panda
bd98486de0
创建了完整的JRXML语义检索RAG项目，包含： 1. 新增.gitignore忽略项目生成的缓存、依赖目录和本地文件 2. 编写详细的项目README文档 3. 补充文件功能说明文档 4. 实现向量导入、向量化、查询等核心脚本
211 lines
7.8 KiB
Python
211 lines
7.8 KiB
Python
"""
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embed_chunks.py
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使用本地 Qwen3-Embedding-4B 模型对 JRXML chunks 进行向量化
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支持 GPU (CUDA) 或 CPU
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"""
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import os
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import sys
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import json
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import pickle
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from pathlib import Path
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import numpy as np
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import torch
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from sentence_transformers import SentenceTransformer
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def build_text_for_embedding(chunk: dict) -> str:
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"""
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将单个 chunk 转换为适合向量化的文本
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拼接：类型、描述、上下文、关键元数据、部分 XML
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"""
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parts = [
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f"[ChunkType: {chunk.get('chunk_type', 'unknown')}]",
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chunk.get('human_description', ''),
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]
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context = chunk.get('context', '')
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if context:
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parts.append(f"Context: {context}")
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raw_xml = chunk.get('raw_xml', '')
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if raw_xml:
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parts.append(f"XML: {raw_xml[:500]}")
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meta = chunk.get('metadata', {})
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if meta:
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if 'field_names' in meta:
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parts.append(f"Fields: {', '.join(meta['field_names'])}")
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if 'parameter_names' in meta:
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parts.append(f"Parameters: {', '.join(meta['parameter_names'])}")
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if 'report_name' in meta:
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parts.append(f"Report: {meta['report_name']}")
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if 'band_name' in meta:
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parts.append(f"Band: {meta['band_name']}")
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if 'element_kind' in meta:
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parts.append(f"Element: {meta['element_kind']}")
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if 'query_language' in meta:
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parts.append(f"QueryLang: {meta['query_language']}")
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return "\n".join(parts)
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def main(chunks_json_path: str = None, output_dir: str = None,
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model_path: str = None, batch_size: int = 64, normalize: bool = True,
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use_fp16: bool = True):
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"""
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主流程：
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1. 加载 chunk JSON
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2. 加载嵌入模型
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3. 构造文本并向量化
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4. 保存向量及映射文件
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"""
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project_root = Path(__file__).resolve().parent
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if chunks_json_path is None:
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chunks_json_path = project_root / "jrxml_chunker_output" / "all_chunks.json"
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else:
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chunks_json_path = Path(chunks_json_path)
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if output_dir is None:
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output_dir = project_root / "embeddings"
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else:
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output_dir = Path(output_dir)
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if model_path is None:
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model_path = project_root / "models" / "Qwen3-Embedding-4B"
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else:
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model_path = Path(model_path)
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if not chunks_json_path.exists():
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print(f"❌ Chunks 文件不存在: {chunks_json_path}")
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print(f" 请先运行 jrxml_banch_chunker.py 生成 chunks")
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return None
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print(f"\n{'='*60}")
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print(f"JRXML Chunks 向量化")
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print(f"{'='*60}")
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print(f"📄 加载 chunks: {chunks_json_path}")
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with open(chunks_json_path, 'r', encoding='utf-8') as f:
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chunks = json.load(f)
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print(f" Total chunks: {len(chunks)}")
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device = "cuda" if torch.cuda.is_available() else "cpu"
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print(f"\n🧠 加载嵌入模型: {model_path}")
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print(f" 设备: {device}")
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# 检查是否是 HuggingFace Hub 模型（格式为 username/model_name）
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model_path_str = str(model_path)
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# Windows PowerShell 会把 / 自动转成 \，需要还原
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if "\\" in model_path_str and not os.path.exists(model_path_str):
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model_path_str = model_path_str.replace("\\", "/")
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is_hub_model = "/" in model_path_str and not os.path.exists(model_path_str)
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# 如果是本地路径但不存在，则报错
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if not is_hub_model and not os.path.exists(model_path_str):
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print(f"❌ 模型目录不存在: {model_path}")
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print(f" 请先下载模型到 {model_path}")
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print(f" 或者使用 HuggingFace Hub 模型，例如: sentence-transformers/all-MiniLM-L6-v2")
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return None
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model = SentenceTransformer(model_path_str, device=device)
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if device == "cuda" and use_fp16:
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model = model.half()
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torch.cuda.empty_cache()
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mem_used = torch.cuda.memory_allocated(0) / 1024**3
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total_mem = torch.cuda.get_device_properties(0).total_memory / 1024**3
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print(f" FP16 已启用")
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print(f" GPU: {torch.cuda.get_device_name(0)}")
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print(f" GPU memory: {mem_used:.2f} GB / {total_mem:.2f} GB (FP16)")
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elif device == "cuda":
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print(f" GPU: {torch.cuda.get_device_name(0)}")
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print(f" GPU memory: {torch.cuda.memory_allocated(0)/1024**3:.2f} GB / {torch.cuda.get_device_properties(0).total_memory/1024**3:.2f} GB")
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print(f"\n🛠️ 构建文本表示...")
