### Install Pyresparser

Source: https://github.com/omkarpathak/pyresparser/blob/master/docs/index.md

Install the pyresparser package using pip. Ensure you have Python and pip installed.

```bash
pip install pyresparser
```

--------------------------------

### Install spaCy and NLTK Models

Source: https://github.com/omkarpathak/pyresparser/blob/master/docs/index.md

Download necessary models for spaCy and NLTK for NLP operations. These are required for accurate resume parsing.

```bash
# spaCy
python -m spacy download en_core_web_sm

# nltk
python -m nltk.downloader words
python -m nltk.downloader stopwords
```

--------------------------------

### Install spaCy NLP model

Source: https://github.com/omkarpathak/pyresparser/blob/master/README.rst

Download the small English NLP model for spaCy, a dependency for pyresparser.

```bash
python -m spacy download en_core_web_sm
```

--------------------------------

### Extracted Resume Data Structure

Source: https://github.com/omkarpathak/pyresparser/blob/master/README.rst

Example structure of the dictionary object returned by ResumeParser, containing extracted information from a resume.

```python
[
  {
    'college_name': ['Marathwada Mitra Mandal’s College of Engineering'],
    'company_names': None,
    'degree': ['B.E. IN COMPUTER ENGINEERING'],
    'designation': ['Manager',
                    'TECHNICAL CONTENT WRITER',
                    'DATA ENGINEER'],
    'email': 'omkarpathak27@gmail.com',
    'mobile_number': '8087996634',
    'name': 'Omkar Pathak',
    'no_of_pages': 3,
    'skills': ['Operating systems',
              'Linux',
              'Github',
              'Testing',
              'Content',
              'Automation',
              'Python',
              'Css',
              'Website',
              'Django',
              'Opencv',
              'Programming',
              'C',
              ...],
    'total_experience': 1.83
  }
]
```

--------------------------------

### Display CLI help message

Source: https://github.com/omkarpathak/pyresparser/blob/master/docs/cli.md

View the available command-line arguments and options.

```bash
usage: pyresparser [-h] [-f FILE] [-d DIRECTORY] [-r REMOTEFILE]
                   [-sf SKILLSFILE]

optional arguments:
  -h, --help                                show this help message and exit
  -f FILE, --file FILE                      resume file to be extracted
  -d DIRECTORY, --directory DIRECTORY       directory containing all the resumes to be extracted
  -r REMOTEFILE, --remotefile REMOTEFILE    remote path for resume file to be extracted
  -sf SKILLSFILE, --skillsfile SKILLSFILE   custom skills CSV file against which skills are searched for
```

--------------------------------

### Parse Resume with Custom Skills File

Source: https://github.com/omkarpathak/pyresparser/blob/master/docs/index.md

Use a custom CSV file for skills matching instead of the default. Ensure the CSV has no headers.

```python
from pyresparser import ResumeParser
data = ResumeParser('/path/to/resume/file', skills_file='/path/to/skills.csv').get_extracted_data()
```

--------------------------------

### Basic Resume Parsing

Source: https://github.com/omkarpathak/pyresparser/blob/master/docs/index.md

Import ResumeParser and extract data from a resume file. This is the primary method for parsing resumes.

```python
from pyresparser import ResumeParser
data = ResumeParser('/path/to/resume/file').get_extracted_data()
```

--------------------------------

### Download NLTK words corpus

Source: https://github.com/omkarpathak/pyresparser/blob/master/README.rst

Download the 'words' corpus for NLTK, another dependency for pyresparser.

```bash
python -m nltk.downloader words
```

--------------------------------

### Parse Resumes from Remote URLs

Source: https://context7.com/omkarpathak/pyresparser/llms.txt

Fetch and parse resumes directly from remote URLs. The resume is downloaded into memory as a BytesIO object and processed without needing a local file.

```python
import io
from urllib.request import Request, urlopen
from pyresparser import ResumeParser

# Fetch remote resume
remote_url = 'https://example.com/resumes/candidate.pdf'
req = Request(remote_url, headers={'User-Agent': 'Mozilla/5.0'})
webpage = urlopen(req).read()

# Create BytesIO object with filename
resume_file = io.BytesIO(webpage)
resume_file.name = 'candidate.pdf'

# Parse remote resume
parser = ResumeParser(resume_file)
data = parser.get_extracted_data()

print(f"Candidate: {data['name']}")
print(f"Email: {data['email']}")
print(f"Skills: {', '.join(data['skills'] or [])}")
```

