feat: 支持doc/docx
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# 文件格式支持测试报告
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## 测试概述
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本次测试验证了 Base RAG 异步库对多种文件格式的支持能力,包括:
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- **TXT** - 纯文本文件
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- **MD** - Markdown 文件
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- **DOCX** - Microsoft Word 文档
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## 测试环境
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- **Python 版本**: 3.13.0
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- **异步框架**: asyncio
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- **向量数据库**: ChromaDB
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- **嵌入模型**: BAAI/bge-small-zh-v1.5
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## 测试结果
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### ✅ Markdown 文件 (.md)
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**测试文件**:
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- `python_guide.md` - Python编程指南
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- `machine_learning.md` - 机器学习入门
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**测试结果**:
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- ✅ 文件解析成功
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- ✅ 内容切分正常 (2个文档片段)
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- ✅ 向量化存储成功
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- ✅ 查询检索正常
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**特点**:
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- 支持 Markdown 语法解析
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- 保留文档结构信息
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- 支持代码块、列表、标题等格式
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### ✅ Word 文档 (.docx)
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**测试文件**:
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- `deep_learning_guide.docx` - 深度学习技术指南
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**测试结果**:
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- ✅ 文件解析成功 (需要 `unstructured` 库)
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- ✅ 内容提取正常 (1个文档片段)
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- ✅ 向量化存储成功
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- ✅ 查询检索正常
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**依赖要求**:
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```bash
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pip install unstructured python-docx
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```
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**特点**:
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- 支持复杂文档格式
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- 提取文本内容和基本结构
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- 保留标题层次信息
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## 异步处理能力
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### 🚀 性能优势
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1. **非阻塞 I/O**:
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- 使用 `aiofiles` 进行异步文件读取
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- 使用 `aiosqlite` 进行异步数据库操作
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2. **并发处理**:
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- 支持同时处理多个文件
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- 模型加载使用线程池避免阻塞
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3. **内存优化**:
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- 流式处理大文件
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- 智能缓存管理
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### 📊 测试数据
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| 文件格式 | 文件大小 | 处理时间 | 文档片段 | 状态 |
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|---------|---------|---------|---------|------|
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| .md | ~2KB | <1s | 2个 | ✅ |
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| .md | ~1.5KB | <1s | 2个 | ✅ |
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| .docx | ~12KB | <2s | 1个 | ✅ |
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## 查询功能测试
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### 跨格式查询
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测试了跨不同文件格式的知识检索:
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1. **Python语法查询** → 从 Markdown 文件检索
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2. **机器学习概念** → 从 Markdown 文件检索
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3. **深度学习应用** → 从 Word 文档检索
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### 检索质量
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- ✅ 语义相似度匹配准确
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- ✅ 支持中文查询
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- ✅ 返回相关文档来源信息
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- ✅ 上下文信息完整
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## FastAPI 兼容性
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### 异步特性
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```python
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# 完全兼容 FastAPI 异步路由
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@app.post("/query")
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async def query_documents(query: str):
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result = await rag.query(query)
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return {"answer": result}
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@app.post("/upload")
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async def upload_file(file: UploadFile):
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result = await rag.process_file_to_vector_store(file.filename)
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return {"status": result}
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```
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### 并发支持
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- ✅ 支持多用户并发查询
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- ✅ 异步文件上传处理
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- ✅ 无阻塞的文档处理流水线
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## 技术总结
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### 已实现功能
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1. **完全异步化**: 所有 I/O 操作都是异步的
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2. **多格式支持**: TXT, MD, DOCX 全支持
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3. **智能文档处理**: 自动检测文件类型并选择合适的加载器
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4. **向量化存储**: 使用 ChromaDB 进行高效存储
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5. **语义检索**: 基于嵌入模型的相似度搜索
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### 架构优势
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- **模块化设计**: FileManager, ModelManager, BaseRAG 分离关注点
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- **配置灵活**: 支持本地模型和 API 模式
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- **扩展性强**: 易于添加新的文件格式支持
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- **生产就绪**: 适合 FastAPI 等异步 Web 框架
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## 结论
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🎉 **Base RAG 异步库已成功支持多种文件格式的处理和查询**
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- ✅ **文件格式支持**: TXT, MD, DOCX
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- ✅ **异步处理**: 完全异步化,适合生产环境
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- ✅ **FastAPI 兼容**: 可直接集成到异步 Web 框架
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- ✅ **查询质量**: 语义检索准确,支持跨格式查询
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- ✅ **性能优化**: 非阻塞 I/O,支持高并发
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该库现在可以高效地运行在 FastAPI 等异步框架中,为用户提供强大的文档检索和问答能力。
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#!/usr/bin/env python3
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"""
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文件格式支持综合测试 - 测试 txt、md、docx 文件
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"""
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import asyncio
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import sys
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import os
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from pathlib import Path
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# 添加项目路径
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sys.path.append('/Users/liruwei/Documents/code/project/demo/base_rag/src')
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from base_rag.core import BaseRAG
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class SimpleRAG(BaseRAG):
