Just about every application you build is going to need some form of user authentication, and the moment you have user accounts, you have passwords to manage. Storing them safely is only part of the job. You also need to make sure those passwords are strong enough to be worth protecting in the first place, and in many cases, you need to make sure the same password isn't recycled every time it’s rotated.
Password reuse is a bigger deal than it sounds. Compliance frameworks frequently require that the last several passwords cannot be used again, and even outside of compliance, it is good hygiene. If a password leaks today and a user just rotates back to it six months from now, your rotation policy did not actually protect anyone.
MongoDB happens to be a really good fit for this kind of feature. A user document can store the current password hash at the root of the document, along with a growing array of previous hashes, all in the same record. There is no separate join table to set up and no migration to run when a user changes their password for the tenth time.
In this tutorial, we'll see how to build a small TypeScript API using Express Framework, Zod, and the MongoDB Node.js driver. This application registers users with strong password rules, authenticates them, and rejects any password change that has been seen before.
The Prerequisites
Prior to starting this tutorial, you'll need a few things in place:
- A MongoDB instance, either a local server or an Atlas cluster on the free tier
- Node.js 22+
The expectation is that you can already connect to MongoDB with a connection string. If you need help getting an Atlas cluster running, check out the MongoDB documentation for getting started.
We're working from an empty project, so the first thing to do is create the directory and initialize it:
mkdir typescript-password-history-example
cd typescript-password-history-example
npm init -y
The above commands create the project directory and a starter package.json file.
Next, we'll install the runtime dependencies:
npm install express mongodb bcrypt zod dotenv
We're pulling in Express Framework as the HTTP layer, the official MongoDB Node.js driver, bcrypt for hashing passwords, zod for schema validation, and dotenv for loading configuration from an environment file.
For TypeScript and the type definitions, run:
npm install --save-dev typescript ts-node @types/express @types/node @types/bcrypt
The ts-node package lets us run TypeScript directly during development without a separate compile step, keeping the feedback loop tight.
Because this is a TypeScript project, we need a tsconfig.json at the root of the project:
{
"compilerOptions": {
"target": "ES2020",
"module": "commonjs",
"lib": ["ES2020"],
"outDir": "./dist",
"rootDir": "./",
"strict": true,
"esModuleInterop": true,
"skipLibCheck": true,
"forceConsistentCasingInFileNames": true,
"resolveJsonModule": true
},
"include": ["./**/*.ts"],
"exclude": ["node_modules", "dist"]
}
The configuration emits CommonJS to a dist/ directory when we run tsc, and as strict mode is turned on, the compiler actually enforces type safety rather than letting things slide.
It also helps to update the scripts block of package.json, so we have a development command and a production command:
"scripts": {
"build": "tsc",
"start": "node dist/main.js",
"dev": "ts-node main.ts"
}
The dev script will run the TypeScript entry point directly. The build script compiles to dist/, and start runs the compiled output.
By the time we're done, the project will have the following shape:
.
├── .env
├── main.ts
├── tsconfig.json
├── package.json
├── libs/
│ ├── mongodb.ts
│ └── validate.ts
└── routes/
└── passwords.ts
Each of those files will get built up as we work through the tutorial. Go ahead and create the libs and routes directories now so you have somewhere to drop code as we go.
The last piece of setup is the environment file. Create a .env file at the root of the project with the following:
MONGODB_URI=ADD_YOUR_ATLAS_URI_HERE
MONGODB_DB_NAME=password_history_demo
PORT=3000
Replace the MONGODB_URI with the connection string for whatever MongoDB instance you're using. The password_history_demo database specified in MONGODB_DB_NAME does not need to exist ahead of time. MongoDB will create it lazily the first time we write data. If you're using version control, make sure .env is listed in your .gitignore so the connection string does not get committed.
Establish an API Foundation with Express and TypeScript
Before we get into password logic, we need an Express application that listens for HTTP requests and a clean way to start and stop it. The entry point lives in main.ts at the root of the project.
