query/query_parser.py

"""
Query parser - main module for query processing.
Responsibilities are intentionally narrow:
- normalize and rewrite the incoming query
- detect language and tokenize with HanLP
- run translation and embedding requests concurrently
- return parser facts, not Elasticsearch language-planning data
"""
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Tuple
import numpy as np
import logging
import re
from concurrent.futures import ThreadPoolExecutor, wait
from embeddings.text_encoder import TextEmbeddingEncoder
from config import SearchConfig
from translation import create_translation_client
from .language_detector import LanguageDetector
from .query_rewriter import QueryRewriter, QueryNormalizer
logger = logging.getLogger(__name__)
import hanlp  # type: ignore
def simple_tokenize_query(text: str) -> List[str]:
    """
    Lightweight tokenizer for suggestion-side heuristics only.
    - Consecutive CJK characters form one token
    - Latin / digit runs (with internal hyphens) form tokens
    """
    if not text:
        return []
    pattern = re.compile(r"[\u4e00-\u9fff]+|[A-Za-z0-9_]+(?:-[A-Za-z0-9_]+)*")
    return pattern.findall(text)
@dataclass(slots=True)
class ParsedQuery:
    """Container for query parser facts."""
    original_query: str
    query_normalized: str
    rewritten_query: str
    detected_language: Optional[str] = None
    translations: Dict[str, str] = field(default_factory=dict)
    query_vector: Optional[np.ndarray] = None
    query_tokens: List[str] = field(default_factory=list)
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary representation."""
        return {
            "original_query": self.original_query,
            "query_normalized": self.query_normalized,
            "rewritten_query": self.rewritten_query,
            "detected_language": self.detected_language,
            "translations": self.translations,
            "query_tokens": self.query_tokens,
        }
class QueryParser:
    """
    Main query parser that processes queries through multiple stages:
    1. Normalization
    2. Query rewriting (brand/category mappings, synonyms)
    3. Language detection
    4. Translation to caller-provided target languages
    5. Text embedding generation (for semantic search)
    """
    def __init__(
        self,
        config: SearchConfig,
        text_encoder: Optional[TextEmbeddingEncoder] = None,
        translator: Optional[Any] = None,
        tokenizer: Optional[Callable[[str], Any]] = None,
    ):
        """
        Initialize query parser.
        Args:
            config: SearchConfig instance
            text_encoder: Text embedding encoder (initialized at startup if not provided)
            translator: Translator instance (initialized at startup if not provided)
        """
        self.config = config
        self._text_encoder = text_encoder
        self._translator = translator
        # Initialize components
        self.normalizer = QueryNormalizer()
        self.language_detector = LanguageDetector()
        self.rewriter = QueryRewriter(config.query_config.rewrite_dictionary)
        self._tokenizer = tokenizer or self._build_tokenizer()
        # Eager initialization (startup-time failure visibility, no lazy init in request path)
        if self.config.query_config.enable_text_embedding and self._text_encoder is None:
            logger.info("Initializing text encoder at QueryParser construction...")
            self._text_encoder = TextEmbeddingEncoder()
        if self._translator is None:
            from config.services_config import get_translation_config
            cfg = get_translation_config()
            logger.info(
                "Initializing translator client at QueryParser construction (service_url=%s, default_model=%s)...",
                cfg.get("service_url"),
                cfg.get("default_model"),
            )
            self._translator = create_translation_client()
    @property
    def text_encoder(self) -> TextEmbeddingEncoder:
        """Return pre-initialized text encoder."""
        return self._text_encoder
    @property
    def translator(self) -> Any:
        """Return pre-initialized translator."""
        return self._translator
    def _build_tokenizer(self) -> Callable[[str], Any]:
        """Build the tokenizer used by query parsing. No fallback path by design."""
        if hanlp is None:
            raise RuntimeError("HanLP is required for QueryParser tokenization")
        logger.info("Initializing HanLP tokenizer...")
        tokenizer = hanlp.load(hanlp.pretrained.tok.CTB9_TOK_ELECTRA_BASE_CRF)
        tokenizer.config.output_spans = True
        logger.info("HanLP tokenizer initialized")
        return tokenizer
    @staticmethod
    def _pick_query_translation_model(
        source_lang: str,
        target_lang: str,
        config: SearchConfig,
        source_language_in_index: bool,
    ) -> str:
        """Pick the translation capability for query-time translation (configurable)."""
