Merge branch 'main' into bugfix/Concurrent-Chat-Session

# Conflicts:
#	packages/components/nodes/agents/OpenAIFunctionAgent/OpenAIFunctionAgent.ts

README-ZH.md

@@ -145,25 +145,40 @@ Flowise 支持不同的环境变量来配置您的实例。您可以在 `package
 
 ## 🌐 自托管
 
-### [Railway](https://docs.flowiseai.com/deployment/railway)
+在您现有的基础设施中部署自托管的 Flowise，我们支持各种[部署](https://docs.flowiseai.com/configuration/deployment)
 
-[![在 Railway 上部署](https://railway.app/button.svg)](https://railway.app/template/pn4G8S?referralCode=WVNPD9)
+-   [AWS](https://docs.flowiseai.com/deployment/aws)
+-   [Azure](https://docs.flowiseai.com/deployment/azure)
+-   [Digital Ocean](https://docs.flowiseai.com/deployment/digital-ocean)
+-   [GCP](https://docs.flowiseai.com/deployment/gcp)
+-   <details>
+      <summary>其他</summary>
 
-### [Render](https://docs.flowiseai.com/deployment/render)
+    -   [Railway](https://docs.flowiseai.com/deployment/railway)
 
-[![部署到 Render](https://render.com/images/deploy-to-render-button.svg)](https://docs.flowiseai.com/deployment/render)
+        [![在 Railway 上部署](https://railway.app/button.svg)](https://railway.app/template/pn4G8S?referralCode=WVNPD9)
 
-### [HuggingFace Spaces](https://docs.flowiseai.com/deployment/hugging-face)
+    -   [Render](https://docs.flowiseai.com/deployment/render)
 
-<a href="https://huggingface.co/spaces/FlowiseAI/Flowise"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="HuggingFace Spaces"></a>
+        [![部署到 Render](https://render.com/images/deploy-to-render-button.svg)](https://docs.flowiseai.com/deployment/render)
 
-### [AWS](https://docs.flowiseai.com/deployment/aws)
+    -   [HuggingFace Spaces](https://docs.flowiseai.com/deployment/hugging-face)
 
-### [Azure](https://docs.flowiseai.com/deployment/azure)
+        <a href="https://huggingface.co/spaces/FlowiseAI/Flowise"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="HuggingFace Spaces"></a>
 
-### [DigitalOcean](https://docs.flowiseai.com/deployment/digital-ocean)
+    -   [Elestio](https://elest.io/open-source/flowiseai)
 
-### [GCP](https://docs.flowiseai.com/deployment/gcp)
+        [![Deploy](https://pub-da36157c854648669813f3f76c526c2b.r2.dev/deploy-on-elestio-black.png)](https://elest.io/open-source/flowiseai)
+
+    -   [Sealos](https://cloud.sealos.io/?openapp=system-template%3FtemplateName%3Dflowise)
+
+        [![部署到 Sealos](https://raw.githubusercontent.com/labring-actions/templates/main/Deploy-on-Sealos.svg)](https://cloud.sealos.io/?openapp=system-template%3FtemplateName%3Dflowise)
+
+    -   [RepoCloud](https://repocloud.io/details/?app_id=29)
+
+        [![部署到 RepoCloud](https://d16t0pc4846x52.cloudfront.net/deploy.png)](https://repocloud.io/details/?app_id=29)
+
+      </details>
 
 ## 💻 云托管
 

README.md

@@ -145,29 +145,40 @@ Flowise support different environment variables to configure your instance. You
 
 ## 🌐 Self Host
 
-### [Railway](https://docs.flowiseai.com/deployment/railway)
+Deploy Flowise self-hosted in your existing infrastructure, we support various [deployments](https://docs.flowiseai.com/configuration/deployment)
 
-[![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/pn4G8S?referralCode=WVNPD9)
+-   [AWS](https://docs.flowiseai.com/deployment/aws)
+-   [Azure](https://docs.flowiseai.com/deployment/azure)
+-   [Digital Ocean](https://docs.flowiseai.com/deployment/digital-ocean)
+-   [GCP](https://docs.flowiseai.com/deployment/gcp)
+-   <details>
+      <summary>Others</summary>
 
-### [Render](https://docs.flowiseai.com/deployment/render)
+    -   [Railway](https://docs.flowiseai.com/deployment/railway)
 
-[![Deploy to Render](https://render.com/images/deploy-to-render-button.svg)](https://docs.flowiseai.com/deployment/render)
+        [![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/pn4G8S?referralCode=WVNPD9)
 
-### [Elestio](https://elest.io/open-source/flowiseai)
+    -   [Render](https://docs.flowiseai.com/deployment/render)
 
-[![Deploy](https://pub-da36157c854648669813f3f76c526c2b.r2.dev/deploy-on-elestio-black.png)](https://elest.io/open-source/flowiseai)
+        [![Deploy to Render](https://render.com/images/deploy-to-render-button.svg)](https://docs.flowiseai.com/deployment/render)
 