Add the following to main.ts:
import * as dotenv from "dotenv";
dotenv.config();
import express from "express";
import { getDb, closeConnection } from "./libs/mongodb";
import passwordRouter from "./routes/passwords";
const app = express();
const PORT = process.env.PORT ?? 3000;
app.use(express.json());
app.use("/users", passwordRouter);
async function start() {
const db = await getDb();
await db.collection("users").createIndex({ username: 1 }, { unique: true });
const server = app.listen(PORT, () => {
console.log(`Server running on http://localhost:${PORT}`);
});
process.on("SIGTERM", async () => {
console.log("SIGTERM received, shutting down gracefully...");
server.close(async () => {
await closeConnection();
process.exit(0);
});
});
process.on("SIGINT", async () => {
console.log("SIGINT received, shutting down gracefully...");
server.close(async () => {
await closeConnection();
process.exit(0);
});
});
}
start();
That file is doing a lot, so let's pull it apart.
At the top, we have two dotenv lines followed by the rest of the imports:
import * as dotenv from "dotenv";
dotenv.config();
import express from "express";
import { getDb, closeConnection } from "./libs/mongodb";
import passwordRouter from "./routes/passwords";
The order matters here. dotenv.config() runs before the MongoDB module is imported, which means the connection string and database name will already be present in process.env by the time libs/mongodb.ts is evaluated.
Next, we have the Express application setup:
const app = express();
const PORT = process.env.PORT ?? 3000;
app.use(express.json());
app.use("/users", passwordRouter);
The express.json() middleware parses incoming JSON bodies so we can read req.body as a plain object later. The router is mounted under /users, so every endpoint in routes/passwords.ts will be reachable beneath that prefix. We'll write that router shortly.
Then we have the start function, which is the part that actually brings the server online:
async function start() {
const db = await getDb();
await db.collection("users").createIndex({ username: 1 }, { unique: true });
const server = app.listen(PORT, () => {
console.log(`Server running on http://localhost:${PORT}`);
});
// ... signal handlers shown below
}
The first thing it does is grab a database reference and create a unique index on the username field of the users collection. Doing it at startup means we don't have to check for duplicate usernames before every insert. MongoDB will raise an error with code 11000 if anyone tries to register a username that already exists, and we'll catch that error in the route handler. It is a much cleaner pattern than running a findOne followed by an insertOne, which is also racy.
After the index is ready, we call app.listen and hold onto the returned server reference so we can close it later.
The final piece is the shutdown handling, which lives inside the same start function:
async function start() {
// ... database, index, and app.listen above
process.on("SIGTERM", async () => {
console.log("SIGTERM received, shutting down gracefully...");
server.close(async () => {
await closeConnection();
process.exit(0);
});
});
process.on("SIGINT", async () => {
console.log("SIGINT received, shutting down gracefully...");
server.close(async () => {
await closeConnection();
process.exit(0);
});
});
}
When the process receives either SIGTERM (which most container orchestrators send) or SIGINT (which is what Ctrl+C sends), we stop accepting new connections, close the MongoDB client, and exit. Without this, you'd leave an open client connection behind every time you restart the server.
Of course, none of this will compile yet because libs/mongodb.ts does not exist. That's our next step.
Create a Singleton MongoDB Class to Manage Database Connections
You could open a new MongoClient on every incoming request, but it is almost never what you want. Connecting is expensive, it adds latency, and it makes graceful shutdown much harder than it needs to be. A long-running API like ours should connect once when the process boots and reuse the same client for every request after that.
Open libs/mongodb.ts and add the following:
import { MongoClient, Db } from "mongodb";
const uri = process.env.MONGODB_URI as string;
const dbName = process.env.MONGODB_DB_NAME as string;
if (!uri) {
throw new Error("MONGODB_URI is not defined in the environment variables");
}
if (!dbName) {
throw new Error("MONGODB_DB_NAME is not defined in the environment variables");
}
let client: MongoClient | null = null;
export async function getClient(): Promise<MongoClient> {
if (!client) {
client = new MongoClient(uri, {
connectTimeoutMS: 5000,
socketTimeoutMS: 30000,
serverSelectionTimeoutMS: 5000,
appName: "devrel-tutorial-typescript-passwordhistory"
});
await client.connect();
}
return client;
}
export async function getDb(): Promise<Db> {
const mongoClient = await getClient();
return mongoClient.db(dbName);
}
export async function closeConnection(): Promise<void> {
if (client) {
await client.close();
client = null;
}
}
The top of the file reads the two environment variables we set earlier and fails fast if either of them is missing. There is no point in starting the server if it cannot reach the database.
