15ea23d5d6
* feat: introduce hindsight-api-slim and hindsight-all-slim packages Closes #552 - Move all source code from hindsight-api/ to new hindsight-api-slim/ - hindsight-api-slim has heavy ML deps (torch, sentence-transformers, transformers, einops, flashrank, mlx, mlx-lm, safetensors) and pg0-embedded as optional extras: [local-ml], [embedded-db], [all] - hindsight-api becomes a zero-code meta-package depending on hindsight-api-slim[all] for full backward compatibility - Add hindsight-all-slim meta-package: hindsight-api-slim + client + embed - hindsight-all updated to depend on hindsight-api-slim[all] - pg0.py: lazy-import pg0 with clear ImportError pointing to [embedded-db] - Dockerfile: replace sed hack with proper uv sync --extra flags - Update release.yml, test.yml, lint.sh, release.sh, CLAUDE.md and all path references throughout the repo * refactor: rename hindsight/ directory to hindsight-all/ * docs: document hindsight-api-slim and hindsight-all-slim package variants Add package variants table and extras explanation to installation.md * docs: remove emojis from installation.md, use professional tone * docs: link Docker slim variant to pip package variants section * docs: consolidate Docker image variants into single table * ci: fix working-directory paths after package restructure - Replace all hindsight-api → hindsight-api-slim in test.yml - Replace hindsight → hindsight-all in test.yml - Add --extra embedded-db to test-embed API install step * ci: add local-ml and embedded-db extras to API sync steps These extras were previously implicit in the old hindsight-api package (which bundled everything). Now that hindsight-api-slim uses optional extras, we must explicitly request local-ml and embedded-db in CI. * ci: add API install step with embedded-db to test-embed smoke test The smoke test starts hindsight-api as a daemon, which requires pg0-embedded. Add a dedicated install step for hindsight-api-slim with embedded-db extra so the daemon can start successfully. * ci: remove --no-install-project when using optional extras When --no-install-project is combined with --extra, the optional deps are not installed because extras require the project to be active. Remove --no-install-project from steps that need local-ml or embedded-db. * ci: fix ordering of uv sync steps to preserve optional extras When uv sync runs for a different workspace member, it removes optional extras installed for other members. Fix by always running extra-requiring API sync last, after other workspace member syncs. Also remove --no-install-project from embedded-db sync in test-embed, as --no-install-project prevents optional extras from being active. * ci: add local-ml extra to test-embed API install for smoke test The smoke test starts the full API server which needs sentence-transformers for local embeddings (default provider). Add local-ml extra to the install. * ci: simplify extras with --all-extras and add slim pip smoke test - Replace explicit --extra local-ml --extra embedded-db with --all-extras for cleaner, more maintainable sync steps - Add test-pip-slim job: tests hindsight-api-slim[embedded-db] without local ML models, using Cohere for embeddings/reranking (mirrors Docker slim smoke test approach) * ci: simplify slim smoke test to health check only (mirrors Docker test)
529 lines
20 KiB
Python
529 lines
20 KiB
Python
"""
|
|
Query analysis abstraction for the memory system.
|
|
|
|
Provides an interface for analyzing natural language queries to extract
|
|
structured information like temporal constraints.
|
|
"""
|
|
|
|
import logging
|
|
import re
|
|
from abc import ABC, abstractmethod
|
|
from datetime import datetime, timedelta
|
|
|
|
from pydantic import BaseModel, Field
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class TemporalConstraint(BaseModel):
|
|
"""
|
|
Temporal constraint extracted from a query.
|
|
|
|
Represents a time range with start and end dates.
|
|
"""
|
|
|
|
start_date: datetime = Field(description="Start of the time range (inclusive)")
|
|
end_date: datetime = Field(description="End of the time range (inclusive)")
|
|
|
|
def __str__(self) -> str:
|
|
return f"{self.start_date.strftime('%Y-%m-%d')} to {self.end_date.strftime('%Y-%m-%d')}"
|
|
|
|
|
|
class QueryAnalysis(BaseModel):
|
|
"""
|
|
Result of analyzing a natural language query.
|
|
|
|
Contains extracted structured information like temporal constraints.
