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 time
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 QueryTextAnalysisCache, contains_han_text, extract_token_strings
from .keyword_extractor import KeywordExtractor, collect_keywords_queries
logger = logging.getLogger(__name__)
import hanlp  # type: ignore
def _async_enrichment_result_summary(
    task_type: str, lang: Optional[str], result: Any
) -> str:
    """One-line description of a completed translation/embedding task for logging."""
    if task_type == "translation":
        if result:
            return f"lang={lang} translated={result!r}"
        return f"lang={lang} empty_translation"
    if task_type in ("embedding", "image_embedding"):
        if result is not None:
            return f"vector_shape={tuple(result.shape)}"
        return "no_vector" if task_type == "embedding" else "no_image_vector"
    return f"unexpected_task_type={task_type!r}"
def _async_enrichment_failure_warning(task_type: str, lang: Optional[str], err: BaseException) -> str:
    """Warning text aligned with historical messages for context.add_warning."""
    msg = str(err)
    if task_type == "translation":
        return f"Translation failed | Language: {lang} | Error: {msg}"
    if task_type == "image_embedding":
        return f"CLIP text query vector generation failed | Error: {msg}"
    return f"Query vector generation failed | Error: {msg}"
def _log_async_enrichment_finished(
    log_info: Callable[[str], None],
    *,
    task_type: str,
    summary: str,
    elapsed_ms: float,
) -> None:
    log_info(
        f"Async enrichment task finished | task_type={task_type} | "
        f"summary={summary} | elapsed_ms={elapsed_ms:.1f}"
    )
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
    _text_analysis_cache: Optional[QueryTextAnalysisCache] = field(default=None, repr=False)
    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.FINE_ELECTRA_SMALL_ZH)
        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))
    @staticmethod
    def _is_ascii_latin_query(text: str) -> bool:
        candidate = str(text or "").strip()
        if not candidate or contains_han_text(candidate):
            return False
        try:
            candidate.encode("ascii")
        except UnicodeEncodeError:
            return False
        return any(ch.isalpha() for ch in candidate)
    def _detect_query_language(
        self,
        query_text: str,
        *,
        target_languages: Optional[List[str]] = None,
    ) -> str:
        normalized_targets = self._normalize_language_codes(target_languages)
        supported_languages = self._normalize_language_codes(
            getattr(self.config.query_config, "supported_languages", None)
        )
        active_languages = normalized_targets or supported_languages
        if active_languages and set(active_languages).issubset({"en", "zh"}):
            if self._is_ascii_latin_query(query_text):
                return "en"
        return self.language_detector.detect(query_text)
    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
        """
        parse_t0 = time.perf_counter()
        # 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)
        before_wait_t0 = time.perf_counter()
        # 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}")
        normalized_targets = self._normalize_language_codes(target_languages)
        # Stage 3: Language detection
        detected_lang = self._detect_query_language(
            query_text,
            target_languages=normalized_targets,
        )
        # 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)
        text_analysis_cache = QueryTextAnalysisCache(tokenizer=self._tokenizer)
        for text_variant in (query, normalized, query_text):
            text_analysis_cache.set_language_hint(text_variant, detected_lang)
        # 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]]] = {}
        future_submit_at: Dict[Any, float] = {}
        async_executor: Optional[ThreadPoolExecutor] = None
        detected_norm = str(detected_lang or "").strip().lower()
        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)
                    future_submit_at[future] = time.perf_counter()
                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)
                    future_submit_at[future] = time.perf_counter()
                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)
                    future_submit_at[future] = time.perf_counter()
        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()
            future_submit_at.clear()