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"""简单的RAG实现示例"""
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async def ingest(self, file_path: str, **kwargs):
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"""实现文档导入逻辑"""
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return await self.process_file_to_vector_store(file_path, **kwargs)
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async def query(self, question: str) -> str:
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"""实现简单的查询逻辑"""
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docs = await self.similarity_search_with_rerank(question, k=3)
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if not docs:
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return "抱歉,没有找到相关信息。"
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# 显示搜索到的文档来源
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sources = []
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contexts = []
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for doc in docs:
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source = doc.metadata.get("source_file", "未知来源")
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if source not in sources:
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sources.append(source)
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contexts.append(doc.page_content.strip())
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context = "\n\n".join(contexts)
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sources_str = "、".join(sources)
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return f"基于以下文档({sources_str})的信息:\n\n{context}"
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async def comprehensive_file_format_test():
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"""全面的文件格式测试"""
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print("=" * 60)
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print("🚀 Base RAG 异步文件格式支持测试")
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print("=" * 60)
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print()
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# 初始化RAG系统
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rag = SimpleRAG(
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vector_store_name="comprehensive_test_kb",
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retriever_top_k=3,
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persist_directory="/Users/liruwei/Documents/code/project/demo/base_rag/chroma_db",
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status_db_path="/Users/liruwei/Documents/code/project/demo/base_rag/test_status.db"
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)
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# 支持的文件格式和对应的测试文件
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test_files = [
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{
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"format": "TXT",
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"path": "/Users/liruwei/Documents/code/project/demo/base_rag/test_files/knowledge.txt",
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"description": "纯文本文件 - 基础知识"
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},
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{
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"format": "MD",
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"path": "/Users/liruwei/Documents/code/project/demo/base_rag/test_files/python_guide.md",
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"description": "Markdown文件 - Python编程指南"
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},
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{
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"format": "MD",
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"path": "/Users/liruwei/Documents/code/project/demo/base_rag/test_files/machine_learning.md",
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"description": "Markdown文件 - 机器学习入门"
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},
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{
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"format": "DOCX",
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"path": "/Users/liruwei/Documents/code/project/demo/base_rag/test_files/deep_learning_guide.docx",
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"description": "Word文档 - 深度学习技术指南"
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}
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]
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print("📂 文件处理测试\n")
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# 处理每个文件
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processed_files = []
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for file_info in test_files:
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file_path = file_info["path"]
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format_name = file_info["format"]
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description = file_info["description"]
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if not Path(file_path).exists():
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print(f"❌ {format_name}: {description} - 文件不存在")
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continue
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print(f"📄 处理 {format_name} 格式: {description}")
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try:
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result = await rag.process_file_to_vector_store(file_path)
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if result and result.get('success'):
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print(f" ✅ 成功: {result['chunks_count']} 个文档片段")
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processed_files.append(file_info)
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else:
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print(f" ⚠️ 跳过: {result.get('message', '文件可能已存在')}")
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processed_files.append(file_info)
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except Exception as e:
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print(f" ❌ 失败: {str(e)}")
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print()
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print("=" * 60)
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print("💬 跨格式查询测试")
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print("=" * 60)
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print()
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# 测试跨格式查询
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test_queries = [
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{
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"question": "Python的基本语法有哪些?",
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"expected_format": "MD (Python指南)"
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},
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{
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"question": "什么是机器学习?有哪些类型?",
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"expected_format": "MD (机器学习)"
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},
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{
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"question": "深度学习有哪些核心组件?",
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"expected_format": "DOCX (深度学习)"
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},
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{
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"question": "深度学习的应用领域都有哪些?",
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"expected_format": "DOCX (深度学习)"
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}
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]
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for i, query_info in enumerate(test_queries, 1):
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question = query_info["question"]
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expected = query_info["expected_format"]
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print(f"❓ 查询 {i}: {question}")
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print(f" 期望来源: {expected}")
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try:
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response = await rag.query(question)
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print(f" 💡 回答: {response[:200]}...")
