Merge branch 'feature/RAG-VectorStores-Updates-2' into feature/RAG-VectorStores-Updates

# Conflicts:
#	packages/components/nodes/retrievers/CohereRerankRetriever/CohereRerank.ts
#	packages/components/nodes/retrievers/CohereRerankRetriever/CohereRerankRetriever.ts

package.json

@@ -1,6 +1,6 @@
 {
     "name": "flowise",
-    "version": "1.4.9",
+    "version": "1.4.10",
     "private": true,
     "homepage": "https://flowiseai.com",
     "workspaces": [
@@ -48,7 +48,7 @@
         "pretty-quick": "^3.1.3",
         "rimraf": "^3.0.2",
         "run-script-os": "^1.1.6",
-        "turbo": "1.7.4",
+        "turbo": "^1.7.4",
         "typescript": "^4.8.4"
     },
     "engines": {

packages/components/nodes/chatmodels/ChatGoogleGenerativeAI/ChatGoogleGenerativeAI.ts

@@ -1,7 +1,9 @@
 import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
-import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
+import { convertMultiOptionsToStringArray, getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { BaseCache } from 'langchain/schema'
-import { ChatGoogleGenerativeAI } from '@langchain/google-genai'
+import { ChatGoogleGenerativeAI, GoogleGenerativeAIChatInput } from '@langchain/google-genai'
+import { HarmBlockThreshold, HarmCategory } from '@google/generative-ai'
+import type { SafetySetting } from '@google/generative-ai'
 
 class GoogleGenerativeAI_ChatModels implements INode {
     label: string
@@ -74,6 +76,73 @@ class GoogleGenerativeAI_ChatModels implements INode {
                 step: 0.1,
                 optional: true,
                 additionalParams: true
+            },
+            {
+                label: 'Top Next Highest Probability Tokens',
+                name: 'topK',
+                type: 'number',
+                description: `Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive`,
+                step: 1,
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Harm Category',
+                name: 'harmCategory',
+                type: 'multiOptions',
+                description:
+                    'Refer to <a target="_blank" href="https://cloud.google.com/vertex-ai/docs/generative-ai/multimodal/configure-safety-attributes#safety_attribute_definitions">official guide</a> on how to use Harm Category',
+                options: [
+                    {
+                        label: 'Dangerous',
+                        name: HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT
+                    },
+                    {
+                        label: 'Harassment',
+                        name: HarmCategory.HARM_CATEGORY_HARASSMENT
+                    },
+                    {
+                        label: 'Hate Speech',
+                        name: HarmCategory.HARM_CATEGORY_HATE_SPEECH
+                    },
+                    {
+                        label: 'Sexually Explicit',
+                        name: HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT
+                    }
+                ],
+                optional: true,
+                additionalParams: true
+            },
+            {
+                label: 'Harm Block Threshold',
+                name: 'harmBlockThreshold',
+                type: 'multiOptions',
+                description:
+                    'Refer to <a target="_blank" href="https://cloud.google.com/vertex-ai/docs/generative-ai/multimodal/configure-safety-attributes#safety_setting_thresholds">official guide</a> on how to use Harm Block Threshold',
+                options: [
+                    {
+                        label: 'Low and Above',
+                        name: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE
+                    },
+                    {
+                        label: 'Medium and Above',
+                        name: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE
+                    },
+                    {
+                        label: 'None',
+                        name: HarmBlockThreshold.BLOCK_NONE
+                    },
+                    {
+                        label: 'Only High',
+                        name: HarmBlockThreshold.BLOCK_ONLY_HIGH
+                    },
+                    {
+                        label: 'Threshold Unspecified',
+                        name: HarmBlockThreshold.HARM_BLOCK_THRESHOLD_UNSPECIFIED
+                    }
+                ],
+                optional: true,
+                additionalParams: true
             }
         ]
     }
@@ -86,9 +155,12 @@ class GoogleGenerativeAI_ChatModels implements INode {
         const modelName = nodeData.inputs?.modelName as string
         const maxOutputTokens = nodeData.inputs?.maxOutputTokens as string
         const topP = nodeData.inputs?.topP as string
+        const topK = nodeData.inputs?.topK as string
+        const harmCategory = nodeData.inputs?.harmCategory as string
+        const harmBlockThreshold = nodeData.inputs?.harmBlockThreshold as string
         const cache = nodeData.inputs?.cache as BaseCache
 
-        const obj = {
+        const obj: Partial<GoogleGenerativeAIChatInput> = {
             apiKey: apiKey,
             modelName: modelName,
             maxOutputTokens: 2048
@@ -98,8 +170,23 @@ class GoogleGenerativeAI_ChatModels implements INode {
 
         const model = new ChatGoogleGenerativeAI(obj)
         if (topP) model.topP = parseFloat(topP)
+        if (topK) model.topK = parseFloat(topK)
         if (cache) model.cache = cache
         if (temperature) model.temperature = parseFloat(temperature)
+
+        // Safety Settings
+        let harmCategories: string[] = convertMultiOptionsToStringArray(harmCategory)
+        let harmBlockThresholds: string[] = convertMultiOptionsToStringArray(harmBlockThreshold)
+        if (harmCategories.length != harmBlockThresholds.length)
+            throw new Error(`Harm Category & Harm Block Threshold are not the same length`)
+        const safetySettings: SafetySetting[] = harmCategories.map((harmCategory, index) => {
+            return {
+                category: harmCategory as HarmCategory,
+                threshold: harmBlockThresholds[index] as HarmBlockThreshold
+            }
+        })
+        if (safetySettings.length > 0) model.safetySettings = safetySettings
+
         return model
     }
 }

