agsamantha/node_modules/langchain/dist/retrievers/multi_query.cjs

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2024-10-02 15:15:21 -05:00
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.MultiQueryRetriever = void 0;
const retrievers_1 = require("@langchain/core/retrievers");
const output_parsers_1 = require("@langchain/core/output_parsers");
const prompts_1 = require("@langchain/core/prompts");
const llm_chain_js_1 = require("../chains/llm_chain.cjs");
class LineListOutputParser extends output_parsers_1.BaseOutputParser {
constructor() {
super(...arguments);
Object.defineProperty(this, "lc_namespace", {
enumerable: true,
configurable: true,
writable: true,
value: ["langchain", "retrievers", "multiquery"]
});
}
static lc_name() {
return "LineListOutputParser";
}
async parse(text) {
const startKeyIndex = text.indexOf("<questions>");
const endKeyIndex = text.indexOf("</questions>");
const questionsStartIndex = startKeyIndex === -1 ? 0 : startKeyIndex + "<questions>".length;
const questionsEndIndex = endKeyIndex === -1 ? text.length : endKeyIndex;
const lines = text
.slice(questionsStartIndex, questionsEndIndex)
.trim()
.split("\n")
.filter((line) => line.trim() !== "");
return { lines };
}
getFormatInstructions() {
throw new Error("Not implemented.");
}
}
// Create template
const DEFAULT_QUERY_PROMPT = /* #__PURE__ */ new prompts_1.PromptTemplate({
inputVariables: ["question", "queryCount"],
template: `You are an AI language model assistant. Your task is
to generate {queryCount} different versions of the given user
question to retrieve relevant documents from a vector database.
By generating multiple perspectives on the user question,
your goal is to help the user overcome some of the limitations
of distance-based similarity search.
Provide these alternative questions separated by newlines between XML tags. For example:
<questions>
Question 1
Question 2
Question 3
</questions>
Original question: {question}`,
});
/**
* @example
* ```typescript
* const retriever = new MultiQueryRetriever.fromLLM({
* llm: new ChatAnthropic({}),
* retriever: new MemoryVectorStore().asRetriever(),
* verbose: true,
* });
* const retrievedDocs = await retriever.getRelevantDocuments(
* "What are mitochondria made of?",
* );
* ```
*/
class MultiQueryRetriever extends retrievers_1.BaseRetriever {
static lc_name() {
return "MultiQueryRetriever";
}
constructor(fields) {
super(fields);
Object.defineProperty(this, "lc_namespace", {
enumerable: true,
configurable: true,
writable: true,
value: ["langchain", "retrievers", "multiquery"]
});
Object.defineProperty(this, "retriever", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "llmChain", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "queryCount", {
enumerable: true,
configurable: true,
writable: true,
value: 3
});
Object.defineProperty(this, "parserKey", {
enumerable: true,
configurable: true,
writable: true,
value: "lines"
});
Object.defineProperty(this, "documentCompressor", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "documentCompressorFilteringFn", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
this.retriever = fields.retriever;
this.llmChain = fields.llmChain;
this.queryCount = fields.queryCount ?? this.queryCount;
this.parserKey = fields.parserKey ?? this.parserKey;
this.documentCompressor = fields.documentCompressor;
this.documentCompressorFilteringFn = fields.documentCompressorFilteringFn;
}
static fromLLM(fields) {
const { retriever, llm, prompt = DEFAULT_QUERY_PROMPT, queryCount, parserKey, ...rest } = fields;
const outputParser = new LineListOutputParser();
const llmChain = new llm_chain_js_1.LLMChain({ llm, prompt, outputParser });
return new this({ retriever, llmChain, queryCount, parserKey, ...rest });
}
// Generate the different queries for each retrieval, using our llmChain
async _generateQueries(question, runManager) {
const response = await this.llmChain.call({ question, queryCount: this.queryCount }, runManager?.getChild());
const lines = response.text[this.parserKey] || [];
if (this.verbose) {
console.log(`Generated queries: ${lines}`);
}
return lines;
}
// Retrieve documents using the original retriever
async _retrieveDocuments(queries, runManager) {
const documents = [];
await Promise.all(queries.map(async (query) => {
const docs = await this.retriever.getRelevantDocuments(query, runManager?.getChild());
documents.push(...docs);
}));
return documents;
}
// Deduplicate the documents that were returned in multiple retrievals
_uniqueUnion(documents) {
const uniqueDocumentsDict = {};
for (const doc of documents) {
const key = `${doc.pageContent}:${JSON.stringify(Object.entries(doc.metadata).sort())}`;
uniqueDocumentsDict[key] = doc;
}
const uniqueDocuments = Object.values(uniqueDocumentsDict);
return uniqueDocuments;
}
async _getRelevantDocuments(question, runManager) {
const queries = await this._generateQueries(question, runManager);
const documents = await this._retrieveDocuments(queries, runManager);
const uniqueDocuments = this._uniqueUnion(documents);
let outputDocs = uniqueDocuments;
if (this.documentCompressor && uniqueDocuments.length) {
outputDocs = await this.documentCompressor.compressDocuments(uniqueDocuments, question, runManager?.getChild());
if (this.documentCompressorFilteringFn) {
outputDocs = this.documentCompressorFilteringFn(outputDocs);
}
}
return outputDocs;
}
}
exports.MultiQueryRetriever = MultiQueryRetriever;