244 lines
11 KiB
Python
244 lines
11 KiB
Python
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import groq
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from datetime import datetime, timedelta
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from typing import List, Tuple
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import config
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from models import Receipt, Transaction, Match
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import time
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import logging
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import asyncio
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# Set up logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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class AIMatcher:
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def __init__(self):
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self.client = groq.Groq(api_key=config.GROQ_API_KEY)
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self.model = "llama3-8b-8192"
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self.max_retries = 3
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self.retry_delay = 2 # seconds - increased for rate limiting
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self.rate_limit_delay = 1.0 # seconds between API calls
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self.last_api_call = 0
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def match_receipts_to_transactions(self, receipts: List[Receipt], transactions: List[Transaction]) -> List[Match]:
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"""Match receipts to transactions using AI"""
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logger.info(f"Starting AI matching for {len(receipts)} receipts against {len(transactions)} transactions")
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matches = []
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for i, receipt in enumerate(receipts):
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logger.info(f"Processing receipt {i+1}/{len(receipts)}: {receipt.vendor} - ${receipt.amount}")
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# Rate limiting
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self._rate_limit()
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# Get the BEST match for this receipt (highest confidence score)
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best_match = self._find_best_match(receipt, transactions)
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if best_match:
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matches.append(best_match)
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logger.info(f"Found match: {best_match.confidence_score:.3f} - {best_match.match_reason}")
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else:
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logger.warning(f"No match found for receipt: {receipt.vendor} - ${receipt.amount}")
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# Sort by confidence score (highest first)
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matches = sorted(matches, key=lambda x: x.confidence_score, reverse=True)
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logger.info(f"AI matching completed. Found {len(matches)} matches")
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return matches
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def _rate_limit(self):
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"""Implement rate limiting to avoid API quota exhaustion"""
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current_time = time.time()
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time_since_last_call = current_time - self.last_api_call
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if time_since_last_call < self.rate_limit_delay:
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sleep_time = self.rate_limit_delay - time_since_last_call
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logger.debug(f"Rate limiting: sleeping for {sleep_time:.2f} seconds")
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time.sleep(sleep_time)
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self.last_api_call = time.time()
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def _find_best_match(self, receipt: Receipt, transactions: List[Transaction]) -> Match:
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"""Find the BEST match for a receipt (highest confidence score)"""
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candidates = self._filter_candidates(receipt, transactions)
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if not candidates:
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logger.warning(f"No candidates found for receipt: {receipt.vendor} - ${receipt.amount}")
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return None
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logger.info(f"Found {len(candidates)} candidates for receipt: {receipt.vendor}")
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best_match = None
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highest_score = 0
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for transaction in candidates:
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score, reason = self._calculate_match_score(receipt, transaction)
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logger.debug(f"Score {score:.3f} for transaction {transaction.vendor}: {reason}")
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# Keep the match with the highest score, regardless of how low it is
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if score > highest_score:
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highest_score = score
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best_match = Match(receipt, transaction, score, reason)
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return best_match
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def _filter_candidates(self, receipt: Receipt, transactions: List[Transaction]) -> List[Transaction]:
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"""Filter transactions to create a reasonable candidate list"""
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candidates = []
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amount_threshold = receipt.amount * 2.0 # 200% threshold - very inclusive
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for transaction in transactions:
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# Use absolute value for transaction amount comparison
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transaction_amount_abs = abs(transaction.amount)
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# Only exclude transactions with obviously different amounts
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if abs(receipt.amount - transaction_amount_abs) <= amount_threshold:
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candidates.append(transaction)
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logger.debug(f"Filtered {len(transactions)} transactions to {len(candidates)} candidates")
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return candidates
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def _calculate_match_score(self, receipt: Receipt, transaction: Transaction) -> Tuple[float, str]:
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"""Calculate match score using AI"""
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# Calculate differences for the AI to consider
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date_diff = abs((receipt.receipt_date - transaction.transaction_date).days)
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transaction_amount_abs = abs(transaction.amount)
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amount_diff = abs(receipt.amount - transaction_amount_abs)
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amount_percent_diff = (amount_diff / receipt.amount) * 100 if receipt.amount > 0 else 0
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prompt = f"""
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Compare this receipt with this transaction and provide a confidence score (0-1) and brief reason.