packages/components/nodes/retrievers/CohereRerankRetriever/Cohere.svg

@@ -0,0 +1 @@
+<svg width="32" height="32" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M11.776 18.304c.64 0 1.92-.032 3.712-.768 2.08-.864 6.176-2.4 9.152-4 2.08-1.12 2.976-2.592 2.976-4.576 0-2.72-2.208-4.96-4.96-4.96h-11.52A7.143 7.143 0 0 0 4 11.136c0 3.936 3.008 7.168 7.776 7.168Z" fill="#39594D"/><path fill-rule="evenodd" clip-rule="evenodd" d="M13.728 23.2c0-1.92 1.152-3.68 2.944-4.416l3.616-1.504C23.968 15.776 28 18.464 28 22.432A5.572 5.572 0 0 1 22.432 28h-3.936c-2.624 0-4.768-2.144-4.768-4.8Z" fill="#D18EE2"/><path d="M8.128 19.232A4.138 4.138 0 0 0 4 23.36v.544C4 26.144 5.856 28 8.128 28a4.138 4.138 0 0 0 4.128-4.128v-.544c-.032-2.24-1.856-4.096-4.128-4.096Z" fill="#FF7759"/></svg>

packages/components/nodes/retrievers/CohereRerankRetriever/CohereRerankRetriever.ts

@@ -1,7 +1,7 @@
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
 import { BaseRetriever } from 'langchain/schema/retriever'
 import { ContextualCompressionRetriever } from 'langchain/retrievers/contextual_compression'
-import { getCredentialData, getCredentialParam } from '../../../src'
+import { getCredentialData, getCredentialParam, handleEscapeCharacters } from '../../../src'
 import { CohereRerank } from './CohereRerank'
 import { VectorStoreRetriever } from 'langchain/vectorstores/base'
 
@@ -15,16 +15,16 @@ class CohereRerankRetriever_Retrievers implements INode {
     category: string
     baseClasses: string[]
     inputs: INodeParams[]
-    outputs: INodeOutputsValue[]
     credential: INodeParams
     badge: string
+    outputs: INodeOutputsValue[]
 
     constructor() {
         this.label = 'Cohere Rerank Retriever'
         this.name = 'cohereRerankRetriever'
         this.version = 1.0
         this.type = 'Cohere Rerank Retriever'
-        this.icon = 'compressionRetriever.svg'
+        this.icon = 'Cohere.svg'
         this.category = 'Retrievers'
         this.badge = 'NEW'
         this.description = 'Cohere Rerank indexes the documents from most to least semantically relevant to the query.'
@@ -37,7 +37,7 @@ class CohereRerankRetriever_Retrievers implements INode {
         }
         this.inputs = [
             {
-                label: 'Base Retriever',
+                label: 'Vector Store Retriever',
                 name: 'baseRetriever',
                 type: 'VectorStoreRetriever'
             },
@@ -58,47 +58,84 @@ class CohereRerankRetriever_Retrievers implements INode {
                 default: 'rerank-english-v2.0',
                 optional: true
             },
+            {
+                label: 'Query',
+                name: 'query',
+                type: 'string',
+                description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
+                optional: true,
+                acceptVariable: true
+            },
             {
                 label: 'Top K',
                 name: 'topK',
                 description: 'Number of top results to fetch. Default to the TopK of the Base Retriever',
-                placeholder: '0',
+                placeholder: '4',
                 type: 'number',
-                default: 0,
                 additionalParams: true,
                 optional: true
             },
             {
-                label: 'Max Chunks Per Document',
+                label: 'Max Chunks Per Doc',
                 name: 'maxChunksPerDoc',
+                description: 'The maximum number of chunks to produce internally from a document. Default to 10',
                 placeholder: '10',
                 type: 'number',
-                default: 10,
                 additionalParams: true,
                 optional: true
             }
         ]
+        this.outputs = [
+            {
+                label: 'Cohere Rerank Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Document',
+                name: 'document',
+                baseClasses: ['Document']
+            },
+            {
+                label: 'Text',
+                name: 'text',
+                baseClasses: ['string', 'json']
+            }
+        ]
     }
 
-    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+    async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
         const baseRetriever = nodeData.inputs?.baseRetriever as BaseRetriever
         const model = nodeData.inputs?.model as string
+        const query = nodeData.inputs?.query as string
         const credentialData = await getCredentialData(nodeData.credential ?? '', options)
         const cohereApiKey = getCredentialParam('cohereApiKey', credentialData, nodeData)
         const topK = nodeData.inputs?.topK as string
-        let k = topK ? parseFloat(topK) : 4
+        const k = topK ? parseFloat(topK) : (baseRetriever as VectorStoreRetriever).k ?? 4
-        const maxChunks = nodeData.inputs?.maxChunksPerDoc as string
+        const maxChunksPerDoc = nodeData.inputs?.maxChunksPerDoc as string
-        let max = maxChunks ? parseInt(maxChunks) : 10
+        const max_chunks_per_doc = maxChunksPerDoc ? parseFloat(maxChunksPerDoc) : 10
+        const output = nodeData.outputs?.output as string
 
-        if (k <= 0) {
+        const cohereCompressor = new CohereRerank(cohereApiKey, model, k, max_chunks_per_doc)
-            k = (baseRetriever as VectorStoreRetriever).k
-        }
 
-        const cohereCompressor = new CohereRerank(cohereApiKey, model, k, max)
+        const retriever = new ContextualCompressionRetriever({
-        return new ContextualCompressionRetriever({
             baseCompressor: cohereCompressor,
             baseRetriever: baseRetriever
         })
+
+        if (output === 'retriever') return retriever
+        else if (output === 'document') return await retriever.getRelevantDocuments(query ? query : input)
+        else if (output === 'text') {
+            let finaltext = ''
+
+            const docs = await retriever.getRelevantDocuments(query ? query : input)
+
+            for (const doc of docs) finaltext += `${doc.pageContent}\n`
+
+            return handleEscapeCharacters(finaltext, false)
+        }
+
+        return retriever
     }
 }
 

packages/components/nodes/retrievers/CohereRerankRetriever/compressionRetriever.svg