|
|
"""
|
|
|
|
temporal_constraint: TemporalConstraint | None = Field(
|
|
default=None, description="Extracted temporal constraint, if any"
|
|
)
|
|
|
|
|
|
class QueryAnalyzer(ABC):
|
|
"""
|
|
Abstract base class for query analysis.
|
|
|
|
Implementations analyze natural language queries to extract structured
|
|
information like temporal constraints, entities, etc.
|
|
"""
|
|
|
|
@abstractmethod
|
|
def load(self) -> None:
|
|
"""
|
|
Load the query analyzer model.
|
|
|
|
This should be called during initialization to load the model
|
|
and avoid cold start latency on first analyze() call.
|
|
"""
|
|
pass
|
|
|
|
@abstractmethod
|
|
def analyze(self, query: str, reference_date: datetime | None = None) -> QueryAnalysis:
|
|
"""
|
|
Analyze a natural language query.
|
|
|
|
Args:
|
|
query: Natural language query to analyze
|
|
reference_date: Reference date for relative terms (defaults to now)
|
|
|
|
Returns:
|
|
QueryAnalysis containing extracted information
|
|
"""
|
|
pass
|
|
|
|
|
|
class DateparserQueryAnalyzer(QueryAnalyzer):
|
|
"""
|
|
Query analyzer using dateparser library.
|
|
|
|
Uses dateparser to extract temporal expressions from natural language
|
|
queries. Supports 200+ languages including English, Spanish, Italian,
|
|
French, German, etc.
|
|
|
|
Performance:
|
|
- ~10-50ms per query
|
|
- No model loading required (lazy import on first use)
|
|
"""
|
|
|
|
def __init__(self):
|
|
"""Initialize dateparser query analyzer."""
|
|
self._search_dates = None
|
|
|
|
def load(self) -> None:
|
|
"""Load dateparser and warm up internal data structures.
|
|
|
|
Triggers the real initialization cost (regex tables, timezone data) at
|
|
load time so the first actual recall doesn't pay the cold-start penalty.
|
|
"""
|
|
if self._search_dates is None:
|
|
from dateparser.search import search_dates
|
|
|
|
self._search_dates = search_dates
|
|
# Warm up: fire a dummy call to trigger lazy-loaded internal tables.
|
|
self._search_dates("today")
|
|
|
|
def analyze(self, query: str, reference_date: datetime | None = None) -> QueryAnalysis:
|
|
"""
|
|
Analyze query using dateparser.
|
|
|
|
Extracts temporal expressions from the query text. Supports multiple
|
|
languages automatically.
|
|
|
|
Args:
|
|
query: Natural language query (any language)
|
|
reference_date: Reference date for relative terms (defaults to now)
|
|
|
|
Returns:
|
|
QueryAnalysis with temporal_constraint if found
|
|
"""
|
|
if reference_date is None:
|
|
reference_date = datetime.now()
|
|
|
|
# Check for period expressions first (these need special handling)
|
|
query_lower = query.lower()
|
|
period_result = self._extract_period(query_lower, reference_date)
|
|
if period_result is not None:
|
|
return QueryAnalysis(temporal_constraint=period_result)
|
|
|
|
# Lazy load dateparser (only imports on first call, then cached)
|
|
self.load()
|
|
|
|
# Use dateparser's search_dates to find temporal expressions
|
|
settings = {
|
|
"RELATIVE_BASE": reference_date,
|
|
"PREFER_DATES_FROM": "past",
|
|
"RETURN_AS_TIMEZONE_AWARE": False,
|
|
}
|
|
|
|
results = self._search_dates(query, settings=settings)
|
|
|
|
if not results:
|
|
return QueryAnalysis(temporal_constraint=None)
|
|
|
|
# Filter out false positives (common words parsed as dates)
|
|
false_positives = {"do", "may", "march", "will", "can", "sat", "sun", "mon", "tue", "wed", "thu", "fri"}
|
|
valid_results = [(text, date) for text, date in results if text.lower() not in false_positives or len(text) > 3]
|
|
|
|
if not valid_results:
|
|
return QueryAnalysis(temporal_constraint=None)
|
|
|
|
# Use the first valid date found
|
|
_, parsed_date = valid_results[0]
|
|
|
|
# Create constraint for single day
|
|
start_date = parsed_date.replace(hour=0, minute=0, second=0, microsecond=0)
|
|
end_date = parsed_date.replace(hour=23, minute=59, second=59, microsecond=999999)
|
|
|
|
return QueryAnalysis(temporal_constraint=TemporalConstraint(start_date=start_date, end_date=end_date))
|
|
|
|
def _extract_period(self, query: str, reference_date: datetime) -> TemporalConstraint | None:
|
|
"""
|
|
Extract period-based temporal expressions (week, month, year, weekend).
