132 lines
5.1 KiB
JavaScript
132 lines
5.1 KiB
JavaScript
import { PromptTemplate, renderTemplate } from "@langchain/core/prompts";
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import { LLMChain } from "../../chains/llm_chain.js";
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import { Agent } from "../agent.js";
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import { deserializeHelper } from "../helpers.js";
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import { ZeroShotAgentOutputParser } from "./outputParser.js";
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import { FORMAT_INSTRUCTIONS, PREFIX, SUFFIX } from "./prompt.js";
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/**
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* Agent for the MRKL chain.
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* @augments Agent
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* @example
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* ```typescript
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*
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* const agent = new ZeroShotAgent({
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* llmChain: new LLMChain({
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* llm: new ChatOpenAI({ temperature: 0 }),
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* prompt: ZeroShotAgent.createPrompt([new SerpAPI(), new Calculator()], {
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* prefix: `Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:`,
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* suffix: `Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Args"
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* Question: {input}
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* {agent_scratchpad}`,
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* inputVariables: ["input", "agent_scratchpad"],
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* }),
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* }),
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* allowedTools: ["search", "calculator"],
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* });
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*
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* const result = await agent.invoke({
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* input: `Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?`,
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* });
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* ```
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*
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* @deprecated Use the {@link https://api.js.langchain.com/functions/langchain.agents.createReactAgent.html | createReactAgent method instead}.
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*/
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export class ZeroShotAgent extends Agent {
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static lc_name() {
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return "ZeroShotAgent";
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}
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constructor(input) {
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const outputParser = input?.outputParser ?? ZeroShotAgent.getDefaultOutputParser();
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super({ ...input, outputParser });
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Object.defineProperty(this, "lc_namespace", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: ["langchain", "agents", "mrkl"]
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});
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}
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_agentType() {
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return "zero-shot-react-description";
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}
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observationPrefix() {
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return "Observation: ";
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}
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llmPrefix() {
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return "Thought:";
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}
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/**
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* Returns the default output parser for the ZeroShotAgent.
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* @param fields Optional arguments for the output parser.
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* @returns An instance of ZeroShotAgentOutputParser.
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*/
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static getDefaultOutputParser(fields) {
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return new ZeroShotAgentOutputParser(fields);
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}
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/**
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* Validates the tools for the ZeroShotAgent. Throws an error if any tool
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* does not have a description.
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* @param tools List of tools to validate.
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*/
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static validateTools(tools) {
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const descriptionlessTool = tools.find((tool) => !tool.description);
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if (descriptionlessTool) {
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const msg = `Got a tool ${descriptionlessTool.name} without a description.` +
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` This agent requires descriptions for all tools.`;
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throw new Error(msg);
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}
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}
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/**
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* Create prompt in the style of the zero shot agent.
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*
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* @param tools - List of tools the agent will have access to, used to format the prompt.
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* @param args - Arguments to create the prompt with.
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* @param args.suffix - String to put after the list of tools.
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* @param args.prefix - String to put before the list of tools.
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* @param args.inputVariables - List of input variables the final prompt will expect.
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*/
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static createPrompt(tools, args) {
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const { prefix = PREFIX, suffix = SUFFIX, inputVariables = ["input", "agent_scratchpad"], } = args ?? {};
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const toolStrings = tools
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.map((tool) => `${tool.name}: ${tool.description}`)
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.join("\n");
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const toolNames = tools.map((tool) => tool.name);
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const formatInstructions = renderTemplate(FORMAT_INSTRUCTIONS, "f-string", {
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tool_names: toolNames,
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});
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const template = [prefix, toolStrings, formatInstructions, suffix].join("\n\n");
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return new PromptTemplate({
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template,
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inputVariables,
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});
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}
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/**
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* Creates a ZeroShotAgent from a Large Language Model and a set of tools.
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* @param llm The Large Language Model to use.
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* @param tools The tools for the agent to use.
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* @param args Optional arguments for creating the agent.
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* @returns A new instance of ZeroShotAgent.
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*/
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static fromLLMAndTools(llm, tools, args) {
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ZeroShotAgent.validateTools(tools);
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const prompt = ZeroShotAgent.createPrompt(tools, args);
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const outputParser = args?.outputParser ?? ZeroShotAgent.getDefaultOutputParser();
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const chain = new LLMChain({
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prompt,
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llm,
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callbacks: args?.callbacks ?? args?.callbackManager,
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});
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return new ZeroShotAgent({
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llmChain: chain,
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allowedTools: tools.map((t) => t.name),
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outputParser,
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});
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}
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static async deserialize(data) {
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const { llm, tools, ...rest } = data;
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return deserializeHelper(llm, tools, rest, (llm, tools, args) => ZeroShotAgent.fromLLMAndTools(llm, tools, {
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prefix: args.prefix,
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suffix: args.suffix,
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inputVariables: args.input_variables,
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}), (args) => new ZeroShotAgent(args));
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}
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}
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