dtamind-components
Version:
Apps integration for Dtamind. Contain Nodes and Credentials.
84 lines • 3.08 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
const modelLoader_1 = require("../../../src/modelLoader");
const utils_1 = require("../../../src/utils");
const llamaindex_1 = require("llamaindex");
class ChatMistral_LlamaIndex_ChatModels {
constructor() {
//@ts-ignore
this.loadMethods = {
async listModels() {
return await (0, modelLoader_1.getModels)(modelLoader_1.MODEL_TYPE.CHAT, 'chatMistral_LlamaIndex');
}
};
this.label = 'ChatMistral';
this.name = 'chatMistral_LlamaIndex';
this.version = 1.0;
this.type = 'ChatMistral';
this.icon = 'MistralAI.svg';
this.category = 'Chat Models';
this.description = 'Wrapper around ChatMistral LLM specific for LlamaIndex';
this.baseClasses = [this.type, 'BaseChatModel_LlamaIndex', ...(0, utils_1.getBaseClasses)(llamaindex_1.MistralAI)];
this.tags = ['LlamaIndex'];
this.credential = {
label: 'Connect Credential',
name: 'credential',
type: 'credential',
credentialNames: ['mistralAIApi']
};
this.inputs = [
{
label: 'Model Name',
name: 'modelName',
type: 'asyncOptions',
loadMethod: 'listModels',
default: 'mistral-tiny'
},
{
label: 'Temperature',
name: 'temperature',
type: 'number',
step: 0.1,
default: 0.9,
optional: true
},
{
label: 'Max Tokens',
name: 'maxTokensToSample',
type: 'number',
step: 1,
optional: true,
additionalParams: true
},
{
label: 'Top P',
name: 'topP',
type: 'number',
step: 0.1,
optional: true,
additionalParams: true
}
];
}
async init(nodeData, _, options) {
const temperature = nodeData.inputs?.temperature;
const modelName = nodeData.inputs?.modelName;
const maxTokensToSample = nodeData.inputs?.maxTokensToSample;
const topP = nodeData.inputs?.topP;
const credentialData = await (0, utils_1.getCredentialData)(nodeData.credential ?? '', options);
const apiKey = (0, utils_1.getCredentialParam)('mistralAIAPIKey', credentialData, nodeData);
const obj = {
temperature: parseFloat(temperature),
model: modelName,
apiKey: apiKey
};
if (maxTokensToSample)
obj.maxTokens = parseInt(maxTokensToSample, 10);
if (topP)
obj.topP = parseFloat(topP);
const model = new llamaindex_1.MistralAI(obj);
return model;
}
}
module.exports = { nodeClass: ChatMistral_LlamaIndex_ChatModels };
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