"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
const ibm_1 = require("@langchain/community/chat_models/ibm");
const utils_1 = require("../../../src/utils");
class ChatIBMWatsonx_ChatModels {
    constructor() {
        this.label = 'ChatIBMWatsonx';
        this.name = 'chatIBMWatsonx';
        this.version = 2.0;
        this.type = 'ChatIBMWatsonx';
        this.icon = 'ibm.png';
        this.category = 'Chat Models';
        this.description = 'Wrapper around IBM watsonx.ai foundation models';
        this.baseClasses = [this.type, ...(0, utils_1.getBaseClasses)(ibm_1.ChatWatsonx)];
        this.credential = {
            label: 'Connect Credential',
            name: 'credential',
            type: 'credential',
            credentialNames: ['ibmWatsonx']
        };
        this.inputs = [
            {
                label: 'Cache',
                name: 'cache',
                type: 'BaseCache',
                optional: true
            },
            {
                label: 'Model',
                name: 'modelName',
                type: 'string',
                placeholder: 'mistralai/mistral-large'
            },
            {
                label: 'Temperature',
                name: 'temperature',
                type: 'number',
                step: 0.1,
                default: 0.9,
                optional: true
            },
            {
                label: 'Streaming',
                name: 'streaming',
                type: 'boolean',
                default: true,
                optional: true,
                additionalParams: true
            },
            {
                label: 'Max Tokens',
                name: 'maxTokens',
                type: 'number',
                step: 1,
                optional: true,
                additionalParams: true
            },
            {
                label: 'Frequency Penalty',
                name: 'frequencyPenalty',
                type: 'number',
                step: 1,
                optional: true,
                additionalParams: true,
                description: "Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim."
            },
            {
                label: 'Log Probs',
                name: 'logprobs',
                type: 'boolean',
                default: false,
                optional: true,
                additionalParams: true,
                description: 'Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message.'
            },
            {
                label: 'N',
                name: 'n',
                type: 'number',
                step: 1,
                default: 1,
                optional: true,
                additionalParams: true,
                description: 'How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep n as 1 to minimize costs.'
            },
            {
                label: 'Presence Penalty',
                name: 'presencePenalty',
                type: 'number',
                step: 1,
                default: 1,
                optional: true,
                additionalParams: true,
                description: "Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics."
            },
            {
                label: 'Top P',
                name: 'topP',
                type: 'number',
                step: 0.1,
                default: 0.1,
                optional: true,
                additionalParams: true,
                description: 'An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.'
            }
        ];
    }
    async init(nodeData, _, options) {
        const cache = nodeData.inputs?.cache;
        const temperature = nodeData.inputs?.temperature;
        const modelName = nodeData.inputs?.modelName;
        const maxTokens = nodeData.inputs?.maxTokens;
        const frequencyPenalty = nodeData.inputs?.frequencyPenalty;
        const logprobs = nodeData.inputs?.logprobs;
        const n = nodeData.inputs?.n;
        const presencePenalty = nodeData.inputs?.presencePenalty;
        const topP = nodeData.inputs?.topP;
        const streaming = nodeData.inputs?.streaming;
        const credentialData = await (0, utils_1.getCredentialData)(nodeData.credential ?? '', options);
        const version = (0, utils_1.getCredentialParam)('version', credentialData, nodeData);
        const serviceUrl = (0, utils_1.getCredentialParam)('serviceUrl', credentialData, nodeData);
        const projectId = (0, utils_1.getCredentialParam)('projectId', credentialData, nodeData);
        const watsonxAIAuthType = (0, utils_1.getCredentialParam)('watsonxAIAuthType', credentialData, nodeData);
        const watsonxAIApikey = (0, utils_1.getCredentialParam)('watsonxAIApikey', credentialData, nodeData);
        const watsonxAIBearerToken = (0, utils_1.getCredentialParam)('watsonxAIBearerToken', credentialData, nodeData);
        const auth = {
            version,
            serviceUrl,
            projectId,
            watsonxAIAuthType,
            watsonxAIApikey,
            watsonxAIBearerToken
        };
        const obj = {
            ...auth,
            streaming: streaming ?? true,
            model: modelName,
            temperature: temperature ? parseFloat(temperature) : undefined
        };
        if (cache)
            obj.cache = cache;
        if (maxTokens)
            obj.maxTokens = parseInt(maxTokens, 10);
        if (frequencyPenalty)
            obj.frequencyPenalty = parseInt(frequencyPenalty, 10);
        if (logprobs)
            obj.logprobs = logprobs;
        if (n)
            obj.maxTokens = parseInt(n, 10);
        if (presencePenalty)
            obj.presencePenalty = parseInt(presencePenalty, 10);
        if (topP)
            obj.topP = parseFloat(topP);
        const model = new ibm_1.ChatWatsonx(obj);
        return model;
    }
}
module.exports = { nodeClass: ChatIBMWatsonx_ChatModels };
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