dtamind-components
Version:
DTAmindai Components
180 lines • 7.33 kB
JavaScript
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
const vectara_1 = require("@langchain/community/vectorstores/vectara");
const utils_1 = require("../../../src/utils");
const src_1 = require("../../../src");
class VectaraUpload_VectorStores {
constructor() {
this.label = 'Vectara Upload File';
this.name = 'vectaraUpload';
this.version = 1.0;
this.type = 'Vectara';
this.icon = 'vectara.png';
this.category = 'Vector Stores';
this.description = 'Upload files to Vectara';
this.baseClasses = [this.type, 'VectorStoreRetriever', 'BaseRetriever'];
this.badge = 'DEPRECATING';
this.credential = {
label: 'Connect Credential',
name: 'credential',
type: 'credential',
credentialNames: ['vectaraApi']
};
this.inputs = [
{
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'
},
{
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
},
{
label: 'Sentences Before',
name: 'sentencesBefore',
description: 'Number of sentences to fetch before the matched sentence. Defaults to 2.',
type: 'number',
additionalParams: true,
optional: true
},
{
label: 'Sentences After',
name: 'sentencesAfter',
description: 'Number of sentences to fetch after the matched sentence. Defaults to 2.',
type: 'number',
additionalParams: true,
optional: true
},
{
label: 'Lambda',
name: 'lambda',
description: 'Improves retrieval accuracy by adjusting the balance (from 0 to 1) between neural search and keyword-based search factors.',
type: 'number',
additionalParams: true,
optional: true
},
{
label: 'Top K',
name: 'topK',
description: 'Number of top results to fetch. Defaults to 4',
placeholder: '4',
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 fileBase64 = nodeData.inputs?.file;
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 ? parseInt(topK, 10) : 4;
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;
let files = [];
const vectaraFiles = [];
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) {
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) {
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) });
}
}
const vectorStore = new vectara_1.VectaraStore(vectaraArgs);
await vectorStore.addFiles(vectaraFiles);
if (output === 'retriever') {
const retriever = vectorStore.asRetriever(k, vectaraFilter);
return retriever;
}
else if (output === 'vectorStore') {
;
vectorStore.k = k;
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: VectaraUpload_VectorStores };
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