Could not import faiss-node. Please install as a dependency with npm install -S faiss-node

Checked other resources

[X] I added a very descriptive title to this issue.
[X] I searched the LangChain.js documentation with the integrated search.
[X] I used the GitHub search to find a similar question and didn't find it.
[X] I am sure that this is a bug in LangChain.js rather than my code.
[X] The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package).

Example Code

import { APIGatewayProxyEvent, APIGatewayProxyResult } from "aws-lambda"; import { BedrockChat } from "@langchain/community/chat_models/bedrock"; import { PromptTemplate } from "@langchain/core/prompts"; import { RunnableLambda, RunnablePassthrough, RunnableSequence, } from "@langchain/core/runnables"; import { StringOutputParser } from "@langchain/core/output_parsers"; / eslint-disable @typescript-eslint/no-non-null-assertion / import { BedrockEmbeddings } from "@langchain/community/embeddings/bedrock"; import { FaissStore } from "@langchain/community/vectorstores/faiss"; import { formatDocumentsAsString } from "langchain/util/document"; import { TextLoader } from "langchain/document_loaders/fs/text"; import express = require("express"); import { Request, Response } from "express"; class TestService { async Test(searchParam:string): Promise { try { const result = await isPiiFunc(searchParam); console.log('========RESult=======', result); if (result === 'NO') { const result = await ragFunc(searchParam); console.log('=========result===========', result); } else { console.log("Your query should not contain any personal information such as name, address, or date. Please ensure your input adheres to this guideline."); } } catch (error) { console.log("Error >>>>>..", error); throw error; } } } const testService = new TestService(); console.log("====here2====="); const app = express(); console.log("====here3====="); app.use(express.json()); console.log("====here4====="); const PORT = 8000; console.log("====here5====="); app.post('/search', async (req : Request, res: Response) => { try { const { searchParam } = req.body; if(!searchParam) { res.status(400).json({error: 'Search parameter is required'}); } const result = await testService.Test(searchParam); res.status(200).json(result); } catch (error) { res.status(500).json({error: 'Internal Server Error'}); } }) // export const handler = async(event: APIGatewayProxyEvent) : Promise => { // try { // if(!event.body) { // return { // statusCode: 400, // headers: { // 'Content-type':'application/json' // }, // body: JSON.stringify({ // message:"Empty request body" // }) // } // } // const reqBody :any = JSON.parse(event.body); // const searchParam: string = reqBody.searchParam // console.log('Search>>>', searchParam); // const response = await Test(searchParam); // return { // statusCode: 200, // headers: { // "Access-Control-Allow-Headers" : "Content-Type", // "Access-Control-Allow-Origin": "", // "Access-Control-Allow-Methods": "OPTIONS,POST,GET" // }, // body: JSON.stringify(response) // } // } catch (error) { // return { // statusCode: 500, // headers: { // "Access-Control-Allow-Headers" : "Content-Type", // "Access-Control-Allow-Origin": "", // "Access-Control-Allow-Methods": "OPTIONS,POST,GET" // }, // body: JSON.stringify(error) // } // } // } // async function Test(searchParam:string) { // try { // const result = await isPiiFunc(searchParam); // console.log('========RESult=======', result); // if (result === 'NO') { // const result = await ragFunc(searchParam); // console.log('=========result===========', result); // } else { // console.log("Your query should not contain any personal information such as name, address, or date. Please ensure your input adheres to this guideline."); // } // } catch (error) { // console.log("Error >>>>>..", error); // throw error; // } // } async function isPiiFunc(question: string): Promise { console.log("Here"); try { const piiPromptTemplate = Human: Based on the provided user input, check if the user input contains personal information that includes name, address or date.Question: ${question}From the above sentence, identify if the above query contains a human's name or address or any date, then return 'YES', else return 'NO'.You are a bot that strictly answers in YES or NO. The only accepted response is YES/NO. Do not explain your answer. Do not answer the query.Assistant:; const model = new BedrockChat({ model: 'anthropic.claude-3-sonnet-20240229-v1:0', region: "us-east-1", modelKwargs: { temperature: 0, } }); const prompt = new PromptTemplate({ template: piiPromptTemplate, inputVariables: ['question'], }); const retrievalChain = RunnableSequence.from([ { question: new RunnablePassthrough() }, prompt, model, new