mirror of
https://github.com/CherryHQ/cherry-studio.git
synced 2025-12-28 05:11:24 +08:00
feat: enhance ToolCallChunkHandler with detailed chunk handling and remove unused plugins
- Updated `handleToolCallCreated` method to support additional chunk types with optional provider metadata. - Removed deprecated `smoothReasoningPlugin` and `textPlugin` files to clean up the codebase. - Cleaned up unused type imports in `tool.ts` for improved clarity and maintainability.
This commit is contained in:
lizhixuan
parent
fcc8836c95
commit
addd5ffdfa
@ -8,6 +8,7 @@ import { ToolCallUnion, ToolResultUnion, ToolSet } from '@cherrystudio/ai-core'
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import Logger from '@renderer/config/logger'
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import { BaseTool, MCPToolResponse, ToolCallResponse } from '@renderer/types'
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import { Chunk, ChunkType } from '@renderer/types/chunk'
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import { type ProviderMetadata } from 'ai'
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// import type {
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// AnthropicSearchOutput,
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// WebSearchPluginConfig
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@ -40,7 +41,27 @@ export class ToolCallChunkHandler {
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// this.onChunk = callback
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// }
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handleToolCallCreated(chunk: { type: 'tool-input-start' | 'tool-input-delta' | 'tool-input-end' }): void {
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handleToolCallCreated(
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chunk:
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| {
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type: 'tool-input-start'
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id: string
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toolName: string
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providerMetadata?: ProviderMetadata
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providerExecuted?: boolean
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}
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| {
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type: 'tool-input-end'
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id: string
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providerMetadata?: ProviderMetadata
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}
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| {
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type: 'tool-input-delta'
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id: string
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delta: string
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providerMetadata?: ProviderMetadata
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}
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): void {
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switch (chunk.type) {
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case 'tool-input-start': {
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// 能拿到说明是mcpTool
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@ -1,152 +0,0 @@
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// // 可能会废弃，在流上做delay还是有问题
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// import { definePlugin } from '@cherrystudio/ai-core'
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// const chunkingRegex = /([\u4E00-\u9FFF])|\S+\s+/
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// const delayInMs = 50
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// export default definePlugin({
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// name: 'reasoningPlugin',
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// transformStream: () => () => {
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// // === smoothing 状态 ===
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// let buffer = ''
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// // === 时间跟踪状态 ===
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// let thinkingStartTime = performance.now()
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// let hasStartedThinking = false
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// let accumulatedThinkingContent = ''
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// // === 日志计数器 ===
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// let chunkCount = 0
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// let delayCount = 0
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// const delay = (ms: number) => new Promise((resolve) => setTimeout(resolve, ms))
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// // 收集所有当前可匹配的chunks
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// const collectMatches = (inputBuffer: string) => {
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// const matches: string[] = []
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// let tempBuffer = inputBuffer
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// let match
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// // 重置regex状态
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// chunkingRegex.lastIndex = 0
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// while ((match = chunkingRegex.exec(tempBuffer)) !== null) {
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// matches.push(match[0])
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// tempBuffer = tempBuffer.slice(match.index + match[0].length)
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// // 重置regex以从头开始匹配剩余内容
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// chunkingRegex.lastIndex = 0
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// }
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// return {
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// matches,
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// remaining: tempBuffer
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// }
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// }
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// return new TransformStream({
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// async transform(chunk, controller) {
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// if (chunk.type !== 'reasoning') {
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// // === 处理 reasoning 结束 ===
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// if (hasStartedThinking && accumulatedThinkingContent) {
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// console.log(
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// `[ReasoningPlugin] Ending reasoning. Final stats: chunks=${chunkCount}, delays=${delayCount}, efficiency=${(chunkCount / Math.max(delayCount, 1)).toFixed(2)}x`
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// )
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// // 先输出剩余的 buffer
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// if (buffer.length > 0) {
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// console.log(`[ReasoningPlugin] Flushing remaining buffer: "${buffer}"`)
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// controller.enqueue({
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// type: 'reasoning',
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// textDelta: buffer,
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// thinking_millsec: performance.now() - thinkingStartTime
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// })
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// buffer = ''
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// }
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// // 生成 reasoning-signature
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// controller.enqueue({
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// type: 'reasoning-signature',
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// text: accumulatedThinkingContent,
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// thinking_millsec: performance.now() - thinkingStartTime
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// })
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// // 重置状态
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// accumulatedThinkingContent = ''
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// hasStartedThinking = false
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// thinkingStartTime = 0
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// chunkCount = 0
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// delayCount = 0
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// }
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// controller.enqueue(chunk)
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// return
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// }
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// // === 处理 reasoning 类型 ===
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// // 1. 时间跟踪逻辑
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// if (!hasStartedThinking) {
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// hasStartedThinking = true
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// thinkingStartTime = performance.now()
