feat: 添加Markdown分块器与统一批量分块入口，支持增量向量化与导入
- 新增 md_chunker.py: Markdown语义分块引擎，支持标题/代码块/表格智能拆分 - 新增 batch_chunker.py: 统一批量分块入口，支持JRXML+Markdown混合处理 - 新增 requirements.txt: 整理项目依赖 - embed_chunks.py: 新增 --incremental 增量模式，追加新向量到已有数据 - import_to_chroma.py: 新增 --incremental 增量模式，不再每次清空数据库 - 更新 README.md 与 docs/file_guide.md 反映最新架构 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
@@ -1,36 +1,39 @@
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# JRXML RAG 项目
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基于 RAG（Retrieval-Augmented Generation）的 JasperReports JRXML 模板智能问答系统，构建JRXML自定义agent的前置工作。
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基于 RAG 的 JasperReports JRXML 模板 + Markdown 文档智能检索系统，作为构建 JRXML 自定义 Agent 的前置工作。
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## 项目简介
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本项目将 JasperReports 的 JRXML 模板文件进行语义分块、向量化，并存入 Chroma 向量数据库，实现通过自然语言查询来检索和理解报表模板的结构、配置和逻辑。
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支持 JRXML 模板和 Markdown 文档的语义分块、向量化、Chroma 持久化存储，以及自然语言查询。
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## 项目结构
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```
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RAG-jaspersoft/
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├── collect_jrxml.py # JRXML 文件收集脚本
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├── jrxml_chunker.py # JRXML 语义分块核心引擎
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├── jrxml_banch_chunker.py # 批量分块入口脚本
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├── down_embedding_model.py # 嵌入模型下载脚本
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├── embed_chunks.py # Chunk 向量化脚本
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├── import_to_chroma.py # 向量导入 Chroma 数据库
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├── query_chroma.py # 语义搜索查询工具
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├── jrxml_source/ # JRXML 源文件目录
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├── jrxml_chunker_output/ # 分块输出目录
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│ ├── all_chunks.json # 所有 chunks 合并文件
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│ ├── processing_stats.json # 处理统计报告
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│ └── per_file/ # 按文件分类的 chunks
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├── models/ # 嵌入模型存放目录
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│ └── Qwen3-Embedding-4B/ # Qwen3 嵌入模型
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├── embeddings/ # 向量输出目录
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│ ├── embeddings.npy # 向量矩阵
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│ ├── chunks.json # 原始 chunks
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│ └── embeddings.pkl # 完整数据 pickle
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├── chroma_db/ # Chroma 向量数据库
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└── docs/ # 项目文档
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└── file_guide.md # 文件功能说明
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rag_jrxml/
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├── collect_jrxml.py # JRXML 文件收集
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├── jrxml_chunker.py # JRXML 语义分块引擎 (v3.0)
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├── jrxml_banch_chunker.py # JRXML 批量分块 (单类型)
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├── md_chunker.py # Markdown 语义分块引擎
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├── batch_chunker.py # 统一批量分块入口 (JRXML + MD)
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├── down_embedding_model.py # 嵌入模型下载
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├── embed_chunks.py # Chunk 向量化 (支持增量)
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├── import_to_chroma.py # Chroma 向量入库 (支持增量)
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├── query_chroma.py # 语义搜索查询
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├── config.py # 统一配置管理 (.env)
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├── .env # 环境变量配置
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├── .env.example # 配置模板
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├── requirements.txt # Python 依赖
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├── jrxml_source/ # JRXML 源文件
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├── jrxml_chunker_output/ # 分块输出
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│ ├── all_chunks.json
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│ ├── processing_stats.json
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│ └── per_file/
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├── models/ # 本地嵌入模型
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├── embeddings/ # 向量输出
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│ ├── embeddings.npy
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│ ├── chunks.json
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│ └── embeddings.pkl
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├── chroma_db/ # Chroma 持久化数据库
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└── docs/
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└── file_guide.md
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```
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## 快速开始
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@@ -38,42 +41,106 @@ RAG-jaspersoft/
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### 环境要求
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- Python 3.11+
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- NVIDIA GPU（推荐，8GB+ 显存）或 CPU
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- CUDA 12.1+（GPU 模式）
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- NVIDIA GPU (推荐 8GB+ 显存) 或 CPU
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- CUDA 12.1+ (GPU 模式)
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### 安装依赖
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### 安装
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```bash
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# 安装 PyTorch (CUDA 版本)
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uv pip install torch --index-url https://download.pytorch.org/whl/cu130
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# 安装其他依赖
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uv pip install sentence-transformers chromadb numpy tqdm
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pip install -r requirements.txt
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```
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### 配置
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```bash
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cp .env.example .env # 编辑 .env 调整模型、路径等参数
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```
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主要配置项：
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| 变量 | 说明 | 默认值 |
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|---|---|---|
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| `EMBEDDING_MODEL_NAME` | 嵌入模型 (Hub 名) | `Qwen/Qwen3-Embedding-0.6B` |
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| `EMBEDDING_MODEL_PATH` | 本地模型路径 | `models/Qwen3-Embedding-0.6B` |
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| `MAX_CHUNK_SIZE` | 单个 chunk 最大字符数 | `2000` |
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| `BATCH_SIZE` | 向量化批大小 | `16` |
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| `CHROMA_COLLECTION_NAME` | Chroma 集合名 | `jrxml_chunks` |
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### 完整流程
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```bash
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# 1. 收集 JRXML 文件
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python collect_jrxml.py
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# 2. 语义分块
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python jrxml_banch_chunker.py ./jrxml_source --output ./jrxml_chunker_output
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# 2. 统一分块 (JRXML + Markdown)
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python batch_chunker.py ./jrxml_source --output ./jrxml_chunker_output
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# 3. 下载嵌入模型（首次运行）
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# 3. 下载嵌入模型 (首次)
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python down_embedding_model.py
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# 4. 向量化
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python embed_chunks.py --batch_size 2
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python embed_chunks.py
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# 5. 导入 Chroma 数据库
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# 5. 导入 Chroma
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python import_to_chroma.py
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# 6. 开始查询
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# 6. 查询
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python query_chroma.py
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```
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### 快速查询
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### 增量更新
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新增或修改部分文件时，无需重新处理全部数据：
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```bash
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# 分块新文件
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python batch_chunker.py ./new_files --output ./jrxml_chunker_output/new_batch
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# 增量向量化 (只处理新 chunks)
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python embed_chunks.py ./jrxml_chunker_output/new_batch/all_chunks.json --incremental
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# 增量导入 (不删除已有数据)
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python import_to_chroma.py --incremental
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```
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## 分块类型
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### JRXML 分块
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| 类型 | 说明 |
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|---|---|
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| `report_overview` | 报表概览 (含数据源分析) |
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| `datasource_config` | 数据源配置 |
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| `query` | 数据查询 (SQL/HQL/XPath/JSON 等) |
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| `parameters` | 参数定义 |
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| `fields` / `field` | 字段定义 |
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| `sortFields` | 排序字段 |
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| `filterExpression` | 过滤表达式 |
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| `variables_*` | 变量定义 (按 resetType) |
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| `styles` | 样式定义 |
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| `dataset` | 数据集定义 |
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| `group` | 分组定义 |
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| `band_*` | 标准带区 (title/detail/pageHeader 等) |
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| `chart` | 图表元素 |
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| `crosstab` | 交叉表元素 |
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| `subreport` | 子报表元素 |
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| `component` | 组件元素 (列表等) |
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### Markdown 分块
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| 类型 | 说明 |
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|---|---|
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| `section_h1` | 一级标题段落 |
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| `section_h2` / `section_h3` | 二/三级标题段落 |
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| `section_installation` | 安装/部署章节 |
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| `section_configuration` | 配置章节 |
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| `section_api` | API 接口章节 |
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| `section_example` | 示例/用法章节 |
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| `section_faq` | FAQ/常见问题章节 |
