feature: Integrate Astra Vectorstore

packages/components/credentials/AstraApi.credential.ts

@@ -0,0 +1,34 @@
+import { INodeParams, INodeCredential } from '../src/Interface'
+
+class AstraApi implements INodeCredential {
+    label: string
+    name: string
+    version: number
+    description: string
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Astra API'
+        this.name = 'AstraApi'
+        this.version = 1.0
+        this.inputs = [
+            {
+                label: 'Colection 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: AstraApi }

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: ['AstraApi']
+        }
+        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 ?? 'dummy',
+                endpoint: credentialData?.dbEndPoint ?? 'dummy'
+            }
+
+            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 ?? 'dummy',
+            endpoint: credentialData?.dbEndPoint ?? 'dummy'
+        }
+
+        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 @@
+<svg width="11.41em" height="1.06em" viewBox="0 0 183 17" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M109.189 2.94031V0H95.0865L91.2567 2.94031V6.97789L95.0865 9.9182H107.377V14.0627H92.0184V17H106.502L110.332 14.0627V9.9182L106.502 6.97789H94.2112V2.94031H109.189Z" fill="currentColor"></path><path d="M35.3706 0H32.6803L22.8126 17H26.2247L34.0316 3.55466L41.8293 17H45.2383L35.3706 0Z" fill="currentColor"></path><path d="M64.7034 0H45.6284V2.94031H53.6902V17H56.6416V2.94031H64.7034V0Z" fill="currentColor"></path><path d="M134.146 0H115.074V2.94031H123.132V17H126.087V2.94031H134.146V0Z" fill="currentColor"></path><path d="M15.2453 0H0V17H15.2453L19.075 14.0597V2.94031L15.2453 0ZM2.95447 2.94031H16.1206V14.0627H2.95447V2.94031Z" fill="currentColor"></path><path d="M169.034 8.5L167.327 5.55969V5.56275L164.099 0H160.687L165.622 8.5L160.687 17H164.099L167.327 11.4403L169.034 8.5Z" fill="currentColor"></path><path d="M173.887 8.5L175.594 5.55969V5.56275L178.825 0H182.234L177.302 8.5L182.234 17H178.825L175.594 11.4403L173.887 8.5Z" fill="currentColor"></path><path d="M74.9611 0H77.6515L87.5191 17H84.1071L76.3001 3.55466L68.5024 17H65.0934L74.9611 0Z" fill="currentColor"></path><path d="M144.403 0H147.094L156.961 17H153.552L145.742 3.55466L137.948 17H134.536L144.403 0Z" fill="currentColor"></path></svg>

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",