mirror of
https://github.com/farcasclaudiu/Flowise.git
synced 2026-06-28 17:01:00 +03:00
Chore/update deprecating nodes (#2540)
* update deprecating nodes * add filters use cases to marketplace * update log level
This commit is contained in:
Henry Heng
@@ -39,7 +39,6 @@ class Qdrant_VectorStores implements INode {
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this.description =
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'Upsert embedded data and perform similarity search upon query using Qdrant, a scalable open source vector database written in Rust'
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this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
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this.badge = 'NEW'
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this.credential = {
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label: 'Connect Credential',
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name: 'credential',
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@@ -1,194 +0,0 @@
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import { QdrantClient } from '@qdrant/js-client-rest'
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import { QdrantVectorStore, QdrantLibArgs } from '@langchain/community/vectorstores/qdrant'
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import { Embeddings } from '@langchain/core/embeddings'
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import { VectorStoreRetrieverInput } from '@langchain/core/vectorstores'
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import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
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import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
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type RetrieverConfig = Partial<VectorStoreRetrieverInput<QdrantVectorStore>>
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class Qdrant_Existing_VectorStores implements INode {
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label: string
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name: string
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version: number
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description: string
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type: string
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icon: string
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category: string
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badge: string
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baseClasses: string[]
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inputs: INodeParams[]
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credential: INodeParams
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outputs: INodeOutputsValue[]
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constructor() {
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this.label = 'Qdrant Load Existing Index'
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this.name = 'qdrantExistingIndex'
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this.version = 2.0
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this.type = 'Qdrant'
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this.icon = 'qdrant.png'
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this.category = 'Vector Stores'
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this.description = 'Load existing index from Qdrant (i.e., documents have been upserted)'
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this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
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this.badge = 'DEPRECATING'
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this.credential = {
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label: 'Connect Credential',
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name: 'credential',
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type: 'credential',
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description: 'Only needed when using Qdrant cloud hosted',
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optional: true,
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credentialNames: ['qdrantApi']
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}
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this.inputs = [
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{
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label: 'Embeddings',
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name: 'embeddings',
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type: 'Embeddings'
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},
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{
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label: 'Qdrant Server URL',
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name: 'qdrantServerUrl',
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type: 'string',
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placeholder: 'http://localhost:6333'
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},
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{
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label: 'Qdrant Collection Name',
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name: 'qdrantCollection',
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type: 'string'
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},
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{
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label: 'Vector Dimension',
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name: 'qdrantVectorDimension',
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type: 'number',
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default: 1536,
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additionalParams: true
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},
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{
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label: 'Similarity',
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name: 'qdrantSimilarity',
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description: 'Similarity measure used in Qdrant.',
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type: 'options',
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default: 'Cosine',
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options: [
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{
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label: 'Cosine',
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name: 'Cosine'
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},
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{
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label: 'Euclid',
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name: 'Euclid'
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},
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{
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label: 'Dot',
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name: 'Dot'
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}
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],
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additionalParams: true
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},
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{
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label: 'Additional Collection Cofiguration',
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name: 'qdrantCollectionConfiguration',
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description:
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'Refer to <a target="_blank" href="https://qdrant.tech/documentation/concepts/collections">collection docs</a> for more reference',
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type: 'json',
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optional: true,
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additionalParams: true
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},
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{
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label: 'Top K',
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name: 'topK',
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description: 'Number of top results to fetch. Default to 4',
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placeholder: '4',
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type: 'number',
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additionalParams: true,
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optional: true
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},
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{
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label: 'Qdrant Search Filter',
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name: 'qdrantFilter',
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description: 'Only return points which satisfy the conditions',
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type: 'json',
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additionalParams: true,
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optional: true
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}
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]
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this.outputs = [
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{
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label: 'Qdrant Retriever',
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name: 'retriever',
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baseClasses: this.baseClasses
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},
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{
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label: 'Qdrant Vector Store',
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name: 'vectorStore',
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baseClasses: [this.type, ...getBaseClasses(QdrantVectorStore)]
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}
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]
