refactor(agent): 迁移AI会话引擎至pi-agent-core库
将原有基于Anthropic/OpenAI SDK的直播聊天代理重构为使用`@earendil-works/pi-agent-core`和`@earendil-works/pi-ai`库的统一API。 新增pi-bridge、pi-model、pi-persist、pi-tools四个模块，封装Agent路由、模型配置、消息持久化和工具适配逻辑。移除`chat.ts`中大量死代码，简化WebSocket处理流程。 BREAKING CHANGE: 移除`VideoAgent`类的`getAnthropicClient`、`getOpenAIClient`、`executeTool`等方法，外部调用需迁移至新pi-bridge API。`PROJECT_ROOT`路径计算方式变更，从`../../..`变为`../../`。
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2277
web/package-lock.json
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web/package-lock.json
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@@ -12,6 +12,8 @@
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},
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"dependencies": {
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"@anthropic-ai/sdk": "^0.95.0",
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"@earendil-works/pi-agent-core": "^0.74.0",
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"@earendil-works/pi-ai": "^0.74.0",
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"@types/multer": "^2.1.0",
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"better-sqlite3": "^11.6.0",
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"cors": "^2.8.5",
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@@ -1,102 +1,12 @@
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import Anthropic from '@anthropic-ai/sdk';
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import OpenAI from 'openai';
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import { tools, ToolDefinition } from './tools/index';
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import { getDb } from '../db';
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import fs from 'fs';
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import path from 'path';
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import { fileURLToPath } from 'url';
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const __filename = fileURLToPath(import.meta.url);
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const __dirname = path.dirname(__filename);
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const PROJECT_ROOT = path.resolve(__dirname, '..', '..', '..', '..');
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export type Protocol = 'anthropic' | 'openai';
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interface ApiConfig {
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protocol: Protocol;
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apiKey: string;
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baseURL: string | undefined;
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model: string;
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}
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function getApiConfig(): ApiConfig {
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const configRow = getDb().prepare('SELECT value FROM configs WHERE key = ?').get('api_keys') as { value: string } | undefined;
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let apiKey = process.env.ANTHROPIC_API_KEY || '';
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let baseURL: string | undefined;
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let model = process.env.ANTHROPIC_MODEL || 'claude-sonnet-4-6';
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let protocol: Protocol = 'anthropic';
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if (configRow) {
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try {
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const cfg = JSON.parse(configRow.value);
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if (cfg.ANTHROPIC_AUTH_TOKEN) apiKey = cfg.ANTHROPIC_AUTH_TOKEN;
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if (cfg.ANTHROPIC_BASE_URL) baseURL = cfg.ANTHROPIC_BASE_URL;
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if (cfg.ANTHROPIC_MODEL) model = cfg.ANTHROPIC_MODEL;
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if (cfg.PROTOCOL === 'openai') protocol = 'openai';
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} catch {}
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}
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return { protocol, apiKey, baseURL, model };
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}
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function getAnthropicClient(): Anthropic {
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const { apiKey, baseURL } = getApiConfig();
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return new Anthropic({ apiKey, baseURL });
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}
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function getOpenAIClient(): OpenAI {
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const { apiKey, baseURL } = getApiConfig();
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return new OpenAI({ apiKey, baseURL: baseURL || 'https://api.openai.com/v1' });
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}
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const PROJECT_ROOT = path.resolve(__dirname, '..', '..', '..');
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export class VideoAgent {
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private tools: ToolDefinition[];
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constructor() {
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this.tools = tools;
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}
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getProtocol(): Protocol {
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return getApiConfig().protocol;
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}
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getModel(): string {
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return getApiConfig().model;
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}
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getAnthropicClient(): Anthropic {
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return getAnthropicClient();
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}
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getOpenAIClient(): OpenAI {
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return getOpenAIClient();
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}
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getAnthropicTools(): Anthropic.Tool[] {
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return this.tools.map((t) => ({
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name: t.name,
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description: t.description,
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input_schema: t.input_schema,
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}));
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}
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getOpenAITools(): OpenAI.ChatCompletionTool[] {
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return this.tools.map((t) => ({
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type: 'function' as const,
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function: {
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name: t.name,
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description: t.description,
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parameters: t.input_schema,
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},
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}));
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}
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async executeTool(name: string, params: Record<string, unknown>): Promise<string> {
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const tool = this.tools.find((t) => t.name === name);
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if (!tool) throw new Error(`Unknown tool: ${name}`);
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return tool.execute(params);
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}
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getSystemPrompt(): string {
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const accountsDir = path.join(PROJECT_ROOT, 'accounts');
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117
web/server/agent/pi-bridge.ts
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117
web/server/agent/pi-bridge.ts
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@@ -0,0 +1,117 @@
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import { Agent } from '@earendil-works/pi-agent-core';
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import type { AgentEvent } from '@earendil-works/pi-agent-core';
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import { streamSimple } from '@earendil-works/pi-ai';
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import type { AssistantMessage } from '@earendil-works/pi-ai';
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import { WebSocket } from 'ws';
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import { createPiModel } from './pi-model';
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import { createPiTools } from './pi-tools';
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import { tools } from './tools/index';
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import { videoAgent } from './index';
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import { dbToPiMessages, saveUserMessage, saveAssistantMessage, saveToolResult, type DbMessage } from './pi-persist';
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import { getDb } from '../db';