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// console.log(`[ReasoningPlugin] Starting reasoning session`)
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// }
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// accumulatedThinkingContent += chunk.textDelta
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// // 2. 动态Smooth处理逻辑
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// const beforeBuffer = buffer
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// buffer += chunk.textDelta
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// console.log(`[ReasoningPlugin] Received chunk: "${chunk.textDelta}", buffer: "${beforeBuffer}" → "${buffer}"`)
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// // 收集所有当前可以匹配的chunks
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// const { matches, remaining } = collectMatches(buffer)
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// if (matches.length > 0) {
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// console.log(
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// `[ReasoningPlugin] Collected ${matches.length} matches: [${matches.map((m) => `"${m}"`).join(', ')}], remaining: "${remaining}"`
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// )
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// // 批量输出所有匹配的chunks
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// for (const matchText of matches) {
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// controller.enqueue({
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// type: 'reasoning',
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// textDelta: matchText,
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// thinking_millsec: performance.now() - thinkingStartTime
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// })
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// chunkCount++
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// }
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// // 更新buffer为剩余内容
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// buffer = remaining
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// // 只等待一次，而不是每个chunk都等待
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// delayCount++
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// console.log(
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// `[ReasoningPlugin] Delaying ${delayInMs}ms (delay #${delayCount}, efficiency: ${(chunkCount / delayCount).toFixed(2)} chunks/delay)`
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// )
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// const delayStart = performance.now()
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// await delay(delayInMs)
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// const actualDelay = performance.now() - delayStart
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// console.log(`[ReasoningPlugin] Delay completed: expected=${delayInMs}ms, actual=${actualDelay.toFixed(1)}ms`)
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// } else {
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// console.log(`[ReasoningPlugin] No matches found, keeping in buffer: "${buffer}"`)
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// }
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// // 如果没有匹配，保留在buffer中等待下次数据
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// },
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// // === flush 处理剩余 buffer ===
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// flush(controller) {
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// if (buffer.length > 0) {
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// console.log(`[ReasoningPlugin] Final flush: "${buffer}"`)
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// controller.enqueue({
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// type: 'reasoning',
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// textDelta: buffer,
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// thinking_millsec: hasStartedThinking ? performance.now() - thinkingStartTime : 0
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// })
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// }
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// }
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// })
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// }
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// })
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@ -1,13 +0,0 @@
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// // 可能会废弃，在流上做delay还是有问题
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// import { definePlugin, smoothStream } from '@cherrystudio/ai-core'
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// export default definePlugin({
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// name: 'textPlugin',
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// transformStream: () =>
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// smoothStream({
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// delayInMs: 50,
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// // 中文3个字符一个chunk,英文一个单词一个chunk
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// chunking: /([\u4E00-\u9FFF]{3})|\S+\s+/
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// })
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// })
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140
src/renderer/src/aiCore/tools/MemorySearchTool.ts
Normal file
140
src/renderer/src/aiCore/tools/MemorySearchTool.ts
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@ -0,0 +1,140 @@
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import store from '@renderer/store'
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import { selectCurrentUserId, selectGlobalMemoryEnabled, selectMemoryConfig } from '@renderer/store/memory'
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import type { Assistant } from '@renderer/types'
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import { type InferToolOutput, tool } from 'ai'
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import { z } from 'zod'
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import { MemoryProcessor } from '../../services/MemoryProcessor'
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/**
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* 🧠 基础记忆搜索工具
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* AI 可以主动调用的简单记忆搜索
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*/
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export const memorySearchTool = () => {
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return tool({
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name: 'builtin_memory_search',
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description: 'Search through conversation memories and stored facts for relevant context',
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inputSchema: z.object({
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query: z.string().describe('Search query to find relevant memories'),
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limit: z.number().min(1).max(20).default(5).describe('Maximum number of memories to return')
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}),
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execute: async ({ query, limit = 5 }) => {
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console.log('🧠 [memorySearchTool] Searching memories:', { query, limit })
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try {
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const globalMemoryEnabled = selectGlobalMemoryEnabled(store.getState())
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if (!globalMemoryEnabled) {
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return []
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}
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const memoryConfig = selectMemoryConfig(store.getState())
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if (!memoryConfig.llmApiClient || !memoryConfig.embedderApiClient) {
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console.warn('Memory search skipped: embedding or LLM model not configured')
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return []
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}
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const currentUserId = selectCurrentUserId(store.getState())
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const processorConfig = MemoryProcessor.getProcessorConfig(memoryConfig, 'default', currentUserId)
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const memoryProcessor = new MemoryProcessor()
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const relevantMemories = await memoryProcessor.searchRelevantMemories(query, processorConfig, limit)
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if (relevantMemories?.length > 0) {
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console.log('🧠 [memorySearchTool] Found memories:', relevantMemories.length)
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return relevantMemories
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}
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return []
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} catch (error) {
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console.error('🧠 [memorySearchTool] Error:', error)
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return []
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}