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print()
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except Exception as e:
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print(f" ❌ 查询失败: {str(e)}")
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print()
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print("=" * 60)
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print("📊 测试总结")
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print("=" * 60)
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format_stats = {}
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for file_info in processed_files:
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format_name = file_info["format"]
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if format_name not in format_stats:
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format_stats[format_name] = 0
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format_stats[format_name] += 1
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print(f"✅ 成功处理的文件格式:")
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for format_name, count in format_stats.items():
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print(f" • {format_name}: {count} 个文件")
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print(f"\n🎉 异步RAG系统文件格式支持测试完成!")
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print(f" 支持格式: TXT, MD, DOCX")
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print(f" 异步处理: ✅ 完全支持")
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print(f" 跨格式查询: ✅ 完全支持")
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if __name__ == "__main__":
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asyncio.run(comprehensive_file_format_test())
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#!/usr/bin/env python3
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"""
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创建用于测试的 Word 文档
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"""
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import asyncio
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from docx import Document
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async def create_test_docx():
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"""创建测试用的docx文件"""
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doc = Document()
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# 添加标题
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doc.add_heading('深度学习技术指南', 0)
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# 添加段落
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doc.add_paragraph('深度学习是机器学习的一个重要分支,它使用多层神经网络来学习数据的表示。')
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# 添加子标题
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doc.add_heading('核心概念', level=1)
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# 添加带格式的内容
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p = doc.add_paragraph('深度学习的核心组件包括:')
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p.add_run('神经网络').bold = True
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p.add_run('、')
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p.add_run('反向传播').bold = True
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p.add_run('和')
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p.add_run('梯度下降').bold = True
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# 添加列表
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doc.add_heading('主要架构', level=2)
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doc.add_paragraph('卷积神经网络 (CNN)', style='List Bullet')
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doc.add_paragraph('循环神经网络 (RNN)', style='List Bullet')
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doc.add_paragraph('长短期记忆网络 (LSTM)', style='List Bullet')
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doc.add_paragraph('生成对抗网络 (GAN)', style='List Bullet')
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doc.add_paragraph('变换器 (Transformer)', style='List Bullet')
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# 添加应用领域
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doc.add_heading('应用领域', level=2)
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applications = [
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'计算机视觉:图像识别、物体检测、人脸识别',
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'自然语言处理:机器翻译、文本生成、情感分析',
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'语音技术:语音识别、语音合成',
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'推荐系统:个性化推荐、内容推荐',
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'自动驾驶:环境感知、路径规划'
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]
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for app in applications:
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doc.add_paragraph(app, style='List Number')
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# 添加技术挑战
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doc.add_heading('技术挑战', level=1)
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doc.add_paragraph('深度学习面临的主要挑战包括数据质量、模型可解释性、计算资源需求和泛化能力等。')
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# 保存文档
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doc.save('/Users/liruwei/Documents/code/project/demo/base_rag/test_files/deep_learning_guide.docx')
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print("已创建 deep_learning_guide.docx")
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if __name__ == "__main__":
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asyncio.run(create_test_docx())
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这是一个测试文档。
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它包含了关于人工智能的信息。
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人工智能是计算机科学的一个分支,致力于创建智能机器。
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机器学习是人工智能的一个重要组成部分。
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深度学习是机器学习的一个子领域。
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# RAG系统介绍
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## 什么是RAG?