StringOutputParser(), ]); const chain = retrievalChain.invoke(question); return chain; } catch (error: any) { console.log("=======Error=============", error.message); throw error; } } async function ragFunc(question: string): Promise { const embeddings = new BedrockEmbeddings({ model: "amazon.titan-embed-text-v1", // Default value }); console.log("==============embeddings==========", embeddings); console.log("Here"); const res = await embeddings.embedQuery(question); console.log("==============res==========", {res}); console.log("here2"); // Create docs with a loader const loader = new TextLoader("example.txt"); const docs = await loader.load(); const vectorStore = await FaissStore.fromDocuments( docs, embeddings ); console.log("=====Vectorstore==========", vectorStore); // const resultOne = await vectorStore.similaritySearch(question, 1); // console.log("==============resultOne==========", resultOne); // Initialize a retriever wrapper around the vector store const retriever = vectorStore.asRetriever({ k: 1, searchType: "similarity", }); console.log("==============retriever==========", retriever); const promptTemplate = Human:Context:{context}Relevant documents is provided above in the context. Please look into those and select the best answer for the question asked. DO NOT answer using information that is NOT present in the context.If you do not know the answer, just say 'I dont know'. Only provide relevant answers.Now here is the question for you to answer: {question}You will only return a JSON object containing the key answer.Pull relevant sentences from the documents that pertain to the answer and write them inside answer JSON key.Include guidelines, comparisons, challenges and examples pertaining to the question if the documents contains any of them without enclosing them in quotation marks. Ensure that you provide examples from the documents to every point you mention. Do not use any such similar phrases such as "According to the documents" or "The documents state that" because a human would not talk like that. Do not talk as if you are refering to any documents. Remember to rephrase your answer in such a way as if you are talking to a human. Elaborate every point in detail. Make use of second person pronouns or third person pronouns.The JSON object must strictly not contain any other keys. Please display only the answer and not the references from the douments.Assistant:; const model = new BedrockChat({ model: 'anthropic.claude-3-sonnet-20240229-v1:0', region: 'us-east-1', streaming: true, modelKwargs: { temperature: 0.1, //max_tokens_to_sample: 2048 }, }); const prompt = new PromptTemplate({ template: promptTemplate, inputVariables: ['context','question'], }); console.log('=============PROMPT==============', prompt); // const retrievalChain = RunnableSequence.from([ // { question: new RunnablePassthrough() }, // prompt, // model, // retriever, // new StringOutputParser(), // ]); const retrievalChain = RunnableSequence.from([ RunnableLambda.from((input: any) => input.question), { context: retriever.pipe(formatDocumentsAsString), question: new RunnablePassthrough(), }, prompt, model, new StringOutputParser(), ]); console.log("===========RETRIVAL============", retrievalChain); const chain = retrievalChain.invoke({question}); return chain; } //=================================Testing=============================================// // Test2(); // async function Test2() { // try { // const searchParam: string = "how many leaves are there" // console.log('Search>>>', searchParam); // const response = await Test(searchParam); // return { // statusCode: 200, // headers: { // "Access-Control-Allow-Headers" : "Content-Type", // "Access-Control-Allow-Origin": "", // "Access-Control-Allow-Methods": "OPTIONS,POST,GET" // }, // body: JSON.stringify(response) // } // } catch (error) { // return { // statusCode: 500, // headers: { // "Access-Control-Allow-Headers" : "Content-Type", // "Access-Control-Allow-Origin": "", // "Access-Control-Allow-Methods": "OPTIONS,POST,GET" // }, // body: JSON.stringify(error) // } // } // } app.listen(PORT, () => { console.log(Server is running on port ${PORT}); });

Error Message and Stack Trace (if applicable)

2024-04-21T16:13:20.512+05:30Error >>>>>.. Error: Could not import faiss-node. Please install faiss-node as a dependency with, e.g. `npm install -S faiss-node`.	Error >>>>>.. Error: Could not import faiss-node. Please install faiss-node as a dependency with, e.g. `npm install -S faiss-node`.
	2024-04-21T16:13:20.512+05:30Error: Could not locate the bindings file. Tried:

System is linux When I run it on AWS lambda or when I creat ECS task there I am getting this error

langchain-ai / langchainjs