@n8n/n8n-nodes-langchain
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JavaScript
;
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)
__defProp(target, name, { get: all[name], enumerable: true });
};
var __copyProps = (to, from, except, desc) => {
if (from && typeof from === "object" || typeof from === "function") {
for (let key of __getOwnPropNames(from))
if (!__hasOwnProp.call(to, key) && key !== except)
__defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable });
}
return to;
};
var __toCommonJS = (mod) => __copyProps(__defProp({}, "__esModule", { value: true }), mod);
var EmbeddingsGoogleGemini_node_exports = {};
__export(EmbeddingsGoogleGemini_node_exports, {
EmbeddingsGoogleGemini: () => EmbeddingsGoogleGemini
});
module.exports = __toCommonJS(EmbeddingsGoogleGemini_node_exports);
var import_google_genai = require("@langchain/google-genai");
var import_n8n_workflow = require("n8n-workflow");
var import_logWrapper = require("../../../utils/logWrapper");
var import_sharedFields = require("../../../utils/sharedFields");
class EmbeddingsGoogleGemini {
constructor() {
this.description = {
displayName: "Embeddings Google Gemini",
name: "embeddingsGoogleGemini",
icon: "file:google.svg",
group: ["transform"],
version: 1,
description: "Use Google Gemini Embeddings",
defaults: {
name: "Embeddings Google Gemini"
},
requestDefaults: {
ignoreHttpStatusErrors: true,
baseURL: "={{ $credentials.host }}"
},
credentials: [
{
name: "googlePalmApi",
required: true
}
],
codex: {
categories: ["AI"],
subcategories: {
AI: ["Embeddings"]
},
resources: {
primaryDocumentation: [
{
url: "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.embeddingsgooglegemini/"
}
]
}
},
inputs: [],
outputs: [import_n8n_workflow.NodeConnectionTypes.AiEmbedding],
outputNames: ["Embeddings"],
properties: [
(0, import_sharedFields.getConnectionHintNoticeField)([import_n8n_workflow.NodeConnectionTypes.AiVectorStore]),
{
displayName: "Each model is using different dimensional density for embeddings. Please make sure to use the same dimensionality for your vector store. The default model is using 768-dimensional embeddings.",
name: "notice",
type: "notice",
default: ""
},
{
displayName: "Model",
name: "modelName",
type: "options",
description: 'The model which will generate the embeddings. <a href="https://developers.generativeai.google/api/rest/generativelanguage/models/list">Learn more</a>.',
typeOptions: {
loadOptions: {
routing: {
request: {
method: "GET",
url: "/v1beta/models"
},
output: {
postReceive: [
{
type: "rootProperty",
properties: {
property: "models"
}
},
{
type: "filter",
properties: {
pass: "={{ $responseItem.name.includes('embedding') }}"
}
},
{
type: "setKeyValue",
properties: {
name: "={{$responseItem.name}}",
value: "={{$responseItem.name}}",
description: "={{$responseItem.description}}"
}
},
{
type: "sort",
properties: {
key: "name"
}
}
]
}
}
}
},
routing: {
send: {
type: "body",
property: "model"
}
},
default: "models/text-embedding-004"
}
]
};
}
async supplyData(itemIndex) {
this.logger.debug("Supply data for embeddings Google Gemini");
const modelName = this.getNodeParameter(
"modelName",
itemIndex,
"models/text-embedding-004"
);
const credentials = await this.getCredentials("googlePalmApi");
const embeddings = new import_google_genai.GoogleGenerativeAIEmbeddings({
apiKey: credentials.apiKey,
baseUrl: credentials.host,
model: modelName
});
return {
response: (0, import_logWrapper.logWrapper)(embeddings, this)
};
}
}
// Annotate the CommonJS export names for ESM import in node:
0 && (module.exports = {
EmbeddingsGoogleGemini
});
//# sourceMappingURL=EmbeddingsGoogleGemini.node.js.map