|
|
|
|
These need special handling as they represent date ranges, not single dates.
|
|
Supports multiple languages.
|
|
"""
|
|
|
|
def constraint(start: datetime, end: datetime) -> TemporalConstraint:
|
|
return TemporalConstraint(
|
|
start_date=start.replace(hour=0, minute=0, second=0, microsecond=0),
|
|
end_date=end.replace(hour=23, minute=59, second=59, microsecond=999999),
|
|
)
|
|
|
|
# Yesterday patterns (English, Spanish, Italian, French, German)
|
|
if re.search(r"\b(yesterday|ayer|ieri|hier|gestern)\b", query, re.IGNORECASE):
|
|
d = reference_date - timedelta(days=1)
|
|
return constraint(d, d)
|
|
|
|
# Today patterns
|
|
if re.search(r"\b(today|hoy|oggi|aujourd\'?hui|heute)\b", query, re.IGNORECASE):
|
|
return constraint(reference_date, reference_date)
|
|
|
|
# "a couple of days ago" / "a few days ago" patterns
|
|
# These are imprecise so we create a range
|
|
if re.search(r"\b(a\s+)?couple\s+(of\s+)?days?\s+ago\b", query, re.IGNORECASE):
|
|
# "a couple of days" = approximately 2 days, give range of 1-3 days
|
|
return constraint(reference_date - timedelta(days=3), reference_date - timedelta(days=1))
|
|
|
|
if re.search(r"\b(a\s+)?few\s+days?\s+ago\b", query, re.IGNORECASE):
|
|
# "a few days" = approximately 3-4 days, give range of 2-5 days
|
|
return constraint(reference_date - timedelta(days=5), reference_date - timedelta(days=2))
|
|
|
|
# "a couple of weeks ago" / "a few weeks ago" patterns
|
|
if re.search(r"\b(a\s+)?couple\s+(of\s+)?weeks?\s+ago\b", query, re.IGNORECASE):
|
|
# "a couple of weeks" = approximately 2 weeks, give range of 1-3 weeks
|
|
return constraint(reference_date - timedelta(weeks=3), reference_date - timedelta(weeks=1))
|
|
|
|
if re.search(r"\b(a\s+)?few\s+weeks?\s+ago\b", query, re.IGNORECASE):
|
|
# "a few weeks" = approximately 3-4 weeks, give range of 2-5 weeks
|
|
return constraint(reference_date - timedelta(weeks=5), reference_date - timedelta(weeks=2))
|
|
|
|
# "a couple of months ago" / "a few months ago" patterns
|
|
if re.search(r"\b(a\s+)?couple\s+(of\s+)?months?\s+ago\b", query, re.IGNORECASE):
|
|
# "a couple of months" = approximately 2 months, give range of 1-3 months
|
|
return constraint(reference_date - timedelta(days=90), reference_date - timedelta(days=30))
|
|
|
|
if re.search(r"\b(a\s+)?few\s+months?\s+ago\b", query, re.IGNORECASE):
|
|
# "a few months" = approximately 3-4 months, give range of 2-5 months
|
|
return constraint(reference_date - timedelta(days=150), reference_date - timedelta(days=60))
|
|
|
|
# Last week patterns (English, Spanish, Italian, French, German)
|
|
if re.search(
|
|
r"\b(last\s+week|la\s+semana\s+pasada|la\s+settimana\s+scorsa|la\s+semaine\s+derni[eè]re|letzte\s+woche)\b",
|
|
query,
|
|
re.IGNORECASE,
|
|
):
|
|
start = reference_date - timedelta(days=reference_date.weekday() + 7)
|
|
return constraint(start, start + timedelta(days=6))
|
|
|
|
# Last month patterns
|
|
if re.search(
|
|
r"\b(last\s+month|el\s+mes\s+pasado|il\s+mese\s+scorso|le\s+mois\s+dernier|letzten?\s+monat)\b",
|
|
query,
|
|
re.IGNORECASE,
|
|
):
|
|
first = reference_date.replace(day=1)
|
|
end = first - timedelta(days=1)
