dtamind-components
Version:
DTAmindai Components
111 lines • 4.67 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
const contextual_compression_1 = require("langchain/retrievers/contextual_compression");
const embeddings_filter_1 = require("langchain/retrievers/document_compressors/embeddings_filter");
const utils_1 = require("../../../src/utils");
class EmbeddingsFilterRetriever_Retrievers {
constructor() {
this.label = 'Embeddings Filter Retriever';
this.name = 'embeddingsFilterRetriever';
this.version = 1.0;
this.type = 'EmbeddingsFilterRetriever';
this.icon = 'compressionRetriever.svg';
this.category = 'Retrievers';
this.description = 'A document compressor that uses embeddings to drop documents unrelated to the query';
this.baseClasses = [this.type, 'BaseRetriever'];
this.inputs = [
{
label: 'Vector Store Retriever',
name: 'baseRetriever',
type: 'VectorStoreRetriever'
},
{
label: 'Embeddings',
name: 'embeddings',
type: 'Embeddings'
},
{
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: 'Similarity Threshold',
name: 'similarityThreshold',
description: 'Threshold for determining when two documents are similar enough to be considered redundant. Must be specified if `k` is not set',
type: 'number',
default: 0.8,
step: 0.1,
optional: true
},
{
label: 'K',
name: 'k',
description: 'The number of relevant documents to return. Can be explicitly set to undefined, in which case similarity_threshold must be specified. Defaults to 20',
type: 'number',
default: 20,
step: 1,
optional: true,
additionalParams: true
}
];
this.outputs = [
{
label: 'Embeddings Filter 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 baseRetriever = nodeData.inputs?.baseRetriever;
const embeddings = nodeData.inputs?.embeddings;
const query = nodeData.inputs?.query;
const similarityThreshold = nodeData.inputs?.similarityThreshold;
const k = nodeData.inputs?.k;
const output = nodeData.outputs?.output;
if (k === undefined && similarityThreshold === undefined) {
throw new Error(`Must specify one of "k" or "similarity_threshold".`);
}
const similarityThresholdNumber = similarityThreshold ? parseFloat(similarityThreshold) : 0.8;
const kNumber = k ? parseFloat(k) : undefined;
const baseCompressor = new embeddings_filter_1.EmbeddingsFilter({
embeddings: embeddings,
similarityThreshold: similarityThresholdNumber,
k: kNumber
});
const retriever = new contextual_compression_1.ContextualCompressionRetriever({
baseCompressor,
baseRetriever: baseRetriever
});
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, utils_1.handleEscapeCharacters)(finaltext, false);
}
return retriever;
}
}
module.exports = { nodeClass: EmbeddingsFilterRetriever_Retrievers };
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