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interface RunContext {
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currentAssistantMsgId: string | null;
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}
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export async function runAgentChat(ws: WebSocket, convId: string, userContent: string) {
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const userMsgId = saveUserMessage(convId, userContent);
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ws.send(JSON.stringify({ type: 'message', data: { id: userMsgId, role: 'user', content: userContent } }));
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const msgCount = getDb().prepare('SELECT COUNT(*) as count FROM messages WHERE conversation_id = ?').get(convId) as { count: number };
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if (msgCount.count <= 1) {
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const title = userContent.slice(0, 30) + (userContent.length > 30 ? '...' : '');
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getDb().prepare("UPDATE conversations SET title = ?, updated_at = datetime('now') WHERE id = ?").run(title, convId);
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}
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getDb().prepare("UPDATE conversations SET updated_at = datetime('now') WHERE id = ?").run(convId);
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const history = getDb().prepare(
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'SELECT * FROM messages WHERE conversation_id = ? AND id != ? ORDER BY created_at'
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).all(convId, userMsgId) as DbMessage[];
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const piMessages = dbToPiMessages(history);
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const { model, apiKey } = createPiModel();
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const piTools = createPiTools(tools);
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const agent = new Agent({
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initialState: {
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systemPrompt: videoAgent.getSystemPrompt(),
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model,
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thinkingLevel: 'off',
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tools: piTools,
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messages: piMessages,
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},
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streamFn: streamSimple,
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getApiKey: () => apiKey,
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});
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const ctx: RunContext = { currentAssistantMsgId: null };
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agent.subscribe((event: AgentEvent) => {
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handleAgentEvent(ws, convId, event, ctx);
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});
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ws.send(JSON.stringify({ type: 'status', data: { status: 'thinking' } }));
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try {
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await agent.prompt(userContent);
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} catch (err) {
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const errMsg = (err as Error).message;
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console.error('[pi-bridge] Agent error:', errMsg);
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ws.send(JSON.stringify({
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type: 'message',
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data: { id: '', role: 'assistant', content: `抱歉，出错了：${errMsg}` },
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}));
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}
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}
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function handleAgentEvent(ws: WebSocket, convId: string, event: AgentEvent, ctx: RunContext) {
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switch (event.type) {
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case 'message_start': {
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if (event.message.role === 'assistant') {
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const id = crypto.randomUUID();
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ctx.currentAssistantMsgId = id;
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ws.send(JSON.stringify({ type: 'message_start', data: { id } }));
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}
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break;
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}
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case 'message_update': {
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const piEvent = event.assistantMessageEvent;
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const id = ctx.currentAssistantMsgId || '';
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if (piEvent.type === 'text_delta') {
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ws.send(JSON.stringify({ type: 'text_delta', data: { id, text: piEvent.delta } }));
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} else if (piEvent.type === 'thinking_delta') {
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ws.send(JSON.stringify({ type: 'reasoning_delta', data: { id, text: piEvent.delta } }));
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}
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break;
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}
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case 'message_end': {
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if (event.message.role === 'assistant') {
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const id = ctx.currentAssistantMsgId || '';
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ws.send(JSON.stringify({ type: 'message_end', data: { id } }));
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saveAssistantMessage(convId, event.message as AssistantMessage);
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ctx.currentAssistantMsgId = null;
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}
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break;
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}
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case 'tool_execution_start': {
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ws.send(JSON.stringify({ type: 'tool_start', data: { tool: event.toolName, input: event.args } }));
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break;
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}
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case 'tool_execution_end': {
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const resultText = event.result?.content?.map((c: any) => c.text || '').join('') || '';
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if (event.isError) {
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ws.send(JSON.stringify({ type: 'tool_error', data: { tool: event.toolName, error: resultText } }));
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} else {
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ws.send(JSON.stringify({ type: 'tool_result', data: { tool: event.toolName, result: resultText.slice(0, 1000) } }));
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}
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saveToolResult(convId, event.toolCallId, event.toolName, resultText, event.isError);
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break;
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}
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}
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}
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57
web/server/agent/pi-model.ts
Normal file
57
web/server/agent/pi-model.ts
Normal file
@@ -0,0 +1,57 @@
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import { registerBuiltInApiProviders } from '@earendil-works/pi-ai';
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import type { Model } from '@earendil-works/pi-ai';
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import { getDb } from '../db';
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registerBuiltInApiProviders();
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export interface PiModelConfig {
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model: Model<any>;
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apiKey: string;
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}
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export function createPiModel(): PiModelConfig {
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const row = getDb().prepare('SELECT value FROM configs WHERE key = ?').get('api_keys') as { value: string } | undefined;
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let apiKey = process.env.ANTHROPIC_API_KEY || '';
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let baseURL: string | undefined;
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let modelId = process.env.ANTHROPIC_MODEL || 'claude-sonnet-4-6';
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let protocol: 'anthropic' | 'openai' = 'anthropic';
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if (row) {
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try {