--------------------------------

### Parse multiple resumes from a directory

Source: https://github.com/omkarpathak/pyresparser/blob/master/docs/cli.md

Extract data from all resume files located within a specified directory.

```bash
pyresparser -d /path/to/resume/directory/
```

--------------------------------

### Pyresparser CLI Usage

Source: https://github.com/omkarpathak/pyresparser/blob/master/README.md

Command-line interface for running the resume extractor. Supports specifying resume files, directories, remote files, custom regex, skills files, and export formats.

```bash
usage: pyresparser [-h] [-f FILE] [-d DIRECTORY] [-r REMOTEFILE]
                   [-re CUSTOM_REGEX] [-sf SKILLSFILE] [-e EXPORT_FORMAT]

optional arguments:
  -h, --help            show this help message and exit
  -f FILE, --file FILE  resume file to be extracted
  -d DIRECTORY, --directory DIRECTORY
                        directory containing all the resumes to be extracted
  -r REMOTEFILE, --remotefile REMOTEFILE
                        remote path for resume file to be extracted
  -re CUSTOM_REGEX, --custom-regex CUSTOM_REGEX
                        custom regex for parsing mobile numbers
  -sf SKILLSFILE, --skillsfile SKILLSFILE
                        custom skills CSV file against which skills are
                        searched for
  -e EXPORT_FORMAT, --export-format EXPORT_FORMAT
                        the information export format (json)
```

--------------------------------

### Parse a single resume file

Source: https://github.com/omkarpathak/pyresparser/blob/master/docs/cli.md

Extract data from a local resume file.

```bash
pyresparser -f /path/to/resume/file
```

--------------------------------

### pyresparser CLI Usage

Source: https://github.com/omkarpathak/pyresparser/blob/master/README.rst

Command-line interface for pyresparser, supporting various options for file input, custom regex, and export formats.

```bash
usage: pyresparser [-h] [-f FILE] [-d DIRECTORY] [-r REMOTEFILE]
                       [-re CUSTOM_REGEX] [-sf SKILLSFILE] [-e EXPORT_FORMAT]

optional arguments:
  -h, --help            show this help message and exit
  -f FILE, --file FILE  resume file to be extracted
  -d DIRECTORY, --directory DIRECTORY
                        directory containing all the resumes to be extracted
  -r REMOTEFILE, --remotefile REMOTEFILE
                        remote path for resume file to be extracted
  -re CUSTOM_REGEX, --custom-regex CUSTOM_REGEX
                        custom regex for parsing mobile numbers
  -sf SKILLSFILE, --skillsfile SKILLSFILE
                        custom skills CSV file against which skills are
                        searched for
  -e EXPORT_FORMAT, --export-format EXPORT_FORMAT
                        the information export format (json)
```

--------------------------------

### Batch Process Resumes with Multiprocessing

Source: https://context7.com/omkarpathak/pyresparser/llms.txt

Use a process pool to parse multiple resume files in parallel for improved performance.

```python
import os
import multiprocessing as mp
from pyresparser import ResumeParser

def resume_result_wrapper(resume_path):
    """Wrapper function for multiprocessing"""
    parser = ResumeParser(resume_path)
    return parser.get_extracted_data()

# Collect all resume files from a directory
resume_directory = '/path/to/resumes/'
resumes = []
for root, dirs, files in os.walk(resume_directory):
    for filename in files:
        if filename.endswith(('.pdf', '.docx', '.doc')):
            resumes.append(os.path.join(root, filename))

# Process resumes in parallel using all CPU cores
pool = mp.Pool(mp.cpu_count())
results = [pool.apply_async(resume_result_wrapper, args=(resume,)) for resume in resumes]
parsed_data = [result.get() for result in results]
pool.close()
pool.join()

# Print summary of all candidates
for data in parsed_data:
    print(f"Name: {data['name']}, Experience: {data['total_experience']} years")
```

