"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
const lodash_1 = require("lodash");
const vectara_1 = require("@langchain/community/vectorstores/vectara");
const documents_1 = require("@langchain/core/documents");
const utils_1 = require("../../../src/utils");
const src_1 = require("../../../src");
class Vectara_VectorStores {
    constructor() {
        //@ts-ignore
        this.vectorStoreMethods = {
            async upsert(nodeData, options) {
                const credentialData = await (0, utils_1.getCredentialData)(nodeData.credential ?? '', options);
                const apiKey = (0, utils_1.getCredentialParam)('apiKey', credentialData, nodeData);
                const customerId = (0, utils_1.getCredentialParam)('customerID', credentialData, nodeData);
                const corpusId = (0, utils_1.getCredentialParam)('corpusID', credentialData, nodeData).split(',');
                const docs = nodeData.inputs?.document;
                const embeddings = {};
                const vectaraMetadataFilter = nodeData.inputs?.filter;
                const sentencesBefore = nodeData.inputs?.sentencesBefore;
                const sentencesAfter = nodeData.inputs?.sentencesAfter;
                const lambda = nodeData.inputs?.lambda;
                const fileBase64 = nodeData.inputs?.file;
                const vectaraArgs = {
                    apiKey: apiKey,
                    customerId: customerId,
                    corpusId: corpusId,
                    source: 'flowise'
                };
                const vectaraFilter = {};
                if (vectaraMetadataFilter)
                    vectaraFilter.filter = vectaraMetadataFilter;
                if (lambda)
                    vectaraFilter.lambda = lambda;
                const vectaraContextConfig = {};
                if (sentencesBefore)
                    vectaraContextConfig.sentencesBefore = sentencesBefore;
                if (sentencesAfter)
                    vectaraContextConfig.sentencesAfter = sentencesAfter;
                vectaraFilter.contextConfig = vectaraContextConfig;
                const flattenDocs = docs && docs.length ? (0, lodash_1.flatten)(docs) : [];
                const finalDocs = [];
                for (let i = 0; i < flattenDocs.length; i += 1) {
                    if (flattenDocs[i] && flattenDocs[i].pageContent) {
                        finalDocs.push(new documents_1.Document(flattenDocs[i]));
                    }
                }
                const vectaraFiles = [];
                let files = [];
                if (fileBase64.startsWith('FILE-STORAGE::')) {
                    const fileName = fileBase64.replace('FILE-STORAGE::', '');
                    if (fileName.startsWith('[') && fileName.endsWith(']')) {
                        files = JSON.parse(fileName);
                    }
                    else {
                        files = [fileName];
                    }
                    const orgId = options.orgId;
                    const chatflowid = options.chatflowid;
                    for (const file of files) {
                        if (!file)
                            continue;
                        const fileData = await (0, src_1.getFileFromStorage)(file, orgId, chatflowid);
                        const blob = new Blob([fileData]);
                        vectaraFiles.push({ blob: blob, fileName: getFileName(file) });
                    }
                }
                else {
                    if (fileBase64.startsWith('[') && fileBase64.endsWith(']')) {
                        files = JSON.parse(fileBase64);
                    }
                    else {
                        files = [fileBase64];
                    }
                    for (const file of files) {
                        if (!file)
                            continue;
                        const splitDataURI = file.split(',');
                        splitDataURI.pop();
                        const bf = Buffer.from(splitDataURI.pop() || '', 'base64');
                        const blob = new Blob([bf]);
                        vectaraFiles.push({ blob: blob, fileName: getFileName(file) });
                    }
                }
                try {
                    if (finalDocs.length)
                        await vectara_1.VectaraStore.fromDocuments(finalDocs, embeddings, vectaraArgs);
                    if (vectaraFiles.length) {
                        const vectorStore = new vectara_1.VectaraStore(vectaraArgs);
                        await vectorStore.addFiles(vectaraFiles);
                    }
                    return { numAdded: finalDocs.length, addedDocs: finalDocs };
                }
                catch (e) {
                    throw new Error(e);
                }
            }
        };
        this.label = 'Vectara';
        this.name = 'vectara';
        this.version = 2.0;
        this.type = 'Vectara';
        this.icon = 'vectara.png';
        this.category = 'Vector Stores';
        this.description = 'Upsert embedded data and perform similarity search upon query using Vectara, a LLM-powered search-as-a-service';
        this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever'];
        this.credential = {
            label: 'Connect Credential',
            name: 'credential',
            type: 'credential',
            credentialNames: ['vectaraApi']
        };
        this.inputs = [