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const cfg = JSON.parse(row.value);
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if (cfg.ANTHROPIC_AUTH_TOKEN) apiKey = cfg.ANTHROPIC_AUTH_TOKEN;
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if (cfg.ANTHROPIC_BASE_URL) baseURL = cfg.ANTHROPIC_BASE_URL;
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if (cfg.ANTHROPIC_MODEL) modelId = cfg.ANTHROPIC_MODEL;
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if (cfg.PROTOCOL === 'openai') protocol = 'openai';
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} catch {}
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}
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const model: Model<any> = protocol === 'openai'
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? {
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id: modelId,
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name: modelId,
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api: 'openai-completions',
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provider: 'openai',
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baseUrl: baseURL || 'https://api.openai.com/v1',
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reasoning: false,
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input: ['text'] as ('text' | 'image')[],
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cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
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contextWindow: 128000,
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maxTokens: 8192,
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}
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: {
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id: modelId,
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name: modelId,
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api: 'anthropic-messages',
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provider: 'anthropic',
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baseUrl: baseURL || 'https://api.anthropic.com',
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reasoning: true,
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input: ['text', 'image'] as ('text' | 'image')[],
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cost: { input: 3, output: 15, cacheRead: 0.3, cacheWrite: 3.75 },
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contextWindow: 200000,
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maxTokens: 8192,
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};
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return { model, apiKey };
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}
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164
web/server/agent/pi-persist.ts
Normal file
164
web/server/agent/pi-persist.ts
Normal file
@@ -0,0 +1,164 @@
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import { randomUUID } from 'crypto';
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import { getDb } from '../db';
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import type { Message, AssistantMessage, TextContent, ToolCall } from '@earendil-works/pi-ai';
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export interface DbMessage {
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id: string;
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conversation_id: string;
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role: string;
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content: string;
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tool_calls: string | null;
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created_at: string;
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}
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export function dbToPiMessages(dbMessages: DbMessage[]): Message[] {
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const sanitized = sanitizeHistory(dbMessages);
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return sanitized.map(dbToPiMessage);
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}
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function dbToPiMessage(msg: DbMessage): Message {
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if (msg.role === 'user') {
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return { role: 'user', content: msg.content, timestamp: Date.parse(msg.created_at) || Date.now() };
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}
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if (msg.role === 'assistant') {
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const content: (TextContent | ToolCall)[] = [];
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const textContent = msg.content && msg.content !== '(调用工具)' ? msg.content : '';
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if (textContent) {
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content.push({ type: 'text', text: textContent });
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}
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if (msg.tool_calls) {
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try {
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const parsed = JSON.parse(msg.tool_calls);
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const blocks = Array.isArray(parsed) ? parsed : parsed.content_blocks;
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if (Array.isArray(blocks)) {
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for (const b of blocks) {
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if (b.type === 'tool_use') {
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content.push({
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type: 'toolCall',
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id: b.id,
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name: b.name,
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arguments: b.input || b.arguments || {},
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});
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}
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}
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}
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} catch {}
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}
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return {
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role: 'assistant',
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content,
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api: 'unknown' as any,
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provider: 'unknown',
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model: 'unknown',
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usage: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, totalTokens: 0, cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0, total: 0 } },
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stopReason: 'stop',
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timestamp: Date.parse(msg.created_at) || Date.now(),
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||||
};
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||||
}
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|
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if (msg.role === 'tool') {
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try {
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const { tool_use_id, content: resultContent } = JSON.parse(msg.content);
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return {
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||||
role: 'toolResult',
|
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toolCallId: tool_use_id,
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toolName: '',
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||||
content: [{ type: 'text', text: resultContent }],
|
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isError: false,
|
||||
timestamp: Date.parse(msg.created_at) || Date.now(),
|
||||
};
|
||||
} catch {
|
||||
return {
|
||||
role: 'toolResult',
|
||||
toolCallId: 'unknown',
|
||||
toolName: '',
|
||||
content: [{ type: 'text', text: msg.content }],
|
||||
isError: true,
|
||||
timestamp: Date.parse(msg.created_at) || Date.now(),
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
return { role: 'user', content: msg.content, timestamp: Date.now() };
|
||||
}
|
||||
|
||||
function sanitizeHistory(messages: DbMessage[]): DbMessage[] {
|
||||
const cleaned = messages.filter((m) => {
|
||||
if (m.role === 'assistant' && m.content.startsWith('抱歉，出错了：')) return false;
|
||||
return true;
|
||||
});
|
||||
|
||||
const result: DbMessage[] = [];
|
||||
for (const msg of cleaned) {
|
||||
if (msg.role === 'tool') {
|
||||
let hasPrecedingToolCall = false;
|
||||
for (let j = result.length - 1; j >= 0; j--) {
|
||||
const prev = result[j];
|
||||
if (prev.role === 'assistant') {
|
||||
if (prev.tool_calls) {
|
||||
try {
|
||||
const parsed = JSON.parse(prev.tool_calls);
|
||||
const blocks = Array.isArray(parsed) ? parsed : parsed.content_blocks;
|
||||
if (blocks?.some((b: any) => b.type === 'tool_use')) {
|
||||
hasPrecedingToolCall = true;
|
||||
}
|
||||
} catch {}
|
||||
}
|
||||
break;
|
||||
}
|
||||
if (prev.role === 'tool') continue;
|
||||
break;
|
||||
}
|
||||
if (!hasPrecedingToolCall) continue;
|
||||
}
|
||||
result.push(msg);
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
export function saveUserMessage(convId: string, content: string): string {
|
||||
const id = randomUUID();
|
||||
getDb().prepare(
|
||||
'INSERT INTO messages (id, conversation_id, role, content) VALUES (?, ?, ?, ?)'