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| `section_changelog` | 更新日志章节 |
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| `code` | 代码块 |
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## 查询
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```bash
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# 交互模式
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@@ -83,48 +150,23 @@ python query_chroma.py
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python query_chroma.py "如何修改报表标题"
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# 按类型过滤
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python query_chroma.py "SQL查询怎么写" --filter_field query
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python query_chroma.py "SQL怎么写" --filter_field query
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# 设置阈值和数量
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python query_chroma.py "报表参数" --threshold 0.5 --n_results 10
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```
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## 分块类型
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## 支持的 JRXML 数据源
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系统将 JRXML 模板按以下语义类型进行分块：
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| 类型 | 说明 |
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|------|------|
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| `report_overview` | 报告整体概览，含数据源分析 |
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| `datasource_config` | 数据源配置属性 |
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| `query` | 数据查询（SQL/HQL/XPath 等） |
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| `parameters` | 参数定义 |
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| `fields` | 字段定义 |
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| `sortFields` | 排序字段 |
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| `filterExpression` | 过滤表达式 |
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| `variables_*` | 变量定义（按重置类型分组） |
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| `styles` | 样式定义 |
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| `groups` | 分组定义 |
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| `band_*` | 标准带（title/detail/pageHeader 等） |
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| `chart` | 图表元素 |
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| `crosstab` | 交叉表元素 |
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| `subreport` | 子报表元素 |
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| `component` | 组件元素（列表等） |
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| `dataset` | 数据集定义 |
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SQL/JDBC · HQL/Hibernate · XPath/XML · JSON · JSONQL · CSV · Data Adapter (Excel/XML/HTTP) · Bean Collection · Empty
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## 技术栈
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- **分块引擎**: 基于 XML 解析的语义分块器
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- **嵌入模型**: Qwen3-Embedding-4B（支持 FP16 半精度）
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- **向量数据库**: ChromaDB（持久化模式，余弦相似度）
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- **分块引擎**: XML 语义解析 (JRXML) + Markdown 结构化解析
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- **嵌入模型**: Qwen3-Embedding (支持 FP16, 可替换)
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- **嵌入框架**: Sentence-Transformers
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- **深度学习**: PyTorch + CUDA
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## 性能参考
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| 硬件 | 模型 | Batch Size | 速度 |
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|------|------|-----------|------|
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| RTX 4060 Laptop 8GB | Qwen3-Embedding-4B (FP16) | 2 | ~1.2s/chunk |
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| RTX 4060 Laptop 8GB | all-MiniLM-L6-v2 | 64 | ~0.001s/chunk |
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> 离线建库是一次性开销，在线查询仅需 1-2 秒。
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- **向量数据库**: ChromaDB (持久化, 余弦相似度)
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- **深度学习**: PyTorch + CUDA (CPU 兼容)
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## License
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@@ -0,0 +1,222 @@
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"""
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batch_chunker.py
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统一批量分块入口，支持 JRXML 和 Markdown 文件混合处理
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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 time
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from pathlib import Path
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from datetime import datetime
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from collections import defaultdict
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from jrxml_chunker import JRXMLSemanticChunker
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from md_chunker import MarkdownSemanticChunker, save_chunks_to_json
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SUPPORTED_EXTENSIONS = ('.jrxml', '.JRXML', '.md', '.markdown')
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def batch_chunk_with_report(input_dir: str = None, output_dir: str = None,
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max_chunk_size: int = 2000):
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"""
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批量分块，支持 JRXML 和 Markdown 混合处理
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Args:
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input_dir: 输入目录
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output_dir: 输出目录
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max_chunk_size: 单个 chunk 最大字符数
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"""
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if input_dir is None:
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print("错误：请指定输入目录")
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return None
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input_path = Path(input_dir).resolve()
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if not input_path.exists():
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print(f"❌ 目录不存在: {input_path}")
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return None
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if not input_path.is_dir():
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print(f"❌ 不是目录: {input_path}")
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return None
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if output_dir is None:
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output_dir = input_path.parent / f"{input_path.stem}_chunks"
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output_path = Path(output_dir)
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output_path.mkdir(parents=True, exist_ok=True)
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print(f"\n{'='*60}")
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print(f"统一批量分块 v1.0")
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print(f"{'='*60}")
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print(f"输入目录: {input_path}")
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print(f"输出目录: {output_path}")
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print(f"{'='*60}\n")
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# 初始化分块器
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jrxml_chunker = JRXMLSemanticChunker(max_chunk_size=max_chunk_size)
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md_chunker = MarkdownSemanticChunker(max_chunk_size=max_chunk_size)
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# 收集所有支持的文件
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files_by_ext = defaultdict(list)
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for ext in SUPPORTED_EXTENSIONS:
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files_by_ext[ext] = list(input_path.rglob(f"*{ext}"))
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total_files = sum(len(f) for f in files_by_ext.values())
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print(f"找到 {total_files} 个文件:")
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for ext, files in files_by_ext.items():
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print(f" {ext}: {len(files)} 个")
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if total_files == 0:
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print("⚠️ 未找到支持的文件")
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return None
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# 统计变量
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all_chunks = []
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stats = {
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"total_files": total_files,
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"success": 0,
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"failed": 0,
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"total_chunks": 0,
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"failed_files": [],
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"chunks_per_file": defaultdict(int),
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"chunk_types": defaultdict(int),
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"files_by_type": {"jrxml": 0, "markdown": 0},
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"started_at": datetime.now().isoformat()
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}
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start_time = time.time()
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# 处理 JRXML 文件
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jrxml_files = files_by_ext.get('.jrxml', []) + files_by_ext.get('.JRXML', [])
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if jrxml_files:
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print(f"\n📄 处理 JRXML 文件 ({len(jrxml_files)} 个)...")
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for i, jrxml_file in enumerate(jrxml_files, 1):
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relative_path = jrxml_file.relative_to(input_path)
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try:
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file_start = time.time()
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chunks = jrxml_chunker.chunk_file(str(jrxml_file))
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file_duration = time.time() - file_start
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all_chunks.extend(chunks)
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stats["success"] += 1
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stats["files_by_type"]["jrxml"] += 1
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stats["total_chunks"] += len(chunks)
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stats["chunks_per_file"][str(relative_path)] = len(chunks)
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for chunk in chunks:
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stats["chunk_types"][f"jrxml_{chunk['chunk_type']}"] += 1
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print(f"[{i}/{len(jrxml_files)}] ✅ JRXML: {relative_path} → {len(chunks)} chunks ({file_duration:.2f}s)")
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except Exception as e:
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stats["failed"] += 1
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error_info = {"file": str(relative_path), "type": "jrxml", "error": str(e)}
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stats["failed_files"].append(error_info)
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print(f"[{i}/{len(jrxml_files)}] ❌ JRXML: {relative_path} → {e}")
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# 处理 Markdown 文件
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md_files = files_by_ext.get('.md', []) + files_by_ext.get('.markdown', [])
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if md_files:
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print(f"\n📝 处理 Markdown 文件 ({len(md_files)} 个)...")