            {
                label: 'Document',
                name: 'document',
                type: 'Document',
                list: true,
                optional: true
            },
            {
                label: 'File',
                name: 'file',
                description: 'File to upload to Vectara. Supported file types: https://docs.vectara.com/docs/api-reference/indexing-apis/file-upload/file-upload-filetypes',
                type: 'file',
                optional: true
            },
            {
                label: 'Metadata Filter',
                name: 'filter',
                description: 'Filter to apply to Vectara metadata. Refer to the <a target="_blank" href="https://docs.flowiseai.com/vector-stores/vectara">documentation</a> on how to use Vectara filters with Flowise.',
                type: 'string',
                additionalParams: true,
                optional: true,
                acceptVariable: true
            },
            {
                label: 'Sentences Before',
                name: 'sentencesBefore',
                description: 'Number of sentences to fetch before the matched sentence. Defaults to 2.',
                type: 'number',
                default: 2,
                additionalParams: true,
                optional: true
            },
            {
                label: 'Sentences After',
                name: 'sentencesAfter',
                description: 'Number of sentences to fetch after the matched sentence. Defaults to 2.',
                type: 'number',
                default: 2,
                additionalParams: true,
                optional: true
            },
            {
                label: 'Lambda',
                name: 'lambda',
                description: '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
            },
            {
                label: 'Top K',
                name: 'topK',
                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
            }
        ];
        this.outputs = [
            {
                label: 'Vectara Retriever',
                name: 'retriever',
                baseClasses: this.baseClasses
            },
            {
                label: 'Vectara Vector Store',
                name: 'vectorStore',
                baseClasses: [this.type, ...(0, utils_1.getBaseClasses)(vectara_1.VectaraStore)]
            }
        ];
    }
    async init(nodeData, _, options) {
        const credentialData = await (0, utils_1.getCredentialData)(nodeData.credential ?? '', options);
        const apiKey = (0, utils_1.getCredentialParam)('apiKey', credentialData, nodeData);
        const customerId = (0, utils_1.getCredentialParam)('customerID', credentialData, nodeData);
        const corpusId = (0, utils_1.getCredentialParam)('corpusID', credentialData, nodeData).split(',');
        const vectaraMetadataFilter = nodeData.inputs?.filter;
        const sentencesBefore = nodeData.inputs?.sentencesBefore;
        const sentencesAfter = nodeData.inputs?.sentencesAfter;
        const lambda = nodeData.inputs?.lambda;
        const output = nodeData.outputs?.output;
        const topK = nodeData.inputs?.topK;
        const k = topK ? parseFloat(topK) : 5;
        const mmrK = nodeData.inputs?.mmrK;
        const mmrDiversityBias = nodeData.inputs?.mmrDiversityBias;
        const vectaraArgs = {
            apiKey: apiKey,
            customerId: customerId,
            corpusId: corpusId,
            source: 'flowise'
        };
        const vectaraFilter = {};
        if (vectaraMetadataFilter)
            vectaraFilter.filter = vectaraMetadataFilter;
        if (lambda)
            vectaraFilter.lambda = lambda;
        const vectaraContextConfig = {};
        if (sentencesBefore)
            vectaraContextConfig.sentencesBefore = sentencesBefore;
        if (sentencesAfter)
            vectaraContextConfig.sentencesAfter = sentencesAfter;
        vectaraFilter.contextConfig = vectaraContextConfig;
        const mmrConfig = {};
        mmrConfig.enabled = mmrDiversityBias > 0;
        mmrConfig.mmrTopK = mmrK;
        mmrConfig.diversityBias = mmrDiversityBias;
        vectaraFilter.mmrConfig = mmrConfig;
        const vectorStore = new vectara_1.VectaraStore(vectaraArgs);
        if (output === 'retriever') {
            const retriever = vectorStore.asRetriever(k, vectaraFilter);
            return retriever;
        }
        else if (output === 'vectorStore') {
            ;
            vectorStore.k = k;
            if (vectaraMetadataFilter) {
                ;
                vectorStore.filter = vectaraFilter.filter;
            }
            return vectorStore;
        }
        return vectorStore;
    }
}
const getFileName = (fileBase64) => {
    let fileNames = [];
    if (fileBase64.startsWith('[') && fileBase64.endsWith(']')) {
        const files = JSON.parse(fileBase64);
        for (const file of files) {
            const splitDataURI = file.split(',');
            const filename = splitDataURI[splitDataURI.length - 1].split(':')[1];
            fileNames.push(filename);
        }
        return fileNames.join(', ');
    }
    else {
        const splitDataURI = fileBase64.split(',');
        const filename = splitDataURI[splitDataURI.length - 1].split(':')[1];
        return filename;
    }
};
module.exports = { nodeClass: Vectara_VectorStores };
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