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| import re | |
| from typing import Dict, Any, List, Set | |
| class QueryIntent: | |
| GLOBAL_SUMMARY = "global_summary" | |
| TAXONOMY_ARCHITECTURE = "taxonomy_architecture" | |
| CROSS_DOC_SUMMARY = "cross_doc_summary" | |
| ACADEMIC_RESEARCH = "academic_research" | |
| SPECIFIC_FACT = "specific_fact" | |
| class QueryIntentClassifier: | |
| """ | |
| Ultra-fast deterministic query intent classifier for Atlas Vectorless RAG. | |
| Zero external dependencies. Operates in < 0.1ms using compiled regular expressions. | |
| """ | |
| # Academic & Literature Research Triggers | |
| _EN_ACADEMIC_PATTERNS = [ | |
| r"\b(arxiv|research papers?|scientific papers?|literature reviews?|citations?)\b", | |
| r"\b(academic papers?|journal articles?|recent research|preprints?|proceedings)\b", | |
| r"\b(sota|state of the art|peer-reviewed|whitepapers?)\b", | |
| ] | |
| _AR_ACADEMIC_PATTERNS = [ | |
| r"(أوراق بحثية|ورقة بحثية|أبحاث|أحدث الأبحاث|دراسة أكاديمية|دراسات|أرشيف|مراجع علمية|مراجعة أدبيات|استشهاد|اقتباسات|مجلات علمية)", | |
| ] | |
| # English Triggers | |
| _EN_GLOBAL_PATTERNS = [ | |
| r"\b(summar(y|ize|ization)|overview|synopsis|abstract|tl;?dr|recap|outline)\b", | |
| r"\b(what (is|are) (this|the) (document|paper|file|report|text) about)\b", | |
| r"\b(what does (this|the) (document|paper|report) discuss)\b", | |
| r"\b(main (points|topics|themes|takeaways|findings|contributions|ideas|results))\b", | |
| r"\b(core (findings|themes|insights|contributions))\b", | |
| r"\b(executive summary|high-?level (view|overview|summary))\b", | |
| r"\b(table of contents|general (idea|summary|overview))\b", | |
| r"\b(big picture|conclusions?)\b", | |
| ] | |
| _EN_TAXONOMY_ARCH_PATTERNS = [ | |
| r"\b(taxonom(y|ies)|architectur(e|al)|framework|system design|components?)\b", | |
| r"\b(categor(y|ies|ization)|classification|hierarch(y|ical))\b", | |
| r"\b(workflow|pipeline|roadmap|building blocks|structural?|diagrams?)\b", | |
| ] | |
| _EN_CROSS_DOC_PATTERNS = [ | |
| r"\b(compar(e|ison|ative)|contrast|difference between)\b", | |
| r"\b(across (the )?(documents|papers|files|reports))\b", | |
| r"\b(both (documents|papers|files)|all documents)\b", | |
| r"\b(similarit(y|ies)|commonalit(y|ies))\b", | |
| ] | |
| # Arabic Triggers | |
| _AR_GLOBAL_PATTERNS = [ | |
| r"(ملخص|لخص|نبذة|نظرة عامة|فحوى|فكرة عامة|المحاور الرئيسية|الفهرس|مواضيع)", | |
| r"(عن ماذا يتحدث|عن ايش يتكلم|عن إيه بيتكلم|عن ايه بيتكلم|عن إيه|عن ايه|ما هو موضوع|ما هو محتوى|محتوى المستند)", | |
| r"(أهم النقاط|النتائج الرئيسية|الاستنتاجات العامة|خلاصة|إيجاز|ملخص تنفيذي)", | |
| r"(دا عن ايه|ده عن ايه|دي عن ايه|دا بيتكلم عن ايه|ده بيتكلم عن ايه)", | |
| r"^(دا|ده|دي|لخصه|لخصها|الملخص)$", | |
| ] | |
| _AR_TAXONOMY_ARCH_PATTERNS = [ | |
| r"(تصنيف|تصنيفات|معمارية|هيكلية|تصميم النظام|مكونات|فئات)", | |
| r"(مخطط|سير العمل|أقسام|أبواب|إطار العمل|خارطة طريق|بنية النظام)", | |
| ] | |
| _AR_CROSS_DOC_PATTERNS = [ | |
| r"(مقارنة|قارن|الفرق بين|أوجه التشابه|أوجه الاختلاف)", | |
| r"(بين المستندين|بين الملفين|بين الوثيقتين|جميع المستندات|عبر الوثائق)", | |
| ] | |
| def __init__(self): | |
| self.en_academic_re = [re.compile(p, re.IGNORECASE) for p in self._EN_ACADEMIC_PATTERNS] | |
| self.en_global_re = [re.compile(p, re.IGNORECASE) for p in self._EN_GLOBAL_PATTERNS] | |
| self.en_tax_re = [re.compile(p, re.IGNORECASE) for p in self._EN_TAXONOMY_ARCH_PATTERNS] | |
| self.en_cross_re = [re.compile(p, re.IGNORECASE) for p in self._EN_CROSS_DOC_PATTERNS] | |
| self.ar_academic_re = [re.compile(p) for p in self._AR_ACADEMIC_PATTERNS] | |
| self.ar_global_re = [re.compile(p) for p in self._AR_GLOBAL_PATTERNS] | |
| self.ar_tax_re = [re.compile(p) for p in self._AR_TAXONOMY_ARCH_PATTERNS] | |
| self.ar_cross_re = [re.compile(p) for p in self._AR_CROSS_DOC_PATTERNS] | |
| def classify(self, query: str, doc_count: int = 1) -> Dict[str, Any]: | |
