@n8n/n8n-nodes-langchain
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var __defProp = Object.defineProperty;
var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
var __getOwnPropNames = Object.getOwnPropertyNames;
var __hasOwnProp = Object.prototype.hasOwnProperty;
var __export = (target, all) => {
for (var name in all)
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}
return to;
};
var __toCommonJS = (mod) => __copyProps(__defProp({}, "__esModule", { value: true }), mod);
var LmOpenHuggingFaceInference_node_exports = {};
__export(LmOpenHuggingFaceInference_node_exports, {
LmOpenHuggingFaceInference: () => LmOpenHuggingFaceInference
});
module.exports = __toCommonJS(LmOpenHuggingFaceInference_node_exports);
var import_hf = require("@langchain/community/llms/hf");
var import_n8n_workflow = require("n8n-workflow");
var import_sharedFields = require("../../../utils/sharedFields");
var import_n8nLlmFailedAttemptHandler = require("../n8nLlmFailedAttemptHandler");
var import_N8nLlmTracing = require("../N8nLlmTracing");
class LmOpenHuggingFaceInference {
constructor() {
this.description = {
displayName: "Hugging Face Inference Model",
name: "lmOpenHuggingFaceInference",
icon: "file:huggingface.svg",
group: ["transform"],
version: 1,
description: "Language Model HuggingFaceInference",
defaults: {
name: "Hugging Face Inference Model"
},
codex: {
categories: ["AI"],
subcategories: {
AI: ["Language Models", "Root Nodes"],
"Language Models": ["Text Completion Models"]
},
resources: {
primaryDocumentation: [
{
url: "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.lmopenhuggingfaceinference/"
}
]
}
},
inputs: [],
outputs: [import_n8n_workflow.NodeConnectionTypes.AiLanguageModel],
outputNames: ["Model"],
credentials: [
{
name: "huggingFaceApi",
required: true
}
],
properties: [
(0, import_sharedFields.getConnectionHintNoticeField)([import_n8n_workflow.NodeConnectionTypes.AiChain, import_n8n_workflow.NodeConnectionTypes.AiAgent]),
{
displayName: "Model",
name: "model",
type: "string",
default: "mistralai/Mistral-Nemo-Base-2407"
},
{
displayName: "Options",
name: "options",
placeholder: "Add Option",
description: "Additional options to add",
type: "collection",
default: {},
options: [
{
displayName: "Custom Inference Endpoint",
name: "endpointUrl",
default: "",
description: "Custom endpoint URL",
type: "string"
},
{
displayName: "Frequency Penalty",
name: "frequencyPenalty",
default: 0,
typeOptions: { maxValue: 2, minValue: -2, numberPrecision: 1 },
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",
type: "number"
},
{
displayName: "Maximum Number of Tokens",
name: "maxTokens",
default: 128,
description: "The maximum number of tokens to generate in the completion. Most models have a context length of 2048 tokens (except for the newest models, which support 32,768).",
type: "number",
typeOptions: {
maxValue: 32768
}
},
{
displayName: "Presence Penalty",
name: "presencePenalty",
default: 0,
typeOptions: { maxValue: 2, minValue: -2, numberPrecision: 1 },
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",
type: "number"
},
{
displayName: "Sampling Temperature",
name: "temperature",
default: 1,
typeOptions: { maxValue: 1, minValue: 0, numberPrecision: 1 },
description: "Controls randomness: Lowering results in less random completions. As the temperature approaches zero, the model will become deterministic and repetitive.",
type: "number"
},
{
displayName: "Top K",
name: "topK",
default: 1,
typeOptions: { maxValue: 1, minValue: 0, numberPrecision: 1 },
description: "Controls the top tokens to consider within the sample operation to create new text",
type: "number"
},
{
displayName: "Top P",
name: "topP",
default: 1,
typeOptions: { maxValue: 1, minValue: 0, numberPrecision: 1 },
description: "Controls diversity via nucleus sampling: 0.5 means half of all likelihood-weighted options are considered. We generally recommend altering this or temperature but not both.",
type: "number"
}
]
}
]
};
}
async supplyData(itemIndex) {
const credentials = await this.getCredentials("huggingFaceApi");
const modelName = this.getNodeParameter("model", itemIndex);
const options = this.getNodeParameter("options", itemIndex, {});
const model = new import_hf.HuggingFaceInference({
model: modelName,
apiKey: credentials.apiKey,
...options,
callbacks: [new import_N8nLlmTracing.N8nLlmTracing(this)],
onFailedAttempt: (0, import_n8nLlmFailedAttemptHandler.makeN8nLlmFailedAttemptHandler)(this)
});
return {
response: model
};
}
}
// Annotate the CommonJS export names for ESM import in node:
0 && (module.exports = {
LmOpenHuggingFaceInference
});
//# sourceMappingURL=LmOpenHuggingFaceInference.node.js.map