only export getDocumentVectors

backend/src/document.ts

@@ -86,7 +86,7 @@ function getDocumentMetas(folder: string) {
 }
 
 // store vectorised documents for each level as array
-const vectorisedDocuments = (() => {
+const documentVectors = (() => {
 	let docs: DocumentsVector[] = [];
 	return {
 		get: () => docs,
@@ -95,6 +95,7 @@ const vectorisedDocuments = (() => {
 		},
 	};
 })();
+const getDocumentVectors = documentVectors.get;
 
 // create and store the document vectors for each level
 async function initDocumentVectors() {
@@ -120,7 +121,7 @@ async function initDocumentVectors() {
 			docVector,
 		});
 	}
-	vectorisedDocuments.set(docVectors);
+	documentVectors.set(docVectors);
 	console.debug(
 		`Initialised document vectors for each level. count=${docVectors.length}`
 	);
@@ -131,5 +132,5 @@ export {
 	getLevelDocuments,
 	getSandboxDocumentMetas,
 	initDocumentVectors,
-	vectorisedDocuments,
+	getDocumentVectors,
 };

backend/src/langchain.ts

@@ -3,7 +3,7 @@ import { ChatOpenAI } from 'langchain/chat_models/openai';
 import { OpenAI } from 'langchain/llms/openai';
 import { PromptTemplate } from 'langchain/prompts';
 
-import { vectorisedDocuments } from './document';
+import { getDocumentVectors } from './document';
 import { CHAT_MODELS, ChatAnswer } from './models/chat';
 import { PromptEvaluationChainReply, QaChainReply } from './models/langchain';
 import { LEVEL_NAMES } from './models/level';
@@ -39,7 +39,7 @@ function getChatModel() {
 
 function initQAModel(level: LEVEL_NAMES, Prompt: string) {
 	const openAIApiKey = getOpenAIKey();
-	const documentVectors = vectorisedDocuments.get()[level].docVector;
+	const documentVectors = getDocumentVectors()[level].docVector;
 	// use gpt-4 if avaliable to apiKey
 	const modelName = getChatModel();
 

backend/test/unit/langchain.ts/initialiseQAModel.test.ts

@@ -3,6 +3,7 @@ import { RetrievalQAChain } from 'langchain/chains';
 import { ChatOpenAI } from 'langchain/chat_models/openai';
 import { PromptTemplate } from 'langchain/prompts';
 
+import { getDocumentVectors } from '@src/document';
 import { queryDocuments } from '@src/langchain';
 import { LEVEL_NAMES } from '@src/models/level';
 import { getOpenAIKey } from '@src/openai';
@@ -59,28 +60,13 @@ jest.mock('@src/openai', () => {
 	};
 });
 
-const mockGetVectorisedDocuments = jest.fn();
-jest.mock('@src/langchain', () => {
-	const originalModule =
-		jest.requireActual<typeof import('@src/langchain')>('@src/langchain');
-	return {
-		...originalModule,
-		vectorisedDocuments: {
-			get: () => mockGetVectorisedDocuments(),
-			set: jest.fn(),
-		},
-	};
-});
-mockGetVectorisedDocuments.mockReturnValue([]);
-
-jest.mock('@src/document', () => ({
-	vectorisedDocuments: {
-		get: jest
-			.fn()
-			.mockReturnValue([{ docVector: { asRetriever: () => 'retriever' } }]),
-		set: jest.fn(),
-	},
-}));
+jest.mock('@src/document');
+const mockGetDocumentVectors = getDocumentVectors as unknown as jest.Mock<
+	() => { docVector: { asRetriever: () => string } }[]
+>;
+mockGetDocumentVectors.mockReturnValue([
+	{ docVector: { asRetriever: () => 'retriever' } },
+]);
 
 beforeEach(() => {
 	mockFromLLM.mockImplementation(() => mockRetrievalQAChain); // this is weird