The interesting bit is getClient:
export async function getClient(): Promise<MongoClient> {
if (!client) {
client = new MongoClient(uri, {
connectTimeoutMS: 5000,
socketTimeoutMS: 30000,
serverSelectionTimeoutMS: 5000,
appName: "devrel-github-typescript-passwordhistory"
});
await client.connect();
}
return client;
}
The module-level client variable starts as null. The first call to getClient instantiates the MongoClient, awaits connect, and caches it. Every subsequent call returns the cached instance immediately. That is the entire singleton pattern. We did not bother setting a maxPoolSize because this is a traditional long-running OLTP server, and the default value of 100 is appropriate for that workload.
The three timeout options are worth a quick mention. connectTimeoutMS caps how long a single socket connection attempt can take, socketTimeoutMS caps how long a socket can sit idle, and serverSelectionTimeoutMS caps how long the driver will spend looking for a suitable server before giving up. The appName value is cosmetic but useful. It shows up in MongoDB Atlas logs and metrics, so you can tell which application is generating which queries.
Then we have the two helpers that the rest of the application actually uses:
export async function getDb(): Promise<Db> {
const mongoClient = await getClient();
return mongoClient.db(dbName);
}
export async function closeConnection(): Promise<void> {
if (client) {
await client.close();
client = null;
}
}
The getDb function returns a Db reference scoped to the database name from the environment file. Anywhere in the codebase that needs to talk to MongoDB will go through this function. The closeConnection function shuts the client down and resets the module-level variable. This is what gets called by the SIGTERM and SIGINT handlers back in main.ts.
Test Password Strength with Regular Expressions and Zod
A password is only as good as the rules you enforce around it. For this tutorial, we're going to require a minimum of eight characters, at least one uppercase letter, at least one lowercase letter, at least one digit, and at least one character that is not a letter or a number. None of these rules is particularly exotic, but together they keep users from registering with something like password or 12345678.
We're going to enforce them with Zod schemas wired into Express as middleware. That way, the route handlers themselves stay focused on database work and never have to inspect raw request bodies.
Open libs/validate.ts and add the following:
import { Request, Response, NextFunction } from "express";
import { ZodSchema } from "zod";
export function validate(schema: ZodSchema) {
return (req: Request, res: Response, next: NextFunction): void => {
const result = schema.safeParse(req.body);
if (!result.success) {
res.status(400).json({ errors: result.error.flatten().fieldErrors });
return;
}
req.body = result.data;
next();
};
}
The validate function is a middleware factory. You hand it a Zod schema, and it hands you back an Express middleware function that runs that schema against the incoming req.body. If validation fails, the middleware sends back a 400 with a flattened map of field errors, and the route handler is never called. If validation succeeds, it replaces req.body with the parsed output before calling next().
Replacing req.body with result.data is intentional. Zod will strip unknown keys and apply any transformations defined on the schema, so by the time the route handler runs, the body is guaranteed to look exactly like the schema declared. There is no risk of a stray field sneaking through.
Now we can define the actual schemas. These live alongside the route handlers in routes/passwords.ts, but we'll write that file in pieces throughout the rest of the tutorial. For now, here's the schema that encodes the password strength rules:
import { z } from "zod";
const strongPasswordSchema = z
.string()
.min(8, "Password must be at least 8 characters")
.regex(/[A-Z]/, "Password must contain at least one uppercase letter")
.regex(/[a-z]/, "Password must contain at least one lowercase letter")
.regex(/[0-9]/, "Password must contain at least one digit")
.regex(/[^A-Za-z0-9]/, "Password must contain at least one special character");
Each rule is its own .regex call with a custom error message. If a user submits something that breaks two rules, Zod will report both errors at once, which makes for a much better registration experience than trickling them out one at a time.
The character class [^A-Za-z0-9] is doing the work for the special character rule. It matches anything that is not an uppercase letter, a lowercase letter, or a digit. We're not picky about which special character. Punctuation, symbols, and even whitespace will satisfy it.
With the strong password schema in place, we can compose the three request schemas the API needs:
const registerSchema = z.object({
username: z.string().min(1, "Username is required").trim(),
password: strongPasswordSchema,
});
const loginSchema = z.object({
username: z.string().min(1, "Username is required").trim(),
password: z.string().min(1, "Password is required"),
});
const changePasswordSchema = z.object({
current_password: z.string().min(1, "Current password is required"),
new_password: strongPasswordSchema,
});
Registration uses the strict rules on password because that's a brand new password being committed to the database. The change-password schema uses the strict rules on new_password for the same reason.
