@promptbook/remote-client/esm/typings/src/llm-providers/_multiple/MultipleLlmExecutionTools.d.ts

Promptbook: Turn your company's scattered knowledge into AI ready books

import type { ChatParticipant } from '../../book-components/Chat/types/ChatParticipant';
import type { AvailableModel } from '../../execution/AvailableModel';
import type { LlmExecutionTools } from '../../execution/LlmExecutionTools';
import type { ChatPromptResult, CompletionPromptResult, EmbeddingPromptResult, PromptResult } from '../../execution/PromptResult';
import type { ChatPrompt, CompletionPrompt, EmbeddingPrompt, Prompt } from '../../types/Prompt';
import type { string_markdown, string_markdown_text, string_title } from '../../types/typeAliases';
/**
 * Multiple LLM Execution Tools is a proxy server that uses multiple execution tools internally and exposes the executor interface externally.
 *
 * Note: Internal utility of `joinLlmExecutionTools` but exposed type
 * @public exported from `@promptbook/core`
 */
export declare class MultipleLlmExecutionTools implements LlmExecutionTools {
    readonly title: string_title & string_markdown_text;
    /**
     * Array of execution tools in order of priority
     */
    readonly llmExecutionTools: ReadonlyArray<LlmExecutionTools>;
    /**
     * Gets array of execution tools in order of priority
     */
    constructor(title: string_title & string_markdown_text, ...llmExecutionTools: ReadonlyArray<LlmExecutionTools>);
    get description(): string_markdown;
    get profile(): ChatParticipant;
    /**
     * Check the configuration of all execution tools
     */
    checkConfiguration(): Promise<void>;
    /**
     * List all available models that can be used
     * This lists is a combination of all available models from all execution tools
     */
    listModels(): Promise<ReadonlyArray<AvailableModel>>;
    /**
     * Calls the best available chat model
     */
    callChatModel(prompt: ChatPrompt): Promise<ChatPromptResult>;
    /**
     * Calls the best available completion model
     */
    callCompletionModel(prompt: CompletionPrompt): Promise<CompletionPromptResult>;
    /**
     * Calls the best available embedding model
     */
    callEmbeddingModel(prompt: EmbeddingPrompt): Promise<EmbeddingPromptResult>;
    /**
     * Calls the best available model
     *
     * Note: This should be private or protected but is public to be usable with duck typing
     */
    callCommonModel(prompt: Prompt): Promise<PromptResult>;
}
/**
 * TODO: [🧠][🎛] Aggregating multiple models - have result not only from one first available model BUT all of them
 * TODO: [🏖] If no llmTools have for example not defined `callCompletionModel` this will still return object with defined `callCompletionModel` which just throws `PipelineExecutionError`, make it undefined instead
 *       Look how `countTotalUsage` (and `cacheLlmTools`) implements it
 */