--------------------------------

### Parse Single Resume with ResumeParser

Source: https://context7.com/omkarpathak/pyresparser/llms.txt

Use the ResumeParser class to extract structured data from a local resume file. Supports PDF and DOCX formats. The output includes name, email, mobile, skills, education, experience, and company details.

```python
from pyresparser import ResumeParser

# Parse a local PDF resume
parser = ResumeParser('/path/to/resume.pdf')
data = parser.get_extracted_data()

print(data)
# Output:
# {
#     'name': 'John Smith',
#     'email': 'john.smith@email.com',
#     'mobile_number': '555-123-4567',
#     'skills': ['Python', 'Machine Learning', 'Django', 'SQL', 'Git'],
#     'college_name': ['Stanford University'],
#     'degree': ['M.S. IN COMPUTER SCIENCE', 'B.S. IN MATHEMATICS'],
#     'designation': ['Software Engineer', 'Data Scientist'],
#     'experience': ['Jan 2020 - Present: Senior Developer at Tech Corp'],
#     'company_names': ['Tech Corp', 'StartupXYZ'],
#     'no_of_pages': 2,
#     'total_experience': 4.5
# }
```

```python
# Parse a DOCX file
docx_parser = ResumeParser('/path/to/resume.docx')
docx_data = docx_parser.get_extracted_data()
```

--------------------------------

### Remote File Parsing - Parse Resumes from URLs

Source: https://context7.com/omkarpathak/pyresparser/llms.txt

Fetch and parse resume files directly from remote URLs without manual downloading.

```APIDOC
## Remote File Parsing - Parse Resumes from URLs

### Description
Pyresparser can fetch and parse resumes directly from remote URLs without requiring a local file download. The file is downloaded into memory as a BytesIO object and processed like a local file.

### Method
Fetch the remote file content and pass a BytesIO object to the `ResumeParser` constructor.

### Endpoint
N/A (Python Class)

### Parameters
#### Path Parameters
- **resume_file** (BytesIO object) - Required - A BytesIO object containing the resume content, with a `name` attribute set to the filename.

### Request Example
```python
import io
from urllib.request import Request, urlopen
from pyresparser import ResumeParser

# Fetch remote resume
remote_url = 'https://example.com/resumes/candidate.pdf'
req = Request(remote_url, headers={'User-Agent': 'Mozilla/5.0'})
webpage = urlopen(req).read()

# Create BytesIO object with filename
resume_file = io.BytesIO(webpage)
resume_file.name = 'candidate.pdf'

# Parse remote resume
parser = ResumeParser(resume_file)
data = parser.get_extracted_data()

print(f"Candidate: {data['name']}")
print(f"Email: {data['email']}")
print(f"Skills: {', '.join(data['skills'] or [])}")
```

### Response
#### Success Response (200)
- **name** (string) - The extracted name of the candidate.
- **email** (string) - The extracted email address.
- **skills** (list of strings) - A list of extracted skills.

#### Response Example
```json
Candidate: Jane Doe
Email: jane.doe@example.com
Skills: Python, Data Analysis, SQL
```
```

--------------------------------

### Parse with custom skills file

Source: https://github.com/omkarpathak/pyresparser/blob/master/docs/cli.md

Use a custom CSV file containing skills to search against during extraction.

```bash
pyresparser -sf /path/to/resume/file.csv -f /path/to/resume/file
```

--------------------------------

### Calculate Total Work Experience using Pyresparser

Source: https://context7.com/omkarpathak/pyresparser/llms.txt

Calculate the total work experience in months or years from a list of experience entries. Handles date ranges and the 'present' keyword. Requires date strings in a recognizable format.

```python
from pyresparser import utils

# Sample experience entries
experience_list = [
    "Software Engineer at Google: Jan 2018 - Dec 2020",
    "Senior Developer at Meta: Jan 2021 - present",
    "Intern at StartupXYZ: Jun 2017 - Aug 2017"
]

# Calculate total experience in months
total_months = utils.get_total_experience(experience_list)
total_years = round(total_months / 12, 2)
print(f"Total experience: {total_years} years")  # Output: Total experience: 5.67 years

# Calculate months between specific dates
months = utils.get_number_of_months_from_dates("Jan 2020", "Dec 2022")
print(f"Duration: {months} months")  # Output: Duration: 35 months

# Handle 'present' as end date
months_to_present = utils.get_number_of_months_from_dates("Jan 2020", "present")
print(f"Duration to present: {months_to_present} months")
```

