@n8n/n8n-nodes-langchain
Version:
124 lines • 5.51 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
exports.EmbeddingsGoogleGemini = void 0;
const google_genai_1 = require("@langchain/google-genai");
const n8n_workflow_1 = require("n8n-workflow");
const ai_utilities_1 = require("@n8n/ai-utilities");
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: [n8n_workflow_1.NodeConnectionTypes.AiEmbedding],
outputNames: ['Embeddings'],
properties: [
(0, ai_utilities_1.getConnectionHintNoticeField)([n8n_workflow_1.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/gemini-embedding-001',
},
],
};
}
async supplyData(itemIndex) {
this.logger.debug('Supply data for embeddings Google Gemini');
const modelName = this.getNodeParameter('modelName', itemIndex, 'models/gemini-embedding-001');
const credentials = await this.getCredentials('googlePalmApi');
const embeddings = new google_genai_1.GoogleGenerativeAIEmbeddings({
apiKey: credentials.apiKey,
baseUrl: credentials.host,
model: modelName,
});
return {
response: (0, ai_utilities_1.logWrapper)(embeddings, this),
};
}
}
exports.EmbeddingsGoogleGemini = EmbeddingsGoogleGemini;
//# sourceMappingURL=EmbeddingsGoogleGemini.node.js.map