fix code
This commit is contained in:
Felix
@@ -600,10 +600,47 @@ class DictService:
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# 如果查询成功且返回了字典条目，直接将结果存入Redis缓存
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if success and dict_entry:
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try:
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uk_phone = dict_entry.uk_phone if dict_entry.uk_phone else None
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uk_phone = dict_entry.uk_phone if dict_entry.uk_phone else dict_entry.us_phone
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await DictService._cache_word_result(word, uk_phone)
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except Exception as e:
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logger.error(f"Error caching word {word} after successful lookup: {e}")
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# 若包含连字符，拆分为多个子词并额外触发查询
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if '-' in word:
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parts = [p.strip() for p in word.split('-') if p.strip()]
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for part in parts:
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try:
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part_success, part_entry = await youdao_word_api.lookup_and_process(
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word=part,
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category=category,
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dict_type=dict_type,
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)
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if part_success and part_entry:
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try:
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uk_phone_part = part_entry.uk_phone if part_entry.uk_phone else dict_entry.us_phone
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await DictService._cache_word_result(part, uk_phone_part)
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except Exception as ce:
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logger.error(f"Error caching sub-word {part} after successful lookup: {ce}")
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# 只要任一查询成功，则整体视为成功
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success = success or part_success
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except Exception as se:
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logger.error(f"Error looking up sub-word '{part}' for '{word}': {se}")
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# 若原词未获得音标，尝试使用子词的音标拼接缓存到原词
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try:
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orig_has_phone = bool(getattr(dict_entry, 'us_phone', None) or getattr(dict_entry, 'uk_phone', None)) if dict_entry else False
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except Exception:
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orig_has_phone = False
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if not orig_has_phone:
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try:
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phonetics = []
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for part in parts:
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_, part_phone = await DictService._is_word_already_processed(part)
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phonetics.append(part_phone if part_phone else "*")
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composed_phone = " - ".join(phonetics) if phonetics else None
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if composed_phone:
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await DictService._cache_word_result(word, composed_phone)
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except Exception as ce2:
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logger.error(f"Error composing phonetics for hyphenated word '{word}': {ce2}")
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return success
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except Exception as e:
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@@ -18,10 +18,11 @@ class ProductService:
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@staticmethod
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async def init_products(items: List[dict] | None = None) -> int:
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default = [
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{"title": "500积分", "description": "仅限一次", "points": 500, "amount_cents": 100, "one_time": True},
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{"title": "100积分", "description": "充值100积分", "points": 100, "amount_cents": 100, "one_time": False},
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{"title": "500积分", "description": "充值500积分", "points": 500, "amount_cents": 500, "one_time": False},
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{"title": "100积分", "description": "测试100积分", "points": 100, "amount_cents": 1, "one_time": False},
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{"title": "+1000%", "description": "仅限一次", "points": 100, "amount_cents": 100, "one_time": True},
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{"title": "+100%", "description": "充值100积分", "points": 20, "amount_cents": 100, "one_time": False},
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{"title": "+200%", "description": "充值500积分", "points": 150, "amount_cents": 500, "one_time": False},
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{"title": "+500%", "description": "充值500积分", "points": 500, "amount_cents": 1000, "one_time": False},
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# {"title": "100积分", "description": "测试100积分", "points": 100, "amount_cents": 1, "one_time": False},
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]
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payload = items or default
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async with async_db_session.begin() as db:
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@@ -62,14 +62,64 @@ class Qwen:
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# "Focus: primary/central/artificial objects."
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# v2:
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"Vision-to-English-Chinese education module. Analyze and describe the image in three levels: "
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"LEVEL1 (simple vocabulary and basic grammar, ~10 words),"
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"LEVEL2 (detailed and complex vocabulary, 15-20 words),"
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"LEVEL3 (professional, uncommon words and complex grammar, ≤25 words)."
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"For each level, provide 6-8 English sentences and Chinese translations."
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"Output JSON: {LEVEL1: {desc_en:[], desc_zh:[]}, LEVEL2: {}, LEVEL3: {}}."
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"Ensure all description are unique - no repetition."
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# "Vision-to-English-Chinese education module. Analyze and describe the image in three levels: "
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# "LEVEL1 (simple vocabulary and basic grammar, ~10 words),"
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# "LEVEL2 (detailed and complex vocabulary, 15-20 words),"
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# "LEVEL3 (professional, uncommon words and complex grammar, ≤25 words)."
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# "For each level, provide 6-8 English sentences and Chinese translations."
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# "Output JSON: {LEVEL1: {desc_en:[], desc_zh:[]}, LEVEL2: {}, LEVEL3: {}}."
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# "Ensure all description are unique - no repetition."
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# "Focus: primary/central/artificial objects."
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# v3
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"""
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Vision-to-English-Chinese education module (for image recognition). Follow these steps and rules strictly to generate descriptions:
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# Step 1: Image Pre-Analysis (Model Must Do First)
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Identify core subject: What is the main object/scene/person in the image?
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Extract key details: Color, shape, position, action, environment, quantity (as applicable).
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Confirm context: Is it a static scene/dynamic action/daily scene/professional scenario?
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# Step 2: Three-Level Description Requirements (No Repetition Across Levels)
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level1: Basic Introduction
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Rules: Simple vocabulary + elementary grammar, ≤15 words per sentence.
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Quantity: 5-7 unique English sentences + corresponding Chinese translations.
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Focus: Only core subject (e.g., "A red cat sits on the chair" → 一只红色的猫坐在椅子上).
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level2: Detailed Expansion
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Rules: Richer vocabulary + complex structures, 15-25 words per sentence.
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Quantity: 8-10 unique English sentences + corresponding Chinese translations.
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Focus: Add key details (color/action/environment) not mentioned in level1.
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level3: Coherent Narrative
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Rules:
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Total words around 300, ≤30 words per sentence (strictly enforced).
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Logical continuity: Follow "overall scene → local details → implicit context" order.
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Use connecting words (e.g., however, besides, therefore) to link sentences.
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Quantity: A continuous article split into independent sentences (array elements).
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Output: English sentences + corresponding Chinese translations (no overlap with level1/2).
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# Step 3: Output Format (Strictly Follow JSON Structure)
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{
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"level1": {
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"desc_en": ["sentence1 (≤10 words)", "sentence2 (≤10 words)", ...],
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"desc_zh": ["准确对应翻译 1", "准确对应翻译 2", ...]
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},
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"level2": {
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"desc_en": ["sentence1 (15-20 words)", "sentence2 (15-20 words)", ...],
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"desc_zh": ["准确对应翻译 1", "准确对应翻译 2", ...]
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},
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"level3": {
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"desc_en": ["sentence1 (≤25 words)", "sentence2 (≤25 words)", ...], //coherent article
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"desc_zh": ["准确对应翻译 1", "准确对应翻译 2", ...] //same logic as English
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}
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}
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Mandatory Rules (Model Must Obey)
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All English sentences and Chinese translations are 100% unique across three levels.
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LEVEL3 translations must maintain the same logical flow as English.
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No abbreviations, slang, or untranslated words; use standard English/Chinese.
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If image details are unclear, describe observable elements (avoid speculation).
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Strictly adhere to word/sentence quantity limits (no under/over).
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"""
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)
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if exclude_words:
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