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for i, md_file in enumerate(md_files, 1):
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relative_path = md_file.relative_to(input_path)
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try:
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file_start = time.time()
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chunks = md_chunker.chunk_file(str(md_file))
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file_duration = time.time() - file_start
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all_chunks.extend(chunks)
|
||||
|
||||
stats["success"] += 1
|
||||
stats["files_by_type"]["markdown"] += 1
|
||||
stats["total_chunks"] += len(chunks)
|
||||
stats["chunks_per_file"][str(relative_path)] = len(chunks)
|
||||
|
||||
for chunk in chunks:
|
||||
stats["chunk_types"][f"md_{chunk['chunk_type']}"] += 1
|
||||
|
||||
print(f"[{i}/{len(md_files)}] ✅ MD: {relative_path} → {len(chunks)} chunks ({file_duration:.2f}s)")
|
||||
|
||||
except Exception as e:
|
||||
stats["failed"] += 1
|
||||
error_info = {"file": str(relative_path), "type": "markdown", "error": str(e)}
|
||||
stats["failed_files"].append(error_info)
|
||||
print(f"[{i}/{len(md_files)}] ❌ MD: {relative_path} → {e}")
|
||||
|
||||
total_duration = time.time() - start_time
|
||||
stats["processing_time"] = round(total_duration, 2)
|
||||
stats["finished_at"] = datetime.now().isoformat()
|
||||
|
||||
# 保存所有 chunks
|
||||
all_chunks_path = output_path / "all_chunks.json"
|
||||
save_chunks_to_json(all_chunks, str(all_chunks_path))
|
||||
|
||||
# 保存统计报告
|
||||
stats_path = output_path / "processing_stats.json"
|
||||
# Convert defaultdict to dict for JSON serialization
|
||||
stats_serializable = {k: (dict(v) if isinstance(v, defaultdict) else v) for k, v in stats.items()}
|
||||
with open(stats_path, "w", encoding="utf-8") as f:
|
||||
json.dump(stats_serializable, f, ensure_ascii=False, indent=2)
|
||||
|
||||
# 打印总结
|
||||
print(f"\n{'='*60}")
|
||||
print(f"处理完成！")
|
||||
print(f"{'='*60}")
|
||||
print(f"✅ 成功: {stats['success']} 文件 (JRXML: {stats['files_by_type']['jrxml']}, MD: {stats['files_by_type']['markdown']})")
|
||||
print(f"❌ 失败: {stats['failed']} 文件")
|
||||
print(f"📦 总 Chunks: {stats['total_chunks']}")
|
||||
print(f"⏱️ 总耗时: {total_duration:.2f}s")
|
||||
print(f"📂 输出目录: {output_path}")
|
||||
print(f"\n主要文件:")
|
||||
print(f" - {all_chunks_path}")
|
||||
print(f" - {stats_path}")
|
||||
|
||||
print(f"\nChunk 类型分布 (前 10):")
|
||||
sorted_types = sorted(stats["chunk_types"].items(), key=lambda x: -x[1])[:10]
|
||||
for ct, count in sorted_types:
|
||||
print(f" {ct}: {count}")
|
||||
|
||||
if stats["failed_files"]:
|
||||
print(f"\n⚠️ 失败文件详情:")
|
||||
for fail in stats["failed_files"][:10]:
|
||||
print(f" - {fail['file']} ({fail['type']}): {fail['error']}")
|
||||
|
||||
return {
|
||||
"chunks": all_chunks,
|
||||
"stats": stats_serializable,
|
||||
"output_path": str(output_path)
|
||||
}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if len(sys.argv) < 2:
|
||||
print("=" * 60)
|
||||
print("统一批量分块 v1.0")
|
||||
print("支持 JRXML 和 Markdown 文件")
|
||||
print("=" * 60)
|
||||
print("\n用法:")
|
||||
print(" python batch_chunker.py <目录路径>")
|
||||
print(" python batch_chunker.py <目录路径> --output <输出目录>")
|
||||
print("\n示例:")
|
||||
print(" python batch_chunker.py ./jrxml_source")
|
||||
print(" python batch_chunker.py ./docs")
|
||||
print(" python batch_chunker.py ./ --output ./chunks")
|
||||
sys.exit(0)
|
||||
|
||||
input_path = sys.argv[1]
|
||||
|
||||
output_dir = None
|
||||
if "--output" in sys.argv:
|
||||
idx = sys.argv.index("--output")
|
||||
if idx + 1 < len(sys.argv):
|
||||
output_dir = sys.argv[idx + 1]
|
||||
|
||||
if os.path.isdir(input_path):
|
||||
batch_chunk_with_report(input_path, output_dir)
|
||||
else:
|
||||
print(f"❌ 路径无效或不是目录: {input_path}")
|
||||
+152
-171
@@ -4,110 +4,106 @@
|
||||
|
||||
---
|
||||
|
||||
## 1. collect_jrxml.py — JRXML 文件收集脚本
|
||||
## 1. collect_jrxml.py — JRXML 文件收集
|
||||
|
||||
**功能**: 从指定的 JasperReports 模板库目录递归收集所有 `.jrxml` 文件，复制到项目的 `jrxml_source` 目录。
|
||||
**功能**: 从 JasperReports 模板库目录递归收集 `.jrxml` 文件，复制到项目 `jrxml_source` 目录。
|
||||
|
||||
**输入**:
|
||||
- 源目录: `C:\Users\zy187\JaspersoftWorkspace\JasperReportsSamples`（可修改）
|
||||
**输入**: 源目录路径（硬编码，可按需修改）
|
||||
|
||||
**输出**:
|
||||
- `jrxml_source/` 目录，包含所有收集到的 JRXML 文件
|
||||
**输出**: `jrxml_source/` 目录
|
||||
|
||||
**使用方式**:
|
||||
```bash
|
||||
python collect_jrxml.py
|
||||
```
|
||||
|
||||
**核心逻辑**:
|
||||
- 使用 `os.walk()` 递归遍历源目录
|
||||
- 筛选 `.jrxml` 后缀文件
|
||||
- 自动处理文件名冲突（添加数字后缀）
|
||||
- 使用 `shutil.copy2()` 保留文件元数据
|
||||
---
|
||||
|
||||
## 2. jrxml_chunker.py — JRXML 语义分块引擎 (v3.0)
|
||||
|
||||
**功能**: 将单个 JRXML 文件按语义结构拆分，每个 chunk 包含人类可读描述、原始 XML 和结构化元数据。
|
||||
|
||||
**输入**: 单个 `.jrxml` 文件路径（或目录）
|
||||
|
||||
**输出**: `JRXMLChunk` 列表，字段包括:
|
||||
- `chunk_id`: 文件内序号
|
||||
- `chunk_type`: 分块类型 (如 `query`, `band_detail`, `chart` 等)
|
||||
- `human_description`: 人类可读描述
|
||||
- `raw_xml`: 原始 XML 片段
|
||||
- `context`: 所属报表名称
|
||||
- `metadata`: 元数据 (report_name, band_name, element_kind 等)
|
||||
|
||||
**支持的数据源**: SQL, HQL, XPath, JSON, JSONQL, CSV, Data Adapter, Bean Collection, Empty
|
||||
|
||||
**使用方式**:
|
||||
```bash
|
||||
python jrxml_chunker.py report.jrxml # 单文件
|
||||
python jrxml_chunker.py ./jrxml_source/ # 目录
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 2. jrxml_chunker.py — JRXML 语义分块核心引擎
|
||||
## 3. jrxml_banch_chunker.py — JRXML 批量分块 (单类型)
|
||||
|
||||
**功能**: 将单个 JRXML 文件按语义结构拆分为多个 chunk，每个 chunk 包含人类可读描述、原始 XML 和元数据。
|
||||
**功能**: 批量处理目录下所有 JRXML 文件，生成统计报告和按文件分类的输出。是旧版入口，被 `batch_chunker.py` 取代。
|
||||
|
||||
**输入**:
|
||||
- 单个 JRXML 文件路径
|
||||
**使用方式**:
|
||||
```bash
|
||||
python jrxml_banch_chunker.py ./jrxml_source --output ./output
|
||||
```
|
||||
|
||||
**输出**:
|
||||
