MMR Search for Pinecone, Weaviate, Zep and Supabase

packages/components/nodes/vectorstores/Pinecone/Pinecone.ts

@@ -5,6 +5,7 @@ import { Embeddings } from 'langchain/embeddings/base'
 import { Document } from 'langchain/document'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
 import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
 
 class Pinecone_VectorStores implements INode {
     label: string
@@ -23,11 +24,11 @@ class Pinecone_VectorStores implements INode {
     constructor() {
         this.label = 'Pinecone'
         this.name = 'pinecone'
-        this.version = 2.0
+        this.version = 3.0
         this.type = 'Pinecone'
         this.icon = 'pinecone.svg'
         this.category = 'Vector Stores'
-        this.description = `Upsert embedded data and perform search upon query using Pinecone, a leading fully managed hosted vector database`
+        this.description = `Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database`
         this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
         this.badge = 'NEW'
         this.credential = {
@@ -77,45 +78,9 @@ class Pinecone_VectorStores implements INode {
                 type: 'number',
                 additionalParams: true,
                 optional: true
-            },
-            {
-                label: 'Search Type',
-                name: 'searchType',
-                type: 'options',
-                default: 'similarity',
-                options: [
-                    {
-                        label: 'Similarity',
-                        name: 'similarity'
-                    },
-                    {
-                        label: 'Max Marginal Relevance',
-                        name: 'mmr'
-                    }
-                ],
-                additionalParams: true,
-                optional: true
-            },
-            {
-                label: 'Fetch K (for MMR Search)',
-                name: 'fetchK',
-                description: 'Number of initial documents to fetch for MMR reranking. Default to 20. Used only when the search type is MMR',
-                placeholder: '20',
-                type: 'number',
-                additionalParams: true,
-                optional: true
-            },
-            {
-                label: 'Lambda (for MMR Search)',
-                name: 'lambda',
-                description:
-                    'Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Used only when the search type is MMR',
-                placeholder: '0.5',
-                type: 'number',
-                additionalParams: true,
-                optional: true
             }
         ]
+        addMMRInputParams(this.inputs)
         this.outputs = [
             {
                 label: 'Pinecone Retriever',
@@ -177,10 +142,6 @@ class Pinecone_VectorStores implements INode {
         const pineconeMetadataFilter = nodeData.inputs?.pineconeMetadataFilter
         const docs = nodeData.inputs?.document as Document[]
         const embeddings = nodeData.inputs?.embeddings as Embeddings
-        const output = nodeData.outputs?.output as string
-        const searchType = nodeData.outputs?.searchType as string
-        const topK = nodeData.inputs?.topK as string
-        const k = topK ? parseFloat(topK) : 4
 
         const credentialData = await getCredentialData(nodeData.credential ?? '', options)
         const pineconeApiKey = getCredentialParam('pineconeApiKey', credentialData, nodeData)
@@ -213,31 +174,7 @@ class Pinecone_VectorStores implements INode {
 
         const vectorStore = await PineconeStore.fromExistingIndex(embeddings, obj)
 
-        if (output === 'retriever') {
+        return resolveVectorStoreOrRetriever(nodeData, vectorStore)
-            if ('mmr' === searchType) {
-                const fetchK = nodeData.inputs?.fetchK as string
-                const lambda = nodeData.inputs?.lambda as string
-                const f = fetchK ? parseInt(fetchK) : 20
-                const l = lambda ? parseFloat(lambda) : 0.5
-                const retriever = vectorStore.asRetriever({
-                    searchType: 'mmr',
-                    k: 5,
-                    searchKwargs: {
-                        fetchK: f,
-                        lambda: l
-                    }
-                })
-                return retriever
-            } else {
-                // "searchType" is "similarity"
-                const retriever = vectorStore.asRetriever(k)
-                return retriever
-            }
-        } else if (output === 'vectorStore') {
-            ;(vectorStore as any).k = k
-            return vectorStore
-        }
-        return vectorStore
     }
 }
 

packages/components/nodes/vectorstores/Supabase/Supabase.ts

@@ -5,6 +5,7 @@ import { Embeddings } from 'langchain/embeddings/base'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
 import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { SupabaseLibArgs, SupabaseVectorStore } from 'langchain/vectorstores/supabase'
+import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
 
