Background challenges
When building RAG (Retrieval Augmented Generation) systems, the document preprocessing phase often becomes a performance bottleneck, especially when dealing with mixed-format enterprise documents.
Kreuzberg's optimization scheme
- Harmonization of treatment processes: single interface to handle