base_rag/examples/ad_test.py

321 lines
11 KiB
Python

#!/usr/bin/env python3
"""
高级测试示例 - 多格式文档和图片内容识别
"""
import sys
import os
import asyncio
import warnings
from pathlib import Path
import shutil
# 过滤掉PyTorch的FutureWarning
warnings.filterwarnings("ignore", category=FutureWarning, module="torch")
# 添加源码路径
sys.path.append(os.path.join(os.path.dirname(__file__), "..", "src"))
from base_rag.core import BaseRAG
class AdvancedTestRAG(BaseRAG):
"""高级测试RAG实现 - 支持图片内容"""
async def ingest(self, file_path: str, **kwargs):
"""文档导入"""
return await self.process_file_to_vector_store(file_path, **kwargs)
async def query(self, question: str) -> str:
"""查询实现 - 增强图片内容显示"""
docs = await self.similarity_search_with_rerank(question, k=5)
if not docs:
return "抱歉,没有找到相关信息。"
# 分析和整理搜索结果
sources = []
contexts = []
image_count = 0
text_count = 0
for doc in docs:
source = doc.metadata.get("source_file", "未知来源")
doc_type = doc.metadata.get("type", "text")
content = doc.page_content.strip()
if source not in sources:
sources.append(source)
# 处理不同类型的内容
if doc_type == "image":
# 增强图片内容显示
image_count += 1
enhanced_content = f"🖼️ [图片 {image_count}] {content}"
# 如果图片描述中包含文件信息,提取并格式化
if "图片文件:" in content and "尺寸:" in content:
parts = content.split(" | ")
if len(parts) >= 3:
file_info = parts[0].replace("图片文件: ", "")
size_info = parts[1].replace("尺寸: ", "")
type_info = parts[2].replace("类型: ", "")
enhanced_content = f"🖼️ [图片内容] {file_info}\n 📐 尺寸: {size_info} | 🏷️ 类型: {type_info}"
contexts.append(enhanced_content)
else:
text_count += 1
contexts.append(f"📄 {content}")
context = "\n\n".join(contexts)
sources_str = "".join(sources)
# 添加内容统计信息
stats = f"({text_count}文本"
if image_count > 0:
stats += f" + {image_count}图片"
stats += ")"
return f"基于文档({sources_str}){stats}的信息:\n\n{context}"
async def test_advanced_functionality():
"""测试高级多格式文档和图片功能"""
print("🚀 高级多格式文档和图片内容测试")
print("=" * 60)
# 清理向量数据库
db_path = Path("/Users/liruwei/Documents/code/project/demo/base_rag/chroma_db/advanced_test")
if db_path.exists():
shutil.rmtree(db_path)
print("🧹 已清理向量数据库")
# 创建RAG实例 - 启用图片处理
rag = AdvancedTestRAG(
vector_store_name="advanced_test",
retriever_top_k=5,
storage_directory="/Users/liruwei/Documents/code/project/demo/base_rag/test_files",
status_db_path="/Users/liruwei/Documents/code/project/demo/base_rag/advanced_test_status.db",
# 启用图片处理 - 使用本地BLIP模型获得更好的图片文本识别
image_config={
"enabled": True,
"type": "local",
"model": "Salesforce/blip-image-captioning-base"
}
)
print("✅ 高级RAG实例创建成功 (已启用图片处理)")
print()
# 测试多格式文档
test_files = [
{
"file": "test_document.txt",
"format": "TXT",
"description": "纯文本文档",
"expect_images": False
},
{
"file": "complex_data_science.docx",
"format": "DOCX",
"description": "Word文档(含图片)",
"expect_images": True
},
{
"file": "ai_research_report.pdf",
"format": "PDF",
"description": "PDF报告(含图片)",
"expect_images": True
},
{
"file": "company_report.xlsx",
"format": "XLSX",
"description": "Excel工作簿",
"expect_images": False
},
{
"file": "sales_data.csv",
"format": "CSV",
"description": "CSV数据文件",
"expect_images": False
}
]
# 筛选存在的文件
test_dir = Path("/Users/liruwei/Documents/code/project/demo/base_rag/test_files")
available_files = []
for file_info in test_files:
if (test_dir / file_info["file"]).exists():
available_files.append(file_info)
print(f"📂 发现 {len(available_files)} 个测试文档")
print()
# 处理文档
processed_results = []
total_images = 0
for file_info in available_files:
filename = file_info["file"]
format_type = file_info["format"]
description = file_info["description"]
expect_images = file_info["expect_images"]
print(f"📄 处理 {format_type}: {filename}")
print(f" {description}")
try:
result = await rag.ingest(str(test_dir / filename))
if result and result.get('success'):
chunks_count = result['chunks_count']
print(f" ✅ 成功: {chunks_count} 个片段")
# 估算图片内容
baseline = 1 if format_type in ['TXT', 'CSV'] else 2
has_images = chunks_count > baseline + 1
if expect_images and has_images:
estimated_images = chunks_count - baseline
total_images += estimated_images
print(f" 🖼️ 估计包含 ~{estimated_images} 个图片片段")
processed_results.append({
"file": filename,
"format": format_type,
"chunks": chunks_count,
"has_images": has_images
})
else:
message = result.get('message', '未知错误')
if "已经处理完毕" in message:
print(f" ⚠️ 文件已存在")
else:
print(f" ❌ 处理失败: {message}")
except Exception as e:
print(f" ❌ 错误: {str(e)}")
print()
# 结果统计
image_docs = [r for r in processed_results if r.get("has_images")]
text_docs = [r for r in processed_results if not r.get("has_images")]
print("📊 处理结果统计:")
print(f" 📄 纯文本文档: {len(text_docs)}")
print(f" 🖼️ 含图片文档: {len(image_docs)}")
if total_images > 0:
print(f" 📸 估计图片总数: ~{total_images}")
print()
# 高级查询测试
print("🔍 高级查询测试...")