|
|
start = end.replace(day=1)
|
|
return constraint(start, end)
|
|
|
|
# Last year patterns
|
|
if re.search(
|
|
r"\b(last\s+year|el\s+a[ñn]o\s+pasado|l\'anno\s+scorso|l\'ann[ée]e\s+derni[eè]re|letztes?\s+jahr)\b",
|
|
query,
|
|
re.IGNORECASE,
|
|
):
|
|
year = reference_date.year - 1
|
|
return constraint(datetime(year, 1, 1), datetime(year, 12, 31))
|
|
|
|
# Last weekend patterns
|
|
if re.search(
|
|
r"\b(last\s+weekend|el\s+fin\s+de\s+semana\s+pasado|lo\s+scorso\s+fine\s+settimana|le\s+week-?end\s+dernier|letztes?\s+wochenende)\b",
|
|
query,
|
|
re.IGNORECASE,
|
|
):
|
|
days_since_sat = (reference_date.weekday() + 2) % 7
|
|
if days_since_sat == 0:
|
|
days_since_sat = 7
|
|
sat = reference_date - timedelta(days=days_since_sat)
|
|
return constraint(sat, sat + timedelta(days=1))
|
|
|
|
# Month + Year patterns (e.g., "June 2024", "junio 2024", "giugno 2024")
|
|
month_patterns = {
|
|
"january|enero|gennaio|janvier|januar": 1,
|
|
"february|febrero|febbraio|f[ée]vrier|februar": 2,
|
|
"march|marzo|mars|m[äa]rz": 3,
|
|
"april|abril|aprile|avril": 4,
|
|
"may|mayo|maggio|mai": 5,
|
|
"june|junio|giugno|juin|juni": 6,
|
|
"july|julio|luglio|juillet|juli": 7,
|
|
"august|agosto|ao[uû]t": 8,
|
|
"september|septiembre|settembre|septembre": 9,
|
|
"october|octubre|ottobre|octobre|oktober": 10,
|
|
"november|noviembre|novembre": 11,
|
|
"december|diciembre|dicembre|d[ée]cembre|dezember": 12,
|
|
}
|
|
|
|
for pattern, month_num in month_patterns.items():
|
|
match = re.search(rf"\b({pattern})\s+(\d{{4}})\b", query, re.IGNORECASE)
|
|
if match:
|
|
year = int(match.group(2))
|
|
start = datetime(year, month_num, 1)
|
|
if month_num == 12:
|
|
end = datetime(year, 12, 31)
|
|
else:
|
|
end = datetime(year, month_num + 1, 1) - timedelta(days=1)
|
|
return constraint(start, end)
|
|
|
|
return None
|
|
|
|
|
|
class TransformerQueryAnalyzer(QueryAnalyzer):
|
|
"""
|
|
Query analyzer using T5-based generative models.
|
|
|
|
Uses T5 to convert natural language temporal expressions into structured
|
|
date ranges without pattern matching or regex.
|
|
|
|
Performance:
|
|
- ~30-80ms on CPU, ~5-15ms on GPU
|
|
- Model size: ~80M params (~300MB download)
|
|
"""
|
|
|
|
def __init__(self, model_name: str = "google/flan-t5-small", device: str = "cpu"):
|
|
"""
|
|
Initialize T5 query analyzer.
|
|
|
|
Args:
|
|
model_name: Name of the HuggingFace T5 model to use.
|
|
Default: google/flan-t5-small (~80M params, ~300MB download)
|
|
Alternative: google/flan-t5-base (~1GB, more accurate)
|
|
device: Device to run model on ("cpu" or "cuda")
|
|
"""
|
|
self.model_name = model_name
|
|
self.device = device
|
|
self._model = None
|
|
self._tokenizer = None
|
|
|
|
def load(self) -> None:
|
|
"""Load the T5 model for temporal extraction."""
|
|
if self._model is not None:
|
|
return
|
|
|
|
try:
|
|
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
|
|
except ImportError:
|
|
raise ImportError(
|
|
"transformers is required for TransformerQueryAnalyzer. Install it with: pip install transformers"
|
|
)
|
|
|
|
logger.info(f"Loading query analyzer model: {self.model_name}...")
