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
269 lines
9.5 KiB
Python
269 lines
9.5 KiB
Python
"""
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query_chroma.py
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查询 Chroma 数据库，从自然语言查找相关 JRXML chunk
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支持命令行单次查询和交互式连续查询
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"""
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import os
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import sys
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import time
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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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import chromadb
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class JRXMLSearcher:
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def __init__(self, chroma_path: str = None,
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collection_name: str = "jrxml_chunks",
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model_path: str = None,
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use_fp16: bool = True):
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project_root = Path(__file__).resolve().parent
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if chroma_path is None:
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chroma_path = str(project_root / "chroma_db")
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if model_path is None:
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model_path = str(project_root / "models" / "Qwen3-Embedding-4B")
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# 处理 Hub 模型名称
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model_path_str = str(model_path)
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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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# 加载嵌入模型
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device = "cuda" if torch.cuda.is_available() else "cpu"
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print(f"🧠 加载模型: {model_path_str}")
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print(f" 设备: {device}")
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self.model = SentenceTransformer(model_path_str, device=device)
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if device == "cuda" and use_fp16:
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self.model = self.model.half()
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torch.cuda.empty_cache()
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mem = torch.cuda.memory_allocated(0) / 1024**3
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total = torch.cuda.get_device_properties(0).total_memory / 1024**3
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print(f" FP16 已启用, 显存: {mem:.2f} GB / {total:.2f} GB")
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# 连接 Chroma
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print(f"💾 连接 Chroma: {chroma_path}")
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self.client = chromadb.PersistentClient(path=chroma_path)
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self.collection = self.client.get_collection(collection_name)
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print(f" 集合 '{collection_name}': {self.collection.count()} 条记录\n")
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def search(self, query: str, n_results: int = 5, filter_meta: dict = None):
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query_embedding = self.model.encode(
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query,
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normalize_embeddings=True,
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show_progress_bar=False
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).tolist()
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where_filter = filter_meta if filter_meta else None
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results = self.collection.query(
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query_embeddings=[query_embedding],
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n_results=n_results,
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where=where_filter,
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include=["documents", "metadatas", "distances"]
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)
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return results
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def search_with_threshold(self, query: str, n_results: int = 5,
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threshold: float = 0.3, filter_meta: dict = None):
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results = self.search(query, n_results, filter_meta)
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filtered = {"ids": [[]], "documents": [[]], "metadatas": [[]], "distances": [[]]}
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for i, dist in enumerate(results["distances"][0]):
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if dist <= threshold:
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filtered["ids"][0].append(results["ids"][0][i])
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filtered["documents"][0].append(results["documents"][0][i])
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filtered["metadatas"][0].append(results["metadatas"][0][i])
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filtered["distances"][0].append(dist)
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return filtered
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def format_result(self, results: dict) -> str:
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lines = []
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n = len(results["ids"][0])
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lines.append(f"找到 {n} 条结果:")
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for i, (doc_id, doc, dist, meta) in enumerate(zip(
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results["ids"][0],
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results["documents"][0],
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results["distances"][0],
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results["metadatas"][0]
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)):
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chunk_type = meta.get("chunk_type", "N/A")
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report = meta.get("report_name", "")
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band = meta.get("band_name", "")
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lines.append(f"\n--- 结果 {i+1} (相似度={1-dist:.4f}, id={doc_id}) ---")
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lines.append(f"类型: {chunk_type}")
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if report:
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lines.append(f"报表: {report}")
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if band:
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lines.append(f"区域: {band}")
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lines.append(f"内容: {doc[:300]}")
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return "\n".join(lines)
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def main():
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import argparse
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project_root = Path(__file__).resolve().parent
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parser = argparse.ArgumentParser(description="JRXML Chunks 语义搜索工具")
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parser.add_argument("query", nargs="?", default="",
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help="搜索关键词（不提供则进入交互模式）")
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parser.add_argument("--chroma_path", "-c", default=None,
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help=f"Chroma 数据库路径 (默认: chroma_db)")
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parser.add_argument("--collection", "-n", default="jrxml_chunks",
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help="集合名称")
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parser.add_argument("--model_path", "-m", default=None,
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help="嵌入模型路径")
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parser.add_argument("--n_results", "-k", type=int, default=5,
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help="返回结果数 (默认: 5)")
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parser.add_argument("--filter_field", "-f",
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help="按 chunk_type 过滤，例如: field, query, chart")
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parser.add_argument("--threshold", "-t", type=float,
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help="相似度阈值 (0~1, 越高越相似)")
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parser.add_argument("--no_fp16", action="store_true",
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help="禁用 FP16 半精度")
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args = parser.parse_args()
