### Install FlashText via pip

Source: https://flashtext.readthedocs.io

Use this command to install the FlashText package in your Python environment.

```bash
$ pip install flashtext
```

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

### Test FlashText installation

Source: https://flashtext.readthedocs.io

Commands to clone the repository and run tests using pytest.

```bash
$ git clone https://github.com/vi3k6i5/flashtext
$ cd flashtext
$ pip install pytest
$ python setup.py test
```

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

### Get all keywords

Source: https://flashtext.readthedocs.io

Retrieves the entire mapping of keywords to their standardized names as a dictionary.

```python
>>> from flashtext import KeywordProcessor
>>> keyword_processor = KeywordProcessor()
>>> keyword_processor.add_keyword('j2ee', 'Java')
>>> keyword_processor.add_keyword('colour', 'color')
>>> keyword_processor.get_all_keywords()
>>> # output: {'colour': 'color', 'j2ee': 'Java'}
```

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

### Extract keyword spans

Source: https://flashtext.readthedocs.io

Retrieves the start and end indices of extracted keywords by setting span_info to True.

```python
>>> from flashtext import KeywordProcessor
>>> keyword_processor = KeywordProcessor()
>>> keyword_processor.add_keyword('Big Apple', 'New York')
>>> keyword_processor.add_keyword('Bay Area')
>>> keywords_found = keyword_processor.extract_keywords('I love big Apple and Bay Area.', span_info=True)
>>> keywords_found
>>> # [('New York', 7, 16), ('Bay Area', 21, 29)]
```

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

### Build FlashText documentation

Source: https://flashtext.readthedocs.io

Commands to clone the repository and generate HTML documentation using Sphinx.

```bash
$ git clone https://github.com/vi3k6i5/flashtext
$ cd flashtext/docs
$ pip install sphinx
$ make html
$ # open _build/html/index.html in browser to view it locally
```

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

### Extract keywords with FlashText

Source: https://flashtext.readthedocs.io

Demonstrates initializing the KeywordProcessor, adding keywords, and handling non-word boundaries.

```python
>>> from flashtext import KeywordProcessor
>>> keyword_processor = KeywordProcessor()
>>> keyword_processor.add_keyword('Big Apple')
>>> print(keyword_processor.extract_keywords('I love Big Apple/Bay Area.'))
>>> # ['Big Apple']
>>> keyword_processor.add_non_word_boundary('/')
>>> print(keyword_processor.extract_keywords('I love Big Apple/Bay Area.'))
>>> # []
```

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

### Extract keywords with FlashText

Source: https://flashtext.readthedocs.io

Demonstrates basic keyword extraction using standardized names.

```python
>>> from flashtext import KeywordProcessor
>>> keyword_processor = KeywordProcessor()
>>> # keyword_processor.add_keyword(<unclean name>, <standardised name>)
>>> keyword_processor.add_keyword('Big Apple', 'New York')
>>> keyword_processor.add_keyword('Bay Area')
>>> keywords_found = keyword_processor.extract_keywords('I love Big Apple and Bay Area.')
>>> keywords_found
>>> # ['New York', 'Bay Area']
```

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

### Add multiple keywords simultaneously

Source: https://flashtext.readthedocs.io

Populates the processor using dictionaries or lists for bulk keyword management.

```python
>>> from flashtext import KeywordProcessor
>>> keyword_processor = KeywordProcessor()
>>> keyword_dict = {
>>>     "java": ["java_2e", "java programing"],
>>>     "product management": ["PM", "product manager"]
>>> }
>>> # {'clean_name': ['list of unclean names']}
>>> keyword_processor.add_keywords_from_dict(keyword_dict)
>>> # Or add keywords from a list:
>>> keyword_processor.add_keywords_from_list(["java", "python"])
>>> keyword_processor.extract_keywords('I am a product manager for a java_2e platform')
>>> # output ['product management', 'java']
```

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

### Configure case sensitivity

Source: https://flashtext.readthedocs.io

Initializes KeywordProcessor with case_sensitive set to True to enforce strict matching.

```python
>>> from flashtext import KeywordProcessor
>>> keyword_processor = KeywordProcessor(case_sensitive=True)
>>> keyword_processor.add_keyword('Big Apple', 'New York')
>>> keyword_processor.add_keyword('Bay Area')
>>> keywords_found = keyword_processor.extract_keywords('I love big Apple and Bay Area.')
>>> keywords_found
>>> # ['Bay Area']
```

