dtamind-components
Version:
DTAmindai Components
169 lines • 7.06 kB
JavaScript
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
const documents_1 = require("@langchain/core/documents");
const vectorstores_1 = require("@langchain/core/vectorstores");
const src_1 = require("../../../src");
const zod_1 = require("zod");
const commonUtils_1 = require("../../sequentialagents/commonUtils");
const queryPrefix = 'query';
const defaultPrompt = `Extract keywords from the query: {{${queryPrefix}}}`;
class ExtractMetadataRetriever_Retrievers {
constructor() {
this.label = 'Extract Metadata Retriever';
this.name = 'extractMetadataRetriever';
this.version = 1.0;
this.type = 'ExtractMetadataRetriever';
this.icon = 'dynamicMetadataRetriever.svg';
this.category = 'Retrievers';
this.description = 'Extract keywords/metadata from the query and use it to filter documents';
this.baseClasses = [this.type, 'BaseRetriever'];
this.inputs = [
{
label: 'Vector Store',
name: 'vectorStore',
type: 'VectorStore'
},
{
label: 'Chat Model',
name: 'model',
type: 'BaseChatModel'
},
{
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: 'Prompt',
name: 'dynamicMetadataFilterRetrieverPrompt',
type: 'string',
description: 'Prompt to extract metadata from query',
rows: 4,
additionalParams: true,
default: defaultPrompt
},
{
label: 'JSON Structured Output',
name: 'dynamicMetadataFilterRetrieverStructuredOutput',
type: 'datagrid',
description: 'Instruct the model to give output in a JSON structured schema. This output will be used as the metadata filter for connected vector store',
datagrid: [
{ field: 'key', headerName: 'Key', editable: true },
{
field: 'type',
headerName: 'Type',
type: 'singleSelect',
valueOptions: ['String', 'String Array', 'Number', 'Boolean', 'Enum'],
editable: true
},
{ field: 'enumValues', headerName: 'Enum Values', editable: true },
{ field: 'description', headerName: 'Description', flex: 1, editable: true }
],
optional: true,
additionalParams: true
},
{
label: 'Top K',
name: 'topK',
description: 'Number of top results to fetch. Default to vector store topK',
placeholder: '4',
type: 'number',
additionalParams: true,
optional: true
}
];
this.outputs = [
{
label: 'Extract Metadata 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;
let llm = nodeData.inputs?.model;
const llmStructuredOutput = nodeData.inputs?.dynamicMetadataFilterRetrieverStructuredOutput;
const topK = nodeData.inputs?.topK;
const dynamicMetadataFilterRetrieverPrompt = nodeData.inputs?.dynamicMetadataFilterRetrieverPrompt;
const query = nodeData.inputs?.query;
const finalInputQuery = query ? query : input;
const output = nodeData.outputs?.output;
if (llmStructuredOutput && llmStructuredOutput !== '[]') {
try {
const structuredOutput = zod_1.z.object((0, commonUtils_1.convertStructuredSchemaToZod)(llmStructuredOutput));
// @ts-ignore
llm = llm.withStructuredOutput(structuredOutput);
}
catch (exception) {
console.error(exception);
}
}
const retriever = DynamicMetadataRetriever.fromVectorStore(vectorStore, {
structuredLLM: llm,
prompt: dynamicMetadataFilterRetrieverPrompt,
topK: topK ? parseInt(topK, 10) : vectorStore?.k ?? 4
});
retriever.filter = vectorStore?.lc_kwargs?.filter ?? vectorStore.filter;
if (output === 'retriever')
return retriever;
else if (output === 'document')
return await retriever.getRelevantDocuments(finalInputQuery);
else if (output === 'text') {
let finaltext = '';
const docs = await retriever.getRelevantDocuments(finalInputQuery);
for (const doc of docs)
finaltext += `${doc.pageContent}\n`;
return (0, src_1.handleEscapeCharacters)(finaltext, false);
}
return retriever;
}
}
class DynamicMetadataRetriever extends vectorstores_1.VectorStoreRetriever {
constructor(input) {
super(input);
this.topK = 4;
this.prompt = '';
this.topK = input.topK ?? this.topK;
this.structuredLLM = input.structuredLLM ?? this.structuredLLM;
this.prompt = input.prompt ?? this.prompt;
}
async getFilter(query) {
const structuredResponse = await this.structuredLLM.invoke(this.prompt.replace(`{{${queryPrefix}}}`, query));
return structuredResponse;
}
async getRelevantDocuments(query) {
const newFilter = await this.getFilter(query);
// @ts-ignore
this.filter = { ...this.filter, ...newFilter };
const results = await this.vectorStore.similaritySearchWithScore(query, this.topK, this.filter);
const finalDocs = [];
for (const result of results) {
finalDocs.push(new documents_1.Document({
pageContent: result[0].pageContent,
metadata: result[0].metadata
}));
}
return finalDocs;
}
static fromVectorStore(vectorStore, options) {
return new this({ ...options, vectorStore });
}
}
module.exports = { nodeClass: ExtractMetadataRetriever_Retrievers };
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