dtamind-components
Version:
Apps integration for Dtamind. Contain Nodes and Credentials.
125 lines • 4.87 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
const utils_1 = require("../../../src/utils");
const core_1 = require("./core");
class HuggingFaceInference_LLMs {
constructor() {
this.label = 'HuggingFace Inference';
this.name = 'huggingFaceInference_LLMs';
this.version = 2.0;
this.type = 'HuggingFaceInference';
this.icon = 'HuggingFace.svg';
this.category = 'LLMs';
this.description = 'Wrapper around HuggingFace large language models';
this.baseClasses = [this.type, ...(0, utils_1.getBaseClasses)(core_1.HuggingFaceInference)];
this.credential = {
label: 'Connect Credential',
name: 'credential',
type: 'credential',
credentialNames: ['huggingFaceApi']
};
this.inputs = [
{
label: 'Cache',
name: 'cache',
type: 'BaseCache',
optional: true
},
{
label: 'Model',
name: 'model',
type: 'string',
description: 'If using own inference endpoint, leave this blank',
placeholder: 'gpt2',
optional: true
},
{
label: 'Endpoint',
name: 'endpoint',
type: 'string',
placeholder: 'https://xyz.eu-west-1.aws.endpoints.huggingface.cloud/gpt2',
description: 'Using your own inference endpoint',
optional: true
},
{
label: 'Temperature',
name: 'temperature',
type: 'number',
step: 0.1,
description: 'Temperature parameter may not apply to certain model. Please check available model parameters',
optional: true,
additionalParams: true
},
{
label: 'Max Tokens',
name: 'maxTokens',
type: 'number',
step: 1,
description: 'Max Tokens parameter may not apply to certain model. Please check available model parameters',
optional: true,
additionalParams: true
},
{
label: 'Top Probability',
name: 'topP',
type: 'number',
step: 0.1,
description: 'Top Probability parameter may not apply to certain model. Please check available model parameters',
optional: true,
additionalParams: true
},
{
label: 'Top K',
name: 'hfTopK',
type: 'number',
step: 0.1,
description: 'Top K parameter may not apply to certain model. Please check available model parameters',
optional: true,
additionalParams: true
},
{
label: 'Frequency Penalty',
name: 'frequencyPenalty',
type: 'number',
step: 0.1,
description: 'Frequency Penalty parameter may not apply to certain model. Please check available model parameters',
optional: true,
additionalParams: true
}
];
}
async init(nodeData, _, options) {
const model = nodeData.inputs?.model;
const temperature = nodeData.inputs?.temperature;
const maxTokens = nodeData.inputs?.maxTokens;
const topP = nodeData.inputs?.topP;
const hfTopK = nodeData.inputs?.hfTopK;
const frequencyPenalty = nodeData.inputs?.frequencyPenalty;
const endpoint = nodeData.inputs?.endpoint;
const credentialData = await (0, utils_1.getCredentialData)(nodeData.credential ?? '', options);
const huggingFaceApiKey = (0, utils_1.getCredentialParam)('huggingFaceApiKey', credentialData, nodeData);
const cache = nodeData.inputs?.cache;
const obj = {
model,
apiKey: huggingFaceApiKey
};
if (temperature)
obj.temperature = parseFloat(temperature);
if (maxTokens)
obj.maxTokens = parseInt(maxTokens, 10);
if (topP)
obj.topP = parseFloat(topP);
if (hfTopK)
obj.topK = parseFloat(hfTopK);
if (frequencyPenalty)
obj.frequencyPenalty = parseFloat(frequencyPenalty);
if (endpoint)
obj.endpoint = endpoint;
const huggingFace = new core_1.HuggingFaceInference(obj);
if (cache)
huggingFace.cache = cache;
return huggingFace;
}
}
module.exports = { nodeClass: HuggingFaceInference_LLMs };
//# sourceMappingURL=HuggingFaceInference.js.map