88 lines
3.8 KiB
TypeScript
88 lines
3.8 KiB
TypeScript
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import type { BaseLanguageModelInterface } from "@langchain/core/language_models/base";
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import type { ToolInterface } from "@langchain/core/tools";
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import { ChatPromptTemplate } from "@langchain/core/prompts";
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import type { AgentStep } from "@langchain/core/agents";
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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 } from "../types.js";
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import { ChatAgentOutputParser } from "./outputParser.js";
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/**
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* Interface for arguments used to create a chat prompt.
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* @deprecated
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*/
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export interface ChatCreatePromptArgs {
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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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/** String to use directly as the human message template. */
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humanMessageTemplate?: string;
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/** Formattable string to use as the instructions template. */
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formatInstructions?: 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 input data for creating a ChatAgent, extending AgentInput with
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* optional 'outputParser'.
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*
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* @deprecated
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*/
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export type ChatAgentInput = 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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*
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* @deprecated Use the {@link https://api.js.langchain.com/functions/langchain.agents.createStructuredChatAgent.html | createStructuredChatAgent method instead}.
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*/
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export declare class ChatAgent 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: ChatAgentInput);
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_agentType(): "chat-zero-shot-react-description";
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observationPrefix(): string;
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llmPrefix(): string;
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_stop(): string[];
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/**
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* Validates that all tools have descriptions. Throws an error if a tool
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* without a description is found.
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* @param tools Array of Tool instances to validate.
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* @returns void
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*/
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static validateTools(tools: ToolInterface[]): void;
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/**
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* Returns a default output parser for the ChatAgent.
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* @param _fields Optional OutputParserArgs to customize the output parser.
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* @returns ChatAgentOutputParser instance
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*/
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static getDefaultOutputParser(_fields?: OutputParserArgs): ChatAgentOutputParser;
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/**
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* Constructs the agent's scratchpad, which is a string representation of
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* the agent's previous steps.
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* @param steps Array of AgentStep instances representing the agent's previous steps.
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* @returns Promise resolving to a string representing the agent's scratchpad.
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*/
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constructScratchPad(steps: AgentStep[]): Promise<string>;
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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.humanMessageTemplate - String to use directly as the human message template
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* @param args.formatInstructions - Formattable string to use as the instructions template
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*/
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static createPrompt(tools: ToolInterface[], args?: ChatCreatePromptArgs): ChatPromptTemplate<any, any>;
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/**
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* Creates a ChatAgent instance using a language model, tools, and
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* optional arguments.
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* @param llm BaseLanguageModelInterface instance to use in the agent.
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* @param tools Array of Tool instances to include in the agent.
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* @param args Optional arguments to customize the agent and prompt.
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* @returns ChatAgent instance
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*/
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static fromLLMAndTools(llm: BaseLanguageModelInterface, tools: ToolInterface[], args?: ChatCreatePromptArgs & AgentArgs): ChatAgent;
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}
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