|
|
self._tokenizer = AutoTokenizer.from_pretrained(self.model_name)
|
|
self._model = AutoModelForSeq2SeqLM.from_pretrained(self.model_name)
|
|
self._model.to(self.device)
|
|
self._model.eval()
|
|
logger.info("Query analyzer model loaded")
|
|
|
|
def _load_model(self):
|
|
"""Lazy load the T5 model for temporal extraction (calls load())."""
|
|
self.load()
|
|
|
|
def _extract_with_rules(self, query: str, reference_date: datetime) -> TemporalConstraint | None:
|
|
"""
|
|
Extract temporal expressions using rule-based patterns.
|
|
|
|
Handles common patterns reliably and fast. Returns None for
|
|
patterns that need model-based extraction.
|
|
"""
|
|
import re
|
|
|
|
query_lower = query.lower()
|
|
|
|
def get_last_weekday(weekday: int) -> datetime:
|
|
days_ago = (reference_date.weekday() - weekday) % 7
|
|
if days_ago == 0:
|
|
days_ago = 7
|
|
return reference_date - timedelta(days=days_ago)
|
|
|
|
def constraint(start: datetime, end: datetime) -> TemporalConstraint:
|
|
return TemporalConstraint(
|
|
start_date=start.replace(hour=0, minute=0, second=0, microsecond=0),
|
|
end_date=end.replace(hour=23, minute=59, second=59, microsecond=999999),
|
|
)
|
|
|
|
# Yesterday
|
|
if re.search(r"\byesterday\b", query_lower):
|
|
d = reference_date - timedelta(days=1)
|
|
return constraint(d, d)
|
|
|
|
# Last week
|
|
if re.search(r"\blast\s+week\b", query_lower):
|
|
start = reference_date - timedelta(days=reference_date.weekday() + 7)
|
|
return constraint(start, start + timedelta(days=6))
|
|
|
|
# Last month
|
|
if re.search(r"\blast\s+month\b", query_lower):
|
|
first = reference_date.replace(day=1)
|
|
end = first - timedelta(days=1)
|
|
start = end.replace(day=1)
|
|
return constraint(start, end)
|
|
|
|
# Last year
|
|
if re.search(r"\blast\s+year\b", query_lower):
|
|
y = reference_date.year - 1
|
|
return constraint(datetime(y, 1, 1), datetime(y, 12, 31))
|
|
|
|
# Last weekend
|
|
if re.search(r"\blast\s+weekend\b", query_lower):
|
|
sat = get_last_weekday(5)
|
|
return constraint(sat, sat + timedelta(days=1))
|
|
|
|
# Last <weekday>
|
|
weekdays = {"monday": 0, "tuesday": 1, "wednesday": 2, "thursday": 3, "friday": 4, "saturday": 5, "sunday": 6}
|
|
for name, num in weekdays.items():
|
|
if re.search(rf"\blast\s+{name}\b", query_lower):
|
|
d = get_last_weekday(num)
|
|
return constraint(d, d)
|
|
|
|
# Month + Year: "June 2024", "in March 2023"
|
|
months = {
|
|
"january": 1,
|
|
"february": 2,
|
|
"march": 3,
|
|
"april": 4,
|
|
"may": 5,
|
|
"june": 6,
|
|
"july": 7,
|
|
"august": 8,
|
|
"september": 9,
|
|
"october": 10,
|
|
"november": 11,
|
|
"december": 12,
|
|
}
|
|
for name, num in months.items():
|
|
match = re.search(rf"\b{name}\s+(\d{{4}})\b", query_lower)
|
|
if match:
|
|
year = int(match.group(1))
|
|
if num == 12:
|
|
last_day = 31
|
|
else:
|
|
last_day = (datetime(year, num + 1, 1) - timedelta(days=1)).day
|
|
return constraint(datetime(year, num, 1), datetime(year, num, last_day))
|
|
|
|
return None
|
|
|
|
def analyze(self, query: str, reference_date: datetime | None = None) -> QueryAnalysis:
|
|
"""
|
|
Analyze query for temporal expressions.
|
|
|
|
Uses rule-based extraction for common patterns (fast & reliable),
|
|
falls back to T5 model for complex/unusual patterns.
