94 lines
3.9 KiB
TypeScript
94 lines
3.9 KiB
TypeScript
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import type { BaseLanguageModelInterface } from "@langchain/core/language_models/base";
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import { ToolInterface } from "@langchain/core/tools";
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import { PromptTemplate } from "@langchain/core/prompts";
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import { Optional } from "../../types/type-utils.js";
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import { Agent, AgentArgs, OutputParserArgs } from "../agent.js";
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import { AgentInput, SerializedZeroShotAgent } from "../types.js";
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import { ZeroShotAgentOutputParser } from "./outputParser.js";
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/**
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* Interface for creating a prompt for the ZeroShotAgent.
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*/
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export interface ZeroShotCreatePromptArgs {
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/** String to put after the list of tools. */
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suffix?: string;
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/** String to put before the list of tools. */
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prefix?: string;
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/** List of input variables the final prompt will expect. */
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inputVariables?: string[];
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}
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/**
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* Type for the input to the ZeroShotAgent, with the 'outputParser'
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* property made optional.
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*/
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export type ZeroShotAgentInput = Optional<AgentInput, "outputParser">;
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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 declare class ZeroShotAgent extends Agent {
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static lc_name(): string;
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lc_namespace: string[];
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ToolType: ToolInterface;
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constructor(input: ZeroShotAgentInput);
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_agentType(): "zero-shot-react-description";
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observationPrefix(): string;
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llmPrefix(): string;
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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?: OutputParserArgs): ZeroShotAgentOutputParser;
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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: ToolInterface[]): void;
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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: ToolInterface[], args?: ZeroShotCreatePromptArgs): PromptTemplate<any, any>;
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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: BaseLanguageModelInterface, tools: ToolInterface[], args?: ZeroShotCreatePromptArgs & AgentArgs): ZeroShotAgent;
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static deserialize(data: SerializedZeroShotAgent & {
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llm?: BaseLanguageModelInterface;
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tools?: ToolInterface[];
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}): Promise<ZeroShotAgent>;
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}
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