""" embed_chunks.py 使用嵌入模型对 JRXML chunks 进行向量化 支持 GPU (CUDA) 或 CPU，模型通过 .env / config.py 配置 """ import os import sys import json import pickle from pathlib import Path import numpy as np import torch from sentence_transformers import SentenceTransformer from config import ( EMBEDDING_MODEL_PATH, CHUNKER_OUTPUT_DIR, EMBEDDINGS_DIR, USE_FP16, BATCH_SIZE, resolve_model_path ) def build_text_for_embedding(chunk: dict) -> str: """ 将单个 chunk 转换为适合向量化的文本 拼接：类型、描述、上下文、关键元数据、部分内容 支持 JRXML chunks (raw_xml) 和 Markdown chunks (raw_content) """ parts = [ f"[ChunkType: {chunk.get('chunk_type', 'unknown')}]", chunk.get('human_description', ''), ] context = chunk.get('context', '') if context: parts.append(f"Context: {context}") # 支持两种格式：raw_xml (JRXML) 和 raw_content (Markdown) raw_content = chunk.get('raw_xml', '') or chunk.get('raw_content', '') if raw_content: parts.append(f"Content: {raw_content[:500]}") meta = chunk.get('metadata', {}) if meta: if 'field_names' in meta: parts.append(f"Fields: {', '.join(meta['field_names'])}") if 'parameter_names' in meta: parts.append(f"Parameters: {', '.join(meta['parameter_names'])}") if 'report_name' in meta: parts.append(f"Report: {meta['report_name']}") if 'band_name' in meta: parts.append(f"Band: {meta['band_name']}") if 'element_kind' in meta: parts.append(f"Element: {meta['element_kind']}") if 'query_language' in meta: parts.append(f"QueryLang: {meta['query_language']}") if 'language' in meta: parts.append(f"CodeLang: {meta['language']}") if 'heading' in meta: parts.append(f"Section: {meta['heading']}") return "\n".join(parts) def main(chunks_json_path: str = None, output_dir: str = None, model_path: str = None, batch_size: int = None, normalize: bool = True, use_fp16: bool = None, incremental: bool = False): """ 主流程： 1. 加载 chunk JSON 2. 加载嵌入模型 3. 构造文本并向量化 4. 保存向量及映射文件 """ project_root = Path(__file__).resolve().parent if chunks_json_path is None: chunks_json_path = CHUNKER_OUTPUT_DIR / "all_chunks.json" else: chunks_json_path = Path(chunks_json_path) if output_dir is None: output_dir = EMBEDDINGS_DIR else: output_dir = Path(output_dir) if model_path is None: model_path = resolve_model_path() else: model_path = str(model_path) if batch_size is None: batch_size = BATCH_SIZE if use_fp16 is None: use_fp16 = USE_FP16 if not chunks_json_path.exists(): print(f"❌ Chunks 文件不存在: {chunks_json_path}") print(f" 请先运行 batch_chunker.py 生成 chunks") return None print(f"\n{'='*60}") print(f"JRXML Chunks 向量化") print(f"{'='*60}") print(f"📄 加载 chunks: {chunks_json_path}") with open(chunks_json_path, 'r', encoding='utf-8') as f: chunks = json.load(f) print(f" Total chunks: {len(chunks)}") device = "cuda" if torch.cuda.is_available() else "cpu" print(f"\n🧠 加载嵌入模型: {model_path}") print(f" 设备: {device}") model_path_str = str(model_path) if "\\" in model_path_str and not os.path.exists(model_path_str): model_path_str = model_path_str.replace("\\", "/") is_hub_model = "/" in model_path_str and not os.path.exists(model_path_str) if not is_hub_model and not os.path.exists(model_path_str): print(f"❌ 模型目录不存在: {model_path}") print(f" 请先运行 down_embedding_model.py 下载模型") print(f" 或在 .env 中配置 EMBEDDING_MODEL_NAME 为 Hub 模型名") return None model = SentenceTransformer(model_path_str, device=device) if device == "cuda" and use_fp16: model = model.half() torch.cuda.empty_cache() mem_used = torch.cuda.memory_allocated(0) / 1024**3 total_mem = torch.cuda.get_device_properties(0).total_memory / 1024**3 print(f" FP16 已启用") print(f" GPU: {torch.cuda.get_device_name(0)}") print(f" GPU memory: {mem_used:.2f} GB / {total_mem:.2f} GB (FP16)") elif device == "cuda": print(f" GPU: {torch.cuda.get_device_name(0)}") 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") # 增量模式：加载已有向量，只处理新 chunks existing_chunks = [] existing_embeddings = None if incremental: existing_chunks_path = output_dir / "chunks.json" existing_emb_path = output_dir / "embeddings.npy" if existing_chunks_path.exists() and existing_emb_path.exists(): with open(existing_chunks_path, 'r', encoding='utf-8') as f: existing_chunks = json.load(f) existing_embeddings = np.load(existing_emb_path) existing_keys = {(c.get('context', ''), c.get('chunk_id', -1)) for c in existing_chunks} new_chunks = [c for c in chunks if (c.get('context', ''), c.get('chunk_id', -1)) not in existing_keys] skipped = len(chunks) - len(new_chunks) print(f"\n🔄 增量模式: 已有 {len(existing_chunks)} 个 chunks, 跳过 {skipped} 个重复, 新增 {len(new_chunks)} 个") chunks = new_chunks else: print(f"\n🔄 增量模式: 未找到已有向量数据，切换为全量处理") incremental = False if not chunks: print("✅ 没有新 chunks 需要向量化") return { "chunks": len(existing_chunks), "embedding_dim": existing_embeddings.shape[1] if existing_embeddings is not None else 0, "output_dir": str(output_dir) } print(f"\n🛠️ 构建文本表示...") texts = [] chunk_ids = [] chunk_types = [] for chunk in chunks: texts.append(build_text_for_embedding(chunk)) chunk_ids.append(chunk.get('chunk_id', -1)) chunk_types.append(chunk.get('chunk_type', 'unknown')) print(f"\n🔢 向量化 {len(texts)} 个文本 (batch_size={batch_size})...") embeddings = model.encode( texts, batch_size=batch_size, show_progress_bar=True, normalize_embeddings=normalize, convert_to_numpy=True ) print(f" Embeddings shape: {embeddings.shape}") # 合并已有向量 if existing_embeddings is not None and len(existing_chunks) > 0: all_embeddings = np.concatenate([existing_embeddings, embeddings], axis=0) all_chunks = existing_chunks + chunks else: all_embeddings = embeddings all_chunks = chunks output_dir.mkdir(parents=True, exist_ok=True) np.save(output_dir / "embeddings.npy", all_embeddings.astype('float32')) all_chunk_ids = [c.get('chunk_id', -1) for c in all_chunks] all_chunk_types = [c.get('chunk_type', 'unknown') for c in all_chunks] with open(output_dir / "chunk_id_map.json", 'w', encoding='utf-8') as f: json.dump(all_chunk_ids, f, ensure_ascii=False, indent=2) with open(output_dir / "chunk_type_map.json", 'w', encoding='utf-8') as f: json.dump(all_chunk_types, f, ensure_ascii=False, indent=2) with open(output_dir / "chunks.json", 'w', encoding='utf-8') as f: json.dump(all_chunks, f, ensure_ascii=False, indent=2) with open(output_dir / "embeddings.pkl", 'wb') as f: pickle.dump({ 'chunks': all_chunks, 'embeddings': all_embeddings, 'texts': texts, 'normalized': normalize }, f) nan_count = np.isnan(all_embeddings).sum() print(f"\n📊 质量检查:") print(f" NaN values: {nan_count}") norms = np.linalg.norm(all_embeddings, axis=1) print(f" Norms: min={norms.min():.4f}, max={norms.max():.4f}, mean={norms.mean():.4f}") print(f"\n✅ 向量数据已保存到: {output_dir}/") print(f" 文件: embeddings.npy, chunk_id_map.json, chunk_type_map.json, chunks.json, embeddings.pkl") type_counts = {} for ct in all_chunk_types: type_counts[ct] = type_counts.get(ct, 0) + 1 print(f"\n📈 Chunk 类型分布:") for ct, count in sorted(type_counts.items(), key=lambda x: -x[1]): print(f" {ct}: {count}") return { "chunks": len(all_chunks), "embedding_dim": all_embeddings.shape[1], "output_dir": str(output_dir) } if __name__ == "__main__": import argparse project_root = Path(__file__).resolve().parent default_chunks = CHUNKER_OUTPUT_DIR / "all_chunks.json" parser = argparse.ArgumentParser(description="JRXML Chunks 向量化工具") parser.add_argument("chunks_json", nargs="?", default=str(default_chunks), help=f"Chunks JSON 文件路径 (默认: {default_chunks})") parser.add_argument("--output_dir", "-o", default=None, help=f"输出目录 (默认: {EMBEDDINGS_DIR})") parser.add_argument("--model_path", "-m", default=None, help=f"模型路径 (默认: {resolve_model_path()})") parser.add_argument("--batch_size", "-b", type=int, default=BATCH_SIZE, help=f"批处理大小 (默认: {BATCH_SIZE})") parser.add_argument("--no_normalize", action="store_true", help="不做向量归一化") parser.add_argument("--no_fp16", action="store_true", help="禁用 FP16 半精度（默认启用，可节省约 50%% 显存）") parser.add_argument("--incremental", "-i", action="store_true", help="增量模式：只向量化新增 chunks，追加到已有向量数据") args = parser.parse_args() main( chunks_json_path=args.chunks_json, output_dir=args.output_dir, model_path=args.model_path, batch_size=args.batch_size, normalize=not args.no_normalize, use_fp16=not args.no_fp16, incremental=args.incremental )