|
||||
).run(id, convId, 'user', content);
|
||||
return id;
|
||||
}
|
||||
|
||||
export function saveAssistantMessage(convId: string, msg: AssistantMessage): string {
|
||||
const id = randomUUID();
|
||||
const textParts = msg.content.filter((c): c is TextContent => c.type === 'text');
|
||||
const text = textParts.map((c) => c.text).join('');
|
||||
|
||||
const toolCalls = msg.content.filter((c): c is ToolCall => c.type === 'toolCall');
|
||||
|
||||
if (toolCalls.length > 0) {
|
||||
const dbToolCalls = toolCalls.map((tc) => ({
|
||||
type: 'tool_use',
|
||||
id: tc.id,
|
||||
name: tc.name,
|
||||
input: tc.arguments,
|
||||
}));
|
||||
const contentBlocks = text ? [{ type: 'text', text }, ...dbToolCalls] : dbToolCalls;
|
||||
getDb().prepare(
|
||||
'INSERT INTO messages (id, conversation_id, role, content, tool_calls) VALUES (?, ?, ?, ?, ?)'
|
||||
).run(id, convId, 'assistant', text || '(调用工具)', JSON.stringify(contentBlocks));
|
||||
} else {
|
||||
getDb().prepare(
|
||||
'INSERT INTO messages (id, conversation_id, role, content) VALUES (?, ?, ?, ?)'
|
||||
).run(id, convId, 'assistant', text);
|
||||
}
|
||||
|
||||
return id;
|
||||
}
|
||||
|
||||
export function saveToolResult(convId: string, toolCallId: string, toolName: string, result: string, isError: boolean): string {
|
||||
const id = randomUUID();
|
||||
getDb().prepare(
|
||||
'INSERT INTO messages (id, conversation_id, role, content) VALUES (?, ?, ?, ?)'
|
||||
).run(id, convId, 'tool', JSON.stringify({ tool_use_id: toolCallId, tool_name: toolName, content: result }));
|
||||
return id;
|
||||
}
|
||||
18
web/server/agent/pi-tools.ts
Normal file
18
web/server/agent/pi-tools.ts
Normal file
@@ -0,0 +1,18 @@
|
||||
import type { AgentTool, AgentToolResult } from '@earendil-works/pi-agent-core';
|
||||
import type { ToolDefinition } from './tools/types';
|
||||
|
||||
export function createPiTools(tools: ToolDefinition[]): AgentTool[] {
|
||||
return tools.map((t): AgentTool => ({
|
||||
name: t.name,
|
||||
description: t.description,
|
||||
parameters: t.input_schema as any,
|
||||
label: t.name,
|
||||
execute: async (_toolCallId: string, params: any): Promise<AgentToolResult<any>> => {
|
||||
const result = await t.execute(params);
|
||||
return {
|
||||
content: [{ type: 'text' as const, text: result }],
|
||||
details: null,
|
||||
};
|
||||
},
|
||||
}));
|
||||
}
|
||||
@@ -2,8 +2,11 @@ import { Router } from 'express';
|
||||
import fs from 'fs/promises';
|
||||
import fss from 'fs';
|
||||
import path from 'path';
|
||||
import { fileURLToPath } from 'url';
|
||||
import multer from 'multer';
|
||||
|
||||
const __filename = fileURLToPath(import.meta.url);
|
||||
const __dirname = path.dirname(__filename);
|
||||
const ACCOUNTS_DIR = path.resolve(__dirname, '..', '..', '..', 'accounts');
|
||||
|
||||
export const accountsRouter = Router();
|
||||
|
||||
@@ -1,7 +1,10 @@
|
||||
import { Router } from 'express';
|
||||
import fs from 'fs/promises';
|
||||
import path from 'path';
|
||||
import { fileURLToPath } from 'url';
|
||||
|
||||
const __filename = fileURLToPath(import.meta.url);
|
||||
const __dirname = path.dirname(__filename);
|
||||
const PROJECT_ROOT = path.resolve(__dirname, '..', '..', '..');
|
||||
|
||||
export const promptsRouter = Router();
|
||||
|
||||
@@ -1,54 +1,7 @@
|
||||
import { WebSocket } from 'ws';
|
||||
import { randomUUID } from 'crypto';
|
||||
import { getDb } from '../db';
|
||||
import { videoAgent } from '../agent';
|
||||
import type Anthropic from '@anthropic-ai/sdk';
|
||||
import type OpenAI from 'openai';
|
||||
|
||||
type MessageParam = Anthropic.MessageParam;
|
||||
type ContentBlock = Anthropic.ContentBlock;
|
||||
|
||||
interface DbMessage {
|
||||
id: string;
|
||||
conversation_id: string;
|
||||
role: string;
|
||||
content: string;
|
||||
tool_calls: string | null;
|
||||
created_at: string;
|
||||
}
|
||||
|
||||
function filterContent(blocks: ContentBlock[]): ContentBlock[] {
|
||||
return blocks.filter((b) => b.type === 'text' || b.type === 'tool_use');
|
||||
}
|
||||
|
||||