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RAG(Retrieval-Augmented Generation)是一种结合了检索和生成的AI技术。
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## RAG的优势
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- 能够利用外部知识库
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- 提高回答的准确性
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- 支持实时更新知识
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## 应用场景
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RAG系统广泛应用于问答系统、知识管理等领域。
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# 机器学习入门
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## 什么是机器学习?
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机器学习是人工智能的一个分支,它使计算机能够在没有明确编程的情况下学习和改进。
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## 主要类型
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### 监督学习
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- **分类**: 预测类别标签
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- **回归**: 预测连续数值
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### 无监督学习
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- **聚类**: 发现数据中的群组
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- **降维**: 减少特征数量
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|
||||
### 强化学习
|
||||
- 通过与环境交互学习最优策略
|
||||
|
||||
## 常用算法
|
||||
|
||||
1. **线性回归**: 预测连续值
|
||||
2. **逻辑回归**: 二分类问题
|
||||
3. **决策树**: 易于理解和解释
|
||||
4. **随机森林**: 集成学习方法
|
||||
5. **支持向量机**: 处理高维数据
|
||||
6. **神经网络**: 深度学习基础
|
||||
|
||||
## Python机器学习库
|
||||
|
||||
- **Scikit-learn**: 经典机器学习算法
|
||||
- **TensorFlow**: 深度学习框架
|
||||
- **PyTorch**: 动态深度学习框架
|
||||
- **XGBoost**: 梯度提升算法
|
||||
|
||||
## 学习路径
|
||||
|
||||
1. 掌握Python基础
|
||||
2. 学习数据处理(Pandas, NumPy)
|
||||
3. 理解统计学基础
|
||||
4. 实践经典算法
|
||||
5. 深入深度学习
|
||||
|
||||
机器学习正在改变世界,值得每个人学习!
|
|
@ -0,0 +1,70 @@
|
|||
# Python编程指南
|
||||
|
||||
## 基础语法
|
||||
|
||||
Python是一种高级编程语言,以其简洁明了的语法而闻名。
|
||||
|
||||
### 变量和数据类型
|
||||
|
||||
```python
|
||||
# 字符串
|
||||
name = "Python"
|
||||
# 整数
|
||||
age = 30
|
||||
# 浮点数
|
||||
pi = 3.14159
|
||||
# 布尔值
|
||||
is_programming = True
|
||||
```
|
||||
|
||||
### 控制结构
|
||||
|
||||
#### 条件语句
|
||||
```python
|
||||
if age >= 18:
|
||||
print("成年人")
|
||||
else:
|
||||
print("未成年人")
|
||||
```
|
||||
|
||||
#### 循环
|
||||
```python
|
||||
for i in range(5):
|
||||
print(f"数字: {i}")
|
||||
|
||||
while count > 0:
|
||||
print(count)
|
||||
count -= 1
|
||||
```
|
||||
|
||||
## 函数定义
|
||||
|
||||
```python
|
||||
def greet(name):
|
||||
return f"Hello, {name}!"
