failed:', error)
return params
}
},
/**
* 💾 Step 3: 记忆存储阶段
*/
// eslint-disable-next-line @typescript-eslint/no-unused-vars
onRequestEnd: async (context: AiRequestContext, _result: any) => {
console.log('💾 [SearchOrchestration] Starting memory storage...', context.requestId)
try {
const assistant = context.originalParams.assistant as Assistant
const messages = context.originalParams.messages as Message[]
if (messages && assistant) {
await storeConversationMemory(messages, assistant)
}
// 清理缓存
delete intentAnalysisResults[context.requestId]
delete userMessages[context.requestId]
} catch (error) {
console.error('💾 [SearchOrchestration] Memory storage failed:', error)
// 不抛出错误，避免影响主流程
}
}
})
}
export default searchOrchestrationPlugin

8
src/renderer/src/aiCore/transformParameters.ts
View File

@ -50,7 +50,7 @@ import { buildSystemPrompt } from '@renderer/utils/prompt'
import { defaultTimeout } from '@shared/config/constant' import { defaultTimeout } from '@shared/config/constant'
import { isEmpty } from 'lodash' import { isEmpty } from 'lodash'
import { webSearchTool } from './tools/WebSearchTool' // import { webSearchTool } from './tools/WebSearchTool'
// import { jsonSchemaToZod } from 'json-schema-to-zod' // import { jsonSchemaToZod } from 'json-schema-to-zod'
import { setupToolsConfig } from './utils/mcp' import { setupToolsConfig } from './utils/mcp'
import { buildProviderOptions } from './utils/options' import { buildProviderOptions } from './utils/options'
@ -298,9 +298,9 @@ export async function buildStreamTextParams(
enableToolUse: enableTools enableToolUse: enableTools
}) })
if (webSearchProviderId) { // if (webSearchProviderId) {
tools['builtin_web_search'] = webSearchTool(webSearchProviderId) // tools['builtin_web_search'] = webSearchTool(webSearchProviderId)
} // }
// 构建真正的 providerOptions // 构建真正的 providerOptions
const providerOptions = buildProviderOptions(assistant, model, provider, { const providerOptions = buildProviderOptions(assistant, model, provider, {

14
src/renderer/src/services/ApiService.ts
View File

@ -16,7 +16,7 @@ import { getStoreSetting } from '@renderer/hooks/useSettings'
import i18n from '@renderer/i18n' import i18n from '@renderer/i18n'
import store from '@renderer/store' import store from '@renderer/store'
import { Assistant, MCPServer, MCPTool, Model, Provider } from '@renderer/types' import { Assistant, MCPServer, MCPTool, Model, Provider } from '@renderer/types'
import { type Chunk } from '@renderer/types/chunk' import { type Chunk, ChunkType } from '@renderer/types/chunk'
import { Message } from '@renderer/types/newMessage' import { Message } from '@renderer/types/newMessage'
import { SdkModel } from '@renderer/types/sdk' import { SdkModel } from '@renderer/types/sdk'
import { removeSpecialCharactersForTopicName } from '@renderer/utils' import { removeSpecialCharactersForTopicName } from '@renderer/utils'
@ -432,14 +432,16 @@ export async function fetchChatCompletion({
enableReasoning: capabilities.enableReasoning, enableReasoning: capabilities.enableReasoning,
enableTool: assistant.settings?.toolUseMode === 'prompt', enableTool: assistant.settings?.toolUseMode === 'prompt',
enableWebSearch: capabilities.enableWebSearch, enableWebSearch: capabilities.enableWebSearch,
mcpTools mcpTools,
assistant
} }
// --- Call AI Completions --- // if (capabilities.enableWebSearch) {
// onChunkReceived({ type: ChunkType.LLM_RESPONSE_CREATED })
await AI.completions(modelId, aiSdkParams, middlewareConfig)
// if (enableWebSearch) {
// onChunkReceived({ type: ChunkType.LLM_WEB_SEARCH_IN_PROGRESS }) // onChunkReceived({ type: ChunkType.LLM_WEB_SEARCH_IN_PROGRESS })
// } // }
// --- Call AI Completions ---
onChunkReceived({ type: ChunkType.LLM_RESPONSE_CREATED })
await AI.completions(modelId, aiSdkParams, middlewareConfig)
// await AI.completions( // await AI.completions(
// { // {
// callType: 'chat', // callType: 'chat',