-### [HuggingFace Spaces](https://docs.flowiseai.com/deployment/hugging-face)
+    -   [HuggingFace Spaces](https://docs.flowiseai.com/deployment/hugging-face)
 
-<a href="https://huggingface.co/spaces/FlowiseAI/Flowise"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="HuggingFace Spaces"></a>
+        <a href="https://huggingface.co/spaces/FlowiseAI/Flowise"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="HuggingFace Spaces"></a>
 
-### [AWS](https://docs.flowiseai.com/deployment/aws)
+    -   [Elestio](https://elest.io/open-source/flowiseai)
 
-### [Azure](https://docs.flowiseai.com/deployment/azure)
+        [![Deploy](https://pub-da36157c854648669813f3f76c526c2b.r2.dev/deploy-on-elestio-black.png)](https://elest.io/open-source/flowiseai)
 
-### [DigitalOcean](https://docs.flowiseai.com/deployment/digital-ocean)
+    -   [Sealos](https://cloud.sealos.io/?openapp=system-template%3FtemplateName%3Dflowise)
 
-### [GCP](https://docs.flowiseai.com/deployment/gcp)
+        [![](https://raw.githubusercontent.com/labring-actions/templates/main/Deploy-on-Sealos.svg)](https://cloud.sealos.io/?openapp=system-template%3FtemplateName%3Dflowise)
+
+    -   [RepoCloud](https://repocloud.io/details/?app_id=29)
+
+        [![Deploy on RepoCloud](https://d16t0pc4846x52.cloudfront.net/deploy.png)](https://repocloud.io/details/?app_id=29)
+
+      </details>
 
 ## 💻 Cloud Hosted
 

docker/README.md

@@ -1,6 +1,6 @@
 # Flowise Docker Hub Image
 
-Starts Flowise from [DockerHub Image](https://hub.docker.com/repository/docker/flowiseai/flowise/general)
+Starts Flowise from [DockerHub Image](https://hub.docker.com/r/flowiseai/flowise)
 
 ## Usage
 

packages/components/credentials/AstraApi.credential.ts

@@ -0,0 +1,34 @@
+import { INodeParams, INodeCredential } from '../src/Interface'
+
+class AstraDBApi implements INodeCredential {
+    label: string
+    name: string
+    version: number
+    description: string
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Astra DB API'
+        this.name = 'AstraDBApi'
+        this.version = 1.0
+        this.inputs = [
+            {
+                label: 'Astra DB Collection Name',
+                name: 'collectionName',
+                type: 'string'
+            },
+            {
+                label: 'Astra DB Application Token',
+                name: 'applicationToken',
+                type: 'password'
+            },
+            {
+                label: 'Astra DB Api Endpoint',
+                name: 'dbEndPoint',
+                type: 'string'
+            }
+        ]
+    }
+}
+
+module.exports = { credClass: AstraDBApi }

packages/components/credentials/LocalAIApi.credential.ts

@@ -1,24 +1,23 @@
 import { INodeParams, INodeCredential } from '../src/Interface'
 
-class ZapierNLAApi implements INodeCredential {
+class LocalAIApi implements INodeCredential {
     label: string
     name: string
     version: number
-    description: string
     inputs: INodeParams[]
 
     constructor() {
-        this.label = 'Zapier NLA API'
+        this.label = 'LocalAI API'
-        this.name = 'zapierNLAApi'
+        this.name = 'localAIApi'
         this.version = 1.0
         this.inputs = [
             {
-                label: 'Zapier NLA Api Key',
+                label: 'LocalAI Api Key',
-                name: 'zapierNLAApiKey',
+                name: 'localAIApiKey',
                 type: 'password'
             }
         ]
     }
 }
 
-module.exports = { credClass: ZapierNLAApi }
+module.exports = { credClass: LocalAIApi }

packages/components/nodes/agents/OpenAIFunctionAgent/OpenAIFunctionAgent.ts

@@ -112,7 +112,7 @@ const prepareAgent = (
     const inputKey = memory.inputKey ? memory.inputKey : 'input'
 
     const prompt = ChatPromptTemplate.fromMessages([
-        ['ai', systemMessage ? systemMessage : `You are a helpful AI assistant.`],
+        ['system', systemMessage ? systemMessage : `You are a helpful AI assistant.`],
         new MessagesPlaceholder(memoryKey),
         ['human', `{${inputKey}}`],
         new MessagesPlaceholder('agent_scratchpad')

