Flowise/packages/components/nodes/vectorstores/Elasticsearch/Elasticsearch_Existing.ts

112 lines
4.0 KiB
TypeScript

import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { Embeddings } from 'langchain/embeddings/base'
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src'
import { Client, ClientOptions } from '@elastic/elasticsearch'
import { ElasticClientArgs, ElasticVectorSearch } from 'langchain/vectorstores/elasticsearch'
class ElasicsearchExisting_VectorStores implements INode {
label: string
name: string
version: number
description: string
type: string
icon: string
category: string
baseClasses: string[]
inputs: INodeParams[]
credential: INodeParams
outputs: INodeOutputsValue[]
constructor() {
this.label = 'Elasticsearch Load Existing Index'
this.name = 'ElasticsearchIndex'
this.version = 1.0
this.type = 'Elasticsearch'
this.icon = 'elasticsearch.png'
this.category = 'Vector Stores'
this.description = 'Load existing index from Elasticsearch (i.e: Document has been upserted)'
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
this.credential = {
label: 'Connect Credential',
name: 'credential',
type: 'credential',
credentialNames: ['elasticsearchApi', 'elasticSearchUserPassword']
}
this.inputs = [
{
label: 'Embeddings',
name: 'embeddings',
type: 'Embeddings'
},
{
label: 'Index Name',
name: 'indexName',
placeholder: '<INDEX_NAME>',
type: 'string'
},
{
label: 'Top K',
name: 'topK',
description: 'Number of top results to fetch. Default to 4',
placeholder: '4',
type: 'number',
additionalParams: true,
optional: true
}
]
this.outputs = [
{
label: 'Elasticsearch Retriever',
name: 'retriever',
baseClasses: this.baseClasses
},
{
label: 'Elasticsearch Vector Store',
name: 'vectorStore',
baseClasses: [this.type, ...getBaseClasses(ElasticVectorSearch)]
}
]
}
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const endPoint = getCredentialParam('endpoint', credentialData, nodeData)
const apiKey = getCredentialParam('apiKey', credentialData, nodeData)
const indexName = nodeData.inputs?.indexName as string
const embeddings = nodeData.inputs?.embeddings as Embeddings
const topK = nodeData.inputs?.topK as string
const k = topK ? parseFloat(topK) : 4
const output = nodeData.outputs?.output as string
// eslint-disable-next-line no-console
console.log('EndPoint:: ' + endPoint + ', APIKey:: ' + apiKey + ', Index:: ' + indexName)
const elasticSearchClientOptions: ClientOptions = {
node: endPoint,
auth: {
apiKey: apiKey
}
}
const elasticSearchClientArgs: ElasticClientArgs = {
client: new Client(elasticSearchClientOptions),
indexName: indexName
}
const vectorStore = await ElasticVectorSearch.fromExistingIndex(embeddings, elasticSearchClientArgs)
// eslint-disable-next-line no-console
console.log('vectorStore ::' + vectorStore._vectorstoreType())
if (output === 'retriever') {
return vectorStore.asRetriever(k)
} else if (output === 'vectorStore') {
;(vectorStore as any).k = k
return vectorStore
}
return vectorStore
}
}
module.exports = { nodeClass: ElasicsearchExisting_VectorStores }