### Batch Initialization of FuzzySet from Large Datasets

Source: https://context7.com/axiak/fuzzyset/llms.txt

Details efficient initialization of FuzzySet from large iterables by passing them directly to the constructor, which is more efficient than adding items individually. Includes an example of loading data from a gzipped file and querying the dataset.

```python
import gzip
from fuzzyset import FuzzySet

# Load from file
def load_cities():
    with gzip.open('cities.gz', 'rb') as f:
        for line in f:
            yield line.decode().strip()

# Efficient batch initialization
fs = FuzzySet(load_cities())

# Query the dataset
result = fs.get("San Fransisco")  # Misspelled
# Returns: [(0.94..., 'San Francisco')]

result = fs.get("Steemboot Sprints")
# Returns closest city names based on character similarity
```

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

### Fuzzy String Matching with get() in FuzzySet

Source: https://context7.com/axiak/fuzzyset/llms.txt

Illustrates how to perform fuzzy string matching using the `get()` method of the FuzzySet. This method returns a list of tuples containing (score, matched_string) for similar strings or `None` if no matches are found. It also shows how to provide a default value to handle missing matches gracefully.

```python
from fuzzyset import FuzzySet

# Build the dataset
fs = FuzzySet()
fs.add("michael axiak")
fs.add("new york city")
fs.add("san francisco")

# Fuzzy search with typos
result = fs.get("micael asiak")
# Returns: [(0.8461538461538461, 'michael axiak')]

result = fs.get("new yrok")
# Returns: [(0.888..., 'new york city')]

result = fs.get("completely unrelated")
# Returns: None (no matches found)

# Handle missing matches safely
result = fs.get("xyz", default=[])
# Returns: [] instead of None
```

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

### High-Performance Cython Implementation of FuzzySet

Source: https://context7.com/axiak/fuzzyset/llms.txt

Explains how to use the `cFuzzySet` for improved performance, offering a 15% speedup with an identical API to the pure Python `FuzzySet`. It shows the recommended import pattern, building a large dataset, and standard usage, including adding elements and getting the size.

```python
# Recommended import pattern for performance
try:
    from cfuzzyset import cFuzzySet as FuzzySet
except ImportError:
    from fuzzyset import FuzzySet

# Build large dataset
cities = [
    "New York", "Los Angeles", "Chicago", "Houston", "Phoenix",
    "Philadelphia", "San Antonio", "San Diego", "Dallas", "San Jose"
]

fs = FuzzySet(cities)

# Usage identical to pure Python version
result = fs.get("Los Angles")  # Common typo
# Returns: [(0.92..., 'Los Angeles')]

# All features work the same
fs.add("Seattle")
print(len(fs))  # Returns: 11
```

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

### FuzzySet Integration with Spell Checkers

Source: https://context7.com/axiak/fuzzyset/llms.txt

Provides an example of using FuzzySet as a backend for a spell-checking system. It defines a `spell_check` function that takes a word and a threshold, returning suggestions from a predefined dictionary if the word is misspelled or not found.

```python
from fuzzyset import FuzzySet

# Dictionary of correct spellings
dictionary = FuzzySet([
    "accommodate", "achievement", "acquaintance", "argument",
    "beginning", "believe", "calendar", "cemetery", "committee",
    "definitely", "embarrass", "environment", "experience"
])

def spell_check(word, threshold=0.8):
    """Return spelling suggestions if word is misspelled"""
    matches = dictionary.get(word)

    if matches is None:
        return f"No suggestions for '{word}'"

    # Perfect match means correctly spelled
    if matches[0][0] == 1.0:
        return f"'{word}' is spelled correctly"

    # Return suggestions above threshold
    suggestions = [
        match for score, match in matches
        if score >= threshold
    ]

    if suggestions:
        return f"Did you mean: {', '.join(suggestions)}?"
    else:
        return f"No close matches for '{word}'"

# Test the spell checker
print(spell_check("accomodate"))      # Did you mean: accommodate?
print(spell_check("definately"))      # Did you mean: definitely?
print(spell_check("environment"))     # 'environment' is spelled correctly
print(spell_check("xyzabc"))          # No suggestions for 'xyzabc'
```