        src = str(source_lang or "").strip().lower()
        tgt = str(target_lang or "").strip().lower()
        qc = config.query_config
        if source_language_in_index:
            if src == "zh" and tgt == "en":
                return qc.zh_to_en_model
            if src == "en" and tgt == "zh":
                return qc.en_to_zh_model
            return qc.default_translation_model
        if src == "zh" and tgt == "en":
            return qc.zh_to_en_model_source_not_in_index or qc.zh_to_en_model
        if src == "en" and tgt == "zh":
            return qc.en_to_zh_model_source_not_in_index or qc.en_to_zh_model
        return qc.default_translation_model_source_not_in_index or qc.default_translation_model
    @staticmethod
    def _normalize_language_codes(languages: Optional[List[str]]) -> List[str]:
        normalized: List[str] = []
        seen = set()
        for language in languages or []:
            token = str(language or "").strip().lower()
            if not token or token in seen:
                continue
            seen.add(token)
            normalized.append(token)
        return normalized
    @staticmethod
    def _extract_tokens(tokenizer_result: Any) -> List[str]:
        """Normalize tokenizer output into a flat token string list."""
        if not tokenizer_result:
            return []
        if isinstance(tokenizer_result, str):
            token = tokenizer_result.strip()
            return [token] if token else []
        tokens: List[str] = []
        for item in tokenizer_result:
            token: Optional[str] = None
            if isinstance(item, str):
                token = item
            elif isinstance(item, (list, tuple)) and item:
                token = str(item[0])
            elif item is not None:
                token = str(item)
            if token is None:
                continue
            token = token.strip()
            if token:
                tokens.append(token)
        return tokens
    def _get_query_tokens(self, query: str) -> List[str]:
        return self._extract_tokens(self._tokenizer(query))
    def parse(
        self,
        query: str,
        tenant_id: Optional[str] = None,
        generate_vector: bool = True,
        context: Optional[Any] = None,
        target_languages: Optional[List[str]] = None,
    ) -> ParsedQuery:
        """
        Parse query through all processing stages.
        Args:
            query: Raw query string
            tenant_id: Deprecated and ignored by QueryParser. Kept temporarily
                to avoid a wider refactor in this first step.
            generate_vector: Whether to generate query embedding
            context: Optional request context for tracking and logging
            target_languages: Translation target languages decided by the caller
        Returns:
            ParsedQuery object with all processing results
        """
        # Initialize logger if context provided
        active_logger = context.logger if context else logger
        if context and hasattr(context, "logger"):
            context.logger.info(
                f"Starting query parsing | Original query: '{query}' | Generate vector: {generate_vector}",
                extra={'reqid': context.reqid, 'uid': context.uid}
            )
        def log_info(msg):
            if context and hasattr(context, 'logger'):
                context.logger.info(msg, extra={'reqid': context.reqid, 'uid': context.uid})
            else:
                active_logger.info(msg)
        def log_debug(msg):
            if context and hasattr(context, 'logger'):
                context.logger.debug(msg, extra={'reqid': context.reqid, 'uid': context.uid})
            else:
                active_logger.debug(msg)
        # Stage 1: Normalize
        normalized = self.normalizer.normalize(query)
        log_debug(f"Normalization completed | '{query}' -> '{normalized}'")
        if context:
            context.store_intermediate_result('query_normalized', normalized)
        # Stage 2: Query rewriting
        query_text = normalized
        rewritten = normalized
        if self.config.query_config.rewrite_dictionary:  # Enable rewrite if dictionary exists
            rewritten = self.rewriter.rewrite(query_text)
            if rewritten != query_text:
                log_info(f"Query rewritten | '{query_text}' -> '{rewritten}'")
                query_text = rewritten
                if context:
                    context.store_intermediate_result('rewritten_query', rewritten)
                    context.add_warning(f"Query was rewritten: {query_text}")
        # Stage 3: Language detection
        detected_lang = self.language_detector.detect(query_text)
        # Use default language if detection failed (None or "unknown")
        if not detected_lang or detected_lang == "unknown":
            detected_lang = self.config.query_config.default_language
        log_info(f"Language detection | Detected language: {detected_lang}")
        if context:
            context.store_intermediate_result('detected_language', detected_lang)
        # Stage 4: Query analysis (tokenization)
        query_tokens = self._get_query_tokens(query_text)
        log_debug(f"Query analysis | Query tokens: {query_tokens}")
        if context:
            context.store_intermediate_result('query_tokens', query_tokens)