|
|
|
|
Args:
|
|
query: Natural language query
|
|
reference_date: Reference date for relative terms (defaults to now)
|
|
|
|
Returns:
|
|
QueryAnalysis with temporal_constraint if found
|
|
"""
|
|
if reference_date is None:
|
|
reference_date = datetime.now()
|
|
|
|
# Try rule-based extraction first (handles 90%+ of cases)
|
|
result = self._extract_with_rules(query, reference_date)
|
|
if result is not None:
|
|
return QueryAnalysis(temporal_constraint=result)
|
|
|
|
# Fall back to T5 model for unusual patterns
|
|
self._load_model()
|
|
|
|
# Helper to calculate example dates
|
|
def get_last_weekday(weekday: int) -> datetime:
|
|
days_ago = (reference_date.weekday() - weekday) % 7
|
|
if days_ago == 0:
|
|
days_ago = 7
|
|
return reference_date - timedelta(days=days_ago)
|
|
|
|
yesterday = reference_date - timedelta(days=1)
|
|
last_saturday = get_last_weekday(5)
|
|
|
|
# Build prompt for T5
|
|
prompt = f"""Today is {reference_date.strftime("%Y-%m-%d")}. Extract date range or "none".
|
|
|
|
June 2024 = 2024-06-01 to 2024-06-30
|
|
yesterday = {yesterday.strftime("%Y-%m-%d")} to {yesterday.strftime("%Y-%m-%d")}
|
|
last Saturday = {last_saturday.strftime("%Y-%m-%d")} to {last_saturday.strftime("%Y-%m-%d")}
|
|
what is the weather = none
|
|
{query} ="""
|
|
|
|
# Tokenize and generate
|
|
inputs = self._tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True)
|
|
inputs = {k: v.to(self.device) for k, v in inputs.items()}
|
|
|
|
with self._no_grad():
|
|
outputs = self._model.generate(**inputs, max_new_tokens=30, num_beams=3, do_sample=False, temperature=1.0)
|
|
|
|
result = self._tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
|
|
|
|
# Parse the generated output
|
|
temporal = self._parse_generated_output(result, reference_date)
|
|
return QueryAnalysis(temporal_constraint=temporal)
|
|
|
|
def _no_grad(self):
|
|
"""Get torch.no_grad context manager."""
|
|
try:
|
|
import torch
|
|
|
|
return torch.no_grad()
|
|
except ImportError:
|
|
from contextlib import nullcontext
|
|
|
|
return nullcontext()
|
|
|
|
def _parse_generated_output(self, result: str, reference_date: datetime) -> TemporalConstraint | None:
|
|
"""
|
|
Parse T5 generated output into TemporalConstraint.
|
|
|
|
Expected format: "YYYY-MM-DD to YYYY-MM-DD"
|
|
|
|
Args:
|
|
result: Generated text from T5
|
|
reference_date: Reference date for validation
|
|
|
|
Returns:
|
|
TemporalConstraint if valid output, else None
|
|
"""
|
|
if not result or result.lower().strip() in ("none", "null", "no"):
|
|
return None
|
|
|
|
try:
|
|
# Parse "YYYY-MM-DD to YYYY-MM-DD"
|
|
import re
|
|
|
|
pattern = r"(\d{4}-\d{2}-\d{2})\s+to\s+(\d{4}-\d{2}-\d{2})"
|
|
match = re.search(pattern, result, re.IGNORECASE)
|
|
|
|
if match:
|
|
start_str = match.group(1)
|
|
end_str = match.group(2)
|
|
|
|
start_date = datetime.strptime(start_str, "%Y-%m-%d")
|
|
end_date = datetime.strptime(end_str, "%Y-%m-%d")
|
|
|
|
# Set time boundaries
|
|
start_date = start_date.replace(hour=0, minute=0, second=0, microsecond=0)
|
|
end_date = end_date.replace(hour=23, minute=59, second=59, microsecond=999999)
|
|
|
|
# Validation
|
|
if end_date < start_date:
|
|
logger.warning(f"Invalid date range: {start_date} to {end_date}")
|
|
return None
|
|
|
|
return TemporalConstraint(start_date=start_date, end_date=end_date)
|
|
|
|
except (ValueError, AttributeError):
|
|
return None
|
|
|
|
return None
|