- `JRXMLChunk` 对象列表，每个包含:
|
||||
- `chunk_id`: 唯一标识
|
||||
- `chunk_type`: 分块类型（如 `query`, `field`, `band_title` 等）
|
||||
- `human_description`: 人类可读的结构化描述
|
||||
- `raw_xml`: 原始 XML 片段
|
||||
- `context`: 上下文信息（所属报表名称）
|
||||
- `metadata`: 元数据字典
|
||||
---
|
||||
|
||||
**核心类**:
|
||||
- `JRXMLChunk`: 单个 chunk 的数据结构
|
||||
- `JRXMLSemanticChunker`: 主分块器，支持多种数据源类型（SQL、HQL、XPath、JSON、CSV 等）
|
||||
## 4. md_chunker.py — Markdown 语义分块引擎
|
||||
|
||||
**功能**: 将 Markdown 文件按标题层级、代码块、表格等结构化元素智能分块。
|
||||
|
||||
**输入**: 单个 `.md` 文件路径（或目录）
|
||||
|
||||
**输出**: `MDChunk` 列表，字段包括:
|
||||
- `chunk_id`: 文件内序号
|
||||
- `chunk_type`: 分块类型 (`section_h1`, `code`, `section_installation` 等)
|
||||
- `human_description`: 人类可读描述
|
||||
- `raw_content`: 原始 Markdown 内容
|
||||
- `context`: 所属文档标题
|
||||
- `metadata`: 元数据 (heading, heading_level, language 等)
|
||||
|
||||
**分块策略**:
|
||||
- 按 XML 元素类型分类（field、parameter、variable、band、chart 等）
|
||||
- 提取数据源配置和查询语句
|
||||
- 保留元素间的层级关系
|
||||
- 为每个 chunk 生成结构化的人类可读描述
|
||||
- 按标题层级 (H1/H2/H3) 划分段落
|
||||
- 代码块作为独立 chunk
|
||||
- 表格作为独立 chunk
|
||||
- H2 标题自动识别特殊类型（安装、配置、API、示例等）
|
||||
- 过长段落按段落/句子二次拆分
|
||||
|
||||
**使用方式**:
|
||||
```bash
|
||||
# 处理单个文件
|
||||
python jrxml_chunker.py report.jrxml
|
||||
|
||||
# 处理整个目录
|
||||
python jrxml_chunker.py ./jrxml_source/
|
||||
python md_chunker.py doc.md # 单文件
|
||||
python md_chunker.py ./docs/ # 目录
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 3. jrxml_banch_chunker.py — 批量分块入口脚本
|
||||
## 5. batch_chunker.py — 统一批量分块入口
|
||||
|
||||
**功能**: 批量处理目录下所有 JRXML 文件，生成统计报告和分类输出。
|
||||
**功能**: 统一入口，支持 JRXML 和 Markdown 文件混合批量处理，生成合并的 chunks 和统计报告。
|
||||
|
||||
**输入**:
|
||||
- JRXML 文件目录（默认: `jrxml_source`）
|
||||
**输入**: 包含 `.jrxml` / `.md` 文件的目录
|
||||
|
||||
**输出**:
|
||||
- `jrxml_chunker_output/all_chunks.json`: 所有 chunks 合并文件
|
||||
- `jrxml_chunker_output/processing_stats.json`: 处理统计（成功/失败数、耗时、chunk 类型分布）
|
||||
- `jrxml_chunker_output/per_file/`: 按原文件分类的独立 chunk 文件
|
||||
|
||||
**核心函数**:
|
||||
- `batch_chunk_with_report()`: 批量处理目录
|
||||
- `chunk_single_file_with_report()`: 处理单个文件
|
||||
- `all_chunks.json`: 所有 chunks 合并
|
||||
- `processing_stats.json`: 处理统计 (成功/失败/耗时/类型分布)
|
||||
|
||||
**使用方式**:
|
||||
```bash
|
||||
# 使用默认输入目录
|
||||
python jrxml_banch_chunker.py
|
||||
|
||||
# 指定输入目录
|
||||
python jrxml_banch_chunker.py ./jrxml_source
|
||||
|
||||
# 指定输出目录
|
||||
python jrxml_banch_chunker.py ./jrxml_source --output ./my_output
|
||||
python batch_chunker.py ./mixed_source
|
||||
python batch_chunker.py ./mixed_source --output ./my_output
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 4. down_embedding_model.py — 嵌入模型下载脚本
|
||||
## 6. down_embedding_model.py — 嵌入模型下载
|
||||
|
||||
**功能**: 从 HuggingFace Hub 下载 Qwen3-Embedding-4B 嵌入模型到本地。
|
||||
|
||||
**输入**:
|
||||
- HuggingFace 模型仓库: `Qwen/Qwen3-Embedding-4B`
|
||||
|
||||
**输出**:
|
||||
- `models/Qwen3-Embedding-4B/` 目录，包含完整的模型文件
|
||||
|
||||
**特性**:
|
||||
- 使用国内镜像加速下载（`hf-mirror.com`）
|
||||
- 支持断点续传
|
||||
- 自动安装依赖
|
||||
**功能**: 从 HuggingFace Hub 下载嵌入模型到本地。支持国内镜像加速和断点续传。
|
||||
|
||||
**使用方式**:
|
||||
```bash
|
||||
@@ -116,121 +112,103 @@ python down_embedding_model.py
|
||||
|
||||
---
|
||||
|
||||
## 5. embed_chunks.py — Chunk 向量化脚本
|
||||
## 7. embed_chunks.py — Chunk 向量化
|
||||
|
||||
**功能**: 使用嵌入模型将分块后的文本转换为向量表示，支持 GPU 加速和 FP16 半精度。
|
||||
**功能**: 使用嵌入模型将 chunks 转换为向量。支持 GPU/CPU、FP16 半精度，**支持增量模式**。
|
||||
|
||||
**输入**:
|
||||
- `jrxml_chunker_output/all_chunks.json`（默认）
|
||||
**输入**: chunks JSON 文件 (默认 `jrxml_chunker_output/all_chunks.json`)
|
||||
|
||||
**输出**:
|
||||
- `embeddings/embeddings.npy`: 向量矩阵（float32）
|
||||
- `embeddings/chunk_id_map.json`: chunk ID 映射
|
||||
- `embeddings/chunk_type_map.json`: chunk 类型映射
|
||||
- `embeddings/chunks.json`: 原始 chunks 副本
|
||||
- `embeddings/embeddings.npy`: 向量矩阵 (float32)
|
||||
- `embeddings/chunks.json`: 原始 chunks
|
||||
- `embeddings/chunk_id_map.json` / `chunk_type_map.json`: 映射文件
|
||||
- `embeddings/embeddings.pkl`: 完整数据 pickle
|
||||
|
||||
**核心函数**:
|
||||
- `build_text_for_embedding()`: 将 chunk 转换为适合向量化的文本（拼接类型、描述、XML、元数据）
|
||||
- `main()`: 主流程（加载→编码→保存→质量检查）
|
||||
|
||||
**特性**:
|
||||
- 自动检测 CUDA/CPU
|
||||
- 默认启用 FP16 半精度（节省约 50% 显存）
|
||||
- 支持 HuggingFace Hub 在线模型
|
||||
- 向量归一化 + NaN 检测
|
||||
|
||||
**使用方式**:
|
||||
**全量模式**:
|
||||
```bash
|
||||
# 使用默认设置
|
||||
python embed_chunks.py
|
||||
|
||||
# 指定模型和 batch size
|
||||
python embed_chunks.py --model_path "sentence-transformers/all-MiniLM-L6-v2" --batch_size 64
|
||||
|
||||
# 使用本地 Qwen3 模型
|
||||
python embed_chunks.py --batch_size 2
|
||||
|
||||
# 禁用 FP16
|
||||
python embed_chunks.py --no_fp16 --batch_size 1
|
||||
python embed_chunks.py --model_path "sentence-transformers/all-MiniLM-L6-v2"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 6. import_to_chroma.py — 向量导入 Chroma 数据库
|
||||
|
||||
**功能**: 将已生成的向量和 chunks 导入 Chroma 持久化向量数据库。
|
||||
|
||||
**输入**:
|
||||
- `embeddings/embeddings.npy`: 向量矩阵
|
||||
- `embeddings/chunks.json`: chunks 数据
|
||||
|
||||
**输出**:
|
||||
- `chroma_db/`: Chroma 持久化数据库目录
|
||||
- 集合名称: `jrxml_chunks`（默认）
|
||||
|
||||
**核心逻辑**:
|
||||
- 加载向量和 chunks
|
||||
- 初始化 Chroma PersistentClient
|
||||
- 创建集合（余弦相似度）
|
||||
- 分批导入（每批 1000 条）
|
||||
- 提取元数据（chunk_type、report_name、band_name 等）
|
||||
- 快速验证查询
|
||||
|
||||
**使用方式**:
|
||||
**增量模式** (`--incremental` / `-i`):
|
||||
```bash
|
||||
# 使用默认设置
|
||||
python import_to_chroma.py
|
||||
|
||||
# 指定路径
|
||||
python import_to_chroma.py --embeddings_dir ./embeddings --chroma_path ./chroma_db
|
||||
# 只向量化新增 chunks，自动合并到已有向量数据
|
||||
python embed_chunks.py ./new_chunks/all_chunks.json --incremental
|
||||
```
|
||||
|
||||
增量模式逻辑：
|
||||
1. 加载已有 `embeddings.npy` + `chunks.json`
|
||||
2. 按 `(context, chunk_id)` 去重
|
||||
3. 只向量化新 chunks
|
||||
4. 合并新旧数据后保存
|
||||
|
||||
---
|
||||
|
||||
## 7. query_chroma.py — 语义搜索查询工具
|
||||
