dtamind-components
Version:
DTAmindai Components
117 lines • 4.81 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
const utils_1 = require("../../../src/utils");
const core_1 = require("./core");
class Replicate_LLMs {
constructor() {
this.label = 'Replicate';
this.name = 'replicate';
this.version = 2.0;
this.type = 'Replicate';
this.icon = 'replicate.svg';
this.category = 'LLMs';
this.description = 'Use Replicate to run open source models on cloud';
this.baseClasses = [this.type, 'BaseChatModel', ...(0, utils_1.getBaseClasses)(core_1.Replicate)];
this.credential = {
label: 'Connect Credential',
name: 'credential',
type: 'credential',
credentialNames: ['replicateApi']
};
this.inputs = [
{
label: 'Cache',
name: 'cache',
type: 'BaseCache',
optional: true
},
{
label: 'Model',
name: 'model',
type: 'string',
placeholder: 'a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5',
optional: true
},
{
label: 'Temperature',
name: 'temperature',
type: 'number',
step: 0.1,
description: 'Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic, 0.75 is a good starting value.',
default: 0.7,
optional: true
},
{
label: 'Max Tokens',
name: 'maxTokens',
type: 'number',
step: 1,
description: 'Maximum number of tokens to generate. A word is generally 2-3 tokens',
optional: true,
additionalParams: true
},
{
label: 'Top Probability',
name: 'topP',
type: 'number',
step: 0.1,
description: 'When decoding text, samples from the top p percentage of most likely tokens; lower to ignore less likely tokens',
optional: true,
additionalParams: true
},
{
label: 'Repetition Penalty',
name: 'repetitionPenalty',
type: 'number',
step: 0.1,
description: 'Penalty for repeated words in generated text; 1 is no penalty, values greater than 1 discourage repetition, less than 1 encourage it. (minimum: 0.01; maximum: 5)',
optional: true,
additionalParams: true
},
{
label: 'Additional Inputs',
name: 'additionalInputs',
type: 'json',
description: 'Each model has different parameters, refer to the specific model accepted inputs. For example: <a target="_blank" href="https://replicate.com/a16z-infra/llama13b-v2-chat/api#inputs">llama13b-v2</a>',
additionalParams: true,
optional: true
}
];
}
async init(nodeData, _, options) {
const modelName = nodeData.inputs?.model;
const temperature = nodeData.inputs?.temperature;
const maxTokens = nodeData.inputs?.maxTokens;
const topP = nodeData.inputs?.topP;
const repetitionPenalty = nodeData.inputs?.repetitionPenalty;
const additionalInputs = nodeData.inputs?.additionalInputs;
const credentialData = await (0, utils_1.getCredentialData)(nodeData.credential ?? '', options);
const apiKey = (0, utils_1.getCredentialParam)('replicateApiKey', credentialData, nodeData);
const cache = nodeData.inputs?.cache;
const obj = {
model: modelName,
apiKey
};
let inputs = {};
if (maxTokens)
inputs.max_length = parseInt(maxTokens, 10);
if (temperature)
inputs.temperature = parseFloat(temperature);
if (topP)
inputs.top_p = parseFloat(topP);
if (repetitionPenalty)
inputs.repetition_penalty = parseFloat(repetitionPenalty);
if (additionalInputs) {
const parsedInputs = typeof additionalInputs === 'object' ? additionalInputs : additionalInputs ? JSON.parse(additionalInputs) : {};
inputs = { ...inputs, ...parsedInputs };
}
if (Object.keys(inputs).length)
obj.input = inputs;
if (cache)
obj.cache = cache;
const model = new core_1.Replicate(obj);
return model;
}
}
module.exports = { nodeClass: Replicate_LLMs };
//# sourceMappingURL=Replicate.js.map