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from typing import List, Dict, Optional
import re
import weave
from pydantic import BaseModel
class RegexResult(BaseModel):
passed: bool
matched_patterns: Dict[str, List[str]]
failed_patterns: List[str]
class RegexModel(weave.Model):
patterns: Dict[str, str]
def __init__(self, patterns: Dict[str, str]) -> None:
"""
Initialize RegexModel with a dictionary of patterns.
Args:
patterns: Dictionary where key is pattern name and value is regex pattern
Example: {"email": r"[^@ \t\r\n]+@[^@ \t\r\n]+\.[^@ \t\r\n]+",
"phone": r"\b\d{3}[-.]?\d{3}[-.]?\d{4}\b"}
"""
super().__init__(patterns=patterns)
self._compiled_patterns = {
name: re.compile(pattern) for name, pattern in patterns.items()
}
@weave.op()
def check(self, prompt: str) -> RegexResult:
"""
Check text against all patterns and return detailed results.
Args:
text: Input text to check against patterns
Returns:
RegexResult containing pass/fail status and details about matches
"""
matched_patterns = {}
failed_patterns = []
for pattern_name, pattern in self.patterns.items():
matches = []
for match in re.finditer(pattern, prompt):
if match.groups():
# If there are capture groups, join them with a separator
matches.append('-'.join(str(g) for g in match.groups() if g is not None))
else:
# If no capture groups, use the full match
matches.append(match.group(0))
if matches:
matched_patterns[pattern_name] = matches
else:
failed_patterns.append(pattern_name)
return RegexResult(
matched_patterns=matched_patterns,
failed_patterns=failed_patterns,
passed=len(matched_patterns) == 0
)
@weave.op()
def predict(self, text: str) -> RegexResult:
"""
Alias for check() to maintain consistency with other models.
"""
return self.check(text) |