update at 2025-10-16 12:45:05
This commit is contained in:
douboer
335
web/src/services/chat/StreamProcessor.ts
Normal file
335
web/src/services/chat/StreamProcessor.ts
Normal file
@@ -0,0 +1,335 @@
|
||||
/**
|
||||
* StreamProcessor
|
||||
* 负责处理流式响应
|
||||
*
|
||||
* 职责：
|
||||
* - 管理流式请求
|
||||
* - 处理流式数据块
|
||||
* - 缓冲和批量输出
|
||||
* - 性能监控
|
||||
* - 错误处理和取消
|
||||
*/
|
||||
|
||||
import type { Conversation } from '../../types/chat'
|
||||
import { modelServiceManager } from '../modelServiceManager'
|
||||
import { mcpClientService } from '../MCPClientService'
|
||||
import { logger } from '../../utils/logger'
|
||||
import { ServiceError, ErrorCode } from '../../utils/error'
|
||||
|
||||
const log = logger.namespace('StreamProcessor')
|
||||
|
||||
/**
|
||||
* 流式处理选项
|
||||
*/
|
||||
export interface StreamOptions {
|
||||
conversation: Conversation
|
||||
model?: string
|
||||
mcpServerId?: string
|
||||
signal?: AbortSignal
|
||||
onChunk: (chunk: string) => void
|
||||
}
|
||||
|
||||
/**
|
||||
* 流式处理结果
|
||||
*/
|
||||
export interface StreamResult {
|
||||
success: boolean
|
||||
error?: string
|
||||
toolCalls?: any[]
|
||||
metrics?: {
|
||||
totalTime: number
|
||||
firstChunkDelay: number
|
||||
chunkCount: number
|
||||
}
|
||||
}
|
||||
|
||||
export class StreamProcessor {
|
||||
private static readonly MAX_CONTEXT_MESSAGES = 20
|
||||
private static readonly BATCH_SIZE = 3 // 每3个字符输出一次，增强流式效果
|
||||
|
||||
/**
|
||||
* 处理流式请求
|
||||
*/
|
||||
async processStream(options: StreamOptions): Promise<StreamResult> {
|
||||
const startTime = performance.now()
|
||||
log.info('开始流式处理')
|
||||
|
||||
try {
|
||||
// 获取工具列表
|
||||
const { tools, mcpServerName } = await this.prepareTools(options.mcpServerId)
|
||||
|
||||
// 准备消息列表
|
||||
const messages = this.prepareMessages(options.conversation, tools, mcpServerName)
|
||||
|
||||
// 选择服务和模型
|
||||
const { service, selectedModel } = this.selectServiceAndModel(options.model)
|
||||
|
||||
log.info('流式处理配置', {
|
||||
service: service.name,
|
||||
model: selectedModel,
|
||||
mcpServer: options.mcpServerId || '未选择',
|
||||
toolCount: tools.length,
|
||||
messageCount: messages.length
|
||||
})
|
||||
|
||||
// 执行流式请求
|
||||
const result = await this.executeStream(
|
||||
service.id,
|
||||
messages,
|
||||
selectedModel,
|
||||
options.onChunk,
|
||||
tools,
|
||||
options.signal,
|
||||
startTime
|
||||
)
|
||||
|
||||
const endTime = performance.now()
|
||||
log.info('流式处理完成', {
|
||||
totalTime: (endTime - startTime).toFixed(2) + 'ms',
|
||||
chunkCount: result.metrics?.chunkCount
|
||||
})
|
||||
|
||||
return result
|
||||
} catch (error) {
|
||||
log.error('流式处理失败', error)
|
||||
throw new ServiceError(
|
||||
error instanceof Error ? error.message : '流式请求失败',
|
||||
ErrorCode.STREAMING_ERROR,
|
||||
{ conversation: options.conversation.id }
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 准备工具列表
|
||||
*/
|
||||
private async prepareTools(mcpServerId?: string): Promise<{
|
||||
tools: any[]
|
||||