 class Supabase_VectorStores implements INode {
     label: string
@@ -23,11 +24,11 @@ class Supabase_VectorStores implements INode {
     constructor() {
         this.label = 'Supabase'
         this.name = 'supabase'
-        this.version = 1.0
+        this.version = 2.0
         this.type = 'Supabase'
         this.icon = 'supabase.svg'
         this.category = 'Vector Stores'
-        this.description = 'Upsert embedded data and perform similarity search upon query using Supabase via pgvector extension'
+        this.description = 'Upsert embedded data and perform similarity or mmr search upon query using Supabase via pgvector extension'
         this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
         this.badge = 'NEW'
         this.credential = {
@@ -81,6 +82,7 @@ class Supabase_VectorStores implements INode {
                 optional: true
             }
         ]
+        addMMRInputParams(this.inputs)
         this.outputs = [
             {
                 label: 'Supabase Retriever',
@@ -135,9 +137,6 @@ class Supabase_VectorStores implements INode {
         const queryName = nodeData.inputs?.queryName as string
         const embeddings = nodeData.inputs?.embeddings as Embeddings
         const supabaseMetadataFilter = nodeData.inputs?.supabaseMetadataFilter
-        const output = nodeData.outputs?.output as string
-        const topK = nodeData.inputs?.topK as string
-        const k = topK ? parseFloat(topK) : 4
 
         const credentialData = await getCredentialData(nodeData.credential ?? '', options)
         const supabaseApiKey = getCredentialParam('supabaseApiKey', credentialData, nodeData)
@@ -157,14 +156,7 @@ class Supabase_VectorStores implements INode {
 
         const vectorStore = await SupabaseVectorStore.fromExistingIndex(embeddings, obj)
 
-        if (output === 'retriever') {
+        return resolveVectorStoreOrRetriever(nodeData, vectorStore)
-            const retriever = vectorStore.asRetriever(k)
-            return retriever
-        } else if (output === 'vectorStore') {
-            ;(vectorStore as any).k = k
-            return vectorStore
-        }
-        return vectorStore
     }
 }
 

packages/components/nodes/vectorstores/VectorStoreUtils.ts

@@ -0,0 +1,76 @@
+import { INodeData } from '../../src'
+
+export const resolveVectorStoreOrRetriever = (nodeData: INodeData, vectorStore: any) => {
+    const output = nodeData.outputs?.output as string
+    const searchType = nodeData.outputs?.searchType as string
+    const topK = nodeData.inputs?.topK as string
+    const k = topK ? parseFloat(topK) : 4
+
+    if (output === 'retriever') {
+        if ('mmr' === searchType) {
+            const fetchK = nodeData.inputs?.fetchK as string
+            const lambda = nodeData.inputs?.lambda as string
+            const f = fetchK ? parseInt(fetchK) : 20
+            const l = lambda ? parseFloat(lambda) : 0.5
+            const retriever = vectorStore.asRetriever({
+                searchType: 'mmr',
+                k: k,
+                searchKwargs: {
+                    fetchK: f,
+                    lambda: l
+                }
+            })
+            return retriever
+        } else {
+            // "searchType" is "similarity"
+            return vectorStore.asRetriever(k)
+        }
+    } else if (output === 'vectorStore') {
+        ;(vectorStore as any).k = k
+        return vectorStore
+    }
+}
+
+export const addMMRInputParams = (inputs: any[]) => {
+    const mmrInputParams = [
+        {
+            label: 'Search Type',
+            name: 'searchType',
+            type: 'options',
+            default: 'similarity',
+            options: [
+                {
+                    label: 'Similarity',
+                    name: 'similarity'
+                },
+                {
+                    label: 'Max Marginal Relevance',
+                    name: 'mmr'
+                }
+            ],
+            additionalParams: true,
+            optional: true
+        },
+        {
+            label: 'Fetch K (for MMR Search)',
+            name: 'fetchK',
+            description: 'Number of initial documents to fetch for MMR reranking. Default to 20. Used only when the search type is MMR',
+            placeholder: '20',
+            type: 'number',
+            additionalParams: true,
+            optional: true
+        },
+        {
+            label: 'Lambda (for MMR Search)',
+            name: 'lambda',
+            description:
+                'Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Used only when the search type is MMR',
+            placeholder: '0.5',
+            type: 'number',
+            additionalParams: true,
+            optional: true
+        }
+    ]
+
+    inputs.push(...mmrInputParams)
+}

packages/components/nodes/vectorstores/Weaviate/Weaviate.ts

@@ -5,6 +5,7 @@ import { Document } from 'langchain/document'
 import { Embeddings } from 'langchain/embeddings/base'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
 import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
 