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

### FuzzySet Initialization with Similarity Cutoff

Source: https://context7.com/axiak/fuzzyset/llms.txt

Demonstrates how to initialize FuzzySet with a list of strings and a relative similarity cutoff. This limits results to matches within 90% of the best match score. It shows how to retrieve results and how the cutoff affects the output.

```python
from fuzzyset import FuzzySet

fs_strict = FuzzySet(
    ["Boston", "Houston", "Austin", "Newton"],
    rel_sim_cutoff=0.9
)

result = fs_strict.get("Auston")
# Returns only high-confidence matches
# [(0.95..., 'Austin'), (0.91..., 'Boston')]
# 'Houston' and 'Newton' excluded if score < 0.9 * top_score

fs_all = FuzzySet(
    ["Boston", "Houston", "Austin", "Newton"],
    rel_sim_cutoff=1.0
)

result = fs_all.get("Auston")
# May return more results including lower-scoring matches
```

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

### Initialize and Add Strings to FuzzySet

Source: https://context7.com/axiak/fuzzyset/llms.txt

Demonstrates how to initialize a FuzzySet object, either empty or with an initial iterable of strings, and how to add individual strings dynamically. It also shows the return value of the add() method indicating whether a string was newly added.

```python
from fuzzyset import FuzzySet

# Initialize empty set
fs = FuzzySet()

# Add strings individually
fs.add("Michael Axiak")
fs.add("New York")
fs.add("Los Angeles")

# Initialize with iterable
cities = FuzzySet(["San Francisco", "San Diego", "Seattle", "Portland"])

# Check if string was added (returns False if already exists)
result = fs.add("New York")  # Returns False - already exists
result = fs.add("Chicago")   # Returns True - newly added
```

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

### Dictionary-Style Access with __getitem__ in FuzzySet

Source: https://context7.com/axiak/fuzzyset/llms.txt

Shows how to use dictionary-style square bracket notation (`__getitem__`) for fuzzy lookups in FuzzySet. This method raises a `KeyError` if no match is found, mimicking standard Python dictionary behavior. It demonstrates retrieving matches and handling potential `KeyError` exceptions.

```python
from fuzzyset import FuzzySet

fs = FuzzySet(["New York", "New Jersey", "Los Angeles"])

# Access with square brackets
try:
    matches = fs["ew Kork"]
    # Returns: [(0.87..., 'New York')]
    print(f"Best match: {matches[0][1]} with score {matches[0][0]:.2f}")
except KeyError:
    print("No matches found")

# Exact matches return perfect score
matches = fs["los angeles"]
# Returns: [(1.0, 'Los Angeles')]
```

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

### Python FuzzySet Construction Arguments

Source: https://github.com/axiak/fuzzyset/blob/master/README.rst

Illustrates initializing a FuzzySet with an iterable of strings and specifying construction arguments like gram size and Levenshtein distance usage. These parameters affect how strings are processed and matched.

```python
import fuzzyset

initial_data = ["apple", "banana", "apricot", "blueberry"]

# Initialize with default gram sizes (2, 3) and Levenshtein enabled
fs_default = fuzzyset.FuzzySet(initial_data)

# Initialize with custom gram sizes and Levenshtein disabled
fs_custom = fuzzyset.FuzzySet(initial_data, gram_size_lower=3, gram_size_upper=4, use_levenshtein=False)

print(fs_default.get("appel"))
print(fs_custom.get("aple"))
```

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

### Python FuzzySet with Cython Optimization

Source: https://github.com/axiak/fuzzyset/blob/master/README.rst

Shows how to import the optimized Cython version of FuzzySet (cFuzzySet) if available, falling back to the pure Python version otherwise. This can provide a significant performance increase.

```python
try:
    from cfuzzyset import cFuzzySet as FuzzySet
except ImportError:
    from fuzzyset import FuzzySet

# Usage remains the same
a = FuzzySet()
a.add("example string")
print(a.get("exampel string"))
```