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}
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async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
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const qdrantServerUrl = nodeData.inputs?.qdrantServerUrl as string
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const collectionName = nodeData.inputs?.qdrantCollection as string
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let qdrantCollectionConfiguration = nodeData.inputs?.qdrantCollectionConfiguration
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const embeddings = nodeData.inputs?.embeddings as Embeddings
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const qdrantSimilarity = nodeData.inputs?.qdrantSimilarity
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const qdrantVectorDimension = nodeData.inputs?.qdrantVectorDimension
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const output = nodeData.outputs?.output as string
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const topK = nodeData.inputs?.topK as string
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let queryFilter = nodeData.inputs?.qdrantFilter
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const k = topK ? parseFloat(topK) : 4
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const credentialData = await getCredentialData(nodeData.credential ?? '', options)
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const qdrantApiKey = getCredentialParam('qdrantApiKey', credentialData, nodeData)
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const client = new QdrantClient({
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url: qdrantServerUrl,
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apiKey: qdrantApiKey
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})
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const dbConfig: QdrantLibArgs = {
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client,
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collectionName
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}
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const retrieverConfig: RetrieverConfig = {
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k
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}
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if (qdrantCollectionConfiguration) {
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qdrantCollectionConfiguration =
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typeof qdrantCollectionConfiguration === 'object'
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? qdrantCollectionConfiguration
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: JSON.parse(qdrantCollectionConfiguration)
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dbConfig.collectionConfig = {
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...qdrantCollectionConfiguration,
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vectors: {
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...qdrantCollectionConfiguration.vectors,
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size: qdrantVectorDimension ? parseInt(qdrantVectorDimension, 10) : 1536,
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distance: qdrantSimilarity ?? 'Cosine'
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}
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}
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}
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if (queryFilter) {
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retrieverConfig.filter = typeof queryFilter === 'object' ? queryFilter : JSON.parse(queryFilter)
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}
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const vectorStore = await QdrantVectorStore.fromExistingCollection(embeddings, dbConfig)
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if (output === 'retriever') {
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const retriever = vectorStore.asRetriever(retrieverConfig)
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return retriever
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} else if (output === 'vectorStore') {
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;(vectorStore as any).k = k
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if (queryFilter) {
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;(vectorStore as any).filter = retrieverConfig.filter
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}
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return vectorStore
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}
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return vectorStore
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}
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}
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module.exports = { nodeClass: Qdrant_Existing_VectorStores }
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@@ -1,213 +0,0 @@
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import { QdrantClient } from '@qdrant/js-client-rest'
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import { QdrantVectorStore, QdrantLibArgs } from '@langchain/community/vectorstores/qdrant'
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import { Embeddings } from '@langchain/core/embeddings'
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import { Document } from '@langchain/core/documents'
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import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
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import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
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import { flatten } from 'lodash'
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import { VectorStoreRetrieverInput } from '@langchain/core/vectorstores'
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type RetrieverConfig = Partial<VectorStoreRetrieverInput<QdrantVectorStore>>
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class QdrantUpsert_VectorStores implements INode {
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label: string
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name: string
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version: number
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description: string
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type: string
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icon: string
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category: string
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badge: string
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baseClasses: string[]
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inputs: INodeParams[]
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credential: INodeParams
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outputs: INodeOutputsValue[]
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constructor() {
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this.label = 'Qdrant Upsert Document'
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this.name = 'qdrantUpsert'
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this.version = 3.0
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this.type = 'Qdrant'
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this.icon = 'qdrant.png'
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this.category = 'Vector Stores'
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this.description = 'Upsert documents to Qdrant'
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this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
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this.badge = 'DEPRECATING'
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this.credential = {
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label: 'Connect Credential',
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name: 'credential',
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type: 'credential',
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description: 'Only needed when using Qdrant cloud hosted',
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optional: true,
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credentialNames: ['qdrantApi']
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}
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this.inputs = [
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{
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label: 'Document',
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name: 'document',
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type: 'Document',
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list: true
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},
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{
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label: 'Embeddings',
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name: 'embeddings',
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type: 'Embeddings'
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},
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{
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label: 'Qdrant Server URL',
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name: 'qdrantServerUrl',
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type: 'string',
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placeholder: 'http://localhost:6333'
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},
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{
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label: 'Qdrant Collection Name',
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name: 'qdrantCollection',
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type: 'string'
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},
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{
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label: 'Vector Dimension',
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name: 'qdrantVectorDimension',
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type: 'number',