        # Stage 5: Translation + embedding. Parser only coordinates async enrichment work; the
        # caller decides translation targets and later search-field planning.
        translations: Dict[str, str] = {}
        future_to_task: Dict[Any, Tuple[str, Optional[str]]] = {}
        async_executor: Optional[ThreadPoolExecutor] = None
        detected_norm = str(detected_lang or "").strip().lower()
        normalized_targets = self._normalize_language_codes(target_languages)
        translation_targets = [lang for lang in normalized_targets if lang != detected_norm]
        source_language_in_index = bool(normalized_targets) and detected_norm in normalized_targets
        # Stage 6: Text embedding - async execution
        query_vector = None
        should_generate_embedding = (
            generate_vector and
            self.config.query_config.enable_text_embedding
        )
        task_count = len(translation_targets) + (1 if should_generate_embedding else 0)
        if task_count > 0:
            async_executor = ThreadPoolExecutor(
                max_workers=max(1, min(task_count, 4)),
                thread_name_prefix="query-enrichment",
            )
        try:
            if async_executor is not None:
                for lang in translation_targets:
                    model_name = self._pick_query_translation_model(
                        detected_lang,
                        lang,
                        self.config,
                        source_language_in_index,
                    )
                    log_debug(
                        f"Submitting query translation | source={detected_lang} target={lang} model={model_name}"
                    )
                    future = async_executor.submit(
                        self.translator.translate,
                        query_text,
                        lang,
                        detected_lang,
                        "ecommerce_search_query",
                        model_name,
                    )
                    future_to_task[future] = ("translation", lang)
                if should_generate_embedding:
                    if self.text_encoder is None:
                        raise RuntimeError("Text embedding is enabled but text encoder is not initialized")
                    log_debug("Submitting query vector generation")
                    def _encode_query_vector() -> Optional[np.ndarray]:
                        arr = self.text_encoder.encode([query_text], priority=1)
                        if arr is None or len(arr) == 0:
                            return None
                        vec = arr[0]
                        if vec is None:
                            return None
                        return np.asarray(vec, dtype=np.float32)
                    future = async_executor.submit(_encode_query_vector)
                    future_to_task[future] = ("embedding", None)
        except Exception as e:
            error_msg = f"Async query enrichment submission failed | Error: {str(e)}"
            log_info(error_msg)
            if context:
                context.add_warning(error_msg)
            if async_executor is not None:
                async_executor.shutdown(wait=False)
                async_executor = None
            future_to_task.clear()
        # Wait for translation + embedding concurrently; shared budget depends on whether
        # the detected language belongs to caller-provided target_languages.
        qc = self.config.query_config
        source_in_target_languages = bool(normalized_targets) and detected_norm in normalized_targets
        budget_ms = (
            qc.translation_embedding_wait_budget_ms_source_in_index
            if source_in_target_languages
            else qc.translation_embedding_wait_budget_ms_source_not_in_index
        )
        budget_sec = max(0.0, float(budget_ms) / 1000.0)
        if translation_targets:
            log_info(
                f"Translation+embedding shared wait budget | budget_ms={budget_ms} | "
                f"source_in_target_languages={source_in_target_languages} | "
                f"translation_targets={translation_targets}"
            )
        if future_to_task:
            log_debug(
                f"Waiting for async tasks (translation+embedding) | budget_ms={budget_ms} | "
                f"source_in_target_languages={source_in_target_languages}"
            )
            done, not_done = wait(list(future_to_task.keys()), timeout=budget_sec)
            for future in done:
                task_type, lang = future_to_task[future]
                try:
                    result = future.result()
                    if task_type == "translation":
                        if result:
                            translations[lang] = result
                            log_info(
                                f"Translation completed | Query text: '{query_text}' | "
                                f"Target language: {lang} | Translation result: '{result}'"
                            )
                            if context:
                                context.store_intermediate_result(f"translation_{lang}", result)
                    elif task_type == "embedding":
                        query_vector = result
                        if query_vector is not None:
                            log_debug(f"Query vector generation completed | Shape: {query_vector.shape}")
                            if context:
                                context.store_intermediate_result("query_vector_shape", query_vector.shape)
                        else:
                            log_info(
                                "Query vector generation completed but result is None, will process without vector"
                            )
                except Exception as e:
                    if task_type == "translation":
                        error_msg = f"Translation failed | Language: {lang} | Error: {str(e)}"
                    else:
                        error_msg = f"Query vector generation failed | Error: {str(e)}"
                    log_info(error_msg)
                    if context:
                        context.add_warning(error_msg)
            if not_done:
                for future in not_done:
                    task_type, lang = future_to_task[future]
                    if task_type == "translation":
                        timeout_msg = (
                            f"Translation timeout (>{budget_ms}ms) | Language: {lang} | "
                            f"Query text: '{query_text}'"
                        )
                    else:
                        timeout_msg = (
                            f"Query vector generation timeout (>{budget_ms}ms), proceeding without embedding result"
                        )
                    log_info(timeout_msg)
                    if context:
                        context.add_warning(timeout_msg)
            if async_executor:
                async_executor.shutdown(wait=False)
            if translations and context:
                context.store_intermediate_result("translations", translations)
        # Build result
        result = ParsedQuery(
            original_query=query,
            query_normalized=normalized,
            rewritten_query=query_text,
            detected_language=detected_lang,
            translations=translations,
            query_vector=query_vector,
            query_tokens=query_tokens,
        )
        if context and hasattr(context, 'logger'):
            context.logger.info(
                f"Query parsing completed | Original query: '{query}' | Final query: '{rewritten or query_text}' | "
                f"Translation count: {len(translations)} | Vector: {'yes' if query_vector is not None else 'no'}",
                extra={'reqid': context.reqid, 'uid': context.uid}
            )
        else:
            logger.info(
                f"Query parsing completed | Original query: '{query}' | Final query: '{rewritten or query_text}' | "
                f"Language: {detected_lang}"
            )
        return result
    def get_search_queries(self, parsed_query: ParsedQuery) -> List[str]:
        """
        Get list of queries to search (original + translations).
        Args:
            parsed_query: Parsed query object
        Returns:
            List of query strings to search
        """
        queries = [parsed_query.rewritten_query]
        # Add translations
        for lang, translation in parsed_query.translations.items():
            if translation and translation != parsed_query.rewritten_query:
                queries.append(translation)
        return queries
def detect_text_language_for_suggestions(
    text: str,
    *,
    index_languages: Optional[List[str]] = None,
    primary_language: str = "en",
) -> Tuple[str, float, str]:
    """
    Language detection for short strings (mixed-language tags, query-log fallback).
    Uses the same ``LanguageDetector`` as :class:`QueryParser`. Returns a language
    code present in ``index_languages`` when possible, otherwise the tenant primary.
    Returns:
        (lang, confidence, source) where source is ``detector``, ``fallback``, or ``default``.
    """
    langs_list = [x for x in (index_languages or []) if x]
    langs_set = set(langs_list)
    def _norm_lang(raw: Optional[str]) -> Optional[str]:
        if not raw:
            return None
        token = str(raw).strip().lower().replace("-", "_")
        if not token:
            return None
        if token in {"zh_tw", "pt_br"}:
            return token
        return token.split("_")[0]
    primary = _norm_lang(primary_language) or "en"
    if primary not in langs_set and langs_list:
        primary = _norm_lang(langs_list[0]) or langs_list[0]
    if not text or not str(text).strip():
        return primary, 0.0, "default"
    raw_code = LanguageDetector().detect(str(text).strip())
    if not raw_code or raw_code == "unknown":
        return primary, 0.35, "default"
    def _index_lang_base(cand: str) -> str:
        t = str(cand).strip().lower().replace("-", "_")
        return t.split("_")[0] if t else ""
    def _resolve_index_lang(code: str) -> Optional[str]:
        if code in langs_set:
            return code
        for cand in langs_list:
            if _index_lang_base(cand) == code:
                return cand
        return None
    if langs_list:
        resolved = _resolve_index_lang(raw_code)
        if resolved is None:
            return primary, 0.5, "fallback"
        return resolved, 0.92, "detector"
    return raw_code, 0.92, "detector"