@@ -1,7 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-chart-bar" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
-   <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-   <path d="M3 12m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v6a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
-   <path d="M9 8m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v10a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
-   <path d="M15 4m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v14a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
-   <path d="M4 20l14 0" />
-</svg>

packages/components/nodes/retrievers/EmbeddingsFilterRetriever/EmbeddingsFilterRetriever.ts

@@ -3,6 +3,7 @@ import { BaseRetriever } from 'langchain/schema/retriever'
 import { Embeddings } from 'langchain/embeddings/base'
 import { ContextualCompressionRetriever } from 'langchain/retrievers/contextual_compression'
 import { EmbeddingsFilter } from 'langchain/retrievers/document_compressors/embeddings_filter'
+import { handleEscapeCharacters } from '../../../src/utils'
 
 class EmbeddingsFilterRetriever_Retrievers implements INode {
     label: string
@@ -29,15 +30,22 @@ class EmbeddingsFilterRetriever_Retrievers implements INode {
         this.baseClasses = [this.type, 'BaseRetriever']
         this.inputs = [
             {
-                label: 'Base Retriever',
+                label: 'Vector Store Retriever',
                 name: 'baseRetriever',
                 type: 'VectorStoreRetriever'
             },
             {
                 label: 'Embeddings',
                 name: 'embeddings',
-                type: 'Embeddings',
+                type: 'Embeddings'
-                optional: false
+            },
+            {
+                label: 'Query',
+                name: 'query',
+                type: 'string',
+                description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
+                optional: true,
+                acceptVariable: true
             },
             {
                 label: 'Similarity Threshold',
@@ -61,36 +69,64 @@ class EmbeddingsFilterRetriever_Retrievers implements INode {
                 additionalParams: true
             }
         ]
+        this.outputs = [
+            {
+                label: 'Embeddings Filter Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Document',
+                name: 'document',
+                baseClasses: ['Document']
+            },
+            {
+                label: 'Text',
+                name: 'text',
+                baseClasses: ['string', 'json']
+            }
+        ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, input: string): Promise<any> {
         const baseRetriever = nodeData.inputs?.baseRetriever as BaseRetriever
         const embeddings = nodeData.inputs?.embeddings as Embeddings
+        const query = nodeData.inputs?.query as string
         const similarityThreshold = nodeData.inputs?.similarityThreshold as string
         const k = nodeData.inputs?.k as string
+        const output = nodeData.outputs?.output as string
 
         if (k === undefined && similarityThreshold === undefined) {
             throw new Error(`Must specify one of "k" or "similarity_threshold".`)
         }
 
-        let similarityThresholdNumber = 0.8
+        const similarityThresholdNumber = similarityThreshold ? parseFloat(similarityThreshold) : 0.8
-        if (similarityThreshold) {
+        const kNumber = k ? parseFloat(k) : undefined
-            similarityThresholdNumber = parseFloat(similarityThreshold)
+
-        }
-        let kNumber = 0.8
-        if (k) {
-            kNumber = parseFloat(k)
-        }
         const baseCompressor = new EmbeddingsFilter({
             embeddings: embeddings,
             similarityThreshold: similarityThresholdNumber,
             k: kNumber
         })
 
-        return new ContextualCompressionRetriever({
+        const retriever = new ContextualCompressionRetriever({
             baseCompressor,
             baseRetriever: baseRetriever
         })
+
+        if (output === 'retriever') return retriever
+        else if (output === 'document') return await retriever.getRelevantDocuments(query ? query : input)
+        else if (output === 'text') {
+            let finaltext = ''
+
+            const docs = await retriever.getRelevantDocuments(query ? query : input)
+
+            for (const doc of docs) finaltext += `${doc.pageContent}\n`
+
+            return handleEscapeCharacters(finaltext, false)
+        }
+
+        return retriever
     }
 }
 

packages/components/nodes/retrievers/HydeRetriever/HydeRetriever.ts

@@ -1,8 +1,9 @@
 import { VectorStore } from 'langchain/vectorstores/base'
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
 import { HydeRetriever, HydeRetrieverOptions, PromptKey } from 'langchain/retrievers/hyde'
 import { BaseLanguageModel } from 'langchain/base_language'
 import { PromptTemplate } from 'langchain/prompts'
+import { handleEscapeCharacters } from '../../../src/utils'
 
 class HydeRetriever_Retrievers implements INode {
     label: string
@@ -14,11 +15,12 @@ class HydeRetriever_Retrievers implements INode {
     category: string
     baseClasses: string[]
     inputs: INodeParams[]
+    outputs: INodeOutputsValue[]
 
     constructor() {
-        this.label = 'Hyde Retriever'
+        this.label = 'HyDE Retriever'
         this.name = 'HydeRetriever'
-        this.version = 2.0
+        this.version = 3.0
         this.type = 'HydeRetriever'
         this.icon = 'hyderetriever.svg'
         this.category = 'Retrievers'
@@ -35,6 +37,14 @@ class HydeRetriever_Retrievers implements INode {
                 name: 'vectorStore',
                 type: 'VectorStore'
             },
+            {
+                label: 'Query',
+                name: 'query',
+                type: 'string',
+                description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
+                optional: true,
+                acceptVariable: true
+            },
             {
                 label: 'Select Defined Prompt',
                 name: 'promptKey',
@@ -121,15 +131,34 @@ Passage:`
                 optional: true
             }
         ]
+        this.outputs = [
+            {
+                label: 'HyDE Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Document',
+                name: 'document',
+                baseClasses: ['Document']
+            },
+            {
+                label: 'Text',
+                name: 'text',
+                baseClasses: ['string', 'json']
+            }
+        ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, input: string): Promise<any> {
         const llm = nodeData.inputs?.model as BaseLanguageModel
         const vectorStore = nodeData.inputs?.vectorStore as VectorStore
         const promptKey = nodeData.inputs?.promptKey as PromptKey
         const customPrompt = nodeData.inputs?.customPrompt as string
+        const query = nodeData.inputs?.query as string
         const topK = nodeData.inputs?.topK as string
         const k = topK ? parseFloat(topK) : 4
+        const output = nodeData.outputs?.output as string
 