packages/components/nodes/chains/VectaraChain/VectaraChain.ts

@@ -69,22 +69,23 @@ class VectaraChain_Chains implements INode {
                 options: [
                     {
                         label: 'vectara-summary-ext-v1.2.0 (gpt-3.5-turbo)',
-                        name: 'vectara-summary-ext-v1.2.0'
+                        name: 'vectara-summary-ext-v1.2.0',
+                        description: 'base summarizer, available to all Vectara users'
                     },
                     {
                         label: 'vectara-experimental-summary-ext-2023-10-23-small (gpt-3.5-turbo)',
                         name: 'vectara-experimental-summary-ext-2023-10-23-small',
-                        description: 'In beta, available to both Growth and Scale Vectara users'
+                        description: `In beta, available to both Growth and <a target="_blank" href="https://vectara.com/pricing/">Scale</a> Vectara users`
                     },
                     {
                         label: 'vectara-summary-ext-v1.3.0 (gpt-4.0)',
                         name: 'vectara-summary-ext-v1.3.0',
-                        description: 'Only available to paying Scale Vectara users'
+                        description: 'Only available to <a target="_blank" href="https://vectara.com/pricing/">Scale</a> Vectara users'
                     },
                     {
                         label: 'vectara-experimental-summary-ext-2023-10-23-med (gpt-4.0)',
                         name: 'vectara-experimental-summary-ext-2023-10-23-med',
-                        description: 'In beta, only available to paying Scale Vectara users'
+                        description: `In beta, only available to <a target="_blank" href="https://vectara.com/pricing/">Scale</a> Vectara users`
                     }
                 ],
                 default: 'vectara-summary-ext-v1.2.0'
@@ -228,7 +229,7 @@ class VectaraChain_Chains implements INode {
 
     async run(nodeData: INodeData, input: string): Promise<object> {
         const vectorStore = nodeData.inputs?.vectaraStore as VectaraStore
-        const responseLang = (nodeData.inputs?.responseLang as string) ?? 'auto'
+        const responseLang = (nodeData.inputs?.responseLang as string) ?? 'eng'
         const summarizerPromptName = nodeData.inputs?.summarizerPromptName as string
         const maxSummarizedResultsStr = nodeData.inputs?.maxSummarizedResults as string
         const maxSummarizedResults = maxSummarizedResultsStr ? parseInt(maxSummarizedResultsStr, 10) : 7
@@ -247,17 +248,31 @@ class VectaraChain_Chains implements INode {
             lexicalInterpolationConfig: { lambda: vectaraFilter?.lambda ?? 0.025 }
         }))
 
+        // Vectara reranker ID for MMR (https://docs.vectara.com/docs/api-reference/search-apis/reranking#maximal-marginal-relevance-mmr-reranker)
+        const mmrRerankerId = 272725718
+        const mmrEnabled = vectaraFilter?.mmrConfig?.enabled
+
         const data = {
             query: [
                 {
                     query: input,
                     start: 0,
-                    numResults: topK,
+                    numResults: mmrEnabled ? vectaraFilter?.mmrTopK : topK,
+                    corpusKey: corpusKeys,
                     contextConfig: {
                         sentencesAfter: vectaraFilter?.contextConfig?.sentencesAfter ?? 2,
                         sentencesBefore: vectaraFilter?.contextConfig?.sentencesBefore ?? 2
                     },
-                    corpusKey: corpusKeys,
+                    ...(mmrEnabled
+                        ? {
+                              rerankingConfig: {
+                                  rerankerId: mmrRerankerId,
+                                  mmrConfig: {
+                                      diversityBias: vectaraFilter?.mmrConfig.diversityBias
+                                  }
+                              }
+                          }
+                        : {}),
                     summary: [
                         {
                             summarizerPromptName,
@@ -285,6 +300,14 @@ class VectaraChain_Chains implements INode {
             const documents = result.responseSet[0].document
             let rawSummarizedText = ''
 
+            // remove responses that are not in the topK (in case of MMR)
+            // Note that this does not really matter functionally due to the reorder citations, but it is more efficient
+            const maxResponses = mmrEnabled ? Math.min(responses.length, topK) : responses.length
+            if (responses.length > maxResponses) {
+                responses.splice(0, maxResponses)
+            }
+
+            // Add metadata to each text response given its corresponding document metadata
             for (let i = 0; i < responses.length; i += 1) {
                 const responseMetadata = responses[i].metadata
                 const documentMetadata = documents[responses[i].documentIndex].metadata
@@ -301,13 +324,13 @@ class VectaraChain_Chains implements INode {
                 responses[i].metadata = combinedMetadata
             }
 
+            // Create the summarization response
             const summaryStatus = result.responseSet[0].summary[0].status
             if (summaryStatus.length > 0 && summaryStatus[0].code === 'BAD_REQUEST') {
                 throw new Error(
                     `BAD REQUEST: Too much text for the summarizer to summarize. Please try reducing the number of search results to summarize, or the context of each result by adjusting the 'summary_num_sentences', and 'summary_num_results' parameters respectively.`
                 )
             }
-
             if (
                 summaryStatus.length > 0 &&
                 summaryStatus[0].code === 'NOT_FOUND' &&
@@ -316,8 +339,8 @@ class VectaraChain_Chains implements INode {
                 throw new Error(`BAD REQUEST: summarizer ${summarizerPromptName} is invalid for this account.`)
             }
 
