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https://github.com/farcasclaudiu/Flowise.git
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MMR Search for Pinecone, Weaviate, Zep and Supabase
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@@ -5,6 +5,7 @@ import { Embeddings } from 'langchain/embeddings/base'
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import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
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import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
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import { SupabaseLibArgs, SupabaseVectorStore } from 'langchain/vectorstores/supabase'
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import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
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class Supabase_VectorStores implements INode {
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label: string
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@@ -23,11 +24,11 @@ class Supabase_VectorStores implements INode {
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constructor() {
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this.label = 'Supabase'
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this.name = 'supabase'
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this.version = 1.0
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this.version = 2.0
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this.type = 'Supabase'
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this.icon = 'supabase.svg'
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this.category = 'Vector Stores'
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this.description = 'Upsert embedded data and perform similarity search upon query using Supabase via pgvector extension'
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this.description = 'Upsert embedded data and perform similarity or mmr search upon query using Supabase via pgvector extension'
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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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@@ -81,6 +82,7 @@ class Supabase_VectorStores implements INode {
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optional: true
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}
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]
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addMMRInputParams(this.inputs)
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this.outputs = [
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{
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label: 'Supabase Retriever',
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@@ -135,9 +137,6 @@ class Supabase_VectorStores implements INode {
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const queryName = nodeData.inputs?.queryName as string
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const embeddings = nodeData.inputs?.embeddings as Embeddings
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const supabaseMetadataFilter = nodeData.inputs?.supabaseMetadataFilter
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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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const credentialData = await getCredentialData(nodeData.credential ?? '', options)
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const supabaseApiKey = getCredentialParam('supabaseApiKey', credentialData, nodeData)
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@@ -157,14 +156,7 @@ class Supabase_VectorStores implements INode {
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const vectorStore = await SupabaseVectorStore.fromExistingIndex(embeddings, obj)
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if (output === 'retriever') {
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const retriever = vectorStore.asRetriever(k)
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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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return resolveVectorStoreOrRetriever(nodeData, vectorStore)
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}
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}
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