--------------------------------

### Parse Resume Sections with Pyresparser

Source: https://context7.com/omkarpathak/pyresparser/llms.txt

Extract structured data from common resume sections like experience, education, and skills. Two functions are available: one for graduates/undergraduates and another for professionals which includes more section types.

```python
from pyresparser import utils

resume_text = """
John Smith
john@email.com

EXPERIENCE
Software Engineer at Google
Jan 2020 - Present
- Developed microservices architecture

EDUCATION
Stanford University
M.S. Computer Science, 2019

SKILLS
Python, Java, Machine Learning, AWS
"""

# Extract sections for graduates/undergraduates
sections = utils.extract_entity_sections_grad(resume_text)
print(sections)
# Output:
# {
#     'experience': ['Software Engineer at Google', 'Jan 2020 - Present', '- Developed microservices architecture'],
#     'education': ['Stanford University', 'M.S. Computer Science, 2019'],
#     'skills': ['Python, Java, Machine Learning, AWS']
# }

# Extract sections for professionals (includes additional section types)
prof_sections = utils.extract_entity_sections_professional(resume_text)
```

--------------------------------

### Parse a remote resume file

Source: https://github.com/omkarpathak/pyresparser/blob/master/docs/cli.md

Extract data from a resume hosted at a remote URL.

```bash
pyresparser -r https://www.example.com/path/to/resume/file
```

--------------------------------

### Domain-Specific Skill Extraction with Custom Skills File

Source: https://context7.com/omkarpathak/pyresparser/llms.txt

Extract domain-specific skills by providing a custom CSV file. The CSV should contain skill names, one per column in the first row, without headers. Only skills matching this file will be extracted.

```python
from pyresparser import ResumeParser

# Create a custom skills CSV file (skills.csv):
# Python,Java,JavaScript,Machine Learning,Deep Learning,TensorFlow,PyTorch

# Parse resume with custom skills file
parser = ResumeParser(
    '/path/to/resume.pdf',
    skills_file='/path/to/custom_skills.csv'
)
data = parser.get_extracted_data()

# Only skills matching your custom file will be extracted
print(data['skills'])
# Output: ['Python', 'Machine Learning', 'TensorFlow']
```

--------------------------------

### CLI Resume Parsing Operations

Source: https://context7.com/omkarpathak/pyresparser/llms.txt

Perform common parsing tasks via the command line, including single files, directories, and remote URLs.

```bash
# Parse a single PDF resume
pyresparser -f /path/to/resume.pdf
```

```bash
# Parse all resumes in a directory
pyresparser -d /path/to/resumes/
```

```bash
# Parse a resume from a remote URL
pyresparser -r https://example.com/resumes/candidate.pdf

# Parse remote file with custom skills
pyresparser -r https://example.com/resume.pdf -sf /path/to/skills.csv
```

--------------------------------

### Specify export format

Source: https://github.com/omkarpathak/pyresparser/blob/master/docs/cli.md

Define the output format for the extracted data. Currently, only JSON is supported.

```bash
pyresparser -e json -f /path/to/resume/file
```

--------------------------------

### Export Parsed Data to JSON

Source: https://context7.com/omkarpathak/pyresparser/llms.txt

Save extracted resume data into JSON files for external system integration.

```bash
# Export single resume to JSON
pyresparser -f /path/to/resume.pdf -e json -o /path/to/output.json

# Export directory of resumes to JSON
pyresparser -d /path/to/resumes/ -e json -o /path/to/all_candidates.json
```

--------------------------------

### ResumeParser Class - Parse Single Resume

Source: https://context7.com/omkarpathak/pyresparser/llms.txt

The ResumeParser class is the primary interface for parsing resume files. It can handle various formats and extract essential candidate data.

```APIDOC
## ResumeParser Class - Parse Single Resume

### Description
The `ResumeParser` class is the main entry point for extracting data from resume files. It accepts a file path or BytesIO object and returns structured data including name, email, mobile number, skills, education, experience, and company information. The parser automatically detects the file format and uses appropriate extraction methods.

### Method
Instantiate the `ResumeParser` class.

### Endpoint
N/A (Python Class)

### Parameters
#### Path Parameters
- **file_path** (string) - Required - The path to the resume file (e.g., PDF, DOCX, DOC).
- **skills_file** (string) - Optional - Path to a custom CSV file for domain-specific skill extraction.
- **custom_regex** (string) - Optional - A custom regex pattern for parsing phone numbers.

### Request Example
```python
from pyresparser import ResumeParser

