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
from concurrent.futures import ThreadPoolExecutor, wait
from embeddings.image_encoder import CLIPImageEncoder
from embeddings.text_encoder import TextEmbeddingEncoder
from config import SearchConfig
from translation import create_translation_client
from .language_detector import LanguageDetector
from .product_title_exclusion import (
    ProductTitleExclusionDetector,
    ProductTitleExclusionProfile,
    ProductTitleExclusionRegistry,
)
from .query_rewriter import QueryRewriter, QueryNormalizer
from .style_intent import StyleIntentDetector, StyleIntentProfile, StyleIntentRegistry
from .tokenization import extract_token_strings, simple_tokenize_query
from .keyword_extractor import KeywordExtractor, collect_keywords_queries
logger = logging.getLogger(__name__)
import hanlp  # type: ignore
def rerank_query_text(
    original_query: str,
    *,
    detected_language: Optional[str] = None,
    translations: Optional[Dict[str, str]] = None,
) -> str:
    """
    Text substituted for ``{query}`` when calling the reranker.
    Chinese and English queries use the original string. For any other detected
    language, prefer the English translation, then Chinese; if neither exists,
    fall back to the original query.
    """
    lang = (detected_language or "").strip().lower()
    if lang in ("zh", "en"):
        return original_query
    trans = translations or {}
    for key in ("en", "zh"):
        t = (trans.get(key) or "").strip()
        if t:
            return t
    return original_query
@dataclass(slots=True)
class ParsedQuery:
    """
    Container for query parser facts.
    ``keywords_queries`` parallels text variants: key ``base`` (see
    ``keyword_extractor.KEYWORDS_QUERY_BASE_KEY``) for ``rewritten_query``,
    and the same language codes as ``translations`` for each translated string.
    Entries with no extracted nouns are omitted.
    """
    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
    image_query_vector: Optional[np.ndarray] = None
    query_tokens: List[str] = field(default_factory=list)
    keywords_queries: Dict[str, str] = field(default_factory=dict)
    style_intent_profile: Optional[StyleIntentProfile] = None
    product_title_exclusion_profile: Optional[ProductTitleExclusionProfile] = None
    def text_for_rerank(self) -> str:
        """See :func:`rerank_query_text`."""
        return rerank_query_text(
            self.original_query,
            detected_language=self.detected_language,
            translations=self.translations,
        )
    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,
            "has_query_vector": self.query_vector is not None,
            "has_image_query_vector": self.image_query_vector is not None,
            "query_tokens": self.query_tokens,
            "keywords_queries": dict(self.keywords_queries),
            "style_intent_profile": (
                self.style_intent_profile.to_dict() if self.style_intent_profile is not None else None
            ),
            "product_title_exclusion_profile": (
                self.product_title_exclusion_profile.to_dict()
                if self.product_title_exclusion_profile is not None
                else None
            ),
        }
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,
        image_encoder: Optional[CLIPImageEncoder] = 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._image_encoder = image_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()
        self._keyword_extractor = KeywordExtractor(tokenizer=self._tokenizer)
        self.style_intent_registry = StyleIntentRegistry.from_query_config(config.query_config)
        self.style_intent_detector = StyleIntentDetector(
            self.style_intent_registry,
            tokenizer=self._tokenizer,
        )
        self.product_title_exclusion_registry = ProductTitleExclusionRegistry.from_query_config(
            config.query_config
        )
        self.product_title_exclusion_detector = ProductTitleExclusionDetector(
            self.product_title_exclusion_registry,
            tokenizer=self._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.config.query_config.image_embedding_field and self._image_encoder is None:
            logger.info("Initializing image encoder at QueryParser construction...")
            self._image_encoder = CLIPImageEncoder()
        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
    @property
    def image_encoder(self) -> Optional[CLIPImageEncoder]:
        """Return pre-initialized image encoder for CLIP text embeddings."""
        return self._image_encoder
    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."""
        return extract_token_strings(tokenizer_result)
    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
        image_query_vector = None
        should_generate_embedding = (
            generate_vector and
            self.config.query_config.enable_text_embedding
        )
        should_generate_image_embedding = (
            generate_vector and
            bool(self.config.query_config.image_embedding_field)
        )
        task_count = (
            len(translation_targets)
            + (1 if should_generate_embedding else 0)
            + (1 if should_generate_image_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,
                            request_id=(context.reqid if context else None),
                            user_id=(context.uid if context else None),
                        )
                        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)
                if should_generate_image_embedding:
                    if self.image_encoder is None:
                        raise RuntimeError(
                            "Image embedding field is configured but image encoder is not initialized"
                        )
                    log_debug("Submitting CLIP text query vector generation")
                    def _encode_image_query_vector() -> Optional[np.ndarray]:
                        vec = self.image_encoder.encode_clip_text(
                            query_text,
                            normalize_embeddings=True,
                            priority=1,
                            request_id=(context.reqid if context else None),
                            user_id=(context.uid if context else None),
                        )
                        if vec is None:
                            return None
                        return np.asarray(vec, dtype=np.float32)
                    future = async_executor.submit(_encode_image_query_vector)
                    future_to_task[future] = ("image_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"
                            )
                    elif task_type == "image_embedding":
                        image_query_vector = result
                        if image_query_vector is not None:
                            log_debug(
                                f"CLIP text query vector generation completed | Shape: {image_query_vector.shape}"
                            )
                            if context:
                                context.store_intermediate_result(
                                    "image_query_vector_shape",
                                    image_query_vector.shape,
                                )
                        else:
                            log_info(
                                "CLIP text query vector generation completed but result is None, "
                                "will process without image vector"
                            )
                except Exception as e:
                    if task_type == "translation":
                        error_msg = f"Translation failed | Language: {lang} | Error: {str(e)}"
                    elif task_type == "image_embedding":
                        error_msg = f"CLIP text query vector generation failed | 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}'"
                        )
                    elif task_type == "image_embedding":
                        timeout_msg = (
                            f"CLIP text query vector generation timeout (>{budget_ms}ms), "
                            "proceeding without image embedding result"
                        )
                    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)
        keywords_queries: Dict[str, str] = {}
        try:
            keywords_queries = collect_keywords_queries(
                self._keyword_extractor,
                query_text,
                translations,
            )
        except Exception as e:
            log_info(f"Keyword extraction failed | Error: {e}")
        # Build result
        base_result = ParsedQuery(
            original_query=query,
            query_normalized=normalized,
            rewritten_query=query_text,
            detected_language=detected_lang,
            translations=translations,
            query_vector=query_vector,
            image_query_vector=image_query_vector,
            query_tokens=query_tokens,
            keywords_queries=keywords_queries,
        )
        style_intent_profile = self.style_intent_detector.detect(base_result)
        product_title_exclusion_profile = self.product_title_exclusion_detector.detect(base_result)
        if context:
            context.store_intermediate_result(
                "style_intent_profile",
                style_intent_profile.to_dict(),
            )
            context.store_intermediate_result(
                "product_title_exclusion_profile",
                product_title_exclusion_profile.to_dict(),
            )
        result = ParsedQuery(
            original_query=query,
            query_normalized=normalized,
            rewritten_query=query_text,
            detected_language=detected_lang,
            translations=translations,
            query_vector=query_vector,
            image_query_vector=image_query_vector,
            query_tokens=query_tokens,
            keywords_queries=keywords_queries,
            style_intent_profile=style_intent_profile,
            product_title_exclusion_profile=product_title_exclusion_profile,
        )
        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'} | "
                f"Image vector: {'yes' if image_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"