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if args.chroma_path is None:
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args.chroma_path = str(project_root / "chroma_db")
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if args.model_path is None:
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default_model = project_root / "models" / "Qwen3-Embedding-4B"
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if not default_model.exists():
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args.model_path = "sentence-transformers/all-MiniLM-L6-v2"
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else:
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args.model_path = str(default_model)
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# 检查数据库
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if not os.path.exists(args.chroma_path):
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print(f"❌ Chroma 数据库不存在: {args.chroma_path}")
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print(f" 请先运行 import_to_chroma.py 导入数据")
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return
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# 初始化搜索器
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try:
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searcher = JRXMLSearcher(
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chroma_path=args.chroma_path,
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collection_name=args.collection,
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model_path=args.model_path,
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use_fp16=not args.no_fp16
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)
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except Exception as e:
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print(f"❌ 初始化失败: {e}")
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return
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# 准备过滤条件
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filter_meta = None
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if args.filter_field:
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filter_meta = {"chunk_type": args.filter_field}
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# 单次查询模式
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if args.query:
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query = args.query
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print(f"\n� 搜索: '{query}'")
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if filter_meta:
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print(f" 过滤: {filter_meta}")
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start = time.time()
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if args.threshold is not None:
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results = searcher.search_with_threshold(
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query, args.n_results, args.threshold, filter_meta
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)
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else:
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results = searcher.search(query, args.n_results, filter_meta)
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elapsed = time.time() - start
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print(searcher.format_result(results))
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print(f"\n⏱️ 耗时: {elapsed:.2f}s")
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return
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# 交互模式
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print(f"\n{'='*60}")
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print(f"JRXML 语义搜索 - 交互模式")
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print(f"{'='*60}")
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print(f"可用过滤类型: report_overview, query, field, parameter,")
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print(f" variable, band_*, chart, crosstab, subreport, style 等")
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print(f"示例: '如何修改报表标题'")
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print(f" 'filter:query SQL数据源查询'")
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print(f" 't:0.5 band:title 标题区域'")
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print(f"输入 'help' 查看帮助, 'exit' 退出\n")
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while True:
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try:
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user_input = input("🔍 搜索> ").strip()
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except (EOFError, KeyboardInterrupt):
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print("\n👋 再见!")
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break
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if not user_input:
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continue
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if user_input.lower() in ("exit", "quit", "q"):
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print("👋 再见!")
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break
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if user_input.lower() == "help":
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print("""
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特殊命令:
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filter:<类型> 按 chunk_type 过滤 (如 filter:query)
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t:<阈值> 设置相似度阈值 0~1 (如 t:0.5)
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k:<数量> 设置返回结果数 (如 k:10)
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示例:
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filter:field 数据源字段有哪些
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t:0.5 band:title 标题区域怎么设置
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k:10 报表参数定义
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""")
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continue
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# 解析特殊命令
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query_text = user_input
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cur_filter = filter_meta
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cur_n = args.n_results
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cur_threshold = args.threshold
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parts = user_input.split()
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new_parts = []
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for p in parts:
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if p.startswith("filter:"):
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field_val = p[len("filter:"):]
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cur_filter = {"chunk_type": field_val}
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print(f" 📌 过滤: {cur_filter}")
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elif p.startswith("t:"):
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try:
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cur_threshold = float(p[2:])
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print(f" 📌 阈值: {cur_threshold}")
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except ValueError:
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pass
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elif p.startswith("k:"):
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try:
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cur_n = int(p[2:])
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cur_n = max(1, min(cur_n, 50))
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print(f" 📌 返回数量: {cur_n}")
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except ValueError:
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pass
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else:
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new_parts.append(p)
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query_text = " ".join(new_parts)
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if not query_text:
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print(" ⚠️ 请输入搜索内容")
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continue
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print(f"🔍 搜索: '{query_text}'")
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start = time.time()
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if cur_threshold is not None:
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results = searcher.search_with_threshold(
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query_text, cur_n, cur_threshold, cur_filter
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)
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else:
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results = searcher.search(query_text, cur_n, cur_filter)
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elapsed = time.time() - start
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print(searcher.format_result(results))
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print(f"⏱️ 耗时: {elapsed:.2f}s\n")
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if __name__ == "__main__":
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main() |