         const obj: HydeRetrieverOptions<any> = {
             llm,
@@ -141,6 +170,19 @@ Passage:`
         else if (promptKey) obj.promptTemplate = promptKey
 
         const retriever = new HydeRetriever(obj)
+
+        if (output === 'retriever') return retriever
+        else if (output === 'document') return await retriever.getRelevantDocuments(query ? query : input)
+        else if (output === 'text') {
+            let finaltext = ''
+
+            const docs = await retriever.getRelevantDocuments(query ? query : input)
+
+            for (const doc of docs) finaltext += `${doc.pageContent}\n`
+
+            return handleEscapeCharacters(finaltext, false)
+        }
+
         return retriever
     }
 }

packages/components/nodes/retrievers/LLMFilterRetriever/LLMFilterCompressionRetriever.ts

@@ -3,6 +3,7 @@ import { BaseRetriever } from 'langchain/schema/retriever'
 import { ContextualCompressionRetriever } from 'langchain/retrievers/contextual_compression'
 import { BaseLanguageModel } from 'langchain/base_language'
 import { LLMChainExtractor } from 'langchain/retrievers/document_compressors/chain_extract'
+import { handleEscapeCharacters } from '../../../src/utils'
 
 class LLMFilterCompressionRetriever_Retrievers implements INode {
     label: string
@@ -22,7 +23,7 @@ class LLMFilterCompressionRetriever_Retrievers implements INode {
         this.name = 'llmFilterRetriever'
         this.version = 1.0
         this.type = 'LLMFilterRetriever'
-        this.icon = 'compressionRetriever.svg'
+        this.icon = 'llmFilterRetriever.svg'
         this.category = 'Retrievers'
         this.badge = 'NEW'
         this.description =
@@ -30,30 +31,69 @@ class LLMFilterCompressionRetriever_Retrievers implements INode {
         this.baseClasses = [this.type, 'BaseRetriever']
         this.inputs = [
             {
-                label: 'Base Retriever',
+                label: 'Vector Store Retriever',
                 name: 'baseRetriever',
                 type: 'VectorStoreRetriever'
             },
             {
                 label: 'Language Model',
                 name: 'model',
-                type: 'BaseLanguageModel',
+                type: 'BaseLanguageModel'
-                optional: true
+            },
+            {
+                label: 'Query',
+                name: 'query',
+                type: 'string',
+                description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
+                optional: true,
+                acceptVariable: true
+            }
+        ]
+        this.outputs = [
+            {
+                label: 'LLM Filter Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Document',
+                name: 'document',
+                baseClasses: ['Document']
+            },
+            {
+                label: 'Text',
+                name: 'text',
+                baseClasses: ['string', 'json']
             }
         ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, input: string): Promise<any> {
         const baseRetriever = nodeData.inputs?.baseRetriever as BaseRetriever
         const model = nodeData.inputs?.model as BaseLanguageModel
+        const query = nodeData.inputs?.query as string
+        const output = nodeData.outputs?.output as string
 
-        if (model) {
+        if (!model) throw new Error('There must be a LLM model connected to LLM Filter Retriever')
-            return new ContextualCompressionRetriever({
+
-                baseCompressor: LLMChainExtractor.fromLLM(model),
+        const retriever = new ContextualCompressionRetriever({
-                baseRetriever: baseRetriever
+            baseCompressor: LLMChainExtractor.fromLLM(model),
-            })
+            baseRetriever: baseRetriever
+        })
+
+        if (output === 'retriever') return retriever
+        else if (output === 'document') return await retriever.getRelevantDocuments(query ? query : input)
+        else if (output === 'text') {
+            let finaltext = ''
+
+            const docs = await retriever.getRelevantDocuments(query ? query : input)
+
+            for (const doc of docs) finaltext += `${doc.pageContent}\n`
+
+            return handleEscapeCharacters(finaltext, false)
         }
-        return {}
+
+        return retriever
     }
 }
 

packages/components/nodes/retrievers/LLMFilterRetriever/compressionRetriever.svg

@@ -1,7 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-chart-bar" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
-   <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-   <path d="M3 12m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v6a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
-   <path d="M9 8m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v10a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
-   <path d="M15 4m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v14a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
-   <path d="M4 20l14 0" />
-</svg>

packages/components/nodes/retrievers/LLMFilterRetriever/llmFilterRetriever.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-filter-check" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M11.18 20.274l-2.18 .726v-8.5l-4.48 -4.928a2 2 0 0 1 -.52 -1.345v-2.227h16v2.172a2 2 0 0 1 -.586 1.414l-4.414 4.414v3" /><path d="M15 19l2 2l4 -4" /></svg>

packages/components/nodes/retrievers/RRFRetriever/RRFRetriever.ts

@@ -1,9 +1,10 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
 import { BaseLanguageModel } from 'langchain/base_language'
 import { ContextualCompressionRetriever } from 'langchain/retrievers/contextual_compression'
 import { BaseRetriever } from 'langchain/schema/retriever'
 import { ReciprocalRankFusion } from './ReciprocalRankFusion'
 import { VectorStoreRetriever } from 'langchain/vectorstores/base'
+import { handleEscapeCharacters } from '../../../src/utils'
 