## 8. import_to_chroma.py — Chroma 向量入库
|
||||
|
||||
**功能**: 通过自然语言查询 Chroma 数据库，检索相关的 JRXML chunk。
|
||||
**功能**: 将向量数据导入 Chroma 持久化数据库。**支持增量模式**。
|
||||
|
||||
**输入**:
|
||||
- 用户自然语言查询
|
||||
- 可选的元数据过滤条件
|
||||
**输入**: `embeddings/embeddings.npy` + `embeddings/chunks.json`
|
||||
|
||||
**输出**:
|
||||
- 相似度排序的检索结果（含 chunk 类型、报表名称、区域、内容摘要）
|
||||
**输出**: `chroma_db/` 持久化数据库
|
||||
|
||||
**核心类**:
|
||||
- `JRXMLSearcher`: 搜索器，封装模型加载、向量编码和 Chroma 查询
|
||||
**全量模式** (删除旧集合重建):
|
||||
```bash
|
||||
python import_to_chroma.py
|
||||
```
|
||||
|
||||
**核心方法**:
|
||||
- `search()`: 基础语义搜索
|
||||
- `search_with_threshold()`: 带相似度阈值的搜索
|
||||
- `format_result()`: 格式化输出结果
|
||||
**增量模式** (`--incremental` / `-i`):
|
||||
```bash
|
||||
# 追加新记录到已有集合，不删除已有数据
|
||||
python import_to_chroma.py --incremental
|
||||
```
|
||||
|
||||
增量模式逻辑：
|
||||
1. 使用 `get_or_create_collection` (不删除已有数据)
|
||||
2. 查询已有 ID，跳过已导入的记录
|
||||
3. 只追加新增数据
|
||||
|
||||
---
|
||||
|
||||
## 9. query_chroma.py — 语义搜索查询
|
||||
|
||||
**功能**: 通过自然语言查询 Chroma 数据库，检索相关的 JRXML/Markdown chunks。
|
||||
|
||||
**两种模式**:
|
||||
1. **命令行单次查询**: `python query_chroma.py "查询内容"`
|
||||
2. **交互模式**: `python query_chroma.py`（支持连续查询和内联命令）
|
||||
- 命令行单次查询: `python query_chroma.py "查询内容"`
|
||||
- 交互模式: `python query_chroma.py` (支持连续查询)
|
||||
|
||||
**交互模式命令**:
|
||||
```
|
||||
filter:<类型> 按 chunk_type 过滤（如 filter:query）
|
||||
t:<阈值> 设置相似度阈值 0~1（如 t:0.5）
|
||||
k:<数量> 设置返回结果数（如 k:10）
|
||||
filter:<类型> 按 chunk_type 过滤 (如 filter:query)
|
||||
t:<阈值> 设置相似度阈值 0~1
|
||||
k:<数量> 设置返回结果数
|
||||
```
|
||||
|
||||
**使用方式**:
|
||||
```bash
|
||||
# 交互模式
|
||||
python query_chroma.py
|
||||
|
||||
# 单次查询
|
||||
python query_chroma.py "如何修改报表标题"
|
||||
|
||||
# 按类型过滤
|
||||
python query_chroma.py # 交互模式
|
||||
python query_chroma.py "如何修改报表标题" # 单次查询
|
||||
python query_chroma.py "SQL怎么写" --filter_field query
|
||||
python query_chroma.py "参数" --threshold 0.5 --n_results 10
|
||||
```
|
||||
|
||||
# 设置阈值和返回数量
|
||||
python query_chroma.py "报表参数" --threshold 0.5 --n_results 10
|
||||
---
|
||||
|
||||
## 10. config.py — 统一配置管理
|
||||
|
||||
**功能**: 从 `.env` 文件加载所有配置项，所有脚本通过此模块获取配置。
|
||||
|
||||
**配置分组**:
|
||||
- 模型配置: `EMBEDDING_MODEL_NAME`, `EMBEDDING_MODEL_PATH`, `HF_ENDPOINT`
|
||||
- 硬件配置: `USE_GPU`, `USE_FP16`, `BATCH_SIZE`
|
||||
- 目录配置: `JRXML_SOURCE_DIR`, `CHUNKER_OUTPUT_DIR`, `EMBEDDINGS_DIR`, `CHROMA_DB_PATH`
|
||||
- 分块配置: `MAX_CHUNK_SIZE`
|
||||
- 查询配置: `DEFAULT_N_RESULTS`, `SIMILARITY_THRESHOLD`
|
||||
|
||||
```bash
|
||||
python config.py # 打印当前配置
|
||||
```
|
||||
|
||||
---
|
||||
@@ -238,26 +216,27 @@ python query_chroma.py "报表参数" --threshold 0.5 --n_results 10
|
||||
## 数据流全景
|
||||
|
||||
```
|
||||
┌─────────────────┐
|
||||
│ JasperReports │ C:\Users\...\JasperReportsSamples
|
||||
│ 模板库 │
|
||||
└────────┬────────┘
|
||||
│ collect_jrxml.py
|
||||
┌─────────────────────┐
|
||||
│ JasperReports 模板库 │ (.jrxml)
|
||||
│ Markdown 文档 │ (.md)
|
||||
└──────────┬──────────┘
|
||||
│ collect_jrxml.py / 手动放置
|
||||
▼
|
||||
┌─────────────────┐
|
||||
│ jrxml_source/ │ 收集的 JRXML 文件
|
||||
└────────┬────────┘
|
||||
│ jrxml_banch_chunker.py (调用 jrxml_chunker.py)
|
||||
┌─────────────────────┐
|
||||
│ jrxml_source/ │ 源文件目录
|
||||
│ docs/ │
|
||||
└──────────┬──────────┘
|
||||
│ batch_chunker.py (调用 jrxml_chunker.py + md_chunker.py)
|
||||
▼
|
||||
┌──────────────────────┐
|
||||
│ jrxml_chunker_output/│ all_chunks.json + per_file/
|
||||
└────────┬─────────────┘
|
||||
│ embed_chunks.py (使用 Qwen3-Embedding-4B)
|
||||
│ jrxml_chunker_output/│ all_chunks.json + processing_stats.json
|
||||
└──────────┬───────────┘
|
||||
│ embed_chunks.py (Qwen3-Embedding, 支持增量)
|
||||
▼
|
||||
┌─────────────────┐
|
||||
│ embeddings/ │ embeddings.npy + chunks.json
|
||||
└────────┬────────┘
|
||||
│ import_to_chroma.py
|
||||
│ import_to_chroma.py (ChromaDB, 支持增量)
|
||||
▼
|
||||
┌─────────────────┐
|
||||
│ chroma_db/ │ Chroma 向量数据库
|
||||
@@ -265,18 +244,20 @@ python query_chroma.py "报表参数" --threshold 0.5 --n_results 10
|
||||
│ query_chroma.py
|
||||
▼
|
||||
┌─────────────────┐
|
||||
│ 用户查询 │ 自然语言 → 相关 JRXML chunks
|
||||
│ 自然语言查询 │ 返回相关 chunks
|
||||
└─────────────────┘
|
||||
```
|
||||
|
||||
## 依赖关系
|
||||
|
||||
```
|
||||
query_chroma.py ──────► chromadb, sentence_transformers, torch
|
||||
import_to_chroma.py ──► chromadb, numpy
|
||||
embed_chunks.py ──────► sentence_transformers, torch, numpy
|
||||
down_embedding_model.py ► huggingface_hub
|
||||
jrxml_banch_chunker.py ─► jrxml_chunker.py
|
||||
jrxml_chunker.py ─────► xml.etree.ElementTree (标准库)
|
||||
collect_jrxml.py ─────► 标准库 (os, shutil)
|
||||
query_chroma.py ──────────► chromadb, sentence_transformers, torch
|
||||
import_to_chroma.py ──────► chromadb, numpy
|
||||
embed_chunks.py ──────────► sentence_transformers, torch, numpy
|
||||
down_embedding_model.py ──► huggingface_hub
|
||||
batch_chunker.py ─────────► jrxml_chunker.py, md_chunker.py
|
||||
md_chunker.py ────────────► 标准库 (re, json, pathlib)
|
||||
jrxml_chunker.py ─────────► xml.etree.ElementTree (标准库)
|
||||
config.py ────────────────► 标准库 (os, pathlib)
|
||||
collect_jrxml.py ─────────► 标准库 (os, shutil)
|
||||
```
|
||||
+64
-18
@@ -20,7 +20,8 @@ from config import (
|
||||
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 XML
|
||||
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')}]",
|
||||
@@ -30,9 +31,10 @@ def build_text_for_embedding(chunk: dict) -> str:
|
||||
if context:
|
||||
parts.append(f"Context: {context}")
|
||||
|
||||
raw_xml = chunk.get('raw_xml', '')
|
||||
if raw_xml:
|
||||
parts.append(f"XML: {raw_xml[:500]}")
|
||||
# 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:
|
||||
@@ -48,12 +50,16 @@ def build_text_for_embedding(chunk: dict) -> str:
|
||||
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):
|
||||
use_fp16: bool = None, incremental: bool = False):
|
||||
"""
|
||||
主流程：
|
||||