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

### Python FuzzySet Basic Usage

Source: https://github.com/axiak/fuzzyset/blob/master/README.rst

Demonstrates the basic usage of FuzzySet for adding strings and performing fuzzy searches. The result is a list of (score, matched_value) tuples, where the score indicates the match quality.

```python
import fuzzyset

a = fuzzyset.FuzzySet()
a.add("michael axiak")
result = a.get("micael asiak")
print(result)
# Expected output: [(0.8461538461538461, u'michael axiak')]
```

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

### Custom N-Gram Configuration in FuzzySet

Source: https://context7.com/axiak/fuzzyset/llms.txt

Explains how to customize the n-gram range used for indexing and matching by adjusting `gram_size_lower` and `gram_size_upper` parameters during FuzzySet initialization. This allows tuning the trade-off between performance and match quality.

```python
from fuzzyset import FuzzySet

# Default: uses 2-grams and 3-grams
fs_default = FuzzySet(["hello", "world"])

# Use only bigrams (faster but less precise)
fs_bigram = FuzzySet(
    ["hello", "world"],
    gram_size_lower=2,
    gram_size_upper=2
)

# Use bigrams and trigrams (default)
fs_balanced = FuzzySet(
    ["mississippi", "massachusetts", "tennessee"],
    gram_size_lower=2,
    gram_size_upper=3
)

# Use only trigrams (more precise, slower for large datasets)
fs_trigram = FuzzySet(
    ["concatenate", "concentrate", "consecrate"],
    gram_size_lower=3,
    gram_size_upper=3
)

result = fs_trigram.get("consecrate")
# More selective matching with larger grams
```

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

### FuzzySet Case Insensitivity and Normalization

Source: https://context7.com/axiak/fuzzyset/llms.txt

Illustrates FuzzySet's automatic normalization of strings to lowercase and removal of non-word characters (preserving spaces and commas) for matching purposes. It shows that case and extra punctuation do not affect matching accuracy, while original casing is preserved in results.

```python
from fuzzyset import FuzzySet

fs = FuzzySet()
fs.add("New York City")
fs.add("São Paulo")
fs.add("Hello, World!")

# Case doesn't matter for matching
result = fs.get("NEW YORK CITY")
# Returns: [(1.0, 'New York City')]

result = fs.get("new york city")
# Returns: [(1.0, 'New York City')]

# Original casing is preserved in results
result = fs.get("hello world")
# Returns: [(1.0, 'Hello, World!')]

# Non-word characters are ignored during matching
result = fs.get("hello___world")
# Still matches 'Hello, World!' with high score
```

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

### FuzzySet Length and Boolean Operations

Source: https://context7.com/axiak/fuzzyset/llms.txt

Demonstrates how to check the size of a FuzzySet dataset and determine if it contains any entries using standard Python length (`len()`) and boolean (`if fs:`) operators. Shows adding items and verifying that duplicate additions do not increase the count.

```python
from fuzzyset import FuzzySet

fs = FuzzySet()

# Check if empty
if not fs:
    print("FuzzySet is empty")

# Add items
fs.add("apple")
fs.add("banana")
fs.add("cherry")

# Get count
print(len(fs))  # Returns: 3

# Boolean check
if fs:
    print("FuzzySet has data")

# Duplicates don't increase count
fs.add("apple")  # Already exists
print(len(fs))  # Still returns: 3
```

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

### Levenshtein Distance Control in FuzzySet

Source: https://context7.com/axiak/fuzzyset/llms.txt

Demonstrates how to control the inclusion of Levenshtein distance calculation in FuzzySet matching by using the `use_levenshtein` parameter. Enabling Levenshtein distance improves accuracy for character-level edits like transpositions, at a potential performance cost.

```python
from fuzzyset import FuzzySet

# With Levenshtein distance (default - more accurate)
fs_lev = FuzzySet(
    ["Michael", "Michelle", "Mitchell"],
    use_levenshtein=True
)

result = fs_lev.get("Micheal")  # Common typo: swapped 'ea' and 'ae'
# Returns better ranking thanks to Levenshtein
# [(0.92..., 'Michael'), (0.85..., 'Michelle'), ...]

# Without Levenshtein (faster, cosine similarity only)
fs_cosine = FuzzySet(
    ["Michael", "Michelle", "Mitchell"],
    use_levenshtein=False
)

result = fs_cosine.get("Micheal")
# Still works but may have different score ordering
```

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