mcpServerName: string
|
||||
}> {
|
||||
let tools: any[] = []
|
||||
let mcpServerName = ''
|
||||
|
||||
if (mcpServerId) {
|
||||
log.debug('获取 MCP 工具', { mcpServerId })
|
||||
const mcpTools = mcpClientService.getTools(mcpServerId)
|
||||
const serverInfo = mcpClientService.getServerInfo(mcpServerId)
|
||||
mcpServerName = serverInfo?.name || 'mcp'
|
||||
|
||||
tools = this.convertToolsToOpenAIFormat(mcpTools, mcpServerName)
|
||||
log.info('MCP 工具已准备', {
|
||||
serverName: mcpServerName,
|
||||
toolCount: tools.length
|
||||
})
|
||||
} else {
|
||||
log.debug('未选择 MCP 服务器，不注入工具')
|
||||
}
|
||||
|
||||
return { tools, mcpServerName }
|
||||
}
|
||||
|
||||
/**
|
||||
* 准备消息列表
|
||||
*/
|
||||
private prepareMessages(
|
||||
conversation: Conversation,
|
||||
tools: any[],
|
||||
mcpServerName: string
|
||||
): any[] {
|
||||
// 过滤成功的消息
|
||||
let messages = conversation.messages
|
||||
.filter(m => m.status === 'success' || m.status === 'paused')
|
||||
.map(m => ({
|
||||
role: m.role,
|
||||
content: m.content
|
||||
}))
|
||||
|
||||
// 限制上下文
|
||||
if (messages.length > StreamProcessor.MAX_CONTEXT_MESSAGES) {
|
||||
log.info('限制上下文', {
|
||||
from: messages.length,
|
||||
to: StreamProcessor.MAX_CONTEXT_MESSAGES
|
||||
})
|
||||
messages = messages.slice(-StreamProcessor.MAX_CONTEXT_MESSAGES)
|
||||
}
|
||||
|
||||
// 添加工具系统提示词
|
||||
if (tools.length > 0 && messages.length > 0 && messages[0].role !== 'system') {
|
||||
const systemPrompt = this.createSystemPromptWithTools(tools, mcpServerName)
|
||||
messages = [
|
||||
{ role: 'system', content: systemPrompt },
|
||||
...messages
|
||||
]
|
||||
}
|
||||
|
||||
log.debug('消息列表已准备', {
|
||||
messageCount: messages.length,
|
||||
hasSystemPrompt: messages[0]?.role === 'system'
|
||||
})
|
||||
|
||||
return messages
|
||||
}
|
||||
|
||||
/**
|
||||
* 选择服务和模型
|
||||
*/
|
||||
private selectServiceAndModel(requestedModel?: string): {
|
||||
service: any
|
||||
selectedModel: string
|
||||
} {
|
||||
const allServices = modelServiceManager.getAllServices()
|
||||
const services = allServices.filter(s => s.status === 'connected')
|
||||
|
||||
if (services.length === 0) {
|
||||
throw new ServiceError(
|
||||
'没有可用的模型服务，请先在"模型服务"中添加并连接服务',
|
||||
ErrorCode.MODEL_NOT_AVAILABLE
|
||||
)
|
||||
}
|
||||
|
||||
let service = services[0]
|
||||
let selectedModel = requestedModel || service.models?.[0] || 'default'
|
||||
|
||||
// 如果指定了模型，尝试找到拥有该模型的服务
|
||||
if (requestedModel) {
|
||||
const foundService = services.find(s =>
|
||||
s.models && s.models.includes(requestedModel)
|
||||
)
|
||||
if (foundService) {
|
||||
service = foundService
|
||||
selectedModel = requestedModel
|
||||
log.debug('找到匹配服务', { service: foundService.name })
|
||||
} else {
|
||||
log.warn('未找到包含该模型的服务，使用默认服务', {
|
||||
requestedModel
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
return { service, selectedModel }
|
||||
}
|
||||
|
||||
/**
|
||||
* 执行流式请求
|
||||
*/
|
||||
private async executeStream(
|
||||
serviceId: string,
|
||||
