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Code Issues Packages Projects Releases Wiki Activity

Feature/Indexing (#1802)

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* indexing

* fix for multiple files upsert

* fix default Postgres port

* fix SQLite node description

* add MySQLRecordManager node

* fix MySQL unique index

* add upsert history

* update jsx ui

* lint-fix

* update dialog details

* update llamaindex pinecone

---------

Co-authored-by: chungyau97 <chungyau97@gmail.com>
This commit is contained in:
Henry Heng
2024-04-02 23:47:19 +01:00
committed by GitHub
parent 957694a912
commit e422ce287b
67 changed files with 3006 additions and 246 deletions
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packages/components/nodes/vectorstores/Elasticsearch/Elasticsearch.ts
+30 -4
View File
@@ -3,8 +3,9 @@ import { Client, ClientOptions } from '@elastic/elasticsearch'
import { Document } from '@langchain/core/documents'
import { Embeddings } from '@langchain/core/embeddings'
import { ElasticClientArgs, ElasticVectorSearch } from '@langchain/community/vectorstores/elasticsearch'
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams, IndexingResult } from '../../../src/Interface'
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
import { index } from '../../../src/indexing'
class Elasticsearch_VectorStores implements INode {
label: string
@@ -23,7 +24,7 @@ class Elasticsearch_VectorStores implements INode {
constructor() {
this.label = 'Elasticsearch'
this.name = 'elasticsearch'
this.version = 1.0
this.version = 2.0
this.description =
'Upsert embedded data and perform similarity search upon query using Elasticsearch, a distributed search and analytics engine'
this.type = 'Elasticsearch'
@@ -50,6 +51,13 @@ class Elasticsearch_VectorStores implements INode {
name: 'embeddings',
type: 'Embeddings'
},
{
label: 'Record Manager',
name: 'recordManager',
type: 'RecordManager',
description: 'Keep track of the record to prevent duplication',
optional: true
},
{
label: 'Index Name',
name: 'indexName',
@@ -105,13 +113,14 @@ class Elasticsearch_VectorStores implements INode {
//@ts-ignore
vectorStoreMethods = {
async upsert(nodeData: INodeData, options: ICommonObject): Promise<void> {
async upsert(nodeData: INodeData, options: ICommonObject): Promise<Partial<IndexingResult>> {
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const endPoint = getCredentialParam('endpoint', credentialData, nodeData)
const cloudId = getCredentialParam('cloudId', credentialData, nodeData)
const indexName = nodeData.inputs?.indexName as string
const embeddings = nodeData.inputs?.embeddings as Embeddings
const similarityMeasure = nodeData.inputs?.similarityMeasure as string
const recordManager = nodeData.inputs?.recordManager
const docs = nodeData.inputs?.document as Document[]
const flattenDocs = docs && docs.length ? flatten(docs) : []
@@ -134,7 +143,24 @@ class Elasticsearch_VectorStores implements INode {
const vectorStore = new ElasticVectorSearch(embeddings, elasticSearchClientArgs)
try {
await vectorStore.addDocuments(finalDocs)
if (recordManager) {
const vectorStore = await ElasticVectorSearch.fromExistingIndex(embeddings, elasticSearchClientArgs)
await recordManager.createSchema()
const res = await index({
docsSource: finalDocs,
recordManager,
vectorStore,
options: {
cleanup: recordManager?.cleanup,
sourceIdKey: recordManager?.sourceIdKey ?? 'source',
vectorStoreName: indexName
}
})
return res
} else {
await vectorStore.addDocuments(finalDocs)
return { numAdded: finalDocs.length, addedDocs: finalDocs }
}
} catch (e) {
throw new Error(e)
}