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}
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})
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}
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/**
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* 🧠 智能记忆搜索工具（带上下文提取）
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* 从用户消息和对话历史中自动提取关键词进行记忆搜索
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*/
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export const memorySearchToolWithExtraction = (assistant: Assistant) => {
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return tool({
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name: 'memory_search_with_extraction',
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description: 'Search memories with automatic keyword extraction from conversation context',
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inputSchema: z.object({
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userMessage: z.object({
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content: z.string().describe('The main content of the user message'),
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role: z.enum(['user', 'assistant', 'system']).describe('Message role')
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}),
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lastAnswer: z
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.object({
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content: z.string().describe('The main content of the last assistant response'),
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role: z.enum(['user', 'assistant', 'system']).describe('Message role')
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})
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.optional()
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}),
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execute: async ({ userMessage, lastAnswer }) => {
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console.log('🧠 [memorySearchToolWithExtraction] Processing:', { userMessage, lastAnswer })
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try {
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const globalMemoryEnabled = selectGlobalMemoryEnabled(store.getState())
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if (!globalMemoryEnabled || !assistant.enableMemory) {
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return {
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extractedKeywords: 'Memory search disabled',
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searchResults: []
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}
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}
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const memoryConfig = selectMemoryConfig(store.getState())
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if (!memoryConfig.llmApiClient || !memoryConfig.embedderApiClient) {
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console.warn('Memory search skipped: embedding or LLM model not configured')
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return {
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extractedKeywords: 'Memory models not configured',
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searchResults: []
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}
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}
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// 🔍 使用用户消息内容作为搜索关键词
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const content = userMessage.content
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if (!content) {
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return {
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extractedKeywords: 'No content to search',
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searchResults: []
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}
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}
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const currentUserId = selectCurrentUserId(store.getState())
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const processorConfig = MemoryProcessor.getProcessorConfig(memoryConfig, assistant.id, currentUserId)
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const memoryProcessor = new MemoryProcessor()
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const relevantMemories = await memoryProcessor.searchRelevantMemories(
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content,
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processorConfig,
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5 // Limit to top 5 most relevant memories
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)
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if (relevantMemories?.length > 0) {
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console.log('🧠 [memorySearchToolWithExtraction] Found memories:', relevantMemories.length)
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return {
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extractedKeywords: content,
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searchResults: relevantMemories
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}
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}
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return {
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extractedKeywords: content,
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searchResults: []
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}
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} catch (error) {
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console.error('🧠 [memorySearchToolWithExtraction] Error:', error)
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return {
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extractedKeywords: 'Search failed',
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searchResults: []
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}
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}
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}
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})
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}
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export type MemorySearchToolOutput = InferToolOutput<ReturnType<typeof memorySearchTool>>
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export type MemorySearchToolWithExtractionOutput = InferToolOutput<ReturnType<typeof memorySearchToolWithExtraction>>
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@ -1,6 +1,3 @@
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import { WebSearchToolOutputSchema } from '@cherrystudio/ai-core/built-in/plugins'
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import type { WebSearchToolOutput } from '@renderer/aiCore/tools/WebSearchTool'
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import type { MCPToolInputSchema } from './index'
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export type ToolType = 'builtin' | 'provider' | 'mcp'
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@ -12,15 +9,15 @@ export interface BaseTool {
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type: ToolType
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}
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export interface ToolCallResponse {
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id: string
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toolName: string
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arguments: Record<string, unknown> | undefined
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status: 'invoking' | 'completed' | 'error'
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result?: any // AI SDK的工具执行结果
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error?: string
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providerExecuted?: boolean // 标识是Provider端执行还是客户端执行
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}
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// export interface ToolCallResponse {
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// id: string
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// toolName: string
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// arguments: Record<string, unknown> | undefined
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// status: 'invoking' | 'completed' | 'error'
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// result?: any // AI SDK的工具执行结果
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// error?: string
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// providerExecuted?: boolean // 标识是Provider端执行还是客户端执行
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// }
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export interface BuiltinTool extends BaseTool {
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inputSchema: MCPToolInputSchema
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@ -33,5 +30,3 @@ export interface MCPTool extends BaseTool {
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inputSchema: MCPToolInputSchema
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type: 'mcp'
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}
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export type WebSearchToolOutputSchema = WebSearchToolOutput | WebSearchToolOutputSchema
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