messages: any[],
|
||||
model: string,
|
||||
onChunk: (chunk: string) => void,
|
||||
tools: any[],
|
||||
signal?: AbortSignal,
|
||||
startTime?: number
|
||||
): Promise<StreamResult> {
|
||||
const beforeStreamCall = performance.now()
|
||||
let chunkCount = 0
|
||||
let firstChunkDelay = 0
|
||||
let buffer = ''
|
||||
|
||||
log.info('开始流式请求')
|
||||
|
||||
const result = await modelServiceManager.sendChatRequestStream(
|
||||
serviceId,
|
||||
messages,
|
||||
model,
|
||||
(chunk) => {
|
||||
chunkCount++
|
||||
|
||||
// 记录首字延迟
|
||||
if (chunkCount === 1) {
|
||||
firstChunkDelay = performance.now() - beforeStreamCall
|
||||
log.debug('首字延迟', { delay: firstChunkDelay.toFixed(2) + 'ms' })
|
||||
}
|
||||
|
||||
// 批量输出，增强流式效果
|
||||
buffer += chunk
|
||||
if (buffer.length >= StreamProcessor.BATCH_SIZE) {
|
||||
const output = buffer
|
||||
buffer = ''
|
||||
onChunk(output)
|
||||
}
|
||||
},
|
||||
tools.length > 0 ? tools : undefined,
|
||||
signal
|
||||
)
|
||||
|
||||
// 输出剩余缓冲区内容
|
||||
if (buffer.length > 0) {
|
||||
onChunk(buffer)
|
||||
}
|
||||
|
||||
const afterStreamCall = performance.now()
|
||||
const totalTime = startTime ? afterStreamCall - startTime : afterStreamCall - beforeStreamCall
|
||||
|
||||
log.info('流式请求完成', {
|
||||
chunkCount,
|
||||
totalTime: totalTime.toFixed(2) + 'ms',
|
||||
firstChunkDelay: firstChunkDelay.toFixed(2) + 'ms'
|
||||
})
|
||||
|
||||
if (!result.success) {
|
||||
throw new ServiceError(
|
||||
result.error || '流式请求失败',
|
||||
ErrorCode.STREAMING_ERROR
|
||||
)
|
||||
}
|
||||
|
||||
return {
|
||||
success: true,
|
||||
toolCalls: result.data?.toolCalls,
|
||||
metrics: {
|
||||
totalTime,
|
||||
firstChunkDelay,
|
||||
chunkCount
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 转换工具为 OpenAI 格式
|
||||
*/
|
||||
private convertToolsToOpenAIFormat(mcpTools: any[], serverName: string): any[] {
|
||||
if (!Array.isArray(mcpTools)) {
|
||||
log.warn('工具列表不是数组', { mcpTools })
|
||||
return []
|
||||
}
|
||||
|
||||
return mcpTools.map(tool => ({
|
||||
type: 'function',
|
||||
function: {
|
||||
name: `${serverName}__${tool.name}`,
|
||||
description: tool.description || tool.name,
|
||||
parameters: tool.inputSchema || {
|
||||
type: 'object',
|
||||
properties: {},
|
||||
required: []
|
||||
}
|
||||
}
|
||||
}))
|
||||
}
|
||||
|
||||
/**
|
||||
* 创建包含工具信息的系统提示词
|
||||
*/
|
||||
private createSystemPromptWithTools(tools: any[], serverName: string): string {
|
||||
const toolList = tools.map(t => `- ${t.function.name}: ${t.function.description}`).join('\n')
|
||||
|
||||
return `你是一个智能助手，可以使用以下工具来帮助用户：
|
||||
|
||||
可用工具列表：
|
||||
${toolList}
|
||||
|
||||
当需要使用工具时，请按照 OpenAI 的 function calling 格式调用。工具名称格式为：${serverName}__工具名称
|
||||
|
||||
注意事项：
|
||||
1. 仔细阅读工具的描述，确保理解其功能
|
||||
2. 根据用户需求选择合适的工具
|
||||
3. 提供准确的参数
|
||||
4. 可以连续调用多个工具来完成复杂任务
|
||||
|
||||
现在，请根据用户的需求提供帮助。`
|
||||
}
|
||||
}
|
||||
|
||||
// 导出单例
|
||||
export const streamProcessor = new StreamProcessor()
|
||||
Reference in New Issue
Block a user