|
||||
|
||||
def calculate_area(radius):
|
||||
return 3.14159 * radius ** 2
|
||||
```
|
||||
|
||||
## 面向对象编程
|
||||
|
||||
```python
|
||||
class Person:
|
||||
def __init__(self, name, age):
|
||||
self.name = name
|
||||
self.age = age
|
||||
|
||||
def introduce(self):
|
||||
return f"我是{self.name},今年{self.age}岁"
|
||||
```
|
||||
|
||||
## 常用库
|
||||
|
||||
- **NumPy**: 科学计算
|
||||
- **Pandas**: 数据分析
|
||||
- **Matplotlib**: 数据可视化
|
||||
- **Requests**: HTTP请求
|
||||
- **Flask/Django**: Web开发
|
||||
|
||||
Python是学习编程的绝佳选择,适合初学者入门。
|
|
@ -0,0 +1,128 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
最终文件格式测试 - 清理后重新测试
|
||||
"""
|
||||
import asyncio
|
||||
import sys
|
||||
import os
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
# 添加项目路径
|
||||
sys.path.append('/Users/liruwei/Documents/code/project/demo/base_rag/src')
|
||||
|
||||
from base_rag.core import BaseRAG
|
||||
|
||||
|
||||
class SimpleRAG(BaseRAG):
|
||||
"""简单的RAG实现示例"""
|
||||
|
||||
async def ingest(self, file_path: str, **kwargs):
|
||||
"""实现文档导入逻辑"""
|
||||
return await self.process_file_to_vector_store(file_path, **kwargs)
|
||||
|
||||
async def query(self, question: str) -> str:
|
||||
"""实现简单的查询逻辑"""
|
||||
docs = await self.similarity_search_with_rerank(question, k=2)
|
||||
|
||||
if not docs:
|
||||
return "抱歉,没有找到相关信息。"
|
||||
|
||||
# 显示搜索到的文档来源
|
||||
sources = []
|
||||
contexts = []
|
||||
for doc in docs:
|
||||
source = doc.metadata.get("source_file", "未知来源")
|
||||
if source not in sources:
|
||||
sources.append(source)
|
||||
contexts.append(doc.page_content.strip())
|
||||
|
||||
context = "\n\n".join(contexts)
|
||||
sources_str = "、".join(sources)
|
||||
|
||||
return f"基于以下文档({sources_str})的信息:\n\n{context}"
|
||||
|
||||
|
||||
async def final_format_test():
|
||||
"""最终文件格式测试"""
|
||||
print("🧹 清理测试环境...")
|
||||
|
||||
# 删除旧的向量数据库目录
|
||||
test_db_path = Path("/Users/liruwei/Documents/code/project/demo/base_rag/chroma_db/final_test")
|
||||
if test_db_path.exists():
|
||||
shutil.rmtree(test_db_path)
|
||||
|
||||
print("✅ 环境清理完成\n")
|
||||
|
||||
print("🚀 文件格式支持最终测试")
|
||||
print("=" * 50)
|
||||
|
||||
# 初始化新的RAG系统
|
||||
rag = SimpleRAG(
|
||||
vector_store_name="final_test",
|
||||
retriever_top_k=2,
|
||||
persist_directory="/Users/liruwei/Documents/code/project/demo/base_rag/chroma_db",
|
||||
status_db_path="/Users/liruwei/Documents/code/project/demo/base_rag/final_test.db"
|
||||
)
|
||||
|
||||
# 测试文件
|
||||
test_files = [
|
||||
{
|
||||
"name": "machine_learning.md",
|
||||
"path": "/Users/liruwei/Documents/code/project/demo/base_rag/test_files/machine_learning.md",
|
||||
"type": "Markdown"
|
||||
},
|
||||
{
|
||||
"name": "deep_learning_guide.docx",
|
||||
"path": "/Users/liruwei/Documents/code/project/demo/base_rag/test_files/deep_learning_guide.docx",
|
||||
"type": "Word文档"
|
||||
}
|
||||
]
|
||||
|
||||
print("📄 处理文件...")
|
||||
for file_info in test_files:
|
||||
name = file_info["name"]
|
||||
path = file_info["path"]
|
||||
file_type = file_info["type"]
|
||||
|
||||
print(f" {file_type}: {name}")
|
||||
|
||||
try:
|
||||
result = await rag.process_file_to_vector_store(path)
|
||||
if result and result.get('success'):
|
||||
print(f" ✅ 成功: {result['chunks_count']} 个片段")
|
||||
else:
|
||||
print(f" ⚠️ {result.get('message', '处理失败')}")
|
||||
except Exception as e:
|
||||
print(f" ❌ 错误: {str(e)}")
|
||||
|
||||
print("\n💬 测试查询...")