 class RRFRetriever_Retrievers implements INode {
     label: string
@@ -16,20 +17,21 @@ class RRFRetriever_Retrievers implements INode {
     baseClasses: string[]
     inputs: INodeParams[]
     badge: string
+    outputs: INodeOutputsValue[]
 
     constructor() {
         this.label = 'Reciprocal Rank Fusion Retriever'
         this.name = 'RRFRetriever'
-        this.version = 2.0
+        this.version = 1.0
         this.type = 'RRFRetriever'
         this.badge = 'NEW'
-        this.icon = 'compressionRetriever.svg'
+        this.icon = 'rrfRetriever.svg'
         this.category = 'Retrievers'
         this.description = 'Reciprocal Rank Fusion to re-rank search results by multiple query generation.'
         this.baseClasses = [this.type, 'BaseRetriever']
         this.inputs = [
             {
-                label: 'Base Retriever',
+                label: 'Vector Store Retriever',
                 name: 'baseRetriever',
                 type: 'VectorStoreRetriever'
             },
@@ -38,6 +40,14 @@ class RRFRetriever_Retrievers implements INode {
                 name: 'model',
                 type: 'BaseLanguageModel'
             },
+            {
+                label: 'Query',
+                name: 'query',
+                type: 'string',
+                description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
+                optional: true,
+                acceptVariable: true
+            },
             {
                 label: 'Query Count',
                 name: 'queryCount',
@@ -54,7 +64,6 @@ class RRFRetriever_Retrievers implements INode {
                 description: 'Number of top results to fetch. Default to the TopK of the Base Retriever',
                 placeholder: '0',
                 type: 'number',
-                default: 0,
                 additionalParams: true,
                 optional: true
             },
@@ -71,27 +80,56 @@ class RRFRetriever_Retrievers implements INode {
                 optional: true
             }
         ]
+        this.outputs = [
+            {
+                label: 'Reciprocal Rank Fusion Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Document',
+                name: 'document',
+                baseClasses: ['Document']
+            },
+            {
+                label: 'Text',
+                name: 'text',
+                baseClasses: ['string', 'json']
+            }
+        ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, input: string): Promise<any> {
         const llm = nodeData.inputs?.model as BaseLanguageModel
         const baseRetriever = nodeData.inputs?.baseRetriever as BaseRetriever
+        const query = nodeData.inputs?.query as string
         const queryCount = nodeData.inputs?.queryCount as string
         const q = queryCount ? parseFloat(queryCount) : 4
         const topK = nodeData.inputs?.topK as string
-        let k = topK ? parseFloat(topK) : 4
+        const k = topK ? parseFloat(topK) : (baseRetriever as VectorStoreRetriever).k ?? 4
         const constantC = nodeData.inputs?.c as string
-        let c = topK ? parseFloat(constantC) : 60
+        const c = topK ? parseFloat(constantC) : 60
-
+        const output = nodeData.outputs?.output as string
-        if (k <= 0) {
-            k = (baseRetriever as VectorStoreRetriever).k
-        }
 
         const ragFusion = new ReciprocalRankFusion(llm, baseRetriever as VectorStoreRetriever, q, k, c)
-        return new ContextualCompressionRetriever({
+        const retriever = new ContextualCompressionRetriever({
             baseCompressor: ragFusion,
             baseRetriever: baseRetriever
         })
+
+        if (output === 'retriever') return retriever
+        else if (output === 'document') return await retriever.getRelevantDocuments(query ? query : input)
+        else if (output === 'text') {
+            let finaltext = ''
+
+            const docs = await retriever.getRelevantDocuments(query ? query : input)
+
+            for (const doc of docs) finaltext += `${doc.pageContent}\n`
+
+            return handleEscapeCharacters(finaltext, false)
+        }
+
+        return retriever
     }
 }
 

packages/components/nodes/retrievers/RRFRetriever/compressionRetriever.svg

@@ -1,7 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-chart-bar" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
-   <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
-   <path d="M3 12m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v6a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
-   <path d="M9 8m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v10a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
-   <path d="M15 4m0 1a1 1 0 0 1 1 -1h4a1 1 0 0 1 1 1v14a1 1 0 0 1 -1 1h-4a1 1 0 0 1 -1 -1z" />
-   <path d="M4 20l14 0" />
-</svg>

packages/components/nodes/retrievers/RRFRetriever/rrfRetriever.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-math-x-divide-y-2" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"/><path d="M3 21l18 -18" /><path d="M15 14l3 4.5" /><path d="M21 14l-4.5 7" /><path d="M3 4l6 6" /><path d="M3 10l6 -6" /></svg>

packages/components/nodes/retrievers/SimilarityThresholdRetriever/SimilarityThresholdRetriever.ts

@@ -18,7 +18,7 @@ class SimilarityThresholdRetriever_Retrievers implements INode {
     constructor() {
         this.label = 'Similarity Score Threshold Retriever'
         this.name = 'similarityThresholdRetriever'
-        this.version = 1.0
+        this.version = 2.0
         this.type = 'SimilarityThresholdRetriever'
         this.icon = 'similaritythreshold.svg'
         this.category = 'Retrievers'
@@ -30,6 +30,14 @@ class SimilarityThresholdRetriever_Retrievers implements INode {
                 name: 'vectorStore',
                 type: 'VectorStore'
             },
+            {
+                label: 'Query',
+                name: 'query',
+                type: 'string',
+                description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
+                optional: true,
+                acceptVariable: true
+            },
             {
                 label: 'Minimum Similarity Score (%)',
                 name: 'minSimilarityScore',
@@ -44,7 +52,8 @@ class SimilarityThresholdRetriever_Retrievers implements INode {
                 description: `The maximum number of results to fetch`,
                 type: 'number',
                 default: 20,
-                step: 1
+                step: 1,
+                additionalParams: true
             },
             {
                 label: 'K Increment',
@@ -52,7 +61,8 @@ class SimilarityThresholdRetriever_Retrievers implements INode {
                 description: `How much to increase K by each time. It'll fetch N results, then N + kIncrement, then N + kIncrement * 2, etc.`,
                 type: 'number',
                 default: 2,
-                step: 1
+                step: 1,
+                additionalParams: true
             }
         ]
         this.outputs = [
@@ -77,6 +87,7 @@ class SimilarityThresholdRetriever_Retrievers implements INode {
     async init(nodeData: INodeData, input: string): Promise<any> {
         const vectorStore = nodeData.inputs?.vectorStore as VectorStore
         const minSimilarityScore = nodeData.inputs?.minSimilarityScore as number
+        const query = nodeData.inputs?.query as string
         const maxK = nodeData.inputs?.maxK as string
         const kIncrement = nodeData.inputs?.kIncrement as string
 