Notice that loginSchema and the current_password field in changePasswordSchema do not use the strict rules. That's deliberate. The strength rules are about what we are willing to store, not what we are willing to compare against. A user who registered before you tightened your password policy still needs to be able to log in with their old, weak password so they can rotate to a stronger one. If the strong rules applied to the login flow, you would lock those users out permanently.
Store Passwords Safely in MongoDB with Password Change History
Now we get to the part this whole tutorial is named after. The shape of a user document is going to look something like this:
{
"_id": "ObjectId(...)",
"username": "nraboy",
"password": "BCRYPT_HASH",
"password_history": [
{ "password": "BCRYPT_HASH", "created_at": "ISODate(...)" }
],
"created_at": "ISODate(...)",
"updated_at": "ISODate(...)"
}
The current password hash lives at the root of the document. That's the value we compare against during login, and it is the one we update during a password change. Alongside it, we keep a password_history array where each entry is a previous hash with the timestamp it was set. When a user first registers, the history starts with a single entry that mirrors the root password. Every password change appends another entry. To check if a candidate password has been used before, we walk the history and compare against each entry.
Let's start building routes/passwords.ts. The top of the file pulls in the dependencies, defines the data shapes, and declares a salt rounds constant:
import { Router, Request, Response } from "express";
import { z } from "zod";
import bcrypt from "bcrypt";
import { getDb } from "../libs/mongodb";
import { MongoServerError } from "mongodb";
import { validate } from "../libs/validate";
const SALT_ROUNDS = 12;
type PasswordHistoryEntry = {
password: string;
created_at: Date;
};
type UserDocument = {
username: string;
password: string;
password_history: PasswordHistoryEntry[];
created_at: Date;
updated_at: Date;
};
The two types describe the user document and a single history entry. We're using them as generic parameters on db.collection<UserDocument>("users") calls below so TypeScript can give us reasonable autocomplete when we read fields from the results.
Twelve salt rounds is a sensible default for bcrypt. It is slow enough to make brute-force attacks expensive and fast enough that a legitimate login does not feel sluggish on modern hardware.
Next, define three small helper functions for working with bcrypt:
async function hashPassword(plaintext: string): Promise<string> {
return bcrypt.hash(plaintext, SALT_ROUNDS);
}
async function verifyPassword(plaintext: string, hash: string): Promise<boolean> {
return bcrypt.compare(plaintext, hash);
}
async function isPasswordInHistory(
plaintext: string,
history: PasswordHistoryEntry[]
): Promise<boolean> {
for (const entry of history) {
const match = await bcrypt.compare(plaintext, entry.password);
if (match) return true;
}
return false;
}
The first two are thin wrappers around the bcrypt API. They exist mostly to keep the route handlers readable.
The third one, isPasswordInHistory, is where the history check actually happens. It loops through every entry in the array and calls bcrypt.compare against each one. You cannot just hash the candidate password and look for an exact string match because bcrypt mixes a fresh random salt into every hash. Two calls to bcrypt.hash("Nic12345$", 12) will produce two different hash strings. The only way to know if a plaintext matches a stored hash is to feed both into bcrypt.compare.
The loop short-circuits the moment it finds a match, so in the common case where the user picks something genuinely new, the cost is proportional to the size of the history. For typical history sizes of five to ten entries, that overhead is negligible.
With the schemas from the previous section and the helpers above, we can start defining routes. Initialize the router:
const router = Router();
And start with the demo endpoint that lists every user document. This one only exists so you can poke at the database during development:
router.get("/", async (_req: Request, res: Response): Promise<void> => {
try {
const db = await getDb();
const users = await db.collection<UserDocument>("users").find({}).toArray();
res.status(200).json(users);
} catch (err) {
console.error(err);
res.status(500).json({ error: "Internal server error" });
}
});
This endpoint returns the full user document, including the bcrypt hash and the entire history array. You would never ship something like this in production. It is genuinely useful while you're developing, though, because it lets you see exactly what the password history looks like as it grows.