+            // Reorder citations in summary and create the list of returned source documents
             rawSummarizedText = result.responseSet[0].summary[0]?.text
-
             let summarizedText = reorderCitations(rawSummarizedText)
             let summaryResponses = applyCitationOrder(responses, rawSummarizedText)
 

packages/components/nodes/chatmodels/ChatLocalAI/ChatLocalAI.ts

@@ -1,5 +1,5 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
-import { getBaseClasses } from '../../../src/utils'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { OpenAIChat } from 'langchain/llms/openai'
 import { OpenAIChatInput } from 'langchain/chat_models/openai'
 import { BaseCache } from 'langchain/schema'
@@ -14,6 +14,7 @@ class ChatLocalAI_ChatModels implements INode {
     category: string
     description: string
     baseClasses: string[]
+    credential: INodeParams
     inputs: INodeParams[]
 
     constructor() {
@@ -25,6 +26,13 @@ class ChatLocalAI_ChatModels implements INode {
         this.category = 'Chat Models'
         this.description = 'Use local LLMs like llama.cpp, gpt4all using LocalAI'
         this.baseClasses = [this.type, 'BaseChatModel', ...getBaseClasses(OpenAIChat)]
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['localAIApi'],
+            optional: true
+        }
         this.inputs = [
             {
                 label: 'Cache',
@@ -79,13 +87,16 @@ class ChatLocalAI_ChatModels implements INode {
         ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         const temperature = nodeData.inputs?.temperature as string
         const modelName = nodeData.inputs?.modelName as string
         const maxTokens = nodeData.inputs?.maxTokens as string
         const topP = nodeData.inputs?.topP as string
         const timeout = nodeData.inputs?.timeout as string
         const basePath = nodeData.inputs?.basePath as string
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const localAIApiKey = getCredentialParam('localAIApiKey', credentialData, nodeData)
+
         const cache = nodeData.inputs?.cache as BaseCache
 
         const obj: Partial<OpenAIChatInput> & BaseLLMParams & { openAIApiKey?: string } = {
@@ -98,6 +109,7 @@ class ChatLocalAI_ChatModels implements INode {
         if (topP) obj.topP = parseFloat(topP)
         if (timeout) obj.timeout = parseInt(timeout, 10)
         if (cache) obj.cache = cache
+        if (localAIApiKey) obj.openAIApiKey = localAIApiKey
 
         const model = new OpenAIChat(obj, { basePath })
 

packages/components/nodes/embeddings/LocalAIEmbedding/LocalAIEmbedding.ts

@@ -1,4 +1,5 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getCredentialData, getCredentialParam } from '../../../src/utils'
 import { OpenAIEmbeddings, OpenAIEmbeddingsParams } from 'langchain/embeddings/openai'
 
 class LocalAIEmbedding_Embeddings implements INode {
@@ -10,6 +11,7 @@ class LocalAIEmbedding_Embeddings implements INode {
     category: string
     description: string
     baseClasses: string[]
+    credential: INodeParams
     inputs: INodeParams[]
 
     constructor() {
@@ -21,6 +23,13 @@ class LocalAIEmbedding_Embeddings implements INode {
         this.category = 'Embeddings'
         this.description = 'Use local embeddings models like llama.cpp'
         this.baseClasses = [this.type, 'Embeddings']
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['localAIApi'],
+            optional: true
+        }
         this.inputs = [
             {
                 label: 'Base Path',
@@ -37,15 +46,20 @@ class LocalAIEmbedding_Embeddings implements INode {
         ]
     }
 
-    async init(nodeData: INodeData): Promise<any> {
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
         const modelName = nodeData.inputs?.modelName as string
         const basePath = nodeData.inputs?.basePath as string
 
+        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
+        const localAIApiKey = getCredentialParam('localAIApiKey', credentialData, nodeData)
+
         const obj: Partial<OpenAIEmbeddingsParams> & { openAIApiKey?: string } = {
             modelName,
             openAIApiKey: 'sk-'
         }
 
+        if (localAIApiKey) obj.openAIApiKey = localAIApiKey
+
         const model = new OpenAIEmbeddings(obj, { basePath })
 
         return model

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

@@ -65,7 +65,7 @@ class CustomFunction_Utilities implements INode {
                 inputVars =
                     typeof functionInputVariablesRaw === 'object' ? functionInputVariablesRaw : JSON.parse(functionInputVariablesRaw)
             } catch (exception) {
-                throw new Error("Invalid JSON in the PromptTemplate's promptValues: " + exception)
+                throw new Error('Invalid JSON in the Custom Function Input Variables: ' + exception)
             }
         }
 