@@ -89,11 +100,11 @@ class SimilarityThresholdRetriever_Retrievers implements INode {
         })
 
         if (output === 'retriever') return retriever
-        else if (output === 'document') return await retriever.getRelevantDocuments(input)
+        else if (output === 'document') return await retriever.getRelevantDocuments(query ? query : input)
         else if (output === 'text') {
             let finaltext = ''
 
-            const docs = await retriever.getRelevantDocuments(input)
+            const docs = await retriever.getRelevantDocuments(query ? query : input)
 
             for (const doc of docs) finaltext += `${doc.pageContent}\n`
 

packages/components/nodes/utilities/CustomFunction/CustomFunction.ts

@@ -73,7 +73,11 @@ class CustomFunction_Utilities implements INode {
 
         if (Object.keys(inputVars).length) {
             for (const item in inputVars) {
-                sandbox[`$${item}`] = inputVars[item]
+                let value = inputVars[item]
+                if (typeof value === 'string') {
+                    value = handleEscapeCharacters(value, true)
+                }
+                sandbox[`$${item}`] = value
             }
         }
 

packages/components/nodes/vectorstores/Astra/Astra.ts

@@ -4,6 +4,7 @@ import { Document } from 'langchain/document'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
 import { getBaseClasses, getCredentialData } from '../../../src/utils'
 import { AstraDBVectorStore, AstraLibArgs } from '@langchain/community/vectorstores/astradb'
+import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
 
 class Astra_VectorStores implements INode {
     label: string
@@ -26,7 +27,7 @@ class Astra_VectorStores implements INode {
         this.type = 'Astra'
         this.icon = 'astra.svg'
         this.category = 'Vector Stores'
-        this.description = `Upsert embedded data and perform similarity search upon query using DataStax Astra DB, a serverless vector database that’s perfect for managing mission-critical AI workloads`
+        this.description = `Upsert embedded data and perform similarity or mmr search upon query using DataStax Astra DB, a serverless vector database that’s perfect for managing mission-critical AI workloads`
         this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
         this.badge = 'NEW'
         this.credential = {
@@ -74,6 +75,7 @@ class Astra_VectorStores implements INode {
                 optional: true
             }
         ]
+        addMMRInputParams(this.inputs)
         this.outputs = [
             {
                 label: 'Astra Retriever',
@@ -139,9 +141,6 @@ class Astra_VectorStores implements INode {
         const embeddings = nodeData.inputs?.embeddings as Embeddings
         const vectorDimension = nodeData.inputs?.vectorDimension as number
         const similarityMetric = nodeData.inputs?.similarityMetric as 'cosine' | 'euclidean' | 'dot_product' | undefined
-        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)
 
@@ -176,14 +175,7 @@ class Astra_VectorStores implements INode {
 
         const vectorStore = await AstraDBVectorStore.fromExistingIndex(embeddings, astraConfig)
 
-        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/MongoDBAtlas/MongoDBAtlas.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 MongoDBAtlas_VectorStores implements INode {
     label: string
@@ -24,7 +25,7 @@ class MongoDBAtlas_VectorStores implements INode {
         this.label = 'MongoDB Atlas'
         this.name = 'mongoDBAtlas'
         this.version = 1.0
-        this.description = `Upsert embedded data and perform similarity search upon query using MongoDB Atlas, a managed cloud mongodb database`
+        this.description = `Upsert embedded data and perform similarity or mmr search upon query using MongoDB Atlas, a managed cloud mongodb database`
         this.type = 'MongoDB Atlas'
         this.icon = 'mongodb.svg'
         this.category = 'Vector Stores'
@@ -95,6 +96,7 @@ class MongoDBAtlas_VectorStores implements INode {
                 optional: true
             }
         ]
+        addMMRInputParams(this.inputs)
         this.outputs = [
             {
                 label: 'MongoDB Retriever',
@@ -162,9 +164,6 @@ class MongoDBAtlas_VectorStores implements INode {
         let textKey = nodeData.inputs?.textKey as string
         let embeddingKey = nodeData.inputs?.embeddingKey as string
         const embeddings = nodeData.inputs?.embeddings as Embeddings
-        const topK = nodeData.inputs?.topK as string
-        const k = topK ? parseFloat(topK) : 4
-        const output = nodeData.outputs?.output as string
 
         let mongoDBConnectUrl = getCredentialParam('mongoDBConnectUrl', credentialData, nodeData)
 
@@ -181,13 +180,7 @@ class MongoDBAtlas_VectorStores implements INode {
             embeddingKey
         })
 
-        if (output === 'retriever') {
+        return resolveVectorStoreOrRetriever(nodeData, vectorStore)
-            return vectorStore.asRetriever(k)
-        } else if (output === 'vectorStore') {
-            ;(vectorStore as any).k = k
-            return vectorStore
-        }
-        return vectorStore
     }
 }
 