The registration endpoint is where new users get created:
router.post("/", validate(registerSchema), async (req: Request, res: Response): Promise<void> => {
const { username, password } = req.body;
try {
const db = await getDb();
const hashed = await hashPassword(password);
const now = new Date();
await db.collection("users").insertOne({
username,
password: hashed,
password_history: [{ password: hashed, created_at: now }],
created_at: now,
updated_at: now,
});
res.status(201).json({ message: "User created successfully" });
} catch (err) {
if (err instanceof MongoServerError && err.code === 11000) {
res.status(409).json({ error: "Username already exists" });
return;
}
console.error(err);
res.status(500).json({ error: "Internal server error" });
}
});
There are a couple of things worth pointing out here. First, validate(registerSchema) runs before the handler. If validation fails, the handler never executes, so by the time we destructure username and password from req.body, we already know they're well-formed.
Second, the new document seeds password_history with a single entry that contains the same hash we just stored at the root. That way, the very first password is part of the history from day one. If we did not do that, the user could "rotate" their password back to the original on their first password change, which is exactly the behavior we're trying to prevent.
Third, instead of running a findOne to see if the username is taken and then doing an insertOne, we just attempt the insert and catch MongoServerError with code 11000. That's the duplicate-key error code, and it is the cleanest way to enforce uniqueness because MongoDB itself is the only thing that needs to coordinate. There is no window between the check and the insert during which two requests could both decide the username is free.
The login endpoint is much simpler:
router.post("/login", validate(loginSchema), async (req: Request, res: Response): Promise<void> => {
const { username, password } = req.body;
try {
const db = await getDb();
const users = db.collection<UserDocument>("users");
const user = await users.findOne({ username });
if (!user) {
res.status(401).json({ error: "Invalid credentials" });
return;
}
const valid = await verifyPassword(password, user.password);
if (!valid) {
res.status(401).json({ error: "Invalid credentials" });
return;
}
res.status(200).json({ message: "Login successful" });
} catch (err) {
console.error(err);
res.status(500).json({ error: "Internal server error" });
}
});
Notice that both failure cases return the same 401 with the same generic message. There is a temptation to send back "User not found" when the lookup misses and "Wrong password" when the comparison fails, but doing so leaks information. An attacker who can tell those two states apart can enumerate which usernames exist in your system, which is the first half of a credential stuffing attack. Returning a single opaque error keeps both branches indistinguishable.
This is also why a real production version would want some kind of rate limiting on this endpoint. We're not adding that here, but it is worth keeping in mind.
The change-password endpoint is the one where the history check actually pays off:
router.put("/:username/password", validate(changePasswordSchema), async (req: Request, res: Response): Promise<void> => {
const { username } = req.params;
const { current_password, new_password } = req.body;
try {
const db = await getDb();
const users = db.collection<UserDocument>("users");
const user = await users.findOne({ username });
if (!user) {
res.status(404).json({ error: "User not found" });
return;
}
const currentValid = await verifyPassword(current_password, user.password);
if (!currentValid) {
res.status(401).json({ error: "Current password is incorrect" });
return;
}
const history = user.password_history ?? [];
const alreadyUsed = await isPasswordInHistory(new_password, history);
if (alreadyUsed) {
res.status(409).json({ error: "New password has been used before. Please choose a different password." });
return;
}
const hashed = await hashPassword(new_password);
const now = new Date();
await users.updateOne(
{ username },
{
$set: {
password: hashed,
updated_at: now,
},
$push: {
password_history: { password: hashed, created_at: now },
},
}
);
res.status(200).json({ message: "Password updated successfully" });
} catch (err) {
console.error(err);
res.status(500).json({ error: "Internal server error" });
}
});
The handler runs through four checks in order. First, we look up the user by the username from the route parameter, returning a 404 if the user does not exist. Then we verify the supplied current_password against the stored hash. A mismatch returns a 401, which is appropriate because the caller is, in effect, failing to authenticate.
After that, we run the history check:
router.put("/:username/password", validate(changePasswordSchema), async (req: Request, res: Response): Promise<void> => {
// ... user lookup and current password verification above
const history = user.password_history ?? [];
const alreadyUsed = await isPasswordInHistory(new_password, history);
if (alreadyUsed) {
res.status(409).json({ error: "New password has been used before. Please choose a different password." });
return;
}
// ... update logic below
});
The nullish coalescing operator gives us a safe default for any users that might somehow be missing the password_history field, though in practice, the registration handler always sets it. If the candidate password matches any entry in the history, we send back a 409 and explain the situation to the caller.
