dtamind-components
Version:
Apps integration for Dtamind. Contain Nodes and Credentials.
110 lines • 4.81 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
const google_vertexai_1 = require("@langchain/google-vertexai");
const utils_1 = require("../../../src/utils");
const modelLoader_1 = require("../../../src/modelLoader");
class GoogleVertexAI_LLMs {
constructor() {
//@ts-ignore
this.loadMethods = {
async listModels() {
return await (0, modelLoader_1.getModels)(modelLoader_1.MODEL_TYPE.LLM, 'googlevertexai');
}
};
this.label = 'GoogleVertexAI';
this.name = 'googlevertexai';
this.version = 3.0;
this.type = 'GoogleVertexAI';
this.icon = 'GoogleVertex.svg';
this.category = 'LLMs';
this.description = 'Wrapper around GoogleVertexAI large language models';
this.baseClasses = [this.type, ...(0, utils_1.getBaseClasses)(google_vertexai_1.VertexAI)];
this.credential = {
label: 'Connect Credential',
name: 'credential',
type: 'credential',
credentialNames: ['googleVertexAuth'],
optional: true,
description: 'Google Vertex AI credential. If you are using a GCP service like Cloud Run, or if you have installed default credentials on your local machine, you do not need to set this credential.'
};
this.inputs = [
{
label: 'Cache',
name: 'cache',
type: 'BaseCache',
optional: true
},
{
label: 'Model Name',
name: 'modelName',
type: 'asyncOptions',
loadMethod: 'listModels',
default: 'text-bison'
},
{
label: 'Temperature',
name: 'temperature',
type: 'number',
step: 0.1,
default: 0.7,
optional: true
},
{
label: 'max Output Tokens',
name: 'maxOutputTokens',
type: 'number',
step: 1,
optional: true,
additionalParams: true
},
{
label: 'Top Probability',
name: 'topP',
type: 'number',
step: 0.1,
optional: true,
additionalParams: true
}
];
}
async init(nodeData, _, options) {
const credentialData = await (0, utils_1.getCredentialData)(nodeData.credential ?? '', options);
const googleApplicationCredentialFilePath = (0, utils_1.getCredentialParam)('googleApplicationCredentialFilePath', credentialData, nodeData);
const googleApplicationCredential = (0, utils_1.getCredentialParam)('googleApplicationCredential', credentialData, nodeData);
const projectID = (0, utils_1.getCredentialParam)('projectID', credentialData, nodeData);
const authOptions = {};
if (Object.keys(credentialData).length !== 0) {
if (!googleApplicationCredentialFilePath && !googleApplicationCredential)
throw new Error('Please specify your Google Application Credential');
if (!googleApplicationCredentialFilePath && !googleApplicationCredential)
throw new Error('Error: More than one component has been inputted. Please use only one of the following: Google Application Credential File Path or Google Credential JSON Object');
if (googleApplicationCredentialFilePath && !googleApplicationCredential)
authOptions.keyFile = googleApplicationCredentialFilePath;
else if (!googleApplicationCredentialFilePath && googleApplicationCredential)
authOptions.credentials = JSON.parse(googleApplicationCredential);
if (projectID)
authOptions.projectId = projectID;
}
const temperature = nodeData.inputs?.temperature;
const modelName = nodeData.inputs?.modelName;
const maxOutputTokens = nodeData.inputs?.maxOutputTokens;
const topP = nodeData.inputs?.topP;
const cache = nodeData.inputs?.cache;
const obj = {
temperature: parseFloat(temperature),
model: modelName
};
if (Object.keys(authOptions).length !== 0)
obj.authOptions = authOptions;
if (maxOutputTokens)
obj.maxOutputTokens = parseInt(maxOutputTokens, 10);
if (topP)
obj.topP = parseFloat(topP);
if (cache)
obj.cache = cache;
const model = new google_vertexai_1.VertexAI(obj);
return model;
}
}
module.exports = { nodeClass: GoogleVertexAI_LLMs };
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