chore: Update Langchain to 0.3 (no-changelog) (#10958)
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@@ -1,4 +1,5 @@
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/* eslint-disable n8n-nodes-base/node-dirname-against-convention */
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import { BedrockEmbeddings } from '@langchain/aws';
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import {
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NodeConnectionType,
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type IExecuteFunctions,
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@@ -6,7 +7,6 @@ import {
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type INodeTypeDescription,
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type SupplyData,
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} from 'n8n-workflow';
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import { BedrockEmbeddings } from '@langchain/community/embeddings/bedrock';
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import { logWrapper } from '../../../utils/logWrapper';
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import { getConnectionHintNoticeField } from '../../../utils/sharedFields';
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@@ -1,135 +0,0 @@
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/* eslint-disable n8n-nodes-base/node-dirname-against-convention */
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import {
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NodeConnectionType,
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type IExecuteFunctions,
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type INodeType,
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type INodeTypeDescription,
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type SupplyData,
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} from 'n8n-workflow';
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import { GooglePaLMEmbeddings } from '@langchain/community/embeddings/googlepalm';
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import { logWrapper } from '../../../utils/logWrapper';
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import { getConnectionHintNoticeField } from '../../../utils/sharedFields';
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export class EmbeddingsGooglePalm implements INodeType {
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description: INodeTypeDescription = {
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displayName: 'Embeddings Google PaLM',
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name: 'embeddingsGooglePalm',
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icon: 'file:google.svg',
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group: ['transform'],
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version: 1,
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description: 'Use Google PaLM Embeddings',
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defaults: {
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name: 'Embeddings Google PaLM',
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},
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requestDefaults: {
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ignoreHttpStatusErrors: true,
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baseURL: '={{ $credentials.host }}',
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},
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credentials: [
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{
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name: 'googlePalmApi',
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required: true,
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},
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],
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codex: {
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categories: ['AI'],
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subcategories: {
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AI: ['Embeddings'],
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},
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resources: {
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primaryDocumentation: [
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{
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url: 'https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.embeddingsgooglepalm/',
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},
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],
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},
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},
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// eslint-disable-next-line n8n-nodes-base/node-class-description-inputs-wrong-regular-node
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inputs: [],
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// eslint-disable-next-line n8n-nodes-base/node-class-description-outputs-wrong
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outputs: [NodeConnectionType.AiEmbedding],
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outputNames: ['Embeddings'],
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properties: [
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getConnectionHintNoticeField([NodeConnectionType.AiVectorStore]),
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{
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displayName:
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'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.',
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name: 'notice',
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type: 'notice',
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default: '',
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},
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{
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displayName: 'Model',
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name: 'modelName',
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type: 'options',
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description:
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'The model which will generate the embeddings. <a href="https://developers.generativeai.google/api/rest/generativelanguage/models/list">Learn more</a>.',
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typeOptions: {
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loadOptions: {
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routing: {
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request: {
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method: 'GET',
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url: '/v1beta3/models',
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},
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output: {
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postReceive: [
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{
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type: 'rootProperty',
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properties: {
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property: 'models',
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},
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},
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{
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type: 'filter',
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properties: {
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pass: "={{ $responseItem.name.startsWith('models/embedding') }}",
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},
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},
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{
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type: 'setKeyValue',
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properties: {
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name: '={{$responseItem.name}}',
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value: '={{$responseItem.name}}',
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description: '={{$responseItem.description}}',
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},
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},
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{
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type: 'sort',
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properties: {
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key: 'name',
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},
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},
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],
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},
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},
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},
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},
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routing: {
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send: {
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type: 'body',
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property: 'model',
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},
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},
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default: 'models/embedding-gecko-001',
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},
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],
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};
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async supplyData(this: IExecuteFunctions, itemIndex: number): Promise<SupplyData> {
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this.logger.debug('Supply data for embeddings Google PaLM');
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const modelName = this.getNodeParameter(
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'modelName',
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itemIndex,
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'models/embedding-gecko-001',
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) as string;
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const credentials = await this.getCredentials('googlePalmApi');
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const embeddings = new GooglePaLMEmbeddings({
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apiKey: credentials.apiKey as string,
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modelName,
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});
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return {
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response: logWrapper(embeddings, this),
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};
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}
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}
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@@ -1 +0,0 @@
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