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default: 1536,
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additionalParams: true
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},
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{
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label: 'Upsert Batch Size',
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name: 'batchSize',
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type: 'number',
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step: 1,
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description: 'Upsert in batches of size N',
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additionalParams: true,
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optional: true
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},
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{
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label: 'Similarity',
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name: 'qdrantSimilarity',
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description: 'Similarity measure used in Qdrant.',
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type: 'options',
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default: 'Cosine',
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options: [
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{
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label: 'Cosine',
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name: 'Cosine'
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},
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{
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label: 'Euclid',
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name: 'Euclid'
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},
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{
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label: 'Dot',
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name: 'Dot'
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}
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],
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additionalParams: true
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},
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{
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label: 'Top K',
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name: 'topK',
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description: 'Number of top results to fetch. Default to 4',
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placeholder: '4',
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type: 'number',
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additionalParams: true,
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optional: true
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},
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{
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label: 'Qdrant Search Filter',
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name: 'qdrantFilter',
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description: 'Only return points which satisfy the conditions',
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type: 'json',
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additionalParams: true,
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optional: true
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}
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]
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this.outputs = [
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{
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label: 'Qdrant Retriever',
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name: 'retriever',
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baseClasses: this.baseClasses
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},
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{
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label: 'Qdrant Vector Store',
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name: 'vectorStore',
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baseClasses: [this.type, ...getBaseClasses(QdrantVectorStore)]
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}
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]
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}
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async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
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const qdrantServerUrl = nodeData.inputs?.qdrantServerUrl as string
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const collectionName = nodeData.inputs?.qdrantCollection as string
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const docs = nodeData.inputs?.document as Document[]
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const embeddings = nodeData.inputs?.embeddings as Embeddings
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const qdrantSimilarity = nodeData.inputs?.qdrantSimilarity
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const qdrantVectorDimension = nodeData.inputs?.qdrantVectorDimension
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const _batchSize = nodeData.inputs?.batchSize
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const output = nodeData.outputs?.output as string
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const topK = nodeData.inputs?.topK as string
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const k = topK ? parseFloat(topK) : 4
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let queryFilter = nodeData.inputs?.qdrantFilter
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const credentialData = await getCredentialData(nodeData.credential ?? '', options)
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const qdrantApiKey = getCredentialParam('qdrantApiKey', credentialData, nodeData)
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const client = new QdrantClient({
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url: qdrantServerUrl,
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apiKey: qdrantApiKey
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})
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const flattenDocs = docs && docs.length ? flatten(docs) : []
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const finalDocs = []
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for (let i = 0; i < flattenDocs.length; i += 1) {
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if (flattenDocs[i] && flattenDocs[i].pageContent) {
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finalDocs.push(new Document(flattenDocs[i]))
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}
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}
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const dbConfig: QdrantLibArgs = {
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client,
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url: qdrantServerUrl,
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collectionName,
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collectionConfig: {
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vectors: {
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size: qdrantVectorDimension ? parseInt(qdrantVectorDimension, 10) : 1536,
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distance: qdrantSimilarity ?? 'Cosine'
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}
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}
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}
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const retrieverConfig: RetrieverConfig = {
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k
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}
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if (queryFilter) {
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retrieverConfig.filter = typeof queryFilter === 'object' ? queryFilter : JSON.parse(queryFilter)
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}
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let vectorStore: QdrantVectorStore | undefined = undefined
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if (_batchSize) {
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const batchSize = parseInt(_batchSize, 10)
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for (let i = 0; i < finalDocs.length; i += batchSize) {
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const batch = finalDocs.slice(i, i + batchSize)
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vectorStore = await QdrantVectorStore.fromDocuments(batch, embeddings, dbConfig)
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}
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} else {
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vectorStore = await QdrantVectorStore.fromDocuments(finalDocs, embeddings, dbConfig)
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}
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if (vectorStore === undefined) {
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throw new Error('No documents to upsert')
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} else {
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if (output === 'retriever') {
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const retriever = vectorStore.asRetriever(retrieverConfig)
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return retriever
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} else if (output === 'vectorStore') {
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;(vectorStore as any).k = k
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return vectorStore
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}
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return vectorStore
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}
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}
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}
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module.exports = { nodeClass: QdrantUpsert_VectorStores }
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Block a user