# Parse a local PDF resume
parser = ResumeParser('/path/to/resume.pdf')
data = parser.get_extracted_data()

print(data)
```

### Response
#### Success Response (200)
- **name** (string) - The extracted name of the candidate.
- **email** (string) - The extracted email address.
- **mobile_number** (string) - The extracted mobile number.
- **skills** (list of strings) - A list of extracted skills.
- **college_name** (list of strings) - A list of extracted college names.
- **degree** (list of strings) - A list of extracted degrees.
- **designation** (list of strings) - A list of extracted designations.
- **experience** (list of strings) - A list of extracted work experiences.
- **company_names** (list of strings) - A list of extracted company names.
- **no_of_pages** (integer) - The total number of pages in the resume.
- **total_experience** (float) - The total work experience in years.

#### Response Example
```json
{
    "name": "John Smith",
    "email": "john.smith@email.com",
    "mobile_number": "555-123-4567",
    "skills": ["Python", "Machine Learning", "Django", "SQL", "Git"],
    "college_name": ["Stanford University"],
    "degree": ["M.S. IN COMPUTER SCIENCE", "B.S. IN MATHEMATICS"],
    "designation": ["Software Engineer", "Data Scientist"],
    "experience": ["Jan 2020 - Present: Senior Developer at Tech Corp"],
    "company_names": ["Tech Corp", "StartupXYZ"],
    "no_of_pages": 2,
    "total_experience": 4.5
}
```
```

--------------------------------

### Extract Entities from Text using Pyresparser Utils

Source: https://context7.com/omkarpathak/pyresparser/llms.txt

Use these utility functions to extract emails, mobile numbers, names, and skills from raw text. spaCy is required for name and skills extraction. Custom regex can be provided for mobile number extraction.

```python
import spacy
from spacy.matcher import Matcher
from pyresparser import utils

# Load spaCy model
nlp = spacy.load('en_core_web_sm')
text = "John Smith john@email.com 555-123-4567 Python Django Machine Learning"
doc = nlp(text)

# Extract email
email = utils.extract_email(text)
print(f"Email: {email}")  # Output: john@email.com

# Extract mobile number
mobile = utils.extract_mobile_number(text)
print(f"Mobile: {mobile}")  # Output: 555-123-4567

# Extract mobile with custom regex
mobile_custom = utils.extract_mobile_number(text, custom_regex=r'\d{3}-\d{3}-\d{4}')

# Extract name using spaCy matcher
matcher = Matcher(nlp.vocab)
name = utils.extract_name(doc, matcher)
print(f"Name: {name}")  # Output: John Smith

# Extract skills
noun_chunks = list(doc.noun_chunks)
skills = utils.extract_skills(doc, noun_chunks)
print(f"Skills: {skills}")  # Output: ['Python', 'Django', 'Machine Learning']

# Extract skills with custom skills file
custom_skills = utils.extract_skills(doc, noun_chunks, skills_file='/path/to/skills.csv')
```

--------------------------------

### Extract Text from Documents via Utilities

Source: https://context7.com/omkarpathak/pyresparser/llms.txt

Access low-level text extraction functions directly from the utils module.

```python
from pyresparser import utils

# Extract text from PDF
pdf_text = ''
for page in utils.extract_text_from_pdf('/path/to/resume.pdf'):
    pdf_text += page

# Extract text from DOCX
docx_text = utils.extract_text_from_docx('/path/to/resume.docx')

# Extract text from DOC (requires textract)
doc_text = utils.extract_text_from_doc('/path/to/resume.doc')

# Generic text extraction (auto-detects format)
text = utils.extract_text('/path/to/resume.pdf', '.pdf')

# Get number of pages in PDF
num_pages = utils.get_number_of_pages('/path/to/resume.pdf')
print(f"Resume has {num_pages} pages")
```

--------------------------------

### Custom Skills File - Domain-Specific Skill Extraction

Source: https://context7.com/omkarpathak/pyresparser/llms.txt

Utilize a custom CSV file to tailor skill extraction to specific industries or domains.

```APIDOC
## Custom Skills File - Domain-Specific Skill Extraction

### Description
By default, Pyresparser uses a built-in skills CSV file. You can provide a custom skills file to extract domain-specific skills relevant to your industry. The CSV file should contain skill names without headers, one per column in the first row.

### Method
Instantiate `ResumeParser` with the `skills_file` parameter.

### Endpoint
N/A (Python Class)

### Parameters
#### Path Parameters
- **file_path** (string) - Required - The path to the resume file.
- **skills_file** (string) - Required - Path to the custom CSV file containing skills.

### Request Example
```python
from pyresparser import ResumeParser

# Create a custom skills CSV file (skills.csv):
# Python,Java,JavaScript,Machine Learning,Deep Learning,TensorFlow,PyTorch