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texts = []
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chunk_ids = []
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chunk_types = []
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for chunk in chunks:
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texts.append(build_text_for_embedding(chunk))
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chunk_ids.append(chunk.get('chunk_id', -1))
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chunk_types.append(chunk.get('chunk_type', 'unknown'))
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print(f"\n🔢 向量化 {len(texts)} 个文本 (batch_size={batch_size})...")
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embeddings = model.encode(
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texts,
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batch_size=batch_size,
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show_progress_bar=True,
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normalize_embeddings=normalize,
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convert_to_numpy=True
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)
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print(f" Embeddings shape: {embeddings.shape}")
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output_dir.mkdir(parents=True, exist_ok=True)
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np.save(output_dir / "embeddings.npy", embeddings.astype('float32'))
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with open(output_dir / "chunk_id_map.json", 'w', encoding='utf-8') as f:
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json.dump(chunk_ids, f, ensure_ascii=False, indent=2)
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with open(output_dir / "chunk_type_map.json", 'w', encoding='utf-8') as f:
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json.dump(chunk_types, f, ensure_ascii=False, indent=2)
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with open(output_dir / "chunks.json", 'w', encoding='utf-8') as f:
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json.dump(chunks, f, ensure_ascii=False, indent=2)
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with open(output_dir / "embeddings.pkl", 'wb') as f:
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pickle.dump({
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'chunks': chunks,
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'embeddings': embeddings,
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'texts': texts,
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'normalized': normalize
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}, f)
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nan_count = np.isnan(embeddings).sum()
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print(f"\n📊 质量检查:")
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print(f" NaN values: {nan_count}")
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norms = np.linalg.norm(embeddings, axis=1)
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print(f" Norms: min={norms.min():.4f}, max={norms.max():.4f}, mean={norms.mean():.4f}")
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print(f"\n✅ 向量数据已保存到: {output_dir}/")
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print(f" 文件: embeddings.npy, chunk_id_map.json, chunk_type_map.json, chunks.json, embeddings.pkl")
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type_counts = {}
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for ct in chunk_types:
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type_counts[ct] = type_counts.get(ct, 0) + 1
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print(f"\n📈 Chunk 类型分布:")
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for ct, count in sorted(type_counts.items(), key=lambda x: -x[1]):
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print(f" {ct}: {count}")
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return {
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"chunks": len(chunks),
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"embedding_dim": embeddings.shape[1],
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"output_dir": str(output_dir)
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}
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if __name__ == "__main__":
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import argparse
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project_root = Path(__file__).resolve().parent
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default_chunks = project_root / "jrxml_chunker_output" / "all_chunks.json"
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parser = argparse.ArgumentParser(description="JRXML Chunks 向量化工具")
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parser.add_argument("chunks_json", nargs="?", default=str(default_chunks),
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help=f"Chunks JSON 文件路径 (默认: {default_chunks})")
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parser.add_argument("--output_dir", "-o", default=None,
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help="输出目录 (默认: embeddings)")
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parser.add_argument("--model_path", "-m", default=None,
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help="模型路径 (默认: models/Qwen3-Embedding-4B)")
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parser.add_argument("--batch_size", "-b", type=int, default=64,
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help="批处理大小 (默认: 64)")
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parser.add_argument("--no_normalize", action="store_true",
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help="不做向量归一化")
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parser.add_argument("--no_fp16", action="store_true",
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help="禁用 FP16 半精度（默认启用，可节省约 50%% 显存）")
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args = parser.parse_args()
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main(
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chunks_json_path=args.chunks_json,
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output_dir=args.output_dir,
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model_path=args.model_path,
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batch_size=args.batch_size,
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normalize=not args.no_normalize,
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use_fp16=not args.no_fp16
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) |