@jackhua/mini-langchain/dist/chains/llm.d.ts

A lightweight TypeScript implementation of LangChain with cost optimization features

import { BaseChain } from './base';
import { BaseLLM } from '../llms/base';
import { PromptTemplate, ChatPromptTemplate } from '../prompts/prompt';
import { ChainValues, BaseCallbackHandler, Message } from '../core/types';
import { BaseMemory } from '../memory/memory';
/**
 * Chain to run a prompt through an LLM
 */
export declare class LLMChain extends BaseChain {
    private llm;
    private prompt;
    private outputKey;
    constructor(config: {
        llm: BaseLLM;
        prompt: PromptTemplate | ChatPromptTemplate;
        outputKey?: string;
        memory?: BaseMemory;
        callbacks?: BaseCallbackHandler[];
        verbose?: boolean;
    });
    get inputKeys(): string[];
    get outputKeys(): string[];
    call(inputs: ChainValues, callbacks?: BaseCallbackHandler[]): Promise<ChainValues>;
    /**
     * Format the prompt with the inputs
     */
    prepPrompt(inputs: ChainValues): Promise<string | Message[]>;
    /**
     * Stream the response
     */
    streamResponse(inputs: ChainValues): AsyncGenerator<string>;
    /**
     * Create an LLMChain from a prompt template string
     */
    static fromLLM(llm: BaseLLM, prompt: string, config?: {
        outputKey?: string;
        memory?: BaseMemory;
    }): LLMChain;
}
/**
 * Conversation Chain - a chain specifically for conversations
 */
export declare class ConversationChain extends LLMChain {
    constructor(config: {
        llm: BaseLLM;
        prompt?: PromptTemplate | ChatPromptTemplate;
        outputKey?: string;
        memory?: BaseMemory;
        callbacks?: BaseCallbackHandler[];
        verbose?: boolean;
    });
    get inputKeys(): string[];
}
/**
 * Question-Answering Chain
 */
export declare class QAChain extends BaseChain {
    private llm;
    private prompt;
    private inputKey;
    private documentVariableName;
    private outputKey;
    constructor(config: {
        llm: BaseLLM;
        prompt?: PromptTemplate;
        inputKey?: string;
        documentVariableName?: string;
        outputKey?: string;
        memory?: BaseMemory;
    });
    get inputKeys(): string[];
    get outputKeys(): string[];
    call(inputs: ChainValues, callbacks?: BaseCallbackHandler[]): Promise<ChainValues>;
    /**
     * Create a QA chain from documents
     */
    static fromDocuments(llm: BaseLLM, documents: Array<{
        pageContent: string;
        metadata?: any;
    }>, config?: {
        prompt?: PromptTemplate;
        inputKey?: string;
        outputKey?: string;
    }): QAChain;
}
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