dtamind-components
Version:
Apps integration for Dtamind. Contain Nodes and Credentials.
92 lines • 3.55 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
const utils_1 = require("../../../src/utils");
class VectorStoreToDocument_DocumentLoaders {
constructor() {
this.label = 'VectorStore To Document';
this.name = 'vectorStoreToDocument';
this.version = 2.0;
this.type = 'Document';
this.icon = 'vectorretriever.svg';
this.category = 'Document Loaders';
this.description = 'Search documents with scores from vector store';
this.baseClasses = [this.type];
this.inputs = [
{
label: 'Vector Store',
name: 'vectorStore',
type: 'VectorStore'
},
{
label: 'Query',
name: 'query',
type: 'string',
description: 'Query to retrieve documents from vector database. If not specified, user question will be used',
optional: true,
acceptVariable: true
},
{
label: 'Minimum Score (%)',
name: 'minScore',
type: 'number',
optional: true,
placeholder: '75',
step: 1,
description: 'Minumum score for embeddings documents to be included'
}
];
this.outputs = [
{
label: 'Document',
name: 'document',
description: 'Array of document objects containing metadata and pageContent',
baseClasses: [...this.baseClasses, 'json']
},
{
label: 'Text',
name: 'text',
description: 'Concatenated string from pageContent of documents',
baseClasses: ['string', 'json']
}
];
}
async init(nodeData, input) {
const vectorStore = nodeData.inputs?.vectorStore;
const minScore = nodeData.inputs?.minScore;
const query = nodeData.inputs?.query;
const output = nodeData.outputs?.output;
const topK = vectorStore?.k ?? 4;
const _filter = vectorStore?.filter;
// If it is already pre-defined in lc_kwargs, then don't pass it again
const filter = vectorStore.lc_kwargs.filter ? undefined : _filter;
if (vectorStore.lc_kwargs.filter) {
;
vectorStore.filter = vectorStore.lc_kwargs.filter;
}
const docs = await vectorStore.similaritySearchWithScore(query ?? input, topK, filter);
// eslint-disable-next-line no-console
console.log('\x1b[94m\x1b[1m\n*****VectorStore Documents*****\n\x1b[0m\x1b[0m');
// eslint-disable-next-line no-console
console.log(JSON.stringify(docs, null, 2));
if (output === 'document') {
let finaldocs = [];
for (const doc of docs) {
if (minScore && doc[1] < minScore / 100)
continue;
finaldocs.push(doc[0]);
}
return finaldocs;
}
else {
let finaltext = '';
for (const doc of docs) {
if (minScore && doc[1] < minScore / 100)
continue;
finaltext += `${doc[0].pageContent}\n`;
}
return (0, utils_1.handleEscapeCharacters)(finaltext, false);
}
}
}
module.exports = { nodeClass: VectorStoreToDocument_DocumentLoaders };
//# sourceMappingURL=VectorStoreToDocument.js.map