1. 加载 chunk JSON
|
||||
@@ -86,7 +92,7 @@ def main(chunks_json_path: str = None, output_dir: str = None,
|
||||
|
||||
if not chunks_json_path.exists():
|
||||
print(f"❌ Chunks 文件不存在: {chunks_json_path}")
|
||||
print(f" 请先运行 jrxml_banch_chunker.py 生成 chunks")
|
||||
print(f" 请先运行 batch_chunker.py 生成 chunks")
|
||||
return None
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
@@ -127,6 +133,33 @@ def main(chunks_json_path: str = None, output_dir: str = None,
|
||||
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 = []
|
||||
@@ -147,42 +180,52 @@ def main(chunks_json_path: str = None, output_dir: str = None,
|
||||
)
|
||||
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", embeddings.astype('float32'))
|
||||
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(chunk_ids, f, ensure_ascii=False, indent=2)
|
||||
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(chunk_types, f, ensure_ascii=False, indent=2)
|
||||
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(chunks, f, ensure_ascii=False, indent=2)
|
||||
json.dump(all_chunks, f, ensure_ascii=False, indent=2)
|
||||
with open(output_dir / "embeddings.pkl", 'wb') as f:
|
||||
pickle.dump({
|
||||
'chunks': chunks,
|
||||
'embeddings': embeddings,
|
||||
'chunks': all_chunks,
|
||||
'embeddings': all_embeddings,
|
||||
'texts': texts,
|
||||
'normalized': normalize
|
||||
}, f)
|
||||
|
||||
nan_count = np.isnan(embeddings).sum()
|
||||
nan_count = np.isnan(all_embeddings).sum()
|
||||
print(f"\n📊 质量检查:")
|
||||
print(f" NaN values: {nan_count}")
|
||||
norms = np.linalg.norm(embeddings, axis=1)
|
||||
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 chunk_types:
|
||||
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(chunks),
|
||||
"embedding_dim": embeddings.shape[1],
|
||||
"chunks": len(all_chunks),
|
||||
"embedding_dim": all_embeddings.shape[1],
|
||||
"output_dir": str(output_dir)
|
||||
}
|
||||
|
||||
@@ -205,6 +248,8 @@ if __name__ == "__main__":
|
||||
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()
|
||||
|
||||
@@ -214,5 +259,6 @@ if __name__ == "__main__":
|
||||
model_path=args.model_path,
|
||||
batch_size=args.batch_size,
|
||||
normalize=not args.no_normalize,
|
||||
use_fp16=not args.no_fp16
|
||||
use_fp16=not args.no_fp16,
|
||||
incremental=args.incremental
|
||||
)
|
||||
+50
-8
@@ -1,6 +1,7 @@
|
||||
"""
|
||||
import_to_chroma.py
|
||||
将已生成的 chunk 向量导入 Chroma 数据库
|
||||
将 chunk 向量导入 Chroma 数据库
|
||||
支持 JRXML chunks 和 Markdown chunks 混合导入
|
||||
"""
|
||||
|
||||
import os
|
||||
@@ -16,7 +17,8 @@ from config import EMBEDDINGS_DIR, CHROMA_DB_PATH, CHROMA_COLLECTION_NAME
|
||||
|
||||
def main(embeddings_dir: str = None,
|
||||
chroma_path: str = None,
|
||||
collection_name: str = None):
|
||||
collection_name: str = None,
|
||||
incremental: bool = False):
|
||||
"""
|
||||
从 embeddings 目录读取向量和 chunks，导入 Chroma 持久化数据库
|
||||
|
||||
@@ -69,33 +71,55 @@ def main(embeddings_dir: str = None,
|
||||
chroma_path.mkdir(parents=True, exist_ok=True)
|
||||
client = chromadb.PersistentClient(path=str(chroma_path))
|
||||
|
||||
if incremental:
|
||||
try:
|
||||
collection = client.get_collection(collection_name)
|
||||
existing_ids = set(collection.get()['ids'])
|
||||
print(f" 增量模式: 集合 '{collection_name}' 已有 {len(existing_ids)} 条记录")
|
||||
except Exception:
|
||||
collection = client.create_collection(
|
||||
name=collection_name,
|
||||
metadata={"hnsw:space": "cosine"}
|
||||
)
|
||||
existing_ids = set()
|
||||
print(f" 增量模式: 创建新集合 '{collection_name}'")
|
||||
else:
|
||||
try:
|
||||
client.delete_collection(collection_name)
|
||||
print(f" 已删除旧集合 '{collection_name}'")
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
collection = client.create_collection(
|
||||
name=collection_name,
|
||||
metadata={"hnsw:space": "cosine"}
|
||||
)
|
||||
existing_ids = set()
|
||||
|
||||
print(f"\n🛠️ 准备导入数据...")
|
||||
ids = []
|
||||
documents = []
|
||||
metadatas = []
|
||||
embeddings_list = []
|
||||
skipped = 0
|
||||
|
||||
seen_ids = {}
|
||||
for i, chunk in enumerate(tqdm(chunks, desc="准备数据")):
|
||||
raw_id = str(chunk.get("chunk_id", i))
|
||||
context = chunk.get("context", "")
|
||||
|
||||
if raw_id in seen_ids:
|
||||
seen_ids[raw_id] += 1
|
||||
chunk_id = f"{raw_id}_{seen_ids[raw_id]}"
|
||||
unique_chunk_id = f"{raw_id}_{seen_ids[raw_id]}"
|
||||
else:
|
||||
seen_ids[raw_id] = 0
|
||||
chunk_id = raw_id
|
||||
ids.append(chunk_id)
|
||||
unique_chunk_id = raw_id
|
||||
|
||||
# 增量模式：跳过已导入的
|
||||
if incremental and unique_chunk_id in existing_ids:
|
||||
skipped += 1
|
||||
continue
|
||||
|
||||
ids.append(unique_chunk_id)
|
||||
|
||||
doc_text = chunk.get("human_description", "")
|
||||
documents.append(doc_text)
|
||||
@@ -105,7 +129,6 @@ def main(embeddings_dir: str = None,
|
||||
if chunk_type:
|
||||
meta["chunk_type"] = chunk_type
|
||||
|
||||
context = chunk.get("context", "")
|
||||
if context:
|
||||
meta["context"] = context
|
||||
|
||||
@@ -118,10 +141,26 @@ def main(embeddings_dir: str = None,
|
||||
meta["element_kind"] = chunk_meta["element_kind"]
|
||||
if "query_language" in chunk_meta:
|
||||
meta["query_language"] = chunk_meta["query_language"]
|
||||
# Markdown-specific metadata
|
||||
if "heading" in chunk_meta:
|
||||
meta["heading"] = chunk_meta["heading"]
|
||||
if "heading_level" in chunk_meta:
|
||||
meta["heading_level"] = chunk_meta["heading_level"]
|
||||
if "language" in chunk_meta:
|
||||
meta["code_language"] = chunk_meta["language"]
|
||||
|
||||
metadatas.append(meta)
|
||||
embeddings_list.append(embeddings[i].tolist())
|
||||
|
||||
if incremental and skipped > 0:
|
||||
print(f" 增量模式: 跳过 {skipped} 条已存在记录")
|
||||
|
||||
if not ids:
|
||||
print(f"\n✅ 没有新数据需要导入，集合已是最新")
|
||||
print(f" 数据库路径: {chroma_path}")
|
||||
print(f" 集合数量: {collection.count()}")
|
||||
return collection
|
||||
|
||||
print(f"\n📥 分批导入到 Chroma (每批 1000 条)...")