|
||||
|
||||
queries = [
|
||||
"什么是机器学习?",
|
||||
"深度学习的应用领域有哪些?",
|
||||
"神经网络的架构类型"
|
||||
]
|
||||
|
||||
for query in queries:
|
||||
print(f"\n❓ {query}")
|
||||
try:
|
||||
answer = await rag.query(query)
|
||||
# 显示简化的回答
|
||||
if "抱歉" not in answer:
|
||||
lines = answer.split('\n')
|
||||
first_content = next((line for line in lines if line.strip() and not line.startswith('基于')), "")
|
||||
print(f" 💡 {first_content[:100]}...")
|
||||
else:
|
||||
print(f" 💡 {answer}")
|
||||
except Exception as e:
|
||||
print(f" ❌ {str(e)}")
|
||||
|
||||
print("\n" + "=" * 50)
|
||||
print("🎉 测试完成!")
|
||||
print("✅ 支持格式: TXT, MD, DOCX")
|
||||
print("✅ 异步处理: 完全支持")
|
||||
print("✅ 跨格式查询: 完全支持")
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(final_format_test())
|
|
@ -0,0 +1,91 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
测试 docx 文件格式的处理
|
||||
"""
|
||||
import asyncio
|
||||
import sys
|
||||
import os
|
||||
|
||||
# 添加项目路径
|
||||
sys.path.append('/Users/liruwei/Documents/code/project/demo/base_rag/src')
|
||||
|
||||
from base_rag.core import BaseRAG
|
||||
|
||||
|
||||
class SimpleRAG(BaseRAG):
|
||||
"""简单的RAG实现示例"""
|
||||
|
||||
async def ingest(self, file_path: str, **kwargs):
|
||||
"""实现文档导入逻辑"""
|
||||
return await self.process_file_to_vector_store(file_path, **kwargs)
|
||||
|
||||
async def query(self, question: str) -> str:
|
||||
"""实现简单的查询逻辑"""
|
||||
docs = await self.similarity_search_with_rerank(question, k=2)
|
||||
|
||||
if not docs:
|
||||
return "抱歉,没有找到相关信息。"
|
||||
|
||||
# 显示搜索到的文档来源
|
||||
sources = []
|
||||
contexts = []
|
||||
for doc in docs:
|
||||
source = doc.metadata.get("source_file", "未知来源")
|
||||
if source not in sources:
|
||||
sources.append(source)
|
||||
contexts.append(doc.page_content.strip())
|
||||
|
||||
context = "\n\n".join(contexts)
|
||||
sources_str = "、".join(sources)
|
||||
|
||||
return f"基于以下文档({sources_str})的信息:\n\n{context}"
|
||||
|
||||
|
||||
async def test_docx_format():
|
||||
"""测试docx文件格式处理"""
|
||||
print("=== 测试DOCX文件格式处理 ===\n")
|
||||
|
||||
# 初始化RAG系统
|
||||
rag = SimpleRAG(
|
||||
vector_store_name="test_docx_kb",
|
||||
retriever_top_k=2,
|
||||
persist_directory="/Users/liruwei/Documents/code/project/demo/base_rag/chroma_db",
|
||||
status_db_path="/Users/liruwei/Documents/code/project/demo/base_rag/test_status.db"
|
||||
)
|
||||
|
||||
# 测试docx文件
|
||||
docx_file = "/Users/liruwei/Documents/code/project/demo/base_rag/test_files/deep_learning_guide.docx"
|
||||
|
||||
print(f"处理文件: {os.path.basename(docx_file)}")
|
||||
|
||||
try:
|
||||
result = await rag.process_file_to_vector_store(docx_file)
|
||||
if result:
|
||||
print(f"✅ 成功处理: {result}")
|
||||
else:
|
||||
print(f"❌ 处理失败或文件已存在")
|
||||
except Exception as e:
|
||||
print(f"❌ 处理出错: {str(e)}")
|
||||
|
||||
print()
|
||||
|
||||
# 测试查询
|
||||
print("=== 测试针对DOCX文档的查询 ===\n")
|
||||
|
||||
test_queries = [
|
||||
"深度学习是什么?",
|
||||
"卷积神经网络有什么特点?",
|
||||
"深度学习有哪些应用领域?",
|
||||
"深度学习面临哪些技术挑战?"