        # Stage 4: Query analysis (tokenization) now overlaps with async enrichment work.
        query_tokenizer_result = text_analysis_cache.get_tokenizer_result(query_text)
        query_tokens = self._extract_tokens(query_tokenizer_result)
        log_debug(f"Query analysis | Query tokens: {query_tokens}")
        if context:
            context.store_intermediate_result('query_tokens', query_tokens)
        keywords_base_query = ""
        keywords_base_ms = 0.0
        try:
            keywords_base_t0 = time.perf_counter()
            keywords_base_query = self._keyword_extractor.extract_keywords(
                query_text,
                language_hint=detected_lang,
                tokenizer_result=text_analysis_cache.get_tokenizer_result(query_text),
            )
            keywords_base_ms = (time.perf_counter() - keywords_base_t0) * 1000.0
        except Exception as e:
            log_info(f"Base keyword extraction failed | Error: {e}")
        before_wait_ms = (time.perf_counter() - before_wait_t0) * 1000.0
        # 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}"
            )
            async_wait_t0 = time.perf_counter()
            done, not_done = wait(list(future_to_task.keys()), timeout=budget_sec)
            async_wait_ms = (time.perf_counter() - async_wait_t0) * 1000.0
            for future in done:
                task_type, lang = future_to_task[future]
                t0 = future_submit_at.pop(future, None)
                elapsed_ms = (time.perf_counter() - t0) * 1000.0 if t0 is not None else 0.0
                try:
                    result = future.result()
                    if task_type == "translation":
                        if result:
                            translations[lang] = result
                            text_analysis_cache.set_language_hint(result, lang)
                            if context:
                                context.store_intermediate_result(f"translation_{lang}", result)
                    elif task_type == "embedding":
                        query_vector = result
                        if query_vector is not None and context:
                            context.store_intermediate_result("query_vector_shape", query_vector.shape)
                    elif task_type == "image_embedding":
                        image_query_vector = result
                        if image_query_vector is not None and context:
                            context.store_intermediate_result(
                                "image_query_vector_shape",
                                image_query_vector.shape,
                            )
                    _log_async_enrichment_finished(
                        log_info,
                        task_type=task_type,
                        summary=_async_enrichment_result_summary(task_type, lang, result),
                        elapsed_ms=elapsed_ms,
                    )
                except Exception as e:
                    _log_async_enrichment_finished(
                        log_info,
                        task_type=task_type,
                        summary=f"error={e!s}",
                        elapsed_ms=elapsed_ms,
                    )
                    if context:
                        context.add_warning(_async_enrichment_failure_warning(task_type, lang, e))
            if not_done:
                for future in not_done:
                    future_submit_at.pop(future, None)
                    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)
        else:
            async_wait_ms = 0.0
        tail_sync_t0 = time.perf_counter()
        keywords_queries: Dict[str, str] = {}
        keyword_tail_ms = 0.0
        try:
            keywords_t0 = time.perf_counter()
            keywords_queries = collect_keywords_queries(
                self._keyword_extractor,
                query_text,
                translations,
                source_language=detected_lang,
                text_analysis_cache=text_analysis_cache,
                base_keywords_query=keywords_base_query,
            )
            keyword_tail_ms = (time.perf_counter() - keywords_t0) * 1000.0
            if context:
                context.store_intermediate_result("keywords_queries", keywords_queries)
            log_info(f"Keyword extraction completed | keywords_queries={keywords_queries}")
        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,
            _text_analysis_cache=text_analysis_cache,
        )
        style_intent_profile = self.style_intent_detector.detect(base_result)
        product_title_exclusion_profile = self.product_title_exclusion_detector.detect(base_result)
        tail_sync_ms = (time.perf_counter() - tail_sync_t0) * 1000.0
        log_info(
            "Query parse stage timings | "
            f"before_wait_ms={before_wait_ms:.1f} | "
            f"async_wait_ms={async_wait_ms:.1f} | "
            f"base_keywords_ms={keywords_base_ms:.1f} | "
            f"keyword_tail_ms={keyword_tail_ms:.1f} | "
            f"tail_sync_ms={tail_sync_ms:.1f}"
        )
        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,
            _text_analysis_cache=text_analysis_cache,
        )
        parse_total_ms = (time.perf_counter() - parse_t0) * 1000.0
        completion_tail = (
            f"Translation count: {len(translations)} | "
            f"Vector: {'yes' if query_vector is not None else 'no'} | "
            f"Image vector: {'yes' if image_query_vector is not None else 'no'} | "
            f"parse_total_ms={parse_total_ms:.1f}"
        )
        if context and hasattr(context, 'logger'):
            context.logger.info(
                f"Query parsing completed | Original query: '{query}' | Final query: '{rewritten or query_text}' | "
                f"Language: {detected_lang} | {completion_tail}",
                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} | {completion_tail}"
            )
        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"