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

### Check term presence and access

Source: https://flashtext.readthedocs.io

Verifies if a keyword exists and retrieves its standardized value using membership operators or dictionary-like access.

```python
>>> from flashtext import KeywordProcessor
>>> keyword_processor = KeywordProcessor()
>>> keyword_processor.add_keyword('j2ee', 'Java')
>>> 'j2ee' in keyword_processor
>>> # output: True
>>> keyword_processor.get_keyword('j2ee')
>>> # output: Java
>>> keyword_processor['colour'] = 'color'
>>> keyword_processor['colour']
>>> # output: color
```

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

### Check number of terms

Source: https://flashtext.readthedocs.io

Returns the total count of keywords currently stored in the processor.

```python
>>> from flashtext import KeywordProcessor
>>> keyword_processor = KeywordProcessor()
>>> keyword_dict = {
>>>     "java": ["java_2e", "java programing"],
>>>     "product management": ["PM", "product manager"]
>>> }
>>> keyword_processor.add_keywords_from_dict(keyword_dict)
>>> print(len(keyword_processor))
>>> # output 4
```

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

### Replace keywords in text

Source: https://flashtext.readthedocs.io

Replaces identified keywords with their standardized counterparts in a string.

```python
>>> keyword_processor.add_keyword('New Delhi', 'NCR region')
>>> new_sentence = keyword_processor.replace_keywords('I love Big Apple and new delhi.')
>>> new_sentence
>>> # 'I love New York and NCR region.'
```

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

### FlashText citation BibTeX

Source: https://flashtext.readthedocs.io

BibTeX entry for the original FlashText algorithm paper.

```bibtex
@ARTICLE{2017arXiv171100046S,
   author = {{Singh}, V.},
    title = "{Replace or Retrieve Keywords In Documents at Scale}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1711.00046},
 primaryClass = "cs.DS",
 keywords = {Computer Science - Data Structures and Algorithms},
     year = 2017,
    month = oct,
   adsurl = {http://adsabs.harvard.edu/abs/2017arXiv171100046S},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
```

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

### Extract extra information with keywords

Source: https://flashtext.readthedocs.io

Maps keywords to tuples or objects for richer metadata extraction. Note that replace_keywords is not supported with this configuration.

```python
>>> from flashtext import KeywordProcessor
>>> kp = KeywordProcessor()
>>> kp.add_keyword('Taj Mahal', ('Monument', 'Taj Mahal'))
>>> kp.add_keyword('Delhi', ('Location', 'Delhi'))
>>> kp.extract_keywords('Taj Mahal is in Delhi.')
>>> # [('Monument', 'Taj Mahal'), ('Location', 'Delhi')]
>>> # NOTE: replace_keywords feature won't work with this.
```

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

### Extract keywords without clean names

Source: https://flashtext.readthedocs.io

Extracts the original keyword if no standardized name is provided.

```python
>>> from flashtext import KeywordProcessor
>>> keyword_processor = KeywordProcessor()
>>> keyword_processor.add_keyword('Big Apple')
>>> keyword_processor.add_keyword('Bay Area')
>>> keywords_found = keyword_processor.extract_keywords('I love big Apple and Bay Area.')
>>> keywords_found
>>> # ['Big Apple', 'Bay Area']
```

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

### Remove keywords from processor

Source: https://flashtext.readthedocs.io

Removes specific keywords or groups of keywords using individual methods or bulk dictionary/list operations.

```python
>>> from flashtext import KeywordProcessor
>>> keyword_processor = KeywordProcessor()
>>> keyword_dict = {
>>>     "java": ["java_2e", "java programing"],
>>>     "product management": ["PM", "product manager"]
>>> }
>>> keyword_processor.add_keywords_from_dict(keyword_dict)
>>> print(keyword_processor.extract_keywords('I am a product manager for a java_2e platform'))
>>> # output ['product management', 'java']
>>> keyword_processor.remove_keyword('java_2e')
>>> # you can also remove keywords from a list/ dictionary
>>> keyword_processor.remove_keywords_from_dict({"product management": ["PM"]})
>>> keyword_processor.remove_keywords_from_list(["java programing"])
>>> keyword_processor.extract_keywords('I am a product manager for a java_2e platform')
>>> # output ['product management']
```

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