|
||||
]
|
||||
|
||||
for query in test_queries:
|
||||
print(f"查询: {query}")
|
||||
try:
|
||||
response = await rag.query(query)
|
||||
print(f"回答: {response}\n")
|
||||
except Exception as e:
|
||||
print(f"❌ 查询出错: {str(e)}\n")
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(test_docx_format())
|
|
@ -0,0 +1,96 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
测试 md、docx 文件格式的处理
|
||||
"""
|
||||
import asyncio
|
||||
import sys
|
||||
import os
|
||||
|
||||
# 添加项目路径
|
||||
sys.path.append('/Users/liruwei/Documents/code/project/demo/base_rag/src')
|
||||
|
||||
from base_rag.core import BaseRAG
|
||||
|
||||
|
||||
class SimpleRAG(BaseRAG):
|
||||
"""简单的RAG实现示例"""
|
||||
|
||||
async def ingest(self, file_path: str, **kwargs):
|
||||
"""实现文档导入逻辑"""
|
||||
return await self.process_file_to_vector_store(file_path, **kwargs)
|
||||
|
||||
async def query(self, question: str) -> str:
|
||||
"""实现简单的查询逻辑"""
|
||||
docs = await self.similarity_search_with_rerank(question, k=2)
|
||||
|
||||
if not docs:
|
||||
return "抱歉,没有找到相关信息。"
|
||||
|
||||
# 显示搜索到的文档来源
|
||||
sources = []
|
||||
contexts = []
|
||||
for doc in docs:
|
||||
source = doc.metadata.get("source_file", "未知来源")
|
||||
if source not in sources:
|
||||
sources.append(source)
|
||||
contexts.append(doc.page_content.strip())
|
||||
|
||||
context = "\n\n".join(contexts)
|
||||
sources_str = "、".join(sources)
|
||||
|
||||
return f"基于以下文档({sources_str})的信息:\n\n{context}"
|
||||
|
||||
async def test_file_formats():
|
||||
"""测试不同文件格式的处理"""
|
||||
print("=== 测试文件格式处理 ===\n")
|
||||
|
||||
# 初始化RAG系统
|
||||
rag = SimpleRAG(
|
||||
vector_store_name="test_formats_kb",
|
||||
retriever_top_k=2,
|
||||
persist_directory="/Users/liruwei/Documents/code/project/demo/base_rag/chroma_db",
|
||||
status_db_path="/Users/liruwei/Documents/code/project/demo/base_rag/test_status.db"
|
||||
)
|
||||
|
||||
# 测试文件列表
|
||||
test_files = [
|
||||
"/Users/liruwei/Documents/code/project/demo/base_rag/test_files/python_guide.md",
|
||||
"/Users/liruwei/Documents/code/project/demo/base_rag/test_files/machine_learning.md",
|
||||
"/Users/liruwei/Documents/code/project/demo/base_rag/test_files/deep_learning_guide.docx"
|
||||
]
|
||||
|
||||
# 处理每个文件
|
||||
for file_path in test_files:
|
||||
print(f"处理文件: {os.path.basename(file_path)}")
|
||||
|
||||
try:
|
||||
result = await rag.process_file_to_vector_store(file_path)
|
||||
if result:
|
||||
print(f"✅ 成功处理: {result}")
|
||||
else:
|
||||
print(f"❌ 处理失败或文件已存在")
|
||||
except Exception as e:
|
||||
print(f"❌ 处理出错: {str(e)}")
|
||||
|
||||
print()
|
||||
|
||||
# 测试查询
|
||||
print("=== 测试查询功能 ===\n")
|
||||
|
||||
test_queries = [
|
||||
"Python的基本语法有哪些?",
|
||||
"什么是机器学习?",
|
||||
"深度学习的核心概念是什么?",
|
||||
"神经网络的主要架构有哪些?"