# Parse resume with custom skills file
parser = ResumeParser(
    '/path/to/resume.pdf',
    skills_file='/path/to/custom_skills.csv'
)
data = parser.get_extracted_data()

# Only skills matching your custom file will be extracted
print(data['skills'])
```

### Response
#### Success Response (200)
- **skills** (list of strings) - A list of skills extracted based on the custom skills file.

#### Response Example
```json
["Python", "Machine Learning", "TensorFlow"]
```
```

--------------------------------

### Provide custom regex for phone numbers

Source: https://github.com/omkarpathak/pyresparser/blob/master/docs/cli.md

Override the default phone number parsing logic by providing a custom regex pattern.

```bash
pyresparser -re '<pattern>' -f /path/to/resume/file
```

--------------------------------

### Parse Resume with Custom Phone Number Regex

Source: https://github.com/omkarpathak/pyresparser/blob/master/docs/index.md

Provide a custom regular expression pattern to parse phone numbers. This allows for flexibility with non-standard phone number formats.

```python
from pyresparser import ResumeParser
data = ResumeParser('/path/to/resume/file', custom_regex='pattern').get_extracted_data()
```

--------------------------------

### Configure Custom Phone Number Regex

Source: https://context7.com/omkarpathak/pyresparser/llms.txt

Override default phone number extraction logic using the -re flag.

```bash
# Parse with custom phone regex for international numbers
pyresparser -f /path/to/resume.pdf -re '\+\d{1,3}[-.\s]?\d{3}[-.\s]?\d{3}[-.\s]?\d{4}'

# Combine with custom skills file
pyresparser -f /path/to/resume.pdf -sf /path/to/skills.csv -re '\d{10}'
```

--------------------------------

### Custom Phone Number Regex for Specific Formats

Source: https://context7.com/omkarpathak/pyresparser/llms.txt

Provide a custom regex pattern to parse phone numbers in specific formats, such as international numbers or organization-specific formats. This overrides the default phone number parsing.

```python
from pyresparser import ResumeParser

# Custom regex for UK phone numbers
uk_phone_regex = r'(\+44\s?7\d{3}|\(?07\d{3}\)?)\s?\d{3}\s?\d{3}'

parser = ResumeParser(
    '/path/to/resume.pdf',
    custom_regex=uk_phone_regex
)
data = parser.get_extracted_data()

print(data['mobile_number'])
# Output: '+44 7123 456 789'
```

```python
# Custom regex for US phone numbers with extension
us_regex = r'\d{3}[-.\]?\d{3}[-.\]?\d{4}(?:\s*(?:ext|x)\.?\s*\d+)?'
parser_us = ResumeParser('/path/to/resume.pdf', custom_regex=us_regex)
```

--------------------------------

### Custom Phone Number Regex - International Number Support

Source: https://context7.com/omkarpathak/pyresparser/llms.txt

Provide a custom regex pattern to parse phone numbers in specific or international formats.

```APIDOC
## Custom Phone Number Regex - International Number Support

### Description
While Pyresparser handles most phone number formats, you can provide a custom regex pattern for parsing phone numbers in specific formats. This is useful for international phone numbers or organization-specific formats.

### Method
Instantiate `ResumeParser` with the `custom_regex` parameter.

### Endpoint
N/A (Python Class)

### Parameters
#### Path Parameters
- **file_path** (string) - Required - The path to the resume file.
- **custom_regex** (string) - Required - A custom regex pattern for parsing phone numbers.

### Request Example
```python
from pyresparser import ResumeParser

# Custom regex for UK phone numbers
uk_phone_regex = r'(\\+44\\s?7\\d{3}|\\(?07\\d{3}\\)?)\\s?\\d{3}\\s?\\d{3}'

parser = ResumeParser(
    '/path/to/resume.pdf',
    custom_regex=uk_phone_regex
)
data = parser.get_extracted_data()

print(data['mobile_number'])
# Output: '+44 7123 456 789'

# Custom regex for US phone numbers with extension
us_regex = r'\\d{3}[-.\\s]?\\d{3}[-.\\s]?\\d{4}(?:\\s*(?:ext|x)\\.?\\s*\\d+)?'
parser_us = ResumeParser('/path/to/resume.pdf', custom_regex=us_regex)
```

### Response
#### Success Response (200)
- **mobile_number** (string) - The extracted mobile number using the custom regex.

#### Response Example
```json
"+44 7123 456 789"
```
```

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