55 lines
2.7 KiB
TypeScript
55 lines
2.7 KiB
TypeScript
import type { StructuredToolInterface } from "@langchain/core/tools";
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import { BaseLLMOutputParser } from "@langchain/core/output_parsers";
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import { AgentStep } from "@langchain/core/agents";
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import { ChainValues } from "@langchain/core/utils/types";
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import { ChatGeneration, Generation } from "@langchain/core/outputs";
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import { BasePromptTemplate } from "@langchain/core/prompts";
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import { Callbacks, BaseCallbackConfig } from "@langchain/core/callbacks/manager";
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import { BaseChatModel } from "@langchain/core/language_models/chat_models";
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import { AgentTrajectoryEvaluator, EvalOutputType, LLMEvalChainInput, LLMTrajectoryEvaluatorArgs, type ExtractLLMCallOptions } from "../base.js";
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/**
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* A parser for the output of the TrajectoryEvalChain.
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*/
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export declare class TrajectoryOutputParser extends BaseLLMOutputParser<EvalOutputType> {
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static lc_name(): string;
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lc_namespace: string[];
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parseResult(generations: Generation[] | ChatGeneration[], _callbacks: Callbacks | undefined): Promise<EvalOutputType>;
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}
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/**
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* A chain for evaluating ReAct style agents.
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*
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* This chain is used to evaluate ReAct style agents by reasoning about
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* the sequence of actions taken and their outcomes.
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*/
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export declare class TrajectoryEvalChain extends AgentTrajectoryEvaluator {
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static lc_name(): string;
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criterionName?: string;
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evaluationName?: string;
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requiresInput: boolean;
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requiresReference: boolean;
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outputParser: TrajectoryOutputParser;
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static resolveTrajectoryPrompt(prompt?: BasePromptTemplate | undefined, agentTools?: StructuredToolInterface[]): BasePromptTemplate<any, import("@langchain/core/prompt_values").BasePromptValueInterface, any>;
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/**
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* Get the description of the agent tools.
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*
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* @returns The description of the agent tools.
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*/
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static toolsDescription(agentTools: StructuredToolInterface[]): string;
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/**
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* Create a new TrajectoryEvalChain.
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* @param llm
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* @param agentTools - The tools used by the agent.
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* @param chainOptions - The options for the chain.
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*/
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static fromLLM(llm: BaseChatModel, agentTools?: StructuredToolInterface[], chainOptions?: Partial<Omit<LLMEvalChainInput, "llm">>): Promise<TrajectoryEvalChain>;
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_prepareOutput(result: ChainValues): any;
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/**
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* Get the agent trajectory as a formatted string.
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*
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* @param steps - The agent trajectory.
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* @returns The formatted agent trajectory.
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*/
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getAgentTrajectory(steps: AgentStep[]): string;
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formatReference(reference?: string): string;
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_evaluateAgentTrajectory(args: LLMTrajectoryEvaluatorArgs, callOptions: ExtractLLMCallOptions<this["llm"]>, config?: Callbacks | BaseCallbackConfig): Promise<ChainValues>;
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}
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