function dbToAnthropic(msg: DbMessage): MessageParam {
|
||||
if (msg.role === 'user') {
|
||||
return { role: 'user', content: msg.content };
|
||||
}
|
||||
if (msg.role === 'assistant') {
|
||||
if (msg.tool_calls) {
|
||||
try {
|
||||
const parsed = JSON.parse(msg.tool_calls) as ContentBlock[];
|
||||
return { role: 'assistant', content: filterContent(parsed) };
|
||||
} catch {
|
||||
return { role: 'assistant', content: msg.content };
|
||||
}
|
||||
}
|
||||
return { role: 'assistant', content: msg.content };
|
||||
}
|
||||
if (msg.role === 'tool') {
|
||||
try {
|
||||
const { tool_use_id, content } = JSON.parse(msg.content);
|
||||
return {
|
||||
role: 'user',
|
||||
content: [{ type: 'tool_result' as const, tool_use_id, content }],
|
||||
};
|
||||
} catch {
|
||||
return { role: 'user', content: msg.content };
|
||||
}
|
||||
}
|
||||
return { role: 'user', content: msg.content };
|
||||
}
|
||||
import { runAgentChat } from '../agent/pi-bridge';
|
||||
|
||||
export function handleChat(ws: WebSocket) {
|
||||
let conversationId: string | null = null;
|
||||
@@ -61,7 +14,7 @@ export function handleChat(ws: WebSocket) {
|
||||
conversationId = msg.conversationId || randomUUID();
|
||||
const history = getDb().prepare(
|
||||
'SELECT * FROM messages WHERE conversation_id = ? ORDER BY created_at'
|
||||
).all(conversationId) as DbMessage[];
|
||||
).all(conversationId);
|
||||
ws.send(JSON.stringify({ type: 'history', data: { conversationId, messages: history } }));
|
||||
return;
|
||||
}
|
||||
@@ -81,7 +34,7 @@ export function handleChat(ws: WebSocket) {
|
||||
ws.send(JSON.stringify({ type: 'error', data: { message: '没有活跃对话，请先创建或选择一个对话' } }));
|
||||
return;
|
||||
}
|
||||
await handleChatMessage(ws, conversationId, msg.content);
|
||||
await runAgentChat(ws, conversationId, msg.content);
|
||||
}
|
||||
} catch (e) {
|
||||
console.error('WebSocket error:', e);
|
||||
@@ -91,381 +44,3 @@ export function handleChat(ws: WebSocket) {
|
||||
|
||||
ws.on('close', () => {});
|
||||
}
|
||||
|
||||
// Helper: convert DB messages to OpenAI format
|
||||
function extractToolCalls(blocks: ContentBlock[]): OpenAI.ChatCompletionMessageToolCall[] {
|
||||
return blocks
|
||||
.filter((b) => b.type === 'tool_use')
|
||||
.map((b) => ({
|
||||
id: (b as { id: string }).id,
|
||||
type: 'function' as const,
|
||||
function: {
|
||||
name: (b as { name: string }).name,
|
||||
arguments: JSON.stringify((b as { input: unknown }).input),
|
||||
},
|
||||
}));
|
||||
}
|
||||
|
||||
function dbToOpenAI(msg: DbMessage): OpenAI.ChatCompletionMessageParam {
|
||||
if (msg.role === 'user') {
|
||||
return { role: 'user', content: msg.content };
|
||||
}
|
||||
if (msg.role === 'assistant') {
|
||||
const result: Record<string, unknown> = { role: 'assistant', content: msg.content || null };
|
||||
|
||||
if (!msg.tool_calls) return result as unknown as OpenAI.ChatCompletionMessageParam;
|
||||
|
||||
let parsed: unknown;
|
||||
try { parsed = JSON.parse(msg.tool_calls); } catch { return result as unknown as OpenAI.ChatCompletionMessageParam; }
|
||||
|
||||
// Legacy format: parsed is ContentBlock[] (array)
|
||||
if (Array.isArray(parsed)) {
|
||||
const toolCalls = extractToolCalls(parsed);
|
||||
if (toolCalls.length > 0) {
|
||||
result.tool_calls = toolCalls;
|
||||
const textBlocks = parsed.filter((b) => b.type === 'text');
|
||||
result.content = textBlocks.map((b) => (b as { text: string }).text).join('') || null;
|
||||
}
|
||||
return result as unknown as OpenAI.ChatCompletionMessageParam;
|
||||
}
|
||||
|
||||
// New format: parsed is { reasoning_content?, content_blocks? }
|
||||
const meta = parsed as { reasoning_content?: string; content_blocks?: ContentBlock[] };
|
||||
if (meta.reasoning_content) {
|
||||
result.reasoning_content = meta.reasoning_content;
|
||||
}
|
||||
if (meta.content_blocks) {
|
||||
const toolCalls = extractToolCalls(meta.content_blocks);
|
||||
if (toolCalls.length > 0) {
|
||||
result.tool_calls = toolCalls;
|
||||
const textBlocks = meta.content_blocks.filter((b) => b.type === 'text');
|
||||
