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| from __future__ import annotations | |
| import logging | |
| import os | |
| import re | |
| from typing import Optional, Tuple | |
| from ..query_categorization import QueryCategorization, QueryClassification | |
| from .models import QueryType | |
| logger = logging.getLogger(__name__) | |
| class AdaptiveQueryCategorizer: | |
| """ | |
| Classifies queries into the taxonomy: | |
| - fact: exact facts, numbers, dates, IDs, short specific answers. | |
| - detailed: procedural, relational, explanatory questions needing context. | |
| - general: broad summaries, overview, exploratory questions. | |
| - technical_exact: code symbols, API calls, syntax, file paths. | |
| - comparison: differences/similarities between concepts. | |
| - unanswerable / unsupported: detect obvious out-of-scope or absurd queries. | |
| """ | |
| def __init__(self, use_llm_if_available: bool = True) -> None: | |
| offline = os.getenv("OFFLINE_EVAL", "0").lower() in ("1", "true", "yes") | |
| self.use_llm = use_llm_if_available and not offline | |
| self._llm_classifier: Optional[QueryCategorization] = None | |
| if self.use_llm and os.getenv("GROQ_API_KEY"): | |
| try: | |
| self._llm_classifier = QueryCategorization() | |
| except Exception as exc: | |
| logger.warning(f"Could not initialize LLM query classifier: {exc}") | |
| def categorize(self, query: str) -> Tuple[QueryType, float]: | |
| q_lower = query.lower().strip() | |
| # Code identifiers, API signatures, and file references route to exact matching | |
| if any(w in q_lower for w in ("def ", "class ", "function", "api", "syntax", "implementation", ".py", ".json", "code")): | |
| return "technical_exact", 0.95 | |
| # Comparative queries require sub-query decomposition | |
| if any(w in q_lower for w in ("compare", "difference between", "versus", "vs.", " vs ", "pros and cons")): | |
| return "comparison", 0.92 | |
| # Fast heuristic pattern matching (sub-millisecond Layer 1) | |
| if re.search(r"^(who|when|where|which|how much|how many|what is the dimension|what is the value|what was the revenue|what was the bleu|what year|what dropout)\b", q_lower): | |
| return "fact", 0.90 | |
| if re.search(r"^(how does|how do|explain|describe|why|walk me through|what are the steps|what are the instructions|how to)\b", q_lower): | |
| return "detailed", 0.90 | |
| if re.search(r"^(tell me about|give me an overview|summarize|overview of|what are|outline)\b", q_lower): | |
| return "general", 0.85 | |
| # Fallback to LLM classifier if available and pattern is ambiguous | |
| if self._llm_classifier: | |
| try: | |
| classification = self._llm_classifier.classify(query) | |
| cat = classification.category | |
| if cat in ("fact", "detailed", "general"): | |
| return cat, classification.confidence | |
| except Exception as exc: | |
| logger.warning(f"LLM classification failed: {exc}, using rule-based fallback.") | |
| # Word count heuristics | |
| word_count = len(q_lower.split()) | |
| if word_count <= 5: | |
| return "fact", 0.75 | |
| elif word_count <= 14: | |
| return "detailed", 0.75 | |
| else: | |
| return "general", 0.70 | |