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"""Utility functions for handling reasoning model responses."""
import re
def extract_answer_from_reasoning(response: str) -> str:
"""Extract the final answer from a response containing reasoning tags.
The Qwen3 model returns responses in the format:
<think>...reasoning...</think>
Final answer here...
Or sometimes just the reasoning tags without closing tag.
This function extracts only the final answer part.
"""
if not response:
return ""
# Method 1: Split on </think> tag (most common format)
if "</think>" in response:
parts = response.split("</think>", 1)
if len(parts) > 1:
return parts[1].strip()
# Method 2: Remove reasoning tags and their content
# Match <think>...</think> (case insensitive, multi-line)
cleaned = re.sub(
r'<think>.*?</think>',
'',
response,
flags=re.DOTALL | re.IGNORECASE
)
# Clean up any remaining whitespace
cleaned = cleaned.strip()
# If we removed everything, return original (fallback)
if not cleaned:
return response.strip()
return cleaned
def extract_key_terms(text: str) -> list[str]:
"""Extract key financial terms from text.
This is a simple heuristic - could be improved with NLP.
"""
# Common French financial terms patterns
financial_patterns = [
r'\bcrédit\b', r'\bprêt\b', r'\bdette\b', r'\bintérêt\b',
r'\btaux\b', r'\bcapital\b', r'\bdividende\b', r'\baction\b',
r'\bobligation\b', r'\bfonds\b', r'\bépargne\b', r'\binvestissement\b',
r'\bhypothèque\b', r'\bamortissement\b', r'\bvalorisation\b',
r'\bdate de valeur\b', r'\bescompte\b', r'\bconsignation\b',
r'\bmain levée\b', r'\bséquestre\b', r'\bnantissement\b',
]
found_terms = []
text_lower = text.lower()
for pattern in financial_patterns:
if re.search(pattern, text_lower):
# Extract the matched term
match = re.search(pattern, text, re.IGNORECASE)
if match:
term = match.group(0).strip()
if term not in found_terms:
found_terms.append(term)
return found_terms[:10] # Limit to 10 terms
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