---
title: Sample Markdown Document
author: John Doe
date: 2025-01-15
tags:
  - crawler
  - extractor
  - markdown
---

# Introduction

This is a sample Markdown document for testing the MarkdownExtractor.

## Features

The extractor should handle:

- YAML front matter extraction
- Heading structure
- **Bold text** and *italic text*
- Lists and other formatting

### Code Examples

Os modelos Pydantic possuem um método `.dict()` que retorna um `dict` com os dados do modelo.

Então, se criarmos um objeto Pydantic `user_in` como:

```Python
user_in = UserIn(username="john", password="secret", email="john.doe@example.com")
```

e depois chamarmos:

```Python
user_dict = user_in.dict()
```

agora temos um `dict` com os dados na variável `user_dict` (é um `dict` em vez de um objeto de modelo Pydantic).

def get_full_name(first_name, last_name):
    full_name = first_name.title() + " " + last_name.title()
    return full_name

def get_full_name(first_name: str, last_name: str):
    full_name = first_name.title() + " " + last_name.title()
    return full_name

Name,Email,Age,Department
John Doe,******@****.***,30,Engineering
Jane Smith,******@****.***,28,Marketing
Bob Johnson,******@****.***,35,Sales

`user_in`은 Pydantic 모델 클래스인 `UserIn`입니다.

Pydantic 모델은 모델 데이터를 포함한 `dict`를 반환하는 `.dict()` 메서드를 제공합니다.

따라서, 다음과 같이 Pydantic 객체 `user_in`을 생성할 수 있습니다:

```Python
user_in = UserIn(username="john", password="secret", email="john.doe@example.com")
```

그 다음, 다음과 같이 호출합니다:

```Python
user_dict = user_in.dict()
```

이제 변수 `user_dict`에 데이터가 포함된 `dict`를 가지게 됩니다(이는 Pydantic 모델 객체가 아닌 `dict`입니다).

그리고 다음과 같이 호출하면:

```Python

from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
from pydantic import BaseModel

fake_users_db = {
    "johndoe": {
        "username": "johndoe",
        "full_name": "John Doe",
        "email": "******@****.***",
        "hashed_password": "fakehashedsecret",
        "disabled": False,
    },
    "alice": {
        "username": "alice",
        "full_name": "Alice Wonderson",

        assertTrue(content.contains("author"));
        assertTrue(content.contains("John Doe"));

        // Verify metadata extraction
        assertNotNull(extractData.getValues("title"));
        assertEquals("Sample Document", extractData.getValues("title")[0]);
        assertNotNull(extractData.getValues("author"));
        assertEquals("John Doe", extractData.getValues("author")[0]);
    }

    public void test_getText_nested() {

Pydantic models have a `.model_dump()` method that returns a `dict` with the model's data.

So, if we create a Pydantic object `user_in` like:

```Python
user_in = UserIn(username="john", password="secret", email="john.doe@example.com")
```

and then we call:

```Python
user_dict = user_in.model_dump()
```

we now have a `dict` with the data in the variable `user_dict` (it's a `dict` instead of a Pydantic model object).

from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
from pydantic import BaseModel

fake_users_db = {
    "johndoe": {
        "username": "johndoe",
        "full_name": "John Doe",
        "email": "******@****.***",
        "hashed_password": "fakehashedsecret",
        "disabled": False,
    },
    "alice": {
        "username": "alice",
        "full_name": "Alice Wonderson",