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

@@ -0,0 +1,190 @@
+import { flatten } from 'lodash'
+import { Embeddings } from 'langchain/embeddings/base'
+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'
+
+class Astra_VectorStores implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    badge: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+    credential: INodeParams
+    outputs: INodeOutputsValue[]
+
+    constructor() {
+        this.label = 'Astra'
+        this.name = 'Astra'
+        this.version = 1.0
+        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.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
+        this.badge = 'NEW'
+        this.credential = {
+            label: 'Connect Credential',
+            name: 'credential',
+            type: 'credential',
+            credentialNames: ['AstraDBApi']
+        }
+        this.inputs = [
+            {
+                label: 'Document',
+                name: 'document',
+                type: 'Document',
+                list: true,
+                optional: true
+            },
+            {
+                label: 'Embeddings',
+                name: 'embeddings',
+                type: 'Embeddings'
+            },
+            {
+                label: 'Vector Dimension',
+                name: 'vectorDimension',
+                type: 'number',
+                placeholder: '1536',
+                optional: true,
+                description: 'Dimension used for storing vector embedding'
+            },
+            {
+                label: 'Similarity Metric',
+                name: 'similarityMetric',
+                type: 'string',
+                placeholder: 'cosine',
+                optional: true,
+                description: 'cosine | euclidean | dot_product'
+            },
+            {
+                label: 'Top K',
+                name: 'topK',
+                description: 'Number of top results to fetch. Default to 4',
+                placeholder: '4',
+                type: 'number',
+                additionalParams: true,
+                optional: true
+            }
+        ]
+        this.outputs = [
+            {
+                label: 'Astra Retriever',
+                name: 'retriever',
+                baseClasses: this.baseClasses
+            },
+            {
+                label: 'Astra Vector Store',
+                name: 'vectorStore',
+                baseClasses: [this.type, ...getBaseClasses(AstraDBVectorStore)]
+            }
+        ]
+    }
+
+    //@ts-ignore
+    vectorStoreMethods = {
+        async upsert(nodeData: INodeData, options: ICommonObject): Promise<void> {
+            const docs = nodeData.inputs?.document as Document[]
+            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 credentialData = await getCredentialData(nodeData.credential ?? '', options)
+
+            const expectedSimilarityMetric = ['cosine', 'euclidean', 'dot_product']
+            if (similarityMetric && !expectedSimilarityMetric.includes(similarityMetric)) {
+                throw new Error(`Invalid Similarity Metric should be one of 'cosine' | 'euclidean' | 'dot_product'`)
+            }
+
+            const clientConfig = {
+                token: credentialData?.applicationToken,
+                endpoint: credentialData?.dbEndPoint
+            }
+
+            const astraConfig: AstraLibArgs = {
+                ...clientConfig,
+                collection: credentialData.collectionName ?? 'flowise_test',
+                collectionOptions: {
+                    vector: {
+                        dimension: vectorDimension ?? 1536,
+                        metric: similarityMetric ?? 'cosine'
+                    }
+                }
+            }
+
+            const flattenDocs = docs && docs.length ? flatten(docs) : []
+            const finalDocs = []
+            for (let i = 0; i < flattenDocs.length; i += 1) {
+                if (flattenDocs[i] && flattenDocs[i].pageContent) {
+                    finalDocs.push(new Document(flattenDocs[i]))
+                }
+            }
+
+            try {
+                await AstraDBVectorStore.fromDocuments(finalDocs, embeddings, astraConfig)
+            } catch (e) {
+                throw new Error(e)
+            }
+        }
+    }
+
+    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const docs = nodeData.inputs?.document as Document[]
+        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)
+
+        const expectedSimilarityMetric = ['cosine', 'euclidean', 'dot_product']
+        if (similarityMetric && !expectedSimilarityMetric.includes(similarityMetric)) {
+            throw new Error(`Invalid Similarity Metric should be one of 'cosine' | 'euclidean' | 'dot_product'`)
+        }
+
+        const clientConfig = {
+            token: credentialData?.applicationToken,
+            endpoint: credentialData?.dbEndPoint
+        }
+
+        const astraConfig: AstraLibArgs = {
+            ...clientConfig,
+            collection: credentialData.collectionName ?? 'flowise_test',
+            collectionOptions: {
+                vector: {
+                    dimension: vectorDimension ?? 1536,
+                    metric: similarityMetric ?? 'cosine'
+                }
+            }
+        }
+
+        const flattenDocs = docs && docs.length ? flatten(docs) : []
+        const finalDocs = []
+        for (let i = 0; i < flattenDocs.length; i += 1) {
+            if (flattenDocs[i] && flattenDocs[i].pageContent) {
+                finalDocs.push(new Document(flattenDocs[i]))
+            }
+        }
+
+        const vectorStore = await AstraDBVectorStore.fromExistingIndex(embeddings, astraConfig)
+
+        if (output === 'retriever') {
+            const retriever = vectorStore.asRetriever(k)
+            return retriever
+        } else if (output === 'vectorStore') {
+            ;(vectorStore as any).k = k
+            return vectorStore
+        }
+        return vectorStore
+    }
+}
+
+module.exports = { nodeClass: Astra_VectorStores }