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

@@ -24,7 +24,7 @@ class Pinecone_VectorStores implements INode {
     constructor() {
         this.label = 'Pinecone'
         this.name = 'pinecone'
-        this.version = 3.0
+        this.version = 2.0
         this.type = 'Pinecone'
         this.icon = 'pinecone.svg'
         this.category = 'Vector Stores'

packages/components/package.json

@@ -1,6 +1,6 @@
 {
     "name": "flowise-components",
-    "version": "1.5.0",
+    "version": "1.5.1",
     "description": "Flowiseai Components",
     "main": "dist/src/index",
     "types": "dist/src/index.d.ts",
@@ -26,6 +26,7 @@
         "@gomomento/sdk": "^1.51.1",
         "@gomomento/sdk-core": "^1.51.1",
         "@google-ai/generativelanguage": "^0.2.1",
+        "@google/generative-ai": "^0.1.3",
         "@huggingface/inference": "^2.6.1",
         "@langchain/community": "^0.0.16",
         "@langchain/google-genai": "^0.0.6",
@@ -37,7 +38,7 @@
         "@supabase/supabase-js": "^2.29.0",
         "@types/js-yaml": "^4.0.5",
         "@types/jsdom": "^21.1.1",
-        "@upstash/redis": "^1.22.1",
+        "@upstash/redis": "1.22.1",
         "@zilliz/milvus2-sdk-node": "^2.2.24",
         "apify-client": "^2.7.1",
         "axios": "1.6.2",

packages/components/src/utils.ts

@@ -673,3 +673,18 @@ export const convertBaseMessagetoIMessage = (messages: BaseMessage[]): IMessage[
     }
     return formatmessages
 }
+
+/**
+ * Convert MultiOptions String to String Array
+ * @param {string} inputString
+ * @returns {string[]}
+ */
+export const convertMultiOptionsToStringArray = (inputString: string): string[] => {
+    let ArrayString: string[] = []
+    try {
+        ArrayString = JSON.parse(inputString)
+    } catch (e) {
+        ArrayString = []
+    }
+    return ArrayString
+}

packages/server/marketplaces/chatflows/AutoGPT.json

@@ -511,7 +511,7 @@
                 "type": "Pinecone",
                 "baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
                 "category": "Vector Stores",
-                "description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
+                "description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
                 "inputParams": [
                     {
                         "label": "Connect Credential",
@@ -552,6 +552,45 @@
                         "additionalParams": true,
                         "optional": true,
                         "id": "pinecone_0-input-topK-number"
+                    },
+                    {
+                        "label": "Search Type",
+                        "name": "searchType",
+                        "type": "options",
+                        "default": "similarity",
+                        "options": [
+                            {
+                                "label": "Similarity",
+                                "name": "similarity"
+                            },
+                            {
+                                "label": "Max Marginal Relevance",
+                                "name": "mmr"
+                            }
+                        ],
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-searchType-options"
+                    },
+                    {
+                        "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,
+                        "id": "pinecone_0-input-fetchK-number"
+                    },
+                    {
+                        "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,
+                        "id": "pinecone_0-input-lambda-number"
                     }
                 ],
                 "inputAnchors": [
@@ -576,7 +615,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {

packages/server/marketplaces/chatflows/BabyAGI.json

@@ -166,7 +166,7 @@
                 "type": "Pinecone",
                 "baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
                 "category": "Vector Stores",
-                "description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
+                "description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
                 "inputParams": [
                     {
                         "label": "Connect Credential",
@@ -207,6 +207,45 @@
                         "additionalParams": true,
                         "optional": true,
                         "id": "pinecone_0-input-topK-number"
+                    },
+                    {
+                        "label": "Search Type",
+                        "name": "searchType",
+                        "type": "options",
+                        "default": "similarity",
+                        "options": [
+                            {
+                                "label": "Similarity",
+                                "name": "similarity"
+                            },
+                            {
+                                "label": "Max Marginal Relevance",
+                                "name": "mmr"
+                            }
+                        ],
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-searchType-options"
+                    },
+                    {
+                        "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,
+                        "id": "pinecone_0-input-fetchK-number"
+                    },
+                    {
+                        "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,
+                        "id": "pinecone_0-input-lambda-number"
                     }
                 ],
                 "inputAnchors": [
@@ -231,7 +270,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {

packages/server/marketplaces/chatflows/Conversational Retrieval Agent.json

@@ -301,7 +301,7 @@
                 "type": "Pinecone",
                 "baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
                 "category": "Vector Stores",
-                "description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
+                "description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
                 "inputParams": [
                     {
                         "label": "Connect Credential",
@@ -342,6 +342,45 @@
                         "additionalParams": true,
                         "optional": true,
                         "id": "pinecone_0-input-topK-number"
+                    },
+                    {
+                        "label": "Search Type",
+                        "name": "searchType",
+                        "type": "options",
+                        "default": "similarity",
+                        "options": [
+                            {
+                                "label": "Similarity",
+                                "name": "similarity"
+                            },
+                            {
+                                "label": "Max Marginal Relevance",
+                                "name": "mmr"
+                            }
+                        ],
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-searchType-options"
+                    },
+                    {
+                        "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,
+                        "id": "pinecone_0-input-fetchK-number"
+                    },
+                    {
+                        "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,
+                        "id": "pinecone_0-input-lambda-number"
                     }
                 ],
                 "inputAnchors": [
@@ -366,7 +405,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {

packages/server/marketplaces/chatflows/Conversational Retrieval QA Chain.json

@@ -541,7 +541,7 @@
                 "type": "Pinecone",
                 "baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
                 "category": "Vector Stores",
-                "description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
+                "description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
                 "inputParams": [
                     {
                         "label": "Connect Credential",
@@ -582,6 +582,45 @@
                         "additionalParams": true,
                         "optional": true,
                         "id": "pinecone_0-input-topK-number"
+                    },
+                    {
+                        "label": "Search Type",
+                        "name": "searchType",
+                        "type": "options",
+                        "default": "similarity",
+                        "options": [
+                            {
+                                "label": "Similarity",
+                                "name": "similarity"
+                            },
+                            {
+                                "label": "Max Marginal Relevance",
+                                "name": "mmr"
+                            }
+                        ],
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-searchType-options"
+                    },
+                    {
+                        "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,
+                        "id": "pinecone_0-input-fetchK-number"
+                    },
+                    {
+                        "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,
+                        "id": "pinecone_0-input-lambda-number"
                     }
                 ],
                 "inputAnchors": [
@@ -606,7 +645,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {

packages/server/marketplaces/chatflows/Metadata Filter.json

@@ -625,7 +625,7 @@
                 "type": "Pinecone",
                 "baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
                 "category": "Vector Stores",
-                "description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
+                "description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
                 "inputParams": [
                     {
                         "label": "Connect Credential",
@@ -666,6 +666,45 @@
                         "additionalParams": true,
                         "optional": true,
                         "id": "pinecone_0-input-topK-number"
+                    },
+                    {
+                        "label": "Search Type",
+                        "name": "searchType",
+                        "type": "options",
+                        "default": "similarity",
+                        "options": [
+                            {
+                                "label": "Similarity",
+                                "name": "similarity"
+                            },
+                            {
+                                "label": "Max Marginal Relevance",
+                                "name": "mmr"
+                            }
+                        ],
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-searchType-options"
+                    },
+                    {
+                        "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,
+                        "id": "pinecone_0-input-fetchK-number"
+                    },
+                    {
+                        "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,
+                        "id": "pinecone_0-input-lambda-number"
                     }
                 ],
                 "inputAnchors": [
@@ -690,7 +729,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "{\"id\":{\"$in\":[\"doc1\",\"doc2\"]}}",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {

packages/server/marketplaces/chatflows/Multi Retrieval QA Chain.json

@@ -560,7 +560,7 @@
                 "type": "Pinecone",
                 "baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
                 "category": "Vector Stores",
-                "description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
+                "description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
                 "inputParams": [
                     {
                         "label": "Connect Credential",
@@ -601,6 +601,45 @@
                         "additionalParams": true,
                         "optional": true,
                         "id": "pinecone_0-input-topK-number"
+                    },
+                    {
+                        "label": "Search Type",
+                        "name": "searchType",
+                        "type": "options",
+                        "default": "similarity",
+                        "options": [
+                            {
+                                "label": "Similarity",
+                                "name": "similarity"
+                            },
+                            {
+                                "label": "Max Marginal Relevance",
+                                "name": "mmr"
+                            }
+                        ],
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-searchType-options"
+                    },
+                    {
+                        "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,
+                        "id": "pinecone_0-input-fetchK-number"
+                    },
+                    {
+                        "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,
+                        "id": "pinecone_0-input-lambda-number"
                     }
                 ],
                 "inputAnchors": [
@@ -625,7 +664,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {
@@ -840,6 +882,45 @@
                         "additionalParams": true,
                         "optional": true,
                         "id": "supabase_0-input-topK-number"
+                    },
+                    {
+                        "label": "Search Type",
+                        "name": "searchType",
+                        "type": "options",
+                        "default": "similarity",
+                        "options": [
+                            {
+                                "label": "Similarity",
+                                "name": "similarity"
+                            },
+                            {
+                                "label": "Max Marginal Relevance",
+                                "name": "mmr"
+                            }
+                        ],
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-searchType-options"
+                    },
+                    {
+                        "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,
+                        "id": "pinecone_0-input-fetchK-number"
+                    },
+                    {
+                        "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,
+                        "id": "pinecone_0-input-lambda-number"
                     }
                 ],
                 "inputAnchors": [
@@ -865,7 +946,10 @@
                     "tableName": "",
                     "queryName": "",
                     "supabaseMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {

packages/server/marketplaces/chatflows/WebPage QnA.json

@@ -643,7 +643,7 @@
                 "type": "Pinecone",
                 "baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
                 "category": "Vector Stores",
-                "description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
+                "description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
                 "inputParams": [
                     {
                         "label": "Connect Credential",
@@ -684,6 +684,45 @@
                         "additionalParams": true,
                         "optional": true,
                         "id": "pinecone_0-input-topK-number"
+                    },
+                    {
+                        "label": "Search Type",
+                        "name": "searchType",
+                        "type": "options",
+                        "default": "similarity",
+                        "options": [
+                            {
+                                "label": "Similarity",
+                                "name": "similarity"
+                            },
+                            {
+                                "label": "Max Marginal Relevance",
+                                "name": "mmr"
+                            }
+                        ],
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "pinecone_0-input-searchType-options"
+                    },
+                    {
+                        "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,
+                        "id": "pinecone_0-input-fetchK-number"
+                    },
+                    {
+                        "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,
+                        "id": "pinecone_0-input-lambda-number"
                     }
                 ],
                 "inputAnchors": [
@@ -708,7 +747,10 @@
                     "pineconeIndex": "",
                     "pineconeNamespace": "",
                     "pineconeMetadataFilter": "",
-                    "topK": ""
+                    "topK": "",
+                    "searchType": "similarity",
+                    "fetchK": "",
+                    "lambda": ""
                 },
                 "outputAnchors": [
                     {

packages/server/package.json

@@ -1,6 +1,6 @@
 {
     "name": "flowise",
-    "version": "1.4.9",
+    "version": "1.4.10",
     "description": "Flowiseai Server",
     "main": "dist/index",
     "types": "dist/index.d.ts",

packages/ui/package.json

@@ -1,6 +1,6 @@
 {
     "name": "flowise-ui",
-    "version": "1.4.6",
+    "version": "1.4.7",
     "license": "SEE LICENSE IN LICENSE.md",
     "homepage": "https://flowiseai.com",
     "author": {