| """ | |
| Classifies user query intent. | |
| Returns: | |
| { | |
| "intent": QueryIntent.*, | |
| "is_global_or_summary": bool, | |
| "is_academic": bool, | |
| "matched_triggers": List[str], | |
| "confidence": float | |
| } | |
| """ | |
| if not query: | |
| return { | |
| "intent": QueryIntent.SPECIFIC_FACT, | |
| "is_global_or_summary": False, | |
| "is_academic": False, | |
| "matched_triggers": [], | |
| "confidence": 0.0, | |
| } | |
| q_clean = query.strip() | |
| q_lower = q_clean.lower() | |
| matched_triggers: List[str] = [] | |
| # 1. Very short generic queries (e.g. "دا", "لخص", "summary", "overview") | |
| if len(q_clean) <= 12 and ( | |
| q_clean in {"دا", "ده", "دي", "لخص", "لخصه", "ملخص"} | |
| or q_lower in {"summary", "summarize", "overview", "tldr", "tl;dr", "about", "what is it"} | |
| ): | |
| matched_triggers.append(f"short_trigger:{q_clean}") | |
| return { | |
| "intent": QueryIntent.GLOBAL_SUMMARY, | |
| "is_global_or_summary": True, | |
| "is_academic": False, | |
| "matched_triggers": matched_triggers, | |
| "confidence": 0.95, | |
| } | |
| # 2. Academic & Literature Research intent (arXiv papers, literature reviews) | |
| acad_triggers: List[str] = [] | |
| for r in self.en_academic_re: | |
| m = r.search(q_lower) | |
| if m: | |
| acad_triggers.append(f"en_academic:{m.group(0)}") | |
| for r in self.ar_academic_re: | |
| m = r.search(q_clean) | |
| if m: | |
| acad_triggers.append(f"ar_academic:{m.group(0)}") | |
| if acad_triggers: | |
| matched_triggers.extend(acad_triggers) | |
| return { | |
| "intent": QueryIntent.ACADEMIC_RESEARCH, | |
| "is_global_or_summary": False, | |
| "is_academic": True, | |
| "matched_triggers": matched_triggers, | |
| "confidence": 0.92, | |
| } | |
| # 3. Cross-document / Comparison intent | |
| for r in self.en_cross_re: | |
| m = r.search(q_lower) | |
| if m: | |
| matched_triggers.append(f"en_cross:{m.group(0)}") | |
| for r in self.ar_cross_re: | |
| m = r.search(q_clean) | |
| if m: | |
| matched_triggers.append(f"ar_cross:{m.group(0)}") | |
| if matched_triggers and doc_count > 1: | |
| return { | |
| "intent": QueryIntent.CROSS_DOC_SUMMARY, | |
| "is_global_or_summary": True, | |
| "is_academic": False, | |
| "matched_triggers": matched_triggers, | |
| "confidence": 0.90, | |
| } | |
| # 4. Taxonomy / Architecture intent | |
| tax_triggers: List[str] = [] | |
| for r in self.en_tax_re: | |
| m = r.search(q_lower) | |
| if m: | |
| tax_triggers.append(f"en_tax:{m.group(0)}") | |
| for r in self.ar_tax_re: | |
| m = r.search(q_clean) | |
| if m: | |
| tax_triggers.append(f"ar_tax:{m.group(0)}") | |
| if tax_triggers: | |
| matched_triggers.extend(tax_triggers) | |
| return { | |
| "intent": QueryIntent.TAXONOMY_ARCHITECTURE, | |
| "is_global_or_summary": True, | |
| "is_academic": False, | |
| "matched_triggers": matched_triggers, | |
| "confidence": 0.88, | |
| } | |
| # 5. Global / Overview / Summary intent | |
| glob_triggers: List[str] = [] | |
| for r in self.en_global_re: | |
| m = r.search(q_lower) | |
| if m: | |
| glob_triggers.append(f"en_global:{m.group(0)}") | |
| for r in self.ar_global_re: | |
| m = r.search(q_clean) | |
| if m: | |
| glob_triggers.append(f"ar_global:{m.group(0)}") | |
| if glob_triggers: | |
| matched_triggers.extend(glob_triggers) | |
| return { | |
| "intent": QueryIntent.GLOBAL_SUMMARY, | |
| "is_global_or_summary": True, | |
| "is_academic": False, | |
| "matched_triggers": matched_triggers, | |
| "confidence": 0.92, | |
| } | |
| # 6. Default to Specific Fact lookup | |
| return { | |
| "intent": QueryIntent.SPECIFIC_FACT, | |
| "is_global_or_summary": False, | |
| "is_academic": False, | |
| "matched_triggers": [], | |
| "confidence": 0.50, | |
| } | |
| # Global singleton instance for high-throughput zero-alloc usage | |
| intent_classifier = QueryIntentClassifier() | |