packages/components/nodes/vectorstores/Astra/astra.svg

@@ -0,0 +1,12 @@
+<svg width="1200" height="1200" viewBox="0 0 1200 1200" fill="none" xmlns="http://www.w3.org/2000/svg">
+<rect width="1200" height="1200" fill="black"/>
+<g clip-path="url(#clip0_102_1968)">
+<path d="M508.819 464.97H267.001V737.697H508.819L569.566 690.526V512.14L508.819 464.97ZM313.864 512.14H522.703V690.575H313.864V512.14Z" fill="white"/>
+<path d="M917.531 514.121V468H696.425L636.389 514.121V577.447L696.425 623.568H889.124V688.545H648.348V734.667H875.409L935.444 688.545V623.568L875.409 577.447H682.709V514.121H917.531Z" fill="white"/>
+</g>
+<defs>
+<clipPath id="clip0_102_1968">
+<rect width="668.444" height="266.667" fill="white" transform="translate(267 468)"/>
+</clipPath>
+</defs>
+</svg>

packages/components/nodes/vectorstores/Milvus/Milvus.ts

@@ -65,6 +65,14 @@ class Milvus_VectorStores implements INode {
                 name: 'milvusCollection',
                 type: 'string'
             },
+            {
+                label: 'Milvus Text Field',
+                name: 'milvusTextField',
+                type: 'string',
+                placeholder: 'langchain_text',
+                optional: true,
+                additionalParams: true
+            },
             {
                 label: 'Milvus Filter',
                 name: 'milvusFilter',
@@ -150,6 +158,7 @@ class Milvus_VectorStores implements INode {
         const address = nodeData.inputs?.milvusServerUrl as string
         const collectionName = nodeData.inputs?.milvusCollection as string
         const milvusFilter = nodeData.inputs?.milvusFilter as string
+        const textField = nodeData.inputs?.milvusTextField as string
 
         // embeddings
         const embeddings = nodeData.inputs?.embeddings as Embeddings
@@ -169,7 +178,8 @@ class Milvus_VectorStores implements INode {
         // init MilvusLibArgs
         const milVusArgs: MilvusLibArgs = {
             url: address,
-            collectionName: collectionName
+            collectionName: collectionName,
+            textField: textField
         }
 
         if (milvusUser) milVusArgs.username = milvusUser

packages/components/nodes/vectorstores/Vectara/Vectara.ts

@@ -1,5 +1,5 @@
 import { flatten } from 'lodash'
-import { VectaraStore, VectaraLibArgs, VectaraFilter, VectaraContextConfig, VectaraFile } from 'langchain/vectorstores/vectara'
+import { VectaraStore, VectaraLibArgs, VectaraFilter, VectaraContextConfig, VectaraFile, MMRConfig } from 'langchain/vectorstores/vectara'
 import { Document } from 'langchain/document'
 import { Embeddings } from 'langchain/embeddings/base'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
@@ -22,7 +22,7 @@ class Vectara_VectorStores implements INode {
     constructor() {
         this.label = 'Vectara'
         this.name = 'vectara'
-        this.version = 1.0
+        this.version = 2.0
         this.type = 'Vectara'
         this.icon = 'vectara.png'
         this.category = 'Vector Stores'
@@ -82,7 +82,9 @@ class Vectara_VectorStores implements INode {
                 label: 'Lambda',
                 name: 'lambda',
                 description:
-                    'Improves retrieval accuracy by adjusting the balance (from 0 to 1) between neural search and keyword-based search factors.',
+                    'Enable hybrid search to improve retrieval accuracy by adjusting the balance (from 0 to 1) between neural search and keyword-based search factors.' +
+                    'A value of 0.0 means that only neural search is used, while a value of 1.0 means that only keyword-based search is used. Defaults to 0.0 (neural only).',
+                default: 0.0,
                 type: 'number',
                 additionalParams: true,
                 optional: true
@@ -90,8 +92,30 @@ class Vectara_VectorStores implements INode {
             {
                 label: 'Top K',
                 name: 'topK',
-                description: 'Number of top results to fetch. Defaults to 4',
+                description: 'Number of top results to fetch. Defaults to 5',
-                placeholder: '4',
+                placeholder: '5',
+                type: 'number',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'MMR K',
+                name: 'mmrK',
+                description: 'Number of top results to fetch for MMR. Defaults to 50',
+                placeholder: '50',
+                type: 'number',
+                additionalParams: true,
+                optional: true
+            },
+            {
+                label: 'MMR diversity bias',
+                name: 'mmrDiversityBias',
+                step: 0.1,
+                description:
+                    'The diversity bias to use for MMR. This is a value between 0.0 and 1.0' +
+                    'Values closer to 1.0 optimize for the most diverse results.' +
+                    'Defaults to 0 (MMR disabled)',
+                placeholder: '0.0',
                 type: 'number',
                 additionalParams: true,
                 optional: true
@@ -191,7 +215,9 @@ class Vectara_VectorStores implements INode {
         const lambda = nodeData.inputs?.lambda as number
         const output = nodeData.outputs?.output as string
         const topK = nodeData.inputs?.topK as string
-        const k = topK ? parseFloat(topK) : 4
+        const k = topK ? parseFloat(topK) : 5
+        const mmrK = nodeData.inputs?.mmrK as number
+        const mmrDiversityBias = nodeData.inputs?.mmrDiversityBias as number
 
         const vectaraArgs: VectaraLibArgs = {
             apiKey: apiKey,
@@ -208,6 +234,11 @@ class Vectara_VectorStores implements INode {
         if (sentencesBefore) vectaraContextConfig.sentencesBefore = sentencesBefore
         if (sentencesAfter) vectaraContextConfig.sentencesAfter = sentencesAfter
         vectaraFilter.contextConfig = vectaraContextConfig
+        const mmrConfig: MMRConfig = {}
+        mmrConfig.enabled = mmrDiversityBias > 0
+        mmrConfig.mmrTopK = mmrK
+        mmrConfig.diversityBias = mmrDiversityBias
+        vectaraFilter.mmrConfig = mmrConfig
 
         const vectorStore = new VectaraStore(vectaraArgs)
 

packages/components/package.json

@@ -19,6 +19,7 @@
         "@aws-sdk/client-bedrock-runtime": "3.422.0",
         "@aws-sdk/client-dynamodb": "^3.360.0",
         "@aws-sdk/client-s3": "^3.427.0",
+        "@datastax/astra-db-ts": "^0.1.2",
         "@dqbd/tiktoken": "^1.0.7",
         "@elastic/elasticsearch": "^8.9.0",
         "@getzep/zep-js": "^0.9.0",
@@ -26,6 +27,7 @@
         "@gomomento/sdk-core": "^1.51.1",
         "@google-ai/generativelanguage": "^0.2.1",
         "@huggingface/inference": "^2.6.1",
+        "@langchain/community": "^0.0.16",
         "@langchain/google-genai": "^0.0.6",
         "@langchain/mistralai": "^0.0.6",
         "@notionhq/client": "^2.2.8",
@@ -48,7 +50,7 @@
         "faiss-node": "^0.2.2",
         "fast-json-patch": "^3.1.1",
         "form-data": "^4.0.0",
-        "google-auth-library": "^9.0.0",
+        "google-auth-library": "^9.4.0",
         "graphql": "^16.6.0",
         "html-to-text": "^9.0.5",
         "husky": "^8.0.3",

packages/components/src/agents.ts

@@ -606,9 +606,18 @@ class ExceptionTool extends Tool {
 
 export const formatAgentSteps = (steps: AgentStep[]): BaseMessage[] =>
     steps.flatMap(({ action, observation }) => {
+        const create_function_message = (observation: string, action: AgentAction) => {
+            let content: string
+            if (typeof observation !== 'string') {
+                content = JSON.stringify(observation)
+            } else {
+                content = observation
+            }
+            return new FunctionMessage(content, action.tool)
+        }
         if ('messageLog' in action && action.messageLog !== undefined) {
             const log = action.messageLog as BaseMessage[]
-            return log.concat(new FunctionMessage(observation, action.tool))
+            return log.concat(create_function_message(observation, action))
         } else {
             return [new AIMessage(action.log)]
         }

packages/server/marketplaces/chatflows/Vectara LLM Chain Upload.json

@@ -350,12 +350,33 @@
                     {
                         "label": "Top K",
                         "name": "topK",
-                        "description": "Number of top results to fetch. Defaults to 4",
+                        "description": "Number of top results to fetch. Defaults to 5",
-                        "placeholder": "4",
+                        "placeholder": "5",
                         "type": "number",
                         "additionalParams": true,
                         "optional": true,
                         "id": "vectara_0-input-topK-number"
+                    },
+                    {
+                        "label": "MMR K",
+                        "name": "mmrK",
+                        "description": "The number of results to rerank if MMR is enabled.",
+                        "placeholder": "50",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "vectara_0-input-mmrK-number"
+                    },
+                    {
+                        "label": "MMR Diversity Bias",
+                        "name": "mmrDiversityBias",
+                        "step": 0.1,
+                        "description": "Diversity Bias parameter for MMR, if enabled. 0.0 means no diversiry bias, 1.0 means maximum diversity bias. Defaults to 0.0 (MMR disabled).",
+                        "placeholder": "0.0",
+                        "type": "number",
+                        "additionalParams": true,
+                        "optional": true,
+                        "id": "vectara_0-input-mmrDiversityBias-number"
                     }
                 ],
                 "inputAnchors": [
@@ -374,7 +395,9 @@
                     "sentencesBefore": "",
                     "sentencesAfter": "",
                     "lambda": "",
-                    "topK": ""
+                    "topK": "",
+                    "mmrK": "",
+                    "mmrDiversityBias": ""
                 },
                 "outputAnchors": [
                     {

packages/server/src/index.ts

@@ -361,7 +361,8 @@ export class App {
             const chatflow = await this.AppDataSource.getRepository(ChatFlow).findOneBy({
                 id: req.params.id
             })
-            if (chatflow && chatflow.chatbotConfig) {
+            if (!chatflow) return res.status(404).send(`Chatflow ${req.params.id} not found`)
+            if (chatflow.chatbotConfig) {
                 try {
                     const parsedConfig = JSON.parse(chatflow.chatbotConfig)
                     return res.json(parsedConfig)
@@ -369,7 +370,7 @@ export class App {
                     return res.status(500).send(`Error parsing Chatbot Config for Chatflow ${req.params.id}`)
                 }
             }
-            return res.status(404).send(`Chatbot Config for Chatflow ${req.params.id} not found`)
+            return res.status(200).send('OK')
         })
 
         // Save chatflow
@@ -521,7 +522,7 @@ export class App {
                 res.status(404).send(`Chatflow ${chatflowid} not found`)
                 return
             }
-            const chatId = (req.query?.chatId as string) ?? (await getChatId(chatflowid))
+            const chatId = req.query?.chatId as string
             const memoryType = req.query?.memoryType as string | undefined
             const sessionId = req.query?.sessionId as string | undefined
             const chatType = req.query?.chatType as string | undefined
@@ -545,7 +546,8 @@ export class App {
                 return res.status(500).send('Error clearing chat messages')
             }
 
-            const deleteOptions: FindOptionsWhere<ChatMessage> = { chatflowid, chatId }
+            const deleteOptions: FindOptionsWhere<ChatMessage> = { chatflowid }
+            if (chatId) deleteOptions.chatId = chatId
             if (memoryType) deleteOptions.memoryType = memoryType
             if (sessionId) deleteOptions.sessionId = sessionId
             if (chatType) deleteOptions.chatType = chatType
@@ -633,7 +635,7 @@ export class App {
             return res.json(result)
         })
 
-        // Delete all chatmessages from chatflowid
+        // Delete all credentials from chatflowid
         this.app.delete('/api/v1/credentials/:id', async (req: Request, res: Response) => {
             const results = await this.AppDataSource.getRepository(Credential).delete({ id: req.params.id })
             return res.json(results)
@@ -1790,23 +1792,6 @@ export class App {
     }
 }
 
-/**
- * Get first chat message id
- * @param {string} chatflowid
- * @returns {string}
- */
-export async function getChatId(chatflowid: string): Promise<string> {
-    // first chatmessage id as the unique chat id
-    const firstChatMessage = await getDataSource()
-        .getRepository(ChatMessage)
-        .createQueryBuilder('cm')
-        .select('cm.id')
-        .where('chatflowid = :chatflowid', { chatflowid })
-        .orderBy('cm.createdDate', 'ASC')
-        .getOne()
-    return firstChatMessage ? firstChatMessage.id : ''
-}
-
 let serverApp: App | undefined
 
 export async function getAllChatFlow(): Promise<IChatFlow[]> {

packages/server/src/utils/index.ts

@@ -547,7 +547,11 @@ export const getVariableValue = (
         variablePaths.forEach((path) => {
             const variableValue = variableDict[path]
             // Replace all occurrence
-            returnVal = returnVal.split(path).join(variableValue)
+            if (typeof variableValue === 'object') {
+                returnVal = returnVal.split(path).join(JSON.stringify(variableValue).replace(/"/g, '\\"'))
+            } else {
+                returnVal = returnVal.split(path).join(variableValue)
+            }
         })
         return returnVal
     }

packages/ui/src/ui-component/dialog/ExpandTextDialog.js

@@ -67,7 +67,11 @@ const ExpandTextDialog = ({ show, dialogProps, onCancel, onConfirm }) => {
 
     useEffect(() => {
         if (executeCustomFunctionNodeApi.data) {
-            setCodeExecutedResult(executeCustomFunctionNodeApi.data)
+            if (typeof executeCustomFunctionNodeApi.data === 'object') {
+                setCodeExecutedResult(JSON.stringify(executeCustomFunctionNodeApi.data, null, 2))
+            } else {
+                setCodeExecutedResult(executeCustomFunctionNodeApi.data)
+            }
         }
     }, [executeCustomFunctionNodeApi.data])