173 lines
6.4 KiB
JavaScript
173 lines
6.4 KiB
JavaScript
"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.MultiQueryRetriever = void 0;
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const retrievers_1 = require("@langchain/core/retrievers");
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const output_parsers_1 = require("@langchain/core/output_parsers");
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const prompts_1 = require("@langchain/core/prompts");
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const llm_chain_js_1 = require("../chains/llm_chain.cjs");
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class LineListOutputParser extends output_parsers_1.BaseOutputParser {
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constructor() {
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super(...arguments);
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Object.defineProperty(this, "lc_namespace", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: ["langchain", "retrievers", "multiquery"]
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});
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}
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static lc_name() {
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return "LineListOutputParser";
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}
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async parse(text) {
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const startKeyIndex = text.indexOf("<questions>");
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const endKeyIndex = text.indexOf("</questions>");
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const questionsStartIndex = startKeyIndex === -1 ? 0 : startKeyIndex + "<questions>".length;
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const questionsEndIndex = endKeyIndex === -1 ? text.length : endKeyIndex;
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const lines = text
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.slice(questionsStartIndex, questionsEndIndex)
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.trim()
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.split("\n")
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.filter((line) => line.trim() !== "");
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return { lines };
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}
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getFormatInstructions() {
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throw new Error("Not implemented.");
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}
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}
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// Create template
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const DEFAULT_QUERY_PROMPT = /* #__PURE__ */ new prompts_1.PromptTemplate({
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inputVariables: ["question", "queryCount"],
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template: `You are an AI language model assistant. Your task is
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to generate {queryCount} different versions of the given user
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question to retrieve relevant documents from a vector database.
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By generating multiple perspectives on the user question,
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your goal is to help the user overcome some of the limitations
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of distance-based similarity search.
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Provide these alternative questions separated by newlines between XML tags. For example:
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<questions>
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Question 1
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Question 2
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Question 3
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</questions>
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Original question: {question}`,
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});
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/**
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* @example
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* ```typescript
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* const retriever = new MultiQueryRetriever.fromLLM({
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* llm: new ChatAnthropic({}),
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* retriever: new MemoryVectorStore().asRetriever(),
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* verbose: true,
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* });
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* const retrievedDocs = await retriever.getRelevantDocuments(
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* "What are mitochondria made of?",
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* );
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* ```
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*/
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class MultiQueryRetriever extends retrievers_1.BaseRetriever {
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static lc_name() {
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return "MultiQueryRetriever";
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}
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constructor(fields) {
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super(fields);
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Object.defineProperty(this, "lc_namespace", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: ["langchain", "retrievers", "multiquery"]
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});
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Object.defineProperty(this, "retriever", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "llmChain", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "queryCount", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: 3
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});
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Object.defineProperty(this, "parserKey", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: "lines"
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});
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Object.defineProperty(this, "documentCompressor", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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Object.defineProperty(this, "documentCompressorFilteringFn", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: void 0
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});
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this.retriever = fields.retriever;
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this.llmChain = fields.llmChain;
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this.queryCount = fields.queryCount ?? this.queryCount;
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this.parserKey = fields.parserKey ?? this.parserKey;
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this.documentCompressor = fields.documentCompressor;
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this.documentCompressorFilteringFn = fields.documentCompressorFilteringFn;
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}
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static fromLLM(fields) {
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const { retriever, llm, prompt = DEFAULT_QUERY_PROMPT, queryCount, parserKey, ...rest } = fields;
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const outputParser = new LineListOutputParser();
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const llmChain = new llm_chain_js_1.LLMChain({ llm, prompt, outputParser });
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return new this({ retriever, llmChain, queryCount, parserKey, ...rest });
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}
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// Generate the different queries for each retrieval, using our llmChain
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async _generateQueries(question, runManager) {
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const response = await this.llmChain.call({ question, queryCount: this.queryCount }, runManager?.getChild());
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const lines = response.text[this.parserKey] || [];
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if (this.verbose) {
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console.log(`Generated queries: ${lines}`);
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}
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return lines;
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}
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// Retrieve documents using the original retriever
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async _retrieveDocuments(queries, runManager) {
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const documents = [];
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await Promise.all(queries.map(async (query) => {
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const docs = await this.retriever.getRelevantDocuments(query, runManager?.getChild());
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documents.push(...docs);
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}));
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return documents;
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}
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// Deduplicate the documents that were returned in multiple retrievals
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_uniqueUnion(documents) {
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const uniqueDocumentsDict = {};
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for (const doc of documents) {
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const key = `${doc.pageContent}:${JSON.stringify(Object.entries(doc.metadata).sort())}`;
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uniqueDocumentsDict[key] = doc;
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}
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const uniqueDocuments = Object.values(uniqueDocumentsDict);
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return uniqueDocuments;
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}
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async _getRelevantDocuments(question, runManager) {
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const queries = await this._generateQueries(question, runManager);
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const documents = await this._retrieveDocuments(queries, runManager);
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const uniqueDocuments = this._uniqueUnion(documents);
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let outputDocs = uniqueDocuments;
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if (this.documentCompressor && uniqueDocuments.length) {
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outputDocs = await this.documentCompressor.compressDocuments(uniqueDocuments, question, runManager?.getChild());
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if (this.documentCompressorFilteringFn) {
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outputDocs = this.documentCompressorFilteringFn(outputDocs);
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}
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}
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return outputDocs;
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}
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}
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exports.MultiQueryRetriever = MultiQueryRetriever;
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