 class Weaviate_VectorStores implements INode {
     label: string
@@ -23,12 +24,12 @@ class Weaviate_VectorStores implements INode {
     constructor() {
         this.label = 'Weaviate'
         this.name = 'weaviate'
-        this.version = 1.0
+        this.version = 2.0
         this.type = 'Weaviate'
         this.icon = 'weaviate.png'
         this.category = 'Vector Stores'
         this.description =
-            'Upsert embedded data and perform similarity search upon query using Weaviate, a scalable open-source vector database'
+            'Upsert embedded data and perform similarity or mmr search using Weaviate, a scalable open-source vector database'
         this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
         this.badge = 'NEW'
         this.credential = {
@@ -107,6 +108,7 @@ class Weaviate_VectorStores implements INode {
                 optional: true
             }
         ]
+        addMMRInputParams(this.inputs)
         this.outputs = [
             {
                 label: 'Weaviate Retriever',
@@ -174,9 +176,6 @@ class Weaviate_VectorStores implements INode {
         const weaviateTextKey = nodeData.inputs?.weaviateTextKey as string
         const weaviateMetadataKeys = nodeData.inputs?.weaviateMetadataKeys as string
         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 credentialData = await getCredentialData(nodeData.credential ?? '', options)
         const weaviateApiKey = getCredentialParam('weaviateApiKey', credentialData, nodeData)
@@ -199,14 +198,7 @@ class Weaviate_VectorStores implements INode {
 
         const vectorStore = await WeaviateStore.fromExistingIndex(embeddings, obj)
 
-        if (output === 'retriever') {
+        return resolveVectorStoreOrRetriever(nodeData, vectorStore)
-            const retriever = vectorStore.asRetriever(k)
-            return retriever
-        } else if (output === 'vectorStore') {
-            ;(vectorStore as any).k = k
-            return vectorStore
-        }
-        return vectorStore
     }
 }
 

packages/components/nodes/vectorstores/Zep/Zep.ts

@@ -5,6 +5,7 @@ import { Embeddings } from 'langchain/embeddings/base'
 import { Document } from 'langchain/document'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
 import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
 
 class Zep_VectorStores implements INode {
     label: string
@@ -23,12 +24,12 @@ class Zep_VectorStores implements INode {
     constructor() {
         this.label = 'Zep'
         this.name = 'zep'
-        this.version = 1.0
+        this.version = 2.0
         this.type = 'Zep'
         this.icon = 'zep.svg'
         this.category = 'Vector Stores'
         this.description =
-            'Upsert embedded data and perform similarity search upon query using Zep, a fast and scalable building block for LLM apps'
+            'Upsert embedded data and perform similarity or mmr search upon query using Zep, a fast and scalable building block for LLM apps'
         this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
         this.badge = 'NEW'
         this.credential = {
@@ -88,6 +89,7 @@ class Zep_VectorStores implements INode {
                 optional: true
             }
         ]
+        addMMRInputParams(this.inputs)
         this.outputs = [
             {
                 label: 'Zep Retriever',
@@ -144,9 +146,6 @@ class Zep_VectorStores implements INode {
         const zepMetadataFilter = nodeData.inputs?.zepMetadataFilter
         const dimension = nodeData.inputs?.dimension as number
         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 credentialData = await getCredentialData(nodeData.credential ?? '', options)
         const apiKey = getCredentialParam('apiKey', credentialData, nodeData)
@@ -165,14 +164,7 @@ class Zep_VectorStores implements INode {
 
         const vectorStore = await ZepExistingVS.fromExistingIndex(embeddings, zepConfig)
 
-        if (output === 'retriever') {
+        return resolveVectorStoreOrRetriever(nodeData, vectorStore)
-            const retriever = vectorStore.asRetriever(k)
-            return retriever
-        } else if (output === 'vectorStore') {
-            ;(vectorStore as any).k = k
-            return vectorStore
-        }
-        return vectorStore
     }
 }
 
@@ -210,7 +202,7 @@ class ZepExistingVS extends ZepVectorStore {
         this.args = args
     }
 
-    async initalizeCollection(args: IZepConfig & Partial<ZepFilter>) {
+    async initializeCollection(args: IZepConfig & Partial<ZepFilter>) {
         this.client = await ZepClient.init(args.apiUrl, args.apiKey)
         try {
             this.collection = await this.client.document.getCollection(args.collectionName)
@@ -259,7 +251,7 @@ class ZepExistingVS extends ZepVectorStore {
         const newfilter = {
             where: { and: ANDFilters }
         }
-        await this.initalizeCollection(this.args!).catch((err) => {
+        await this.initializeCollection(this.args!).catch((err) => {
             console.error('Error initializing collection:', err)
             throw err
         })