other
Warning
- Location
scripts/auto-research.py:290- Finding
Synthetic Research Results Are Presented as Successfully Retrieved Platform Data
- Content
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List[ContentItem]: """Multi-platform parallel search""" results = [] # Sort by relevance and search high-relevance platforms first sorted_configs = sorted(platform_configs, key=lambda x: x['score'], reverse=True) for cfg in sorted_configs: platform = cfg['name'] count = cfg['fetch_count'] lang = "zh" if platform in ["Zhihu", "CSDN", "Juejin", "Bilibili", "Xiaoyuzhou", "WeChat"] else "en" items = self._simulate_search(platform, keywords[lang], count) results.extend(items) # Update statistics stats = self.platform_stats[platform] stats.fetched_count = len(items) print(f" ✓ {platform}: {len(items)} items") return results def _simulate_search(self, platform: str, keywords: Dict, count: int) -> List[ContentItem]: """Simulate search results (a real API should be called in production)""" items = [] core_word = keywords["core"][0] # Platform-specific fixed title templates are selected here. template_list = templates.get(platform, [f"{core_word} content"]) for i in range(min(count, len(template_list) * 3)): title = template_list[i % len(template_list)] level = self.classifier.classify_level(title) content_type = self.classifier.classify_type(platform) item = ContentItem( title=title, url=f"https://example.com/{platform.lower()}/{i}", platform=platform, content_type=content_type, language="zh" if platform in ["Zhihu", "CSDN", "Juejin", "Bilibili", "Xiaoyuzhou", "WeChat"] else "en", level=level, quality_score=round(0.7 + (i % ...[truncated 2962 chars]- Remediation
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