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