dtamind-components
Version:
Apps integration for Dtamind. Contain Nodes and Credentials.
198 lines (184 loc) • 7.94 kB
text/typescript
import { flatten } from 'lodash'
import { Embeddings } from '@langchain/core/embeddings'
import { SingleStoreVectorStore, SingleStoreVectorStoreConfig } from '@langchain/community/vectorstores/singlestore'
import { Document } from '@langchain/core/documents'
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams, IndexingResult } from '../../../src/Interface'
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
class SingleStore_VectorStores implements INode {
label: string
name: string
version: number
description: string
type: string
icon: string
category: string
badge: string
baseClasses: string[]
inputs: INodeParams[]
credential: INodeParams
outputs: INodeOutputsValue[]
constructor() {
this.label = 'SingleStore'
this.name = 'singlestore'
this.version = 1.0
this.type = 'SingleStore'
this.icon = 'singlestore.svg'
this.category = 'Vector Stores'
this.description =
'Upsert embedded data and perform similarity search upon query using SingleStore, a fast and distributed cloud relational database'
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
this.credential = {
label: 'Connect Credential',
name: 'credential',
type: 'credential',
description: 'Needed when using SingleStore cloud hosted',
optional: true,
credentialNames: ['singleStoreApi']
}
this.inputs = [
{
label: 'Document',
name: 'document',
type: 'Document',
list: true,
optional: true
},
{
label: 'Embeddings',
name: 'embeddings',
type: 'Embeddings'
},
{
label: 'Host',
name: 'host',
type: 'string'
},
{
label: 'Database',
name: 'database',
type: 'string'
},
{
label: 'Table Name',
name: 'tableName',
type: 'string',
placeholder: 'embeddings',
additionalParams: true,
optional: true
},
{
label: 'Content Column Name',
name: 'contentColumnName',
type: 'string',
placeholder: 'content',
additionalParams: true,
optional: true
},
{
label: 'Vector Column Name',
name: 'vectorColumnName',
type: 'string',
placeholder: 'vector',
additionalParams: true,
optional: true
},
{
label: 'Metadata Column Name',
name: 'metadataColumnName',
type: 'string',
placeholder: 'metadata',
additionalParams: true,
optional: true
},
{
label: 'Top K',
name: 'topK',
placeholder: '4',
type: 'number',
additionalParams: true,
optional: true
}
]
this.outputs = [
{
label: 'SingleStore Retriever',
name: 'retriever',
baseClasses: this.baseClasses
},
{
label: 'SingleStore Vector Store',
name: 'vectorStore',
baseClasses: [this.type, ...getBaseClasses(SingleStoreVectorStore)]
}
]
}
//@ts-ignore
vectorStoreMethods = {
async upsert(nodeData: INodeData, options: ICommonObject): Promise<Partial<IndexingResult>> {
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const user = getCredentialParam('user', credentialData, nodeData)
const password = getCredentialParam('password', credentialData, nodeData)
const singleStoreConnectionConfig = {
connectionOptions: {
host: nodeData.inputs?.host as string,
port: 3306,
user,
password,
database: nodeData.inputs?.database as string
},
...(nodeData.inputs?.tableName ? { tableName: nodeData.inputs.tableName as string } : {}),
...(nodeData.inputs?.contentColumnName ? { contentColumnName: nodeData.inputs.contentColumnName as string } : {}),
...(nodeData.inputs?.vectorColumnName ? { vectorColumnName: nodeData.inputs.vectorColumnName as string } : {}),
...(nodeData.inputs?.metadataColumnName ? { metadataColumnName: nodeData.inputs.metadataColumnName as string } : {})
} as SingleStoreVectorStoreConfig
const docs = nodeData.inputs?.document as Document[]
const embeddings = nodeData.inputs?.embeddings as Embeddings
const flattenDocs = docs && docs.length ? flatten(docs) : []
const finalDocs = []
for (let i = 0; i < flattenDocs.length; i += 1) {
if (flattenDocs[i] && flattenDocs[i].pageContent) {
finalDocs.push(new Document(flattenDocs[i]))
}
}
try {
const vectorStore = new SingleStoreVectorStore(embeddings, singleStoreConnectionConfig)
vectorStore.addDocuments.bind(vectorStore)(finalDocs)
return { numAdded: finalDocs.length, addedDocs: finalDocs }
} catch (e) {
throw new Error(e)
}
}
}
async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
const credentialData = await getCredentialData(nodeData.credential ?? '', options)
const user = getCredentialParam('user', credentialData, nodeData)
const password = getCredentialParam('password', credentialData, nodeData)
const singleStoreConnectionConfig = {
connectionOptions: {
host: nodeData.inputs?.host as string,
port: 3306,
user,
password,
database: nodeData.inputs?.database as string
},
...(nodeData.inputs?.tableName ? { tableName: nodeData.inputs.tableName as string } : {}),
...(nodeData.inputs?.contentColumnName ? { contentColumnName: nodeData.inputs.contentColumnName as string } : {}),
...(nodeData.inputs?.vectorColumnName ? { vectorColumnName: nodeData.inputs.vectorColumnName as string } : {}),
...(nodeData.inputs?.metadataColumnName ? { metadataColumnName: nodeData.inputs.metadataColumnName as string } : {})
} as SingleStoreVectorStoreConfig
const embeddings = nodeData.inputs?.embeddings as Embeddings
const output = nodeData.outputs?.output as string
const topK = nodeData.inputs?.topK as string
const k = topK ? parseFloat(topK) : 4
const vectorStore = new SingleStoreVectorStore(embeddings, singleStoreConnectionConfig)
if (output === 'retriever') {
const retriever = vectorStore.asRetriever(k)
return retriever
} else if (output === 'vectorStore') {
;(vectorStore as any).k = k
return vectorStore
}
return vectorStore
}
}
module.exports = { nodeClass: SingleStore_VectorStores }