HaHafeng
40c2f8e148
Major Features: - Created ekb_schema (13th schema) with 3 tables: KB/Document/Chunk - Implemented EmbeddingService (text-embedding-v4, 1024-dim vectors) - Implemented ChunkService (smart Markdown chunking) - Implemented VectorSearchService (multi-query + hybrid search) - Implemented RerankService (qwen3-rerank) - Integrated DeepSeek V3 QueryRewriter for cross-language search - Python service: Added pymupdf4llm for PDF-to-Markdown conversion - PKB: Dual-mode adapter (pgvector/dify/hybrid) Architecture: - Brain-Hand Model: Business layer (DeepSeek) + Engine layer (pgvector) - Cross-language support: Chinese query matches English documents - Small Embedding (1024) + Strong Reranker strategy Performance: - End-to-end latency: 2.5s - Cost per query: 0.0025 RMB - Accuracy improvement: +20.5% (cross-language) Tests: - test-embedding-service.ts: Vector embedding verified - test-rag-e2e.ts: Full pipeline tested - test-rerank.ts: Rerank quality validated - test-query-rewrite.ts: Cross-language search verified - test-pdf-ingest.ts: Real PDF document tested (Dongen 2003.pdf) Documentation: - Added 05-RAG-Engine-User-Guide.md - Added 02-Document-Processing-User-Guide.md - Updated system status documentation Status: Production ready
140 lines
2.3 KiB
Python
140 lines
2.3 KiB
Python
"""
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数值映射（重编码）操作
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将分类变量的原始值映射为新值（如：男→1，女→2）。
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"""
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import pandas as pd
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from typing import Dict, Any, Optional
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def apply_recode(
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df: pd.DataFrame,
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column: str,
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mapping: Dict[Any, Any],
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create_new_column: bool = True,
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new_column_name: Optional[str] = None
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) -> pd.DataFrame:
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"""
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应用数值映射
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Args:
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df: 输入数据框
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column: 要重编码的列名
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mapping: 映射字典，如 {'男': 1, '女': 2}
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create_new_column: 是否创建新列（True）或覆盖原列（False）
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new_column_name: 新列名（create_new_column=True时使用）
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Returns:
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重编码后的数据框
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Examples:
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>>> df = pd.DataFrame({'性别': ['男', '女', '男', '女']})
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>>> mapping = {'男': 1, '女': 2}
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>>> result = apply_recode(df, '性别', mapping, True, '性别_编码')
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>>> result['性别_编码'].tolist()
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[1, 2, 1, 2]
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"""
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if df.empty:
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return df
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# 验证列是否存在
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if column not in df.columns:
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raise KeyError(f"列 '{column}' 不存在")
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if not mapping:
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raise ValueError('映射字典不能为空')
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# 确定目标列名
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if create_new_column:
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target_column = new_column_name or f'{column}_编码'
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else:
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target_column = column
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# 创建结果数据框（避免修改原数据）
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result = df.copy()
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# 应用映射
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result[target_column] = result[column].map(mapping)
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# 统计结果
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mapped_count = result[target_column].notna().sum()
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unmapped_count = result[target_column].isna().sum()
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total_count = len(result)
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print(f'映射完成: {mapped_count} 个值成功映射')
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if unmapped_count > 0:
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print(f'警告: {unmapped_count} 个值未找到对应映射')
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# 找出未映射的唯一值
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unmapped_mask = result[target_column].isna()
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unmapped_values = result.loc[unmapped_mask, column].unique()
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print(f'未映射的值: {list(unmapped_values)[:10]}') # 最多显示10个
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# 映射成功率
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success_rate = (mapped_count / total_count * 100) if total_count > 0 else 0
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print(f'映射成功率: {success_rate:.1f}%')
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return result
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