generator-begcode/dist/agent/agents/Evo/index.js

Spring Boot + Angular/React/Vue in one handy generator

import { MessageChunker, ContextualizedChat, agentFunctionBaseToAgentFunction, tokensToChars, Prompt, } from '../../agent-core/index.js';
import { agentPrompts, prompts } from './prompts.js';
import { Agent, AgentConfig } from '../utils/index.js';
import { findBestAgent } from './findBestAgent.js';
const insertPersonaAsFirstMsg = (persona, logs, tokenizer) => {
    const newLogs = logs.clone();
    newLogs.insert('persistent', [
        {
            role: 'user',
            content: persona,
        },
    ], [tokenizer.encode(persona).length], 0);
    return newLogs;
};
export class Evo extends Agent {
    enableQuickTermination;
    _cChat;
    _chunker;
    previousPrediction;
    loopCounter = 0;
    previousAgent;
    initializedAgents = new Set();
    goal = '';
    constructor(context, timeout, enableQuickTermination) {
        super(new AgentConfig(agentPrompts, [], timeout), context);
        this.enableQuickTermination = enableQuickTermination;
        this._chunker = new MessageChunker({
            maxChunkSize: context.variables.saveThreshold,
        });
        this._cChat = new ContextualizedChat(this.context, this.context.chat, this._chunker, context.variables);
    }
    reset() {
        this.previousPrediction = undefined;
        this.loopCounter = 0;
        this.previousAgent = undefined;
        this.initializedAgents = new Set();
        this.goal = '';
    }
    async init() {
        const { chat } = this.context;
        const initialMessages = [
            { role: 'user', content: prompts.exhaustAllApproaches },
            { role: 'user', content: prompts.variablesExplainer },
            { role: 'user', content: prompts.evoExplainer },
        ];
        await chat.persistent(initialMessages);
    }
    async initRun(args) {
        this.goal = args.goal;
        await this.context.chat.persistent([{ role: 'user', content: args.goal }]);
    }
    async beforeLlmResponse() {
        const { chat } = this.context;
        const { messages } = chat.chatLogs;
        await this.context.logger.notice('Predicting best next step...');
        const prediction = !this.previousPrediction || this.loopCounter % 2 === 0
            ? await this.predictBestNextStep(messages, this.previousAgent, this.enableQuickTermination ? (this.previousPrediction ? 'SUCCESS' : undefined) : undefined)
            : this.previousPrediction;
        this.loopCounter += 1;
        if (prediction === 'SUCCESS' || prediction.includes('"SUCCESS"') || prediction.includes('goal has been achieved')) {
            return {
                logs: chat.chatLogs,
                agentFunctions: [],
                allFunctions: [],
                finalOutput: {
                    type: 'success',
                    title: 'SUCCESS',
                },
            };
        }
        const predictionVector = await this.createEmbeddingVector(prediction);
        await this.context.logger.info(`### Prediction:\n-> ${prediction}`);
        await this.context.logger.notice('Finding best agent to execute step...');
        const [agent, agentFunctions, persona, allFunctions] = await findBestAgent(predictionVector, this.context);
        if (!this.initializedAgents.has(agent.config.prompts.name)) {
            this.initializedAgents.add(agent.config.prompts.name);
            await agent.onFirstRun({ goal: this.goal }, chat);
        }
        this.previousPrediction = prediction;
        this.previousAgent = agent;
        const contextualizedChat = await this.contextualizeChat(await this.createEmbeddingVector(`${persona}\n${prediction}`));
        const logs = insertPersonaAsFirstMsg(persona, contextualizedChat.chatLogs, chat.tokenizer);
        return {
            logs,
            agentFunctions,
            allFunctions: allFunctions.map(agentFunctionBaseToAgentFunction(agent)),
        };
    }
    async contextualizeChat(predictionVector) {
        const maxContextTokens = this.context.llm.getMaxContextTokens();
        const maxResponseTokens = this.context.llm.getMaxResponseTokens();
        const fuzz = 500;
        const maxChatTokens = maxContextTokens - maxResponseTokens - fuzz;
        return this._cChat.contextualize(predictionVector, {
            persistent: maxChatTokens * 0.25,
            temporary: maxChatTokens * 0.75,
        });
    }
    async predictBestNextStep(messages, previousAgent, terminationStr) {
        const agentPersona = previousAgent
            ?
                previousAgent.config.prompts.initialMessages().slice(0, -1)
            : [{ role: 'user', content: prompts.generalAgentPersona }];
        return this.askLlm(new Prompt().json([...agentPersona, ...messages]).line(`
          如果需要使用信息，并且该信息不在对话中，则必须通过web_search搜索或fs_readFile读取该信息。
          结合前面用户和助手之间的对话，认真检查是否所有的任务已经完成。
          如果没有完成，根据您的专业能力，助手下一步最好的步骤是什么？
          ${terminationStr && this.goal.length < 150
            ? `如果您 100% 确定用户的目标已实现，只需回复"${terminationStr}". 用户的目标是："${this.goal}"。`
            : ''}`), {
            model: this.context.llm.getModel(),
        });
    }
    maxContextChars() {
        return tokensToChars(this.maxContextTokens());
    }
    maxContextTokens() {
        return this.context.llm.getMaxContextTokens() ?? 8000;
    }
    isLargeMsg = (message) => {
        return !!message.content && message.content.length > this.maxContextChars() * 0.0625;
    };
}