28
yarn.lock
View File

@ -148,6 +148,18 @@ __metadata:
languageName: node languageName: node
linkType: hard linkType: hard
"@ai-sdk/openai-compatible@npm:1.0.0-beta.8":
version: 1.0.0-beta.8
resolution: "@ai-sdk/openai-compatible@npm:1.0.0-beta.8"
dependencies:
"@ai-sdk/provider": "npm:2.0.0-beta.1"
"@ai-sdk/provider-utils": "npm:3.0.0-beta.5"
peerDependencies:
zod: ^3.25.76 || ^4
checksum: 10c0/047f044bf0da9608e09073957916373bd39760ec00f498ba0c4a597ec70ba9eb4ef31f06b21b363b3c1ba775f64fcc46d41b60a171e0e99250824817ecb19ba8
languageName: node
linkType: hard
"@ai-sdk/openai@npm:2.0.0-beta.9": "@ai-sdk/openai@npm:2.0.0-beta.9":
version: 2.0.0-beta.9 version: 2.0.0-beta.9
resolution: "@ai-sdk/openai@npm:2.0.0-beta.9" resolution: "@ai-sdk/openai@npm:2.0.0-beta.9"
@ -188,6 +200,20 @@ __metadata:
languageName: node languageName: node
linkType: hard linkType: hard
"@ai-sdk/provider-utils@npm:3.0.0-beta.5":
version: 3.0.0-beta.5
resolution: "@ai-sdk/provider-utils@npm:3.0.0-beta.5"
dependencies:
"@ai-sdk/provider": "npm:2.0.0-beta.1"
"@standard-schema/spec": "npm:^1.0.0"
eventsource-parser: "npm:^3.0.3"
zod-to-json-schema: "npm:^3.24.1"
peerDependencies:
zod: ^3.25.76 || ^4
checksum: 10c0/229a53672accc5d9d986da2e18f619dbcfaf64ab269c8cc9e955480c4428d2a87255330c587453d01eb66ac297bb6975f91c24a93f87dd4b84f6428cb60d4211
languageName: node
linkType: hard
"@ai-sdk/provider@npm:2.0.0-beta.1": "@ai-sdk/provider@npm:2.0.0-beta.1":
version: 2.0.0-beta.1 version: 2.0.0-beta.1
resolution: "@ai-sdk/provider@npm:2.0.0-beta.1" resolution: "@ai-sdk/provider@npm:2.0.0-beta.1"
@ -1251,7 +1277,7 @@ __metadata:
"@ai-sdk/deepseek": "npm:1.0.0-beta.6" "@ai-sdk/deepseek": "npm:1.0.0-beta.6"
"@ai-sdk/google": "npm:2.0.0-beta.11" "@ai-sdk/google": "npm:2.0.0-beta.11"
"@ai-sdk/openai": "npm:2.0.0-beta.9" "@ai-sdk/openai": "npm:2.0.0-beta.9"
"@ai-sdk/openai-compatible": "npm:1.0.0-beta.6" "@ai-sdk/openai-compatible": "npm:1.0.0-beta.8"
"@ai-sdk/provider": "npm:2.0.0-beta.1" "@ai-sdk/provider": "npm:2.0.0-beta.1"
"@ai-sdk/provider-utils": "npm:3.0.0-beta.3" "@ai-sdk/provider-utils": "npm:3.0.0-beta.3"
"@ai-sdk/xai": "npm:2.0.0-beta.8" "@ai-sdk/xai": "npm:2.0.0-beta.8"