Boolean Query Parser

A Python package for parsing and evaluating complex boolean text queries with support for AND, OR, NOT operations, parentheses for nesting, and regular expression pattern matching.

Features

• Boolean operators: AND, OR, NOT
• Parentheses for grouping and complex nested expressions
• Regular expression pattern matching with support for:
  • Regular expression flags (i for case-insensitive, m for multiline, s for dotall, x for verbose)
  • Complex patterns including capture groups, lookaheads, and lookbehinds
  • Special character escaping
• Simple, intuitive query syntax
• Comprehensive error handling

Installation

From PyPI

pip install boolean-query-parser

From Source

Clone the repository and install using pip:

git clone https://github.com/yourusername/boolean_query_parser.git
cd boolean_query_parser
pip install .

Usage

Basic Example

from boolean_query_parser import parse_query, apply_query

# Define some sample text data
documents = [
    "The quick brown fox jumps over the lazy dog",
    "Python is a programming language",
    "The Python programming language is powerful and easy to learn",
    "Regular expressions can be complex but useful"
]

# Parse a query
query = 'Python AND programming AND NOT complex'
parsed_query = parse_query(query)

# Apply the query to filter documents
matching_documents = [doc for doc in documents if apply_query(parsed_query, doc)]

# Print results
for doc in matching_documents:
    print(doc)

Output:

Python is a programming language
The Python programming language is powerful and easy to learn

Advanced Example with Nested Expressions

from boolean_query_parser import parse_query, apply_query

# Parse a complex query with parentheses and multiple operations
query = '(Python OR programming) AND (language OR easy) AND NOT (complex OR difficult)'
parsed_query = parse_query(query)

# Sample text data
documents = [
    "Python is a great language for beginners",
    "Programming can be complex and difficult at times",
    "Python makes programming tasks easy to accomplish",
    "This text has nothing relevant"
]

# Apply the query
for doc in documents:
    if apply_query(parsed_query, doc):
        print(f"Match: {doc}")
    else:
        print(f"No match: {doc}")

Using Regular Expressions

from boolean_query_parser import parse_query, apply_query

# Parse a query with regex patterns
query = '/py.*on/i AND NOT /difficult/'
parsed_query = parse_query(query)

documents = [
    "Python is easy to learn",
    "python programming is fun",
    "This is difficult Python code",
    "PyThOn is case-insensitive in this example"
]

# Apply the query
for doc in documents:
    if apply_query(parsed_query, doc):
        print(f"Match: {doc}")

Using Regular Expression Flags

from boolean_query_parser import parse_query, apply_query

# Case-insensitive matching with 'i' flag
query = '/python/i'
parsed_query = parse_query(query)
print(apply_query(parsed_query, "This contains PYTHON"))  # True

# Multiline matching with 'm' flag
multiline_text = "First line\nSecond line with python\nThird line"
query = '/^Second.*python$/m'
parsed_query = parse_query(query)
print(apply_query(parsed_query, multiline_text))  # True

# Dot-all mode with 's' flag (dot matches newlines)
text_with_newlines = "Start\nMiddle\nEnd"
query = '/Start.*End/s'
parsed_query = parse_query(query)
print(apply_query(parsed_query, text_with_newlines))  # True

Complex Regex Patterns

from boolean_query_parser import parse_query, apply_query

# Email validation with regex
email_pattern = '/([A-Za-z0-9]+[._-])*[A-Za-z0-9]+@[A-Za-z0-9-]+(\\.[A-Za-z]{2,})/'
email_query = parse_query(email_pattern)

# HTML tag matching with capture groups and backreferences
html_pattern = '/\\<([a-z][a-z0-9]*)(\\s[^\\>]*)?\\>([^\\<]*)\\<\\/\\1\\>/i'
html_query = parse_query(html_pattern)

# Password validation with lookaheads
password_pattern = '/^(?=.*[a-z])(?=.*[A-Z])(?=.*\\d).{8,}$/'
password_query = parse_query(password_pattern)

# Test them
print(apply_query(email_query, "Contact us at info@example.com"))  # True
print(apply_query(html_query, "<div>Content</div>"))  # True
print(apply_query(password_query, "Password123"))  # True

API Documentation

parse_query(query_str: str) -> Union[dict, str]

Parses a boolean query string into a structured representation.

Parameters:

• query_str (str): The boolean query string to parse.

Returns:

• A nested dictionary structure representing the parsed query.

Raises:

• SyntaxError: If the query has invalid syntax or mismatched parentheses.

Query Syntax:

• Boolean operators: AND, OR, NOT
• Terms can be wrapped in quotes for exact matching: "exact phrase"
• Regular expressions can be specified with forward slashes: /pattern/
• Regular expressions can include flags: /pattern/i (i=case-insensitive, m=multiline, s=dotall, x=verbose)
• Parentheses can be used for grouping expressions

apply_query(parsed_query: Union[dict, str], text: str) -> bool

Applies a parsed query to a text string and returns whether the text matches the query.

Parameters:

• parsed_query (Union[dict, str]): The parsed query structure from parse_query.
• text (str): The text to match against the query.

Returns:

• bool: True if the text matches the query, False otherwise.

Real-World Use Cases

Log Analysis

Parse through server logs to find specific error patterns:

from boolean_query_parser import parse_query, apply_query
import glob

# Query to find critical errors related to database but not connection timeouts
query = '(ERROR OR CRITICAL) AND database AND NOT "connection timeout"'
parsed_query = parse_query(query)

# Process log files
matching_logs = []
for log_file in glob.glob('/var/log/application/*.log'):
    with open(log_file, 'r') as f:
        for line in f:
            if apply_query(parsed_query, line):
                matching_logs.append(line.strip())

print(f"Found {len(matching_logs)} matching log entries")

Document Classification

Categorize documents based on their content:

from boolean_query_parser import parse_query, apply_query

# Define category queries
categories = {
    'finance': parse_query('(banking OR investment OR financial) AND NOT (gaming OR entertainment)'),
    'technology': parse_query('(programming OR software OR hardware OR "machine learning") AND NOT financial'),
    'health': parse_query('(medical OR health OR doctor OR patient) AND NOT (technology OR finance)')
}

# Function to classify a document
def classify_document(text):
    results = []
    for category, query in categories.items():
        if apply_query(query, text):
            results.append(category)
    return results or ['uncategorized']

Email Filtering example

Filter emails based on complex patterns:

from boolean_query_parser import parse_query, apply_query

# Query to find emails that:
# 1. Have attachments (mention .pdf, .doc, etc.)
# 2. Are not from known domains
# 3. Contain specific keywords in the subject
query = parse_query('(/\\.pdf/i OR /\\.doc/i OR /\\.docx/i) AND NOT /from:.*@(company\\.com|trusted\\.org)/ AND /subject:.*urgent/i')

# Apply to email bodies
def filter_suspicious_emails(emails):
    return [email for email in emails if apply_query(query, email)]

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.