fast-parse-time 1.3.2

Natural Language (NLP) Extraction of Date and Time

fast-parse-time

Extract dates and times from text. Fast, deterministic, zero cost.

Why?

LLMs can parse dates, but they're slow, expensive, and non-deterministic. This library gives you:

• Sub-millisecond performance - Process thousands of documents per second
• Zero API costs - No per-request charges
• Deterministic results - Same input always produces same output
• Simple API - One function call, everything extracted

Install

pip install fast-parse-time

Usage

from fast_parse_time import parse_dates

text = "Meeting on 04/08/2024 to discuss issues from 5 days ago"
result = parse_dates(text)

# Explicit dates found in text
print(result.explicit_dates)
# [ExplicitDate(text='04/08/2024', date_type='FULL_EXPLICIT_DATE')]

# Relative time expressions
print(result.relative_times)
# [RelativeTime(cardinality=5, frame='day', tense='past')]

# Convert to Python datetime
for time_ref in result.relative_times:
    print(time_ref.to_datetime())
    # datetime.datetime(2025, 11, 14, ...)

What It Extracts

Explicit dates:

"Event on 04/08/2024"          → 04/08/2024 (full date)
"Meeting scheduled for 3/24"   → 3/24 (month/day)
"Copyright 2024"               → 2024 (year only)
"Ambiguous: 4/8"               → 4/8 (flagged as ambiguous)
"Published March 15, 2024"     → March 15, 2024 (written month)
"Filed in 2024"                → 2024 (year in prose)

Year ranges:

"Active 2014-2015"             → 2014-2015 (year range)
"From 2010 to 2020"           → From 2010 to 2020 (year range)
"Revenue grew 2019–2023"      → 2019-2023 (en/em dash normalized)
"Contract 2023-24"            → 2023-24 (abbreviated)

Relative times:

"5 days ago"                   → 5 days (past)
"last couple of weeks"         → 2 weeks (past)
"30 minutes ago"               → 30 minutes (past)
"half an hour ago"             → 1 hour (past)
"a few days ago"               → 3 days (past)

Examples

Parse everything at once

result = parse_dates("Report from 04/08/2024 covering issues from last week")

result.explicit_dates  # ['04/08/2024']
result.relative_times  # [RelativeTime(cardinality=1, frame='week', tense='past')]

Just get dates

from fast_parse_time import extract_explicit_dates

dates = extract_explicit_dates("Event on 04/08/2024 or maybe 3/24")
# {'04/08/2024': 'FULL_EXPLICIT_DATE', '3/24': 'MONTH_DAY'}

Convert to datetime objects

from fast_parse_time import resolve_to_datetime

datetimes = resolve_to_datetime("Show me data from 5 days ago")
# [datetime.datetime(2025, 11, 14, ...)]

Features

• Multiple date formats: 04/08/2024, 3/24, 2024-06-05, March 15, 2024
• Multiple delimiters: /, -, .
• Year ranges: 2014-2015, 2010 to 2020, from 2018 through 2022, 2023-24
• Unicode normalization: en dash, em dash, and other hyphen variants accepted automatically
• Written months: March 15, 2024, Mar 15, 2024, 15 March 2024
• Year-only in prose: Copyright 2024, filed in 2019
• Relative time expressions: "5 days ago", "last week", "couple of months ago"
• Informal expressions: "half an hour ago", "a few days ago", "several weeks ago"
• Named day and time-of-day references: "last Monday", "this morning", "yesterday"
• Ambiguity detection: Flags dates like 4/8 that could be April 8 or August 4
• Time frame support: seconds, minutes, hours, days, weeks, months, years

Documentation

• API Reference
• Functions - All functions with examples
• Types - Data classes and DateType enum
• System Boundaries - Design decisions and limitations

Performance

Typical extraction takes < 1ms per document. No network calls, no model inference, pure Python.

License

MIT - See LICENSE for details.

Author

Craig Trim - [e-mail]

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