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

# Conflicts: # packages/components/nodes/agents/OpenAIFunctionAgent/OpenAIFunctionAgent.ts
2026-06-28 19:00:59 +03:00 · 2024-01-15 13:22:59 +00:00
parent 02482f1b38 8847fc9d4c
commit 7ba4a063fb
20 changed files with 460 additions and 82 deletions
@@ -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 }
@@ -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.name = 'zapierNLAApi'
+        this.label = 'LocalAI API'
+        this.name = 'localAIApi'
        this.version = 1.0
        this.inputs = [
            {
-                label: 'Zapier NLA Api Key',
-                name: 'zapierNLAApiKey',
+                label: 'LocalAI Api Key',
+                name: 'localAIApiKey',
                type: 'password'
            }
        ]
    }
 }

-module.exports = { credClass: ZapierNLAApi }
+module.exports = { credClass: LocalAIApi }
@@ -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')
@@ -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)

@@ -1,5 +1,5 @@
-import { INode, INodeData, INodeParams } from '../../../src/Interface'
-import { getBaseClasses } from '../../../src/utils'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
+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 })

@@ -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
@@ -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)
            }
        }

@@ -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 }
@@ -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>
@@ -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
@@ -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',
-                placeholder: '4',
+                description: 'Number of top results to fetch. Defaults to 5',
+                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)

@@ -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",
@@ -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)]
        }