211 lines
8 KiB
JavaScript
211 lines
8 KiB
JavaScript
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"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.AutoGPT = void 0;
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const messages_1 = require("@langchain/core/messages");
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const base_1 = require("@langchain/core/language_models/base");
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const llm_chain_js_1 = require("../../chains/llm_chain.cjs");
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const output_parser_js_1 = require("./output_parser.cjs");
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const prompt_js_1 = require("./prompt.cjs");
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// import { HumanInputRun } from "./tools/human/tool"; // TODO
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const schema_js_1 = require("./schema.cjs");
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const text_splitter_js_1 = require("../../text_splitter.cjs");
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/**
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* Class representing the AutoGPT concept with LangChain primitives. It is
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* designed to be used with a set of tools such as a search tool,
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* write-file tool, and a read-file tool.
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* @example
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* ```typescript
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* const autogpt = AutoGPT.fromLLMAndTools(
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* new ChatOpenAI({ temperature: 0 }),
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* [
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* new ReadFileTool({ store: new InMemoryFileStore() }),
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* new WriteFileTool({ store: new InMemoryFileStore() }),
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* new SerpAPI("YOUR_SERPAPI_API_KEY", {
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* location: "San Francisco,California,United States",
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* hl: "en",
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* gl: "us",
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* }),
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* ],
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* {
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* memory: new MemoryVectorStore(new OpenAIEmbeddings()).asRetriever(),
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* aiName: "Tom",
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* aiRole: "Assistant",
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* },
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* );
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* const result = await autogpt.run(["write a weather report for SF today"]);
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* ```
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*/
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class AutoGPT {
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constructor({ aiName, memory, chain, outputParser, tools, feedbackTool, maxIterations, }) {
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Object.defineProperty(this, "aiName", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "memory", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "fullMessageHistory", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "nextActionCount", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "chain", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "outputParser", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "tools", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "feedbackTool", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "maxIterations", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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// Currently not generic enough to support any text splitter.
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Object.defineProperty(this, "textSplitter", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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this.aiName = aiName;
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this.memory = memory;
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this.fullMessageHistory = [];
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this.nextActionCount = 0;
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this.chain = chain;
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this.outputParser = outputParser;
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this.tools = tools;
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this.feedbackTool = feedbackTool;
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this.maxIterations = maxIterations;
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const chunkSize = (0, base_1.getEmbeddingContextSize)("modelName" in memory.vectorStore.embeddings
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? memory.vectorStore.embeddings.modelName
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: undefined);
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this.textSplitter = new text_splitter_js_1.TokenTextSplitter({
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chunkSize,
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chunkOverlap: Math.round(chunkSize / 10),
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});
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}
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/**
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* Creates a new AutoGPT instance from a given LLM and a set of tools.
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* @param llm A BaseChatModel object.
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* @param tools An array of ObjectTool objects.
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* @param options.aiName The name of the AI.
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* @param options.aiRole The role of the AI.
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* @param options.memory A VectorStoreRetriever object that represents the memory of the AI.
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* @param options.maxIterations The maximum number of iterations the AI can perform.
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* @param options.outputParser An AutoGPTOutputParser object that parses the output of the AI.
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* @returns A new instance of the AutoGPT class.
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*/
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static fromLLMAndTools(llm, tools, { aiName, aiRole, memory, maxIterations = 100,
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// humanInTheLoop = false,
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outputParser = new output_parser_js_1.AutoGPTOutputParser(), }) {
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const prompt = new prompt_js_1.AutoGPTPrompt({
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aiName,
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aiRole,
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tools,
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tokenCounter: llm.getNumTokens.bind(llm),
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sendTokenLimit: (0, base_1.getModelContextSize)("modelName" in llm ? llm.modelName : "gpt2"),
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});
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// const feedbackTool = humanInTheLoop ? new HumanInputRun() : null;
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const chain = new llm_chain_js_1.LLMChain({ llm, prompt });
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return new AutoGPT({
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aiName,
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memory,
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chain,
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outputParser,
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tools,
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// feedbackTool,
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maxIterations,
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});
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}
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/**
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* Runs the AI with a given set of goals.
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* @param goals An array of strings representing the goals.
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* @returns A string representing the result of the run or undefined if the maximum number of iterations is reached without a result.
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*/
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async run(goals) {
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const user_input = "Determine which next command to use, and respond using the format specified above:";
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let loopCount = 0;
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while (loopCount < this.maxIterations) {
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loopCount += 1;
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const { text: assistantReply } = await this.chain.call({
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goals,
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user_input,
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memory: this.memory,
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messages: this.fullMessageHistory,
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});
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// Print the assistant reply
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console.log(assistantReply);
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this.fullMessageHistory.push(new messages_1.HumanMessage(user_input));
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this.fullMessageHistory.push(new messages_1.AIMessage(assistantReply));
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const action = await this.outputParser.parse(assistantReply);
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const tools = this.tools.reduce((acc, tool) => ({ ...acc, [tool.name]: tool }), {});
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if (action.name === schema_js_1.FINISH_NAME) {
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return action.args.response;
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}
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let result;
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if (action.name in tools) {
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const tool = tools[action.name];
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let observation;
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try {
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observation = await tool.call(action.args);
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}
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catch (e) {
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observation = `Error in args: ${e}`;
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}
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result = `Command ${tool.name} returned: ${observation}`;
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}
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else if (action.name === "ERROR") {
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result = `Error: ${action.args}. `;
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}
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else {
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result = `Unknown command '${action.name}'. Please refer to the 'COMMANDS' list for available commands and only respond in the specified JSON format.`;
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}
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let memoryToAdd = `Assistant Reply: ${assistantReply}\nResult: ${result} `;
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if (this.feedbackTool) {
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const feedback = `\n${await this.feedbackTool.call("Input: ")}`;
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if (feedback === "q" || feedback === "stop") {
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console.log("EXITING");
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return "EXITING";
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}
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memoryToAdd += feedback;
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}
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const documents = await this.textSplitter.createDocuments([memoryToAdd]);
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await this.memory.addDocuments(documents);
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this.fullMessageHistory.push(new messages_1.SystemMessage(result));
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}
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return undefined;
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}
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}
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exports.AutoGPT = AutoGPT;
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