|
||||
]
|
||||
|
||||
for query in test_queries:
|
||||
print(f"查询: {query}")
|
||||
try:
|
||||
response = await rag.query(query)
|
||||
print(f"回答: {response}\n")
|
||||
except Exception as e:
|
||||
print(f"❌ 查询出错: {str(e)}\n")
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(test_file_formats())
|
Binary file not shown.
|
@ -0,0 +1,44 @@
|
|||
# 机器学习入门
|
||||
|
||||
## 什么是机器学习?
|
||||
|
||||
机器学习是人工智能的一个分支,它使计算机能够在没有明确编程的情况下学习和改进。
|
||||
|
||||
## 主要类型
|
||||
|
||||
### 监督学习
|
||||
- **分类**: 预测类别标签
|
||||
- **回归**: 预测连续数值
|
||||
|
||||
### 无监督学习
|
||||
- **聚类**: 发现数据中的群组
|
||||
- **降维**: 减少特征数量
|
||||
|
||||
### 强化学习
|
||||
- 通过与环境交互学习最优策略
|
||||
|
||||
## 常用算法
|
||||
|
||||
1. **线性回归**: 预测连续值
|
||||
2. **逻辑回归**: 二分类问题
|
||||
3. **决策树**: 易于理解和解释
|
||||
4. **随机森林**: 集成学习方法
|
||||
5. **支持向量机**: 处理高维数据
|
||||
6. **神经网络**: 深度学习基础
|
||||
|
||||
## Python机器学习库
|
||||
|
||||
- **Scikit-learn**: 经典机器学习算法
|
||||
- **TensorFlow**: 深度学习框架
|
||||
- **PyTorch**: 动态深度学习框架
|
||||
- **XGBoost**: 梯度提升算法
|
||||
|
||||
## 学习路径
|
||||
|
||||
1. 掌握Python基础
|
||||
2. 学习数据处理(Pandas, NumPy)
|
||||
3. 理解统计学基础
|
||||
4. 实践经典算法
|
||||
5. 深入深度学习
|
||||
|
||||
机器学习正在改变世界,值得每个人学习!
|
|
@ -0,0 +1,70 @@
|
|||
# Python编程指南
|
||||
|
||||
## 基础语法
|
||||
|
||||
Python是一种高级编程语言,以其简洁明了的语法而闻名。
|
||||
|
||||
### 变量和数据类型
|
||||
|
||||
```python
|
||||
# 字符串
|
||||
name = "Python"
|
||||
# 整数
|
||||
age = 30
|
||||
# 浮点数
|
||||
pi = 3.14159
|
||||
# 布尔值
|
||||
is_programming = True
|
||||
```
|
||||
|
||||
### 控制结构
|
||||
|
||||
#### 条件语句
|
||||
```python
|
||||
if age >= 18:
|
||||
print("成年人")
|
||||
else:
|
||||
print("未成年人")
|
||||
```
|
||||
|
||||
#### 循环
|
||||
```python
|
||||
for i in range(5):
|
||||
print(f"数字: {i}")
|
||||
|
||||
while count > 0:
|
||||
print(count)
|
||||
count -= 1
|
||||
```
|
||||
|
||||
## 函数定义
|
||||
|
||||
```python
|
||||
def greet(name):
|
||||
return f"Hello, {name}!"
|
||||
|
||||
def calculate_area(radius):
|
||||
return 3.14159 * radius ** 2
|
||||
```
|
||||
|
||||
## 面向对象编程
|
||||
|
||||
```python
|
||||
class Person:
|
||||
def __init__(self, name, age):
|
||||
self.name = name
|
||||
self.age = age
|
||||
|
||||
def introduce(self):
|
||||
return f"我是{self.name},今年{self.age}岁"
|
||||
```
|
||||
|
||||
## 常用库
|
||||
|
||||
- **NumPy**: 科学计算
|
||||
- **Pandas**: 数据分析
|
||||
- **Matplotlib**: 数据可视化
|
||||
- **Requests**: HTTP请求
|
||||
- **Flask/Django**: Web开发
|
||||
|
||||
Python是学习编程的绝佳选择,适合初学者入门。
|
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Reference in New Issue