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Receipt: {receipt.vendor}, ${receipt.amount}, {receipt.receipt_date.strftime('%Y-%m-%d')}
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Receipt Description: {receipt.description}
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Receipt Category: {receipt.category}
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Transaction: {transaction.vendor}, ${transaction.amount} (absolute: ${transaction_amount_abs}), {transaction.transaction_date.strftime('%Y-%m-%d')}
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Transaction Notes: {transaction.notes}
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Differences:
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- Date difference: {date_diff} days
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- Amount difference: ${amount_diff} ({amount_percent_diff:.1f}%)
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- Vendor comparison: "{receipt.vendor}" vs "{transaction.vendor}"
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- Description/Notes comparison: "{receipt.description}" vs "{transaction.notes}"
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- Category: {receipt.category}
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Score this potential match based on how likely it is the correct match:
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- Perfect matches (same vendor, amount, date): 0.95-1.0
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- High confidence (minor differences): 0.8-0.94
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- Medium confidence (moderate differences): 0.6-0.79
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- Low confidence (significant differences): 0.4-0.59
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- Very low confidence (major differences): 0.2-0.39
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- Minimal similarity: 0.1-0.19
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- No meaningful similarity: 0.0-0.09
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Consider description and category similarity in your scoring.
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IMPORTANT: Return ONLY the score and reason separated by a pipe character.
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Format: [score]|[reason]
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Example: 0.85|Same vendor, same amount, 2 days apart
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"""
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for attempt in range(self.max_retries):
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try:
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result = self._call_groq_api_with_timeout(prompt, timeout=30) # Increased timeout
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# Parse the result - handle multiple formats
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score, reason = self._parse_ai_response(result)
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logger.debug(f"AI Response: {result}")
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logger.debug(f"Parsed: score={score}, reason={reason}")
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return score, reason
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except Exception as e:
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logger.warning(f"Attempt {attempt + 1} failed for receipt {receipt.id}: {str(e)}")
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if attempt < self.max_retries - 1:
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# Exponential backoff for rate limiting
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sleep_time = self.retry_delay * (2 ** attempt)
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logger.info(f"Waiting {sleep_time} seconds before retry...")
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time.sleep(sleep_time)
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else:
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logger.error(f"All attempts failed for receipt {receipt.id}")
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return 0.0, f"AI error after {self.max_retries} attempts: {str(e)}"
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def _parse_ai_response(self, result: str) -> Tuple[float, str]:
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"""Parse AI response with robust error handling"""
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result = result.strip()
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logger.debug(f"Parsing AI response: {result}")
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# Try to find score in various formats
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if '|' in result:
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parts = result.split('|')
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logger.debug(f"Split response into {len(parts)} parts: {parts}")
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# Look for a numeric score in any part
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for i, part in enumerate(parts):
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part = part.strip()
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try:
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# Remove any non-numeric characters except decimal point
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score_str_clean = ''.join(c for c in part if c.isdigit() or c == '.')
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if score_str_clean:
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score = float(score_str_clean)
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if 0 <= score <= 1: # Valid confidence score
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# Get reason from other parts
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reason_parts = [p.strip() for j, p in enumerate(parts) if j != i and p.strip()]
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reason = ' | '.join(reason_parts) if reason_parts else "Score extracted"
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logger.debug(f"Found score {score} in part {i}, reason: {reason}")
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return score, reason
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except ValueError:
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continue
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# Try to extract just a number from the response
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try:
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import re
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numbers = re.findall(r'\d+\.?\d*', result)
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if numbers:
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for num_str in numbers:
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score = float(num_str)
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if 0 <= score <= 1: # Valid confidence score
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logger.debug(f"Extracted score {score} from response")
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return score, f"Extracted from response: {result[:50]}..."
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except (ValueError, IndexError):
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pass
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# Fallback - try to find any number and normalize it
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try:
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import re
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numbers = re.findall(r'\d+\.?\d*', result)
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if numbers:
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score = float(numbers[0])
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# Normalize to 0-1 range if it's a percentage or other scale
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if score > 1:
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score = score / 100 # Assume percentage
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score = max(0, min(1, score)) # Clamp to 0-1
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logger.debug(f"Normalized score {score} from response")
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return score, f"Normalized from response: {result[:50]}..."
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except (ValueError, IndexError):
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pass
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# Final fallback
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logger.warning(f"Could not parse AI response: {result}")
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return 0.0, f"Unparseable response: {result[:50]}..."
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def _call_groq_api_with_timeout(self, prompt: str, timeout: int = 15) -> str:
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"""Make API call with timeout and retry logic"""
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import concurrent.futures
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def api_call():
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try:
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response = self.client.chat.completions.create(
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model=self.model,
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messages=[{"role": "user", "content": prompt}],
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max_tokens=200,
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temperature=0.1
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)
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return response.choices[0].message.content.strip()
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except Exception as e:
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raise e
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try:
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with concurrent.futures.ThreadPoolExecutor() as executor:
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future = executor.submit(api_call)
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return future.result(timeout=timeout)
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except concurrent.futures.TimeoutError:
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raise Exception(f"API call timed out after {timeout} seconds")
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except Exception as e:
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raise e
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