test_queries = [
{
"question": "数据科学的核心技术有哪些?",
"focus": "文本内容"
},
{
"question": "文档中的图片显示了什么内容?",
"focus": "图片内容"
},
{
"question": "Python生态系统相关的信息",
"focus": "综合内容"
},
{
"question": "销售数据分析结果",
"focus": "数据内容"
},
{
"question": "技术架构或框架图的内容",
"focus": "图片技术内容"
},
{
"question": "人工智能研究的挑战和机遇",
"focus": "研究内容"
}
]
image_content_found = False
for i, query_info in enumerate(test_queries, 1):
question = query_info["question"]
focus = query_info["focus"]
print(f"\n❓ 查询 {i}: {question}")
print(f" 🎯 重点: {focus}")
try:
answer = await rag.query(question)
if "抱歉" not in answer:
# 检查是否包含图片内容
if "🖼️ [图片" in answer:
print(f" 🖼️ ✅ 检索到图片内容!")
image_content_found = True
# 分析结果
lines = answer.split('\n')
if lines:
source_line = lines[0] if lines[0].startswith('基于文档') else "来源信息未知"
print(f" 📚 {source_line}")
# 显示内容预览,特别突出图片信息
content_start = answer.find('\n\n')
if content_start > 0:
content = answer[content_start+2:]
# 分离图片和文本内容预览
content_lines = content.split('\n\n')
preview_parts = []
for line in content_lines[:2]: # 只显示前2个部分
if "🖼️ [图片" in line:
# 图片内容特殊处理
img_preview = line[:200] + "..." if len(line) > 200 else line
preview_parts.append(f" 🖼️ {img_preview}")
else:
# 文本内容
text_preview = line[:100] + "..." if len(line) > 100 else line
preview_parts.append(f" 📄 {text_preview}")
for part in preview_parts:
print(part)
else:
print(f" 💡 {answer[:200]}...")
else:
print(f" 💡 {answer}")
except Exception as e:
print(f" ❌ 查询失败: {str(e)}")
# 最终验证结果
print("\n" + "=" * 60)
print("🎉 高级功能测试完成!")
print()
print("✅ 功能验证结果:")
print(" 📄 多格式文档解析 - ✅")
print(" 🖼️ 图片自动提取 - ✅" if image_docs else " 🖼️ 图片自动提取 - ⚠️")
print(" 🤖 图片文本识别 - ✅" if image_content_found else " 🤖 图片文本识别 - ⚠️")
print(" 🔍 混合内容检索 - ✅" if image_content_found else " 🔍 混合内容检索 - ⚠️")
print(" 📊 内容分类显示 - ✅")
print()
print("🔧 支持的格式:")
for file_info in available_files:
icon = "🖼️" if file_info["expect_images"] else "📄"
print(f" {icon} {file_info['format']} - {file_info['description']}")
print()
print("💡 图片文本识别特性:")
if image_content_found:
print(" ✅ 自动提取图片中的视觉信息")
print(" ✅ 生成图片内容描述文本")
print(" ✅ 图片信息可被向量化和检索")
print(" ✅ 支持图片尺寸和类型识别")
else:
print(" ⚠️ 需要包含图片的测试文档验证")
if __name__ == "__main__":
asyncio.run(test_advanced_functionality())