n8n/packages/@n8n/nodes-langchain/nodes/llms/LmChatAwsBedrock/LmChatAwsBedrock.node.ts
2024-04-09 14:22:48 +02:00

162 lines
4.6 KiB
TypeScript

/* eslint-disable n8n-nodes-base/node-dirname-against-convention */
import {
NodeConnectionType,
type IExecuteFunctions,
type INodeType,
type INodeTypeDescription,
type SupplyData,
} from 'n8n-workflow';
import { BedrockChat } from '@langchain/community/chat_models/bedrock';
import { logWrapper } from '../../../utils/logWrapper';
import { getConnectionHintNoticeField } from '../../../utils/sharedFields';
// Dependencies needed underneath the hood. We add them
// here only to track where what dependency is used
import '@aws-sdk/credential-provider-node';
import '@aws-sdk/client-bedrock-runtime';
export class LmChatAwsBedrock implements INodeType {
description: INodeTypeDescription = {
displayName: 'AWS Bedrock Chat Model',
// eslint-disable-next-line n8n-nodes-base/node-class-description-name-miscased
name: 'lmChatAwsBedrock',
icon: 'file:bedrock.svg',
group: ['transform'],
version: 1,
description: 'Language Model AWS Bedrock',
defaults: {
name: 'AWS Bedrock Chat Model',
},
codex: {
categories: ['AI'],
subcategories: {
AI: ['Language Models'],
},
resources: {
primaryDocumentation: [
{
url: 'https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.lmchatawsbedrock/',
},
],
},
},
// eslint-disable-next-line n8n-nodes-base/node-class-description-inputs-wrong-regular-node
inputs: [],
// eslint-disable-next-line n8n-nodes-base/node-class-description-outputs-wrong
outputs: [NodeConnectionType.AiLanguageModel],
outputNames: ['Model'],
credentials: [
{
// eslint-disable-next-line n8n-nodes-base/node-class-description-credentials-name-unsuffixed
name: 'aws',
required: true,
},
],
requestDefaults: {
ignoreHttpStatusErrors: true,
baseURL: '=https://bedrock.{{$credentials?.region ?? "eu-central-1"}}.amazonaws.com',
},
properties: [
getConnectionHintNoticeField([NodeConnectionType.AiChain, NodeConnectionType.AiChain]),
{
displayName: 'Model',
name: 'model',
type: 'options',
description:
'The model which will generate the completion. <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/foundation-models.html">Learn more</a>.',
typeOptions: {
loadOptions: {
routing: {
request: {
method: 'GET',
url: '/foundation-models?&byOutputModality=TEXT&byInferenceType=ON_DEMAND',
},
output: {
postReceive: [
{
type: 'rootProperty',
properties: {
property: 'modelSummaries',
},
},
{
type: 'setKeyValue',
properties: {
name: '={{$responseItem.modelName}}',
description: '={{$responseItem.modelArn}}',
value: '={{$responseItem.modelId}}',
},
},
{
type: 'sort',
properties: {
key: 'name',
},
},
],
},
},
},
},
routing: {
send: {
type: 'body',
property: 'model',
},
},
default: '',
},
{
displayName: 'Options',
name: 'options',
placeholder: 'Add Option',
description: 'Additional options to add',
type: 'collection',
default: {},
options: [
{
displayName: 'Maximum Number of Tokens',
name: 'maxTokensToSample',
default: 2000,
description: 'The maximum number of tokens to generate in the completion',
type: 'number',
},
{
displayName: 'Sampling Temperature',
name: 'temperature',
default: 0.7,
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',
},
],
},
],
};
async supplyData(this: IExecuteFunctions, itemIndex: number): Promise<SupplyData> {
const credentials = await this.getCredentials('aws');
const modelName = this.getNodeParameter('model', itemIndex) as string;
const options = this.getNodeParameter('options', itemIndex, {}) as {
temperature: number;
maxTokensToSample: number;
};
const model = new BedrockChat({
region: credentials.region as string,
model: modelName,
temperature: options.temperature,
maxTokens: options.maxTokensToSample,
credentials: {
secretAccessKey: credentials.secretAccessKey as string,
accessKeyId: credentials.accessKeyId as string,
sessionToken: credentials.sessionToken as string,
},
});
return {
response: logWrapper(model, this),
};
}
}