dtamind-components
Version:
Apps integration for Dtamind. Contain Nodes and Credentials.
104 lines • 4.22 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
const score_threshold_1 = require("langchain/retrievers/score_threshold");
const src_1 = require("../../../src");
class SimilarityThresholdRetriever_Retrievers {
constructor() {
this.label = 'Similarity Score Threshold Retriever';
this.name = 'similarityThresholdRetriever';
this.version = 2.0;
this.type = 'SimilarityThresholdRetriever';
this.icon = 'similaritythreshold.svg';
this.category = 'Retrievers';
this.description = 'Return results based on the minimum similarity percentage';
this.baseClasses = [this.type, 'BaseRetriever'];
this.inputs = [
{
label: 'Vector Store',
name: 'vectorStore',
type: 'VectorStore'
},
{
label: 'Query',
name: 'query',
type: 'string',
description: 'Query to retrieve documents from retriever. If not specified, user question will be used',
optional: true,
acceptVariable: true
},
{
label: 'Minimum Similarity Score (%)',
name: 'minSimilarityScore',
description: 'Finds results with at least this similarity score',
type: 'number',
default: 80,
step: 1
},
{
label: 'Max K',
name: 'maxK',
description: `The maximum number of results to fetch`,
type: 'number',
default: 20,
step: 1,
additionalParams: true
},
{
label: 'K Increment',
name: 'kIncrement',
description: `How much to increase K by each time. It'll fetch N results, then N + kIncrement, then N + kIncrement * 2, etc.`,
type: 'number',
default: 2,
step: 1,
additionalParams: true
}
];
this.outputs = [
{
label: 'Similarity Threshold Retriever',
name: 'retriever',
baseClasses: this.baseClasses
},
{
label: 'Document',
name: 'document',
description: 'Array of document objects containing metadata and pageContent',
baseClasses: ['Document', '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 minSimilarityScore = nodeData.inputs?.minSimilarityScore;
const query = nodeData.inputs?.query;
const maxK = nodeData.inputs?.maxK;
const kIncrement = nodeData.inputs?.kIncrement;
const output = nodeData.outputs?.output;
const retriever = score_threshold_1.ScoreThresholdRetriever.fromVectorStore(vectorStore, {
minSimilarityScore: minSimilarityScore ? minSimilarityScore / 100 : 0.9,
maxK: maxK ? parseInt(maxK, 10) : 100,
kIncrement: kIncrement ? parseInt(kIncrement, 10) : 2
});
retriever.filter = vectorStore?.lc_kwargs?.filter ?? vectorStore.filter;
if (output === 'retriever')
return retriever;
else if (output === 'document')
return await retriever.getRelevantDocuments(query ? query : input);
else if (output === 'text') {
let finaltext = '';
const docs = await retriever.getRelevantDocuments(query ? query : input);
for (const doc of docs)
finaltext += `${doc.pageContent}\n`;
return (0, src_1.handleEscapeCharacters)(finaltext, false);
}
return retriever;
}
}
module.exports = { nodeClass: SimilarityThresholdRetriever_Retrievers };
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