|
||||
import_batch_size = 1000
|
||||
start_time = time.time()
|
||||
@@ -173,11 +212,14 @@ if __name__ == "__main__":
|
||||
help=f"Chroma 数据库路径 (默认: {CHROMA_DB_PATH})")
|
||||
parser.add_argument("--collection_name", "-n", default=CHROMA_COLLECTION_NAME,
|
||||
help=f"集合名称 (默认: {CHROMA_COLLECTION_NAME})")
|
||||
parser.add_argument("--incremental", "-i", action="store_true",
|
||||
help="增量模式：只导入新增记录，不删除已有数据")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
main(
|
||||
embeddings_dir=args.embeddings_dir,
|
||||
chroma_path=args.chroma_path,
|
||||
collection_name=args.collection_name
|
||||
collection_name=args.collection_name,
|
||||
incremental=args.incremental
|
||||
)
|
||||
+358
@@ -0,0 +1,358 @@
|
||||
"""
|
||||
md_chunker.py
|
||||
Markdown 语义分块器
|
||||
支持标题层级、代码块、表格等元素的智能分块
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
from typing import List, Dict, Tuple
|
||||
from pathlib import Path
|
||||
from dataclasses import dataclass, field, asdict
|
||||
|
||||
|
||||
@dataclass
|
||||
class MDChunk:
|
||||
"""Single Markdown chunk data structure"""
|
||||
chunk_id: int
|
||||
chunk_type: str
|
||||
human_description: str
|
||||
raw_content: str
|
||||
context: str
|
||||
metadata: Dict = field(default_factory=dict)
|
||||
|
||||
|
||||
class MarkdownSemanticChunker:
|
||||
"""
|
||||
Markdown 语义分块器 v1.0
|
||||
分块策略：
|
||||
1. 按标题层级（H1/H2/H3...）划分大段落
|
||||
2. 代码块作为独立 chunk
|
||||
3. 表格作为独立 chunk
|
||||
4. 过长段落内部按句子/段落二次拆分
|
||||
"""
|
||||
|
||||
# Heading patterns
|
||||
HEADING_PATTERN = re.compile(r'^(#{1,6})\s+(.+)$', re.MULTILINE)
|
||||
|
||||
# Code block pattern (fenced)
|
||||
CODE_BLOCK_PATTERN = re.compile(r'```(\w*)\n([\s\S]*?)```', re.MULTILINE)
|
||||
|
||||
# Inline code pattern
|
||||
INLINE_CODE_PATTERN = re.compile(r'`([^`]+)`')
|
||||
|
||||
# Table pattern
|
||||
TABLE_PATTERN = re.compile(r'\|.+\|\n\|[-| :]+\|\n((?:\|.+\|\n)*)', re.MULTILINE)
|
||||
|
||||
# List pattern
|
||||
LIST_PATTERN = re.compile(r'^(\s*[-*+]\s+.+)+', re.MULTILINE)
|
||||
|
||||
def __init__(self, max_chunk_size: int = 2000):
|
||||
self.max_chunk_size = max_chunk_size
|
||||
|
||||
def chunk_file(self, file_path: str) -> List[Dict]:
|
||||
"""处理单个 Markdown 文件"""
|
||||
if not os.path.exists(file_path):
|
||||
raise FileNotFoundError(f"File not found: {file_path}")
|
||||
|
||||
with open(file_path, 'r', encoding='utf-8') as f:
|
||||
content = f.read()
|
||||
|
||||
file_name = Path(file_path).stem
|
||||
chunks = []
|
||||
chunk_id = 0
|
||||
|
||||
# 尝试提取文档标题（第一个 H1）
|
||||
title_match = re.search(r'^#\s+(.+)$', content, re.MULTILINE)
|
||||
doc_title = title_match.group(1).strip() if title_match else file_name
|
||||
|
||||
# 按结构化元素分割
|
||||
segments = self._split_by_structure(content)
|
||||
|
||||
for segment in segments:
|
||||
seg_type = segment['type']
|
||||
seg_content = segment['content']
|
||||
|
||||
if not seg_content.strip():
|
||||
continue
|
||||
|
||||
# 构建描述
|
||||
description = self._build_description(seg_type, seg_content, doc_title)
|
||||
|
||||
# 如果超过最大长度，尝试二次拆分
|
||||
if len(seg_content) > self.max_chunk_size:
|
||||
sub_chunks = self._split_large_chunk(
|
||||
seg_content, seg_type, doc_title, chunk_id
|
||||
)
|
||||
chunks.extend([asdict(c) for c in sub_chunks])
|
||||
chunk_id += len(sub_chunks)
|
||||
else:
|
||||
chunks.append(asdict(MDChunk(
|
||||
chunk_id=chunk_id,
|
||||
chunk_type=seg_type,
|
||||
human_description=description,
|
||||
raw_content=seg_content.strip(),
|
||||
context=f"{doc_title}",
|
||||
metadata=segment.get('metadata', {})
|
||||
)))
|
||||
chunk_id += 1
|
||||
|
||||
return chunks
|
||||
|
||||
def _split_by_structure(self, content: str) -> List[Dict]:
|
||||
"""
|
||||
按 Markdown 结构分割内容
|
||||
返回: [{'type': 'h1/h2/code/table/paragraph', 'content': '...', 'metadata': {...}}]
|
||||
"""
|
||||
segments = []
|
||||
|
||||
# 首先提取所有代码块（保留位置标记，稍后处理）
|
||||
code_blocks = []
|
||||
code_pattern = re.compile(r'(```\w*\n[\s\S]*?```)', re.MULTILINE)
|
||||
|
||||
last_end = 0
|
||||
for match in code_pattern.finditer(content):
|
||||
# 处理代码块前的普通文本
|
||||
before = content[last_end:match.start()]
|
||||
if before.strip():
|
||||
segments.extend(self._process_text_section(before))
|
||||
|
||||
# 添加代码块
|
||||
code_blocks.append(match.group(1))
|
||||
lang_match = re.match(r'```(\w*)', match.group(1))
|
||||
lang = lang_match.group(1) if lang_match else ''
|
||||
segments.append({
|
||||
'type': 'code',
|
||||
'content': match.group(1),
|
||||
'metadata': {'language': lang}
|
||||
})
|
||||
last_end = match.end()
|
||||
|
||||
# 处理剩余文本
|
||||
remaining = content[last_end:]
|
||||
if remaining.strip():
|
||||
segments.extend(self._process_text_section(remaining))
|
||||
|
||||
return segments
|
||||
|
||||
def _process_text_section(self, text: str) -> List[Dict]:
|
||||
"""处理普通文本区域，提取标题和段落"""
|
||||
segments = []
|
||||
|
||||
# 按标题分割
|
||||
lines = text.split('\n')
|
||||
current_section = []
|
||||
current_heading_level = 0
|
||||
current_heading = ''
|
||||
|
||||
for line in lines:
|
||||
heading_match = re.match(r'^(#{1,6})\s+(.+)', line)
|
||||
if heading_match:
|
||||
# 保存之前的段落
|
||||
if current_section:
|
||||
section_text = '\n'.join(current_section).strip()
|
||||
if section_text:
|
||||
segments.append({
|
||||
'type': self._get_section_type(current_heading_level, current_heading),
|
||||
'content': section_text,
|
||||
'metadata': {
|
||||
'heading': current_heading,
|
||||
'heading_level': current_heading_level
|
||||
}
|
||||
})
|
||||
current_section = []
|
||||
|
||||
# 开始新标题区域
|
||||
current_heading_level = len(heading_match.group(1))
|
||||
current_heading = heading_match.group(2).strip()
|
||||
else:
|
||||
current_section.append(line)
|
||||
|
||||
# 保存最后一段
|
||||
if current_section:
|
||||
section_text = '\n'.join(current_section).strip()
|
||||
if section_text:
|
||||
segments.append({
|
||||
'type': self._get_section_type(current_heading_level, current_heading),
|
||||
'content': section_text,
|
||||
'metadata': {
|
||||
'heading': current_heading,
|
||||
'heading_level': current_heading_level
|
||||
}
|
||||
})
|
||||
|
||||
return segments
|
||||
|
||||
def _get_section_type(self, level: int, heading: str) -> str:
|
||||
"""根据标题级别和内容确定段落类型"""
|
||||
heading_lower = heading.lower()
|
||||
|
||||
if level == 1:
|
||||
return 'section_h1'
|
||||
elif level == 2:
|
||||
# 检测特殊章节类型
|
||||
if any(kw in heading_lower for kw in ['install', '安装', 'setup', '部署']):
