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packages/@n8n/nodes-langchain/nodes/embeddings/EmbeddingsGoogleGemini/EmbeddingsGoogleGemini.node.ts

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+import { GoogleGenerativeAIEmbeddings } from '@langchain/google-genai';
+import {
+	NodeConnectionTypes,
+	type INodeType,
+	type INodeTypeDescription,
+	type ISupplyDataFunctions,
+	type SupplyData,
+} from 'n8n-workflow';
+
+import { logWrapper, getConnectionHintNoticeField } from '@n8n/ai-utilities';
+
+export class EmbeddingsGoogleGemini implements INodeType {
+	description: INodeTypeDescription = {
+		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: [NodeConnectionTypes.AiEmbedding],
+		outputNames: ['Embeddings'],
+		properties: [
+			getConnectionHintNoticeField([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(this: ISupplyDataFunctions, itemIndex: number): Promise<SupplyData> {
+		this.logger.debug('Supply data for embeddings Google Gemini');
+		const modelName = this.getNodeParameter(
+			'modelName',
+			itemIndex,
+			'models/text-embedding-004',
+		) as string;
+		const credentials = await this.getCredentials('googlePalmApi');
+		const embeddings = new GoogleGenerativeAIEmbeddings({
+			apiKey: credentials.apiKey as string,
+			baseUrl: credentials.host as string,
+			model: modelName,
+		});
+
+		return {
+			response: logWrapper(embeddings, this),
+		};
+	}
+}

packages/@n8n/nodes-langchain/nodes/embeddings/EmbeddingsGoogleGemini/google.svg

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