If we make it past all four checks, the update itself uses two operators in one call:
router.put("/:username/password", validate(changePasswordSchema), async (req: Request, res: Response): Promise<void> => {
// ... lookup, verification, and history check above
const hashed = await hashPassword(new_password);
const now = new Date();
await users.updateOne(
{ username },
{
$set: {
password: hashed,
updated_at: now,
},
$push: {
password_history: { password: hashed, created_at: now },
},
}
);
res.status(200).json({ message: "Password updated successfully" });
});
The $set operator overwrites the root password field with the new hash and bumps updated_at. At the same time, $push appends the new hash to the password_history array. Because both operators are part of the same update, the change is atomic from MongoDB's perspective. The current password and the history can never disagree about the user's most recent password.
Finally, the bottom of the file exports the router so main.ts can mount it:
export default router;
That completes the route layer. Time to try it out.
Testing the API Endpoints with cURL
We'll exercise the API from the command line with curl. The repository also includes a Bruno collection in the bruno/ directory if you'd rather click through requests in a GUI, but curl is faster to demonstrate in a tutorial.
Start the server in development mode:
npm run dev
You should see Server running on http://localhost:3000 in the terminal. Leave it running and open a second terminal for the requests.
First, register a new user:
curl -s -X POST http://localhost:3000/users \
-H "Content-Type: application/json" \
-d '{"username":"nraboy","password":"Nic12345$"}'
A successful registration responds with a 201 and {"message":"User created successfully"}. If you try the exact same request again, you'll get a 409 because the unique index on username rejects the duplicate.
To prove the validation middleware is doing its job, try sending a weak password:
curl -s -X POST http://localhost:3000/users \
-H "Content-Type: application/json" \
-d '{"username":"weakuser","password":"password"}'
The response is a 400 with a per-field map of the rules that were broken. No insert ever reaches MongoDB.
Now try logging in with the user we just created:
curl -s -X POST http://localhost:3000/users/login \
-H "Content-Type: application/json" \
-d '{"username":"nraboy","password":"Nic12345$"}'
A correct password returns {"message":"Login successful"}. Get one character wrong, and you'll see {"error":"Invalid credentials"} with a 401.
The interesting endpoint is the password change. Rotate to a brand new password:
curl -s -X PUT http://localhost:3000/users/nraboy/password \
-H "Content-Type: application/json" \
-d '{"current_password":"Nic12345$","new_password":"Nic67890$"}'
That responds with a 200 and {"message":"Password updated successfully"}. Now try to rotate back to the original password:
curl -s -X PUT http://localhost:3000/users/nraboy/password \
-H "Content-Type: application/json" \
-d '{"current_password":"Nic67890$","new_password":"Nic12345$"}'
This time the response is a 409 and {"error":"New password has been used before. Please choose a different password."}. The history check did exactly what we wanted.
You can verify what's actually in the database by hitting the demo list endpoint:
curl -s http://localhost:3000/users
The response includes the password_history array with two entries, one for the original password and one for the rotation, each with its own timestamp and bcrypt hash. Two different hashes for the same logical password value, which is the whole point of using bcrypt.compare rather than string equality during the history check.
Conclusion
You just saw how to combine TypeScript, Express Framework, Zod, and the MongoDB Node.js driver to enforce password strength and prevent password reuse. Zod schemas plugged into Express as middleware handle the strength rules, bcrypt handles the hashing, and a password_history array on each user document keeps a running record of every hash that user has ever used. The singleton MongoDB client keeps the connection layer simple, and a unique index on username lets MongoDB itself enforce uniqueness without us having to coordinate it in application code.
What we built is good for a demo, but a few things would need to change before this could be a production system. There is no rate limiting on the login or password change endpoints, both of which are attractive targets for brute-force attempts. There is no session or token mechanism, so right now a successful login just sends back a message. The GET /users endpoint exposes every hash in the database and would need to be removed entirely. And depending on your compliance requirements, you may want to cap the size of password_history so it does not grow without bound.
As a next step, try adding a password expiration field that forces a rotation after some number of days, or capping password_history so only the last ten entries are retained.
The full source for this project is available on GitHub.
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