dtamind-components
Version:
Apps integration for Dtamind. Contain Nodes and Credentials.
117 lines • 4.71 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
const contextual_compression_1 = require("langchain/retrievers/contextual_compression");
const ReciprocalRankFusion_1 = require("./ReciprocalRankFusion");
const utils_1 = require("../../../src/utils");
class RRFRetriever_Retrievers {
constructor() {
this.label = 'Reciprocal Rank Fusion Retriever';
this.name = 'RRFRetriever';
this.version = 1.0;
this.type = 'RRFRetriever';
this.icon = 'rrfRetriever.svg';
this.category = 'Retrievers';
this.description = 'Reciprocal Rank Fusion to re-rank search results by multiple query generation.';
this.baseClasses = [this.type, 'BaseRetriever'];
this.inputs = [
{
label: 'Vector Store Retriever',
name: 'baseRetriever',
type: 'VectorStoreRetriever'
},
{
label: 'Language Model',
name: 'model',
type: 'BaseLanguageModel'
},
{
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: 'Query Count',
name: 'queryCount',
description: 'Number of synthetic queries to generate. Default to 4',
placeholder: '4',
type: 'number',
default: 4,
additionalParams: true,
optional: true
},
{
label: 'Top K',
name: 'topK',
description: 'Number of top results to fetch. Default to the TopK of the Base Retriever',
placeholder: '0',
type: 'number',
additionalParams: true,
optional: true
},
{
label: 'Constant',
name: 'c',
description: 'A constant added to the rank, controlling the balance between the importance of high-ranked items and the consideration given to lower-ranked items.\n' +
'Default is 60',
placeholder: '60',
type: 'number',
default: 60,
additionalParams: true,
optional: true
}
];
this.outputs = [
{
label: 'Reciprocal Rank Fusion 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 llm = nodeData.inputs?.model;
const baseRetriever = nodeData.inputs?.baseRetriever;
const query = nodeData.inputs?.query;
const queryCount = nodeData.inputs?.queryCount;
const q = queryCount ? parseFloat(queryCount) : 4;
const topK = nodeData.inputs?.topK;
const k = topK ? parseFloat(topK) : baseRetriever.k ?? 4;
const constantC = nodeData.inputs?.c;
const c = topK ? parseFloat(constantC) : 60;
const output = nodeData.outputs?.output;
const ragFusion = new ReciprocalRankFusion_1.ReciprocalRankFusion(llm, baseRetriever, q, k, c);
const retriever = new contextual_compression_1.ContextualCompressionRetriever({
baseCompressor: ragFusion,
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: RRFRetriever_Retrievers };
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