|
||||
return 'section_installation'
|
||||
elif any(kw in heading_lower for kw in ['config', '配置', 'setting']):
|
||||
return 'section_configuration'
|
||||
elif any(kw in heading_lower for kw in ['api', '接口']):
|
||||
return 'section_api'
|
||||
elif any(kw in heading_lower for kw in ['example', '示例', 'usage', '使用']):
|
||||
return 'section_example'
|
||||
elif any(kw in heading_lower for kw in ['faq', 'question', '问题', '常见']):
|
||||
return 'section_faq'
|
||||
elif any(kw in heading_lower for kw in ['changelog', '更新', 'release']):
|
||||
return 'section_changelog'
|
||||
return 'section_h2'
|
||||
elif level == 3:
|
||||
return 'section_h3'
|
||||
else:
|
||||
return 'section_other'
|
||||
|
||||
def _build_description(self, chunk_type: str, content: str, doc_title: str) -> str:
|
||||
"""为 chunk 生成人类可读描述"""
|
||||
lines = content.split('\n')[:5]
|
||||
preview = ' '.join(line.strip() for line in lines if line.strip())[:150]
|
||||
|
||||
if chunk_type == 'code':
|
||||
lang = ''
|
||||
lang_match = re.match(r'```(\w*)', content)
|
||||
if lang_match:
|
||||
lang = lang_match.group(1) or 'text'
|
||||
return f"Code block (language: {lang}) in {doc_title}. Preview: {preview}"
|
||||
|
||||
elif chunk_type.startswith('section_'):
|
||||
heading = content.split('\n')[0] if '\n' in content else content[:50]
|
||||
heading_clean = re.sub(r'^#+\s+', '', heading)
|
||||
type_hint = chunk_type.replace('section_', '')
|
||||
return f"[{type_hint.upper()}] {heading_clean}. Content: {preview}"
|
||||
|
||||
else:
|
||||
return f"Document section in {doc_title}. Content: {preview}"
|
||||
|
||||
def _split_large_chunk(self, content: str, chunk_type: str,
|
||||
doc_title: str, start_id: int) -> List[MDChunk]:
|
||||
"""对过长的 chunk 进行二次拆分"""
|
||||
chunks = []
|
||||
|
||||
# 按段落分割（双换行符）
|
||||
paragraphs = re.split(r'\n\n+', content)
|
||||
current_chunk = []
|
||||
current_size = 0
|
||||
|
||||
for para in paragraphs:
|
||||
para_size = len(para)
|
||||
|
||||
if current_size + para_size > self.max_chunk_size and current_chunk:
|
||||
# 当前块已满，生成 chunk
|
||||
chunk_text = '\n\n'.join(current_chunk)
|
||||
chunks.append(MDChunk(
|
||||
chunk_id=start_id + len(chunks),
|
||||
chunk_type=f"{chunk_type}_part",
|
||||
human_description=f"Part of {doc_title} ({chunk_type}): {chunk_text[:100]}...",
|
||||
raw_content=chunk_text,
|
||||
context=f"{doc_title} (continued)",
|
||||
metadata={'part': len(chunks) + 1, 'original_type': chunk_type}
|
||||
))
|
||||
current_chunk = []
|
||||
current_size = 0
|
||||
|
||||
current_chunk.append(para)
|
||||
current_size += para_size + 2
|
||||
|
||||
# 处理剩余内容
|
||||
if current_chunk:
|
||||
chunk_text = '\n\n'.join(current_chunk)
|
||||
chunks.append(MDChunk(
|
||||
chunk_id=start_id + len(chunks),
|
||||
chunk_type=f"{chunk_type}_part",
|
||||
human_description=f"Part of {doc_title} ({chunk_type}): {chunk_text[:100]}...",
|
||||
raw_content=chunk_text,
|
||||
context=f"{doc_title} (continued)",
|
||||
metadata={'part': len(chunks) + 1, 'original_type': chunk_type}
|
||||
))
|
||||
|
||||
return chunks if chunks else [MDChunk(
|
||||
chunk_id=start_id,
|
||||
chunk_type=chunk_type,
|
||||
human_description=f"{doc_title}: {content[:100]}...",
|
||||
raw_content=content[:self.max_chunk_size],
|
||||
context=doc_title,
|
||||
metadata={'truncated': True}
|
||||
)]
|
||||
|
||||
def chunk_directory(self, dir_path: str, extensions: tuple = ('.md', '.markdown')) -> List[Dict]:
|
||||
"""批量处理目录下所有 Markdown 文件"""
|
||||
all_chunks = []
|
||||
file_count = 0
|
||||
|
||||
for root, _, files in os.walk(dir_path):
|
||||
for file in files:
|
||||
if file.lower().endswith(extensions):
|
||||
file_path = os.path.join(root, file)
|
||||
try:
|
||||
chunks = self.chunk_file(file_path)
|
||||
all_chunks.extend(chunks)
|
||||
file_count += 1
|
||||
print(f"OK {file_path}: {len(chunks)} chunks")
|
||||
except Exception as e:
|
||||
print(f"FAIL {file_path}: {e}")
|
||||
|
||||
print(f"\nTotal: {file_count} files, {len(all_chunks)} chunks")
|
||||
return all_chunks
|
||||
|
||||
|
||||
def save_chunks_to_json(chunks: List[Dict], output_path: str):
|
||||
"""保存 chunks 到 JSON 文件"""
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(chunks, f, ensure_ascii=False, indent=2)
|
||||
print(f"Saved {len(chunks)} chunks to {output_path}")
|
||||
|
||||
|
||||
def print_chunk_summary(chunks: List[Dict]):
|
||||
"""打印 chunk 类型统计"""
|
||||
type_counts = {}
|
||||
for chunk in chunks:
|
||||
chunk_type = chunk["chunk_type"]
|
||||
type_counts[chunk_type] = type_counts.get(chunk_type, 0) + 1
|
||||
|
||||
print("\nChunk Type Summary:")
|
||||
for chunk_type, count in sorted(type_counts.items(), key=lambda x: -x[1]):
|
||||
print(f" {chunk_type}: {count}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
|
||||
chunker = MarkdownSemanticChunker(max_chunk_size=2000)
|
||||
|
||||
if len(sys.argv) > 1:
|
||||
path = sys.argv[1]
|
||||
if os.path.isdir(path):
|
||||
all_chunks = chunker.chunk_directory(path)
|
||||
output_path = os.path.join(os.path.dirname(path.rstrip("/\\")) if os.path.dirname(path) else ".",
|
||||
os.path.basename(path.rstrip("/\\")) + "_md_chunks.json")
|
||||
save_chunks_to_json(all_chunks, output_path)
|
||||
print_chunk_summary(all_chunks)
|
||||
else:
|
||||
chunks = chunker.chunk_file(path)
|
||||
output_path = path.replace(".md", "_chunks.json").replace(".markdown", "_chunks.json")
|
||||
save_chunks_to_json(chunks, output_path)
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print("Chunking Results Preview")
|
||||
print(f"{'='*60}")
|
||||
for chunk in chunks[:10]:
|
||||
print(f"\n[Chunk {chunk['chunk_id']}] Type: {chunk['chunk_type']}")
|
||||
print(f"Description: {chunk['human_description'][:120]}...")
|
||||
print(f"Content length: {len(chunk['raw_content'])} chars")
|
||||
if len(chunks) > 10:
|
||||
print(f"\n... and {len(chunks) - 10} more chunks")
|
||||
|
||||
print_chunk_summary(chunks)
|
||||
else:
|
||||
print("=" * 60)
|
||||
print("Markdown Semantic Chunking v1.0")
|
||||
print("=" * 60)
|
||||
print("\nUsage: python md_chunker.py <md_file_or_directory>")
|
||||
@@ -0,0 +1,10 @@
|
||||
# Core dependencies
|
||||
torch>=2.0.0
|
||||
sentence-transformers>=2.2.0
|
||||
chromadb>=0.4.0
|
||||
numpy>=1.24.0
|
||||
tqdm>=4.65.0
|
||||
huggingface_hub>=0.19.0
|
||||
|
||||
# Optional - for LangChain document conversion
|
||||
langchain>=0.1.0
|
||||
Reference in New Issue
Block a user