result.content = textBlocks.map((b) => (b as { text: string }).text).join('') || null;
|
||||
}
|
||||
}
|
||||
|
||||
return result as unknown as OpenAI.ChatCompletionMessageParam;
|
||||
}
|
||||
if (msg.role === 'tool') {
|
||||
try {
|
||||
const { tool_use_id, content } = JSON.parse(msg.content);
|
||||
return { role: 'tool', tool_call_id: tool_use_id, content };
|
||||
} catch {
|
||||
return { role: 'tool', tool_call_id: 'unknown', content: msg.content };
|
||||
}
|
||||
}
|
||||
return { role: 'user', content: msg.content };
|
||||
}
|
||||
|
||||
// --- Anthropic protocol streaming ---
|
||||
async function streamAnthropic(
|
||||
ws: WebSocket,
|
||||
convId: string,
|
||||
messages: MessageParam[],
|
||||
): Promise<void> {
|
||||
const client = videoAgent.getAnthropicClient();
|
||||
const model = videoAgent.getModel();
|
||||
const systemPrompt = videoAgent.getSystemPrompt();
|
||||
|
||||
let currentMessages = messages;
|
||||
let maxLoops = 10;
|
||||
|
||||
while (maxLoops-- > 0) {
|
||||
console.log(`[chat:anthropic] Loop ${9 - maxLoops}, messages: ${currentMessages.length}`);
|
||||
const stream = client.messages.stream({
|
||||
model,
|
||||
max_tokens: 4096,
|
||||
system: systemPrompt,
|
||||
tools: videoAgent.getAnthropicTools(),
|
||||
messages: currentMessages,
|
||||
});
|
||||
|
||||
const assistantMsgId = randomUUID();
|
||||
ws.send(JSON.stringify({ type: 'message_start', data: { id: assistantMsgId } }));
|
||||
|
||||
for await (const event of stream) {
|
||||
if (event.type === 'content_block_delta') {
|
||||
if (event.delta.type === 'text_delta') {
|
||||
ws.send(JSON.stringify({
|
||||
type: 'text_delta',
|
||||
data: { id: assistantMsgId, text: event.delta.text },
|
||||
}));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
const finalMsg = await stream.finalMessage();
|
||||
ws.send(JSON.stringify({ type: 'message_end', data: { id: assistantMsgId } }));
|
||||
|
||||
const toolUses = finalMsg.content.filter((b): b is Anthropic.ToolUseBlock => b.type === 'tool_use');
|
||||
const textBlocks = finalMsg.content.filter((b): b is Anthropic.TextBlock => b.type === 'text');
|
||||
const finalText = textBlocks.map((b) => b.text).join('');
|
||||
|
||||
if (toolUses.length === 0) {
|
||||
getDb().prepare(
|
||||
'INSERT INTO messages (id, conversation_id, role, content) VALUES (?, ?, ?, ?)'
|
||||
).run(assistantMsgId, convId, 'assistant', finalText);
|
||||
return;
|
||||
}
|
||||
|
||||
// Save assistant with tool calls
|
||||
const cleanContent = filterContent(finalMsg.content as ContentBlock[]);
|
||||
getDb().prepare(
|
||||
'INSERT INTO messages (id, conversation_id, role, content, tool_calls) VALUES (?, ?, ?, ?, ?)'
|
||||
).run(assistantMsgId, convId, 'assistant', finalText || '(调用工具)', JSON.stringify(cleanContent));
|
||||
|
||||
currentMessages.push({ role: 'assistant', content: cleanContent });
|
||||
|
||||
// Execute tools
|
||||
const toolResults: Anthropic.ToolResultBlockParam[] = [];
|
||||
for (const tool of toolUses) {
|
||||
ws.send(JSON.stringify({ type: 'tool_start', data: { tool: tool.name, input: tool.input } }));
|
||||
console.log(`[chat:anthropic] Executing tool: ${tool.name}`);
|
||||
|
||||
try {
|
||||
const result = await videoAgent.executeTool(tool.name, tool.input as Record<string, unknown>);
|
||||
toolResults.push({ type: 'tool_result', tool_use_id: tool.id, content: result });
|
||||
|
||||
const toolMsgId = randomUUID();
|
||||
getDb().prepare(
|
||||
'INSERT INTO messages (id, conversation_id, role, content) VALUES (?, ?, ?, ?)'
|
||||
).run(toolMsgId, convId, 'tool', JSON.stringify({ tool_use_id: tool.id, content: result }));
|
||||
|
||||
ws.send(JSON.stringify({ type: 'tool_result', data: { tool: tool.name, result: result.slice(0, 1000) } }));
|
||||
} catch (err) {
|
||||
const errMsg = (err as Error).message;
|
||||
toolResults.push({ type: 'tool_result', tool_use_id: tool.id, content: `Error: ${errMsg}` });
|
||||
|
||||
const toolMsgId = randomUUID();
|
||||
getDb().prepare(
|
||||
'INSERT INTO messages (id, conversation_id, role, content) VALUES (?, ?, ?, ?)'
|
||||
).run(toolMsgId, convId, 'tool', JSON.stringify({ tool_use_id: tool.id, content: `Error: ${errMsg}` }));
|
||||
|
||||
ws.send(JSON.stringify({ type: 'tool_error', data: { tool: tool.name, error: errMsg } }));
|
||||
}
|
||||
}
|
||||
|
||||
currentMessages.push({ role: 'user', content: toolResults });
|
||||
}
|
||||
}
|
||||
|
||||
// --- OpenAI protocol streaming ---
|
||||
async function streamOpenAI(
|
||||
ws: WebSocket,
|
||||
convId: string,
|
||||
dbMessages: DbMessage[],
|
||||
): Promise<void> {
|
||||
const client = videoAgent.getOpenAIClient();
|
||||
const model = videoAgent.getModel();
|
||||
const systemPrompt = videoAgent.getSystemPrompt();
|
||||
|
||||
const openaiTools = videoAgent.getOpenAITools();
|
||||
|
||||
let currentDbMessages = [...dbMessages];
|
||||
let maxLoops = 10;
|
||||
|
||||
while (maxLoops-- > 0) {
|
||||
const openaiMessages: OpenAI.ChatCompletionMessageParam[] = [
|
||||
{ role: 'system', content: systemPrompt },
|
||||
...currentDbMessages.map(dbToOpenAI),
|
||||
];
|
||||
|
||||
console.log(`[chat:openai] Loop ${9 - maxLoops}, messages: ${openaiMessages.length}`);
|
||||
|
||||
const assistantMsgId = randomUUID();
|
||||
ws.send(JSON.stringify({ type: 'message_start', data: { id: assistantMsgId } }));
|
||||
|
||||
let fullText = '';
|
||||
let reasoningContent = '';
|
||||
let toolCallsAcc: Array<{ id: string; name: string; arguments: string }> = [];
|
||||
|
||||
const stream = await client.chat.completions.create({
|
||||
model,
|
||||
messages: openaiMessages,
|
||||
tools: openaiTools.length > 0 ? openaiTools : undefined,
|
||||
stream: true,
|
||||
});
|
||||
|
||||
for await (const chunk of stream) {
|
||||
const delta = chunk.choices[0]?.delta;
|
||||
if (delta?.content) {
|
||||
fullText += delta.content;
|
||||
ws.send(JSON.stringify({
|
||||
type: 'text_delta',
|
||||
data: { id: assistantMsgId, text: delta.content },
|
||||
}));
|
||||
}
|
||||
// DeepSeek thinking mode: capture reasoning_content
|
||||
if ((delta as Record<string, unknown>)?.reasoning_content) {
|
||||
const chunk = (delta as Record<string, unknown>).reasoning_content as string;
|
||||
reasoningContent += chunk;
|
||||
ws.send(JSON.stringify({
|
||||
type: 'reasoning_delta',
|
||||
data: { id: assistantMsgId, text: chunk },
|
||||
}));
|
||||
}
|
||||
if (delta?.tool_calls) {
|
||||
for (const tc of delta.tool_calls) {
|
||||
if (tc.index !== undefined) {
|
||||
while (toolCallsAcc.length <= tc.index) {
|
||||
toolCallsAcc.push({ id: '', name: '', arguments: '' });
|
||||
}
|
||||
if (tc.id) toolCallsAcc[tc.index].id = tc.id;
|
||||
if (tc.function?.name) toolCallsAcc[tc.index].name = tc.function.name;
|
||||
if (tc.function?.arguments) toolCallsAcc[tc.index].arguments += tc.function.arguments;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
ws.send(JSON.stringify({ type: 'message_end', data: { id: assistantMsgId } }));
|
||||
|
||||
// Store extra metadata (reasoning_content, tool_calls) in tool_calls column as JSON
|
||||
const extraMeta: Record<string, unknown> = {};
|
||||
if (reasoningContent) extraMeta.reasoning_content = reasoningContent;
|
||||
|
||||
// No tool calls — save and done
|
||||
if (toolCallsAcc.length === 0) {
|
||||
getDb().prepare(
|
||||
'INSERT INTO messages (id, conversation_id, role, content, tool_calls) VALUES (?, ?, ?, ?, ?)'
|
||||
).run(assistantMsgId, convId, 'assistant', fullText, Object.keys(extraMeta).length > 0 ? JSON.stringify(extraMeta) : null);
|
||||
return;
|
||||
}
|
||||
|
||||
// Save assistant with tool calls in Anthropic-compatible format for DB
|
||||
const dbToolCalls = toolCallsAcc.map((tc) => ({
|
||||
type: 'tool_use',
|
||||
id: tc.id,
|
||||
name: tc.name,
|
||||
input: JSON.parse(tc.arguments || '{}'),
|
||||
}));
|
||||
const cleanContent = fullText
|
||||
? [{ type: 'text', text: fullText }, ...dbToolCalls]
|
||||
: dbToolCalls;
|
||||
extraMeta.content_blocks = cleanContent;
|
||||
|
||||
getDb().prepare(
|
||||
'INSERT INTO messages (id, conversation_id, role, content, tool_calls) VALUES (?, ?, ?, ?, ?)'
|
||||
).run(assistantMsgId, convId, 'assistant', fullText || '(调用工具)', JSON.stringify(extraMeta));
|
||||
|
||||
// Execute tools and collect results
|
||||
for (const tc of toolCallsAcc) {
|
||||
ws.send(JSON.stringify({ type: 'tool_start', data: { tool: tc.name, input: JSON.parse(tc.arguments || '{}') } }));
|
||||
console.log(`[chat:openai] Executing tool: ${tc.name}`);
|
||||
|
||||
try {
|
||||
const params = JSON.parse(tc.arguments || '{}');
|
||||
const result = await videoAgent.executeTool(tc.name, params);
|
||||
|
||||
const toolMsgId = randomUUID();
|
||||
getDb().prepare(
|
||||
'INSERT INTO messages (id, conversation_id, role, content) VALUES (?, ?, ?, ?)'
|
||||
).run(toolMsgId, convId, 'tool', JSON.stringify({ tool_use_id: tc.id, content: result }));
|
||||
|
||||
ws.send(JSON.stringify({ type: 'tool_result', data: { tool: tc.name, result: result.slice(0, 1000) } }));
|
||||
} catch (err) {
|
||||
const errMsg = (err as Error).message;
|
||||
|
||||
const toolMsgId = randomUUID();
|
||||
getDb().prepare(
|
||||
'INSERT INTO messages (id, conversation_id, role, content) VALUES (?, ?, ?, ?)'
|
||||
).run(toolMsgId, convId, 'tool', JSON.stringify({ tool_use_id: tc.id, content: `Error: ${errMsg}` }));
|
||||
|
||||
ws.send(JSON.stringify({ type: 'tool_error', data: { tool: tc.name, error: errMsg } }));
|
||||
}
|
||||
}
|
||||
|
||||
// Reload all messages for next loop
|
||||
currentDbMessages = getDb().prepare(
|
||||
'SELECT * FROM messages WHERE conversation_id = ? ORDER BY created_at'
|
||||
).all(convId) as DbMessage[];
|
||||
}
|
||||
}
|
||||
|
||||
// Clean and validate message history before sending to API
|
||||
function sanitizeHistory(messages: DbMessage[]): DbMessage[] {
|
||||
// 1. Filter out error messages stored in DB
|
||||
const cleaned = messages.filter((m) => {
|
||||
if (m.role === 'assistant' && m.content.startsWith('抱歉，出错了：')) return false;
|
||||
return true;
|
||||
});
|
||||
|
||||
// 2. Validate sequence: tool messages must follow assistant with tool_calls
|
||||
const result: DbMessage[] = [];
|
||||
for (let i = 0; i < cleaned.length; i++) {
|
||||
const msg = cleaned[i];
|
||||
|
||||
// Tool message: check if preceding assistant has tool_calls
|
||||
if (msg.role === 'tool') {
|
||||
let hasPrecedingToolCall = false;
|
||||
for (let j = result.length - 1; j >= 0; j--) {
|
||||
const prev = result[j];
|
||||
if (prev.role === 'assistant') {
|
||||
if (prev.tool_calls) {
|
||||
try {
|
||||
const parsed = JSON.parse(prev.tool_calls);
|
||||
const blocks = Array.isArray(parsed) ? parsed : parsed.content_blocks;
|
||||
if (blocks?.some((b: ContentBlock) => b.type === 'tool_use')) {
|
||||
hasPrecedingToolCall = true;
|
||||
}
|
||||
} catch {}
|
||||
}
|
||||
break; // stop at nearest assistant
|
||||
}
|
||||
if (prev.role === 'tool') continue; // skip consecutive tool messages
|
||||
break;
|
||||
}
|
||||
if (!hasPrecedingToolCall) continue; // skip orphan tool message
|
||||
}
|
||||
|
||||
result.push(msg);
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
async function handleChatMessage(ws: WebSocket, convId: string, content: string) {
|
||||
const userMsgId = randomUUID();
|
||||
getDb().prepare(
|
||||
'INSERT INTO messages (id, conversation_id, role, content) VALUES (?, ?, ?, ?)'
|
||||
).run(userMsgId, convId, 'user', content);
|
||||
ws.send(JSON.stringify({ type: 'message', data: { id: userMsgId, role: 'user', content } }));
|
||||
|
||||
const msgCount = getDb().prepare(
|
||||
'SELECT COUNT(*) as count FROM messages WHERE conversation_id = ?'
|
||||
).get(convId) as { count: number };
|
||||
if (msgCount.count <= 1) {
|
||||
const title = content.slice(0, 30) + (content.length > 30 ? '...' : '');
|
||||
getDb().prepare('UPDATE conversations SET title = ?, updated_at = datetime(\'now\') WHERE id = ?')
|
||||
.run(title, convId);
|
||||
}
|
||||
getDb().prepare('UPDATE conversations SET updated_at = datetime(\'now\') WHERE id = ?').run(convId);
|
||||
|
||||
const rawHistory = getDb().prepare(
|
||||
'SELECT * FROM messages WHERE conversation_id = ? AND id != ? ORDER BY created_at'
|
||||
).all(convId, userMsgId) as DbMessage[];
|
||||
const history = sanitizeHistory(rawHistory);
|
||||
|
||||
ws.send(JSON.stringify({ type: 'status', data: { status: 'thinking' } }));
|
||||
|
||||
try {
|
||||
const protocol = videoAgent.getProtocol();
|
||||
|
||||
if (protocol === 'openai') {
|
||||
// OpenAI protocol
|
||||
await streamOpenAI(ws, convId, history);
|
||||
} else {
|
||||
// Anthropic protocol (default)
|
||||
const messages: MessageParam[] = history.map(dbToAnthropic);
|
||||
await streamAnthropic(ws, convId, messages);
|
||||
}
|
||||
} catch (err) {
|
||||
const errMsg = (err as Error).message;
|
||||
console.error('[chat] LLM error:', errMsg);
|
||||
const errId = randomUUID();
|
||||
// Don't store error in DB to avoid polluting history
|
||||
ws.send(JSON.stringify({
|
||||
type: 'message',
|
||||
data: { id: errId, role: 'assistant', content: `抱歉，出错了：${errMsg}` },
|
||||
}));
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user