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

import { flatten } from 'lodash'
import { Pinecone } from '@pinecone-database/pinecone'
import { PineconeStoreParams, PineconeStore } from '@langchain/pinecone'
import { Embeddings } from '@langchain/core/embeddings'
import { Document } from '@langchain/core/documents'
import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'

class Pinecone_VectorStores implements INode {
    label: string
    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 = 'Pinecone'
        this.name = 'pinecone'
        this.version = 2.0
        this.type = 'Pinecone'
        this.icon = 'pinecone.svg'
        this.category = 'Vector Stores'
        this.description = `Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database`
        this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever']
        this.badge = 'NEW'
        this.credential = {
            label: 'Connect Credential',
            name: 'credential',
            type: 'credential',
            credentialNames: ['pineconeApi']
        }
        this.inputs = [
            {
                label: 'Document',
                name: 'document',
                type: 'Document',
                list: true,
                optional: true
            },
            {
                label: 'Embeddings',
                name: 'embeddings',
                type: 'Embeddings'
            },
            {
                label: 'Pinecone Index',
                name: 'pineconeIndex',
                type: 'string'
            },
            {
                label: 'Pinecone Namespace',
                name: 'pineconeNamespace',
                type: 'string',
                placeholder: 'my-first-namespace',
                additionalParams: true,
                optional: true
            },
            {
                label: 'Pinecone Metadata Filter',
                name: 'pineconeMetadataFilter',
                type: 'json',
                optional: true,
                additionalParams: true
            },
            {
                label: 'Top K',
                name: 'topK',
                description: 'Number of top results to fetch. Default to 4',
                placeholder: '4',
                type: 'number',
                additionalParams: true,
                optional: true
            }
        ]
        addMMRInputParams(this.inputs)
        this.outputs = [
            {
                label: 'Pinecone Retriever',
                name: 'retriever',
                baseClasses: this.baseClasses
            },
            {
                label: 'Pinecone Vector Store',
                name: 'vectorStore',
                baseClasses: [this.type, ...getBaseClasses(PineconeStore)]
            }
        ]
    }

    //@ts-ignore
    vectorStoreMethods = {
        async upsert(nodeData: INodeData, options: ICommonObject): Promise<void> {
            const index = nodeData.inputs?.pineconeIndex as string
            const pineconeNamespace = nodeData.inputs?.pineconeNamespace as string
            const docs = nodeData.inputs?.document as Document[]
            const embeddings = nodeData.inputs?.embeddings as Embeddings

            const credentialData = await getCredentialData(nodeData.credential ?? '', options)
            const pineconeApiKey = getCredentialParam('pineconeApiKey', credentialData, nodeData)

            const client = new Pinecone({
                apiKey: pineconeApiKey
            })

            const pineconeIndex = client.Index(index)

            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 obj: PineconeStoreParams = {
                pineconeIndex
            }

            if (pineconeNamespace) obj.namespace = pineconeNamespace

            try {
                await PineconeStore.fromDocuments(finalDocs, embeddings, obj)
            } catch (e) {
                throw new Error(e)
            }
        }
    }

    async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
        const index = nodeData.inputs?.pineconeIndex as string
        const pineconeNamespace = nodeData.inputs?.pineconeNamespace as string
        const pineconeMetadataFilter = nodeData.inputs?.pineconeMetadataFilter
        const docs = nodeData.inputs?.document as Document[]
        const embeddings = nodeData.inputs?.embeddings as Embeddings

        const credentialData = await getCredentialData(nodeData.credential ?? '', options)
        const pineconeApiKey = getCredentialParam('pineconeApiKey', credentialData, nodeData)

        const client = new Pinecone({
            apiKey: pineconeApiKey
        })

        await client.describeIndex(index)

        const pineconeIndex = client.Index(index)

        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 obj: PineconeStoreParams = {
            pineconeIndex
        }

        if (pineconeNamespace) obj.namespace = pineconeNamespace
        if (pineconeMetadataFilter) {
            const metadatafilter = typeof pineconeMetadataFilter === 'object' ? pineconeMetadataFilter : JSON.parse(pineconeMetadataFilter)
            obj.filter = metadatafilter
        }

        const vectorStore = await PineconeStore.fromExistingIndex(embeddings, obj)

        return resolveVectorStoreOrRetriever(nodeData, vectorStore)
    }
}

module.exports = { nodeClass: Pinecone_VectorStores }