inference-server/dist/engines/node-llama-cpp/util.js

Libraries and server to build AI applications. Adapters to various native bindings allowing local inference. Integrate it with your application, or use as a microservice.

import fs from 'node:fs';
import path from 'node:path';
import { flattenMessageTextContent } from '../../lib/flattenMessageTextContent.js';
export function mapFinishReason(nodeLlamaCppFinishReason) {
    switch (nodeLlamaCppFinishReason) {
        case 'functionCalls':
            return 'toolCalls';
        case 'stopGenerationTrigger':
            return 'stopTrigger';
        case 'customStopTrigger':
            return 'stopTrigger';
        default:
            return nodeLlamaCppFinishReason;
    }
}
export function addFunctionCallToChatHistory({ chatHistory, functionName, functionDescription, callParams, callResult, rawCall, startsNewChunk }) {
    const newChatHistory = chatHistory.slice();
    if (newChatHistory.length === 0 || newChatHistory[newChatHistory.length - 1].type !== "model")
        newChatHistory.push({
            type: "model",
            response: []
        });
    const lastModelResponseItem = newChatHistory[newChatHistory.length - 1];
    const newLastModelResponseItem = { ...lastModelResponseItem };
    newChatHistory[newChatHistory.length - 1] = newLastModelResponseItem;
    const modelResponse = newLastModelResponseItem.response.slice();
    newLastModelResponseItem.response = modelResponse;
    const functionCall = {
        type: "functionCall",
        name: functionName,
        description: functionDescription,
        params: callParams,
        result: typeof callResult === "string" ? callResult : callResult.text,
        rawCall
    };
    if (startsNewChunk)
        functionCall.startsNewChunk = true;
    modelResponse.push(functionCall);
    return newChatHistory;
}
export function createChatMessageArray(messages) {
    const items = [];
    let systemPrompt;
    for (const message of messages) {
        if (message.role === 'user') {
            items.push({
                type: 'user',
                text: flattenMessageTextContent(message.content),
            });
        }
        else if (message.role === 'assistant') {
            items.push({
                type: 'model',
                response: [message.content],
            });
        }
        else if (message.role === 'system') {
            if (systemPrompt) {
                systemPrompt += '\n\n' + flattenMessageTextContent(message.content);
            }
            else {
                systemPrompt = flattenMessageTextContent(message.content);
            }
        }
    }
    if (systemPrompt) {
        items.unshift({
            type: 'system',
            text: systemPrompt,
        });
    }
    return items;
}
export async function readGBNFFiles(directoryPath) {
    const gbnfFiles = fs
        .readdirSync(directoryPath)
        .filter((f) => f.endsWith('.gbnf'));
    const fileContents = await Promise.all(gbnfFiles.map((file) => fs.promises.readFile(path.join(directoryPath, file), 'utf-8')));
    return gbnfFiles.reduce((acc, file, i) => {
        acc[file.replace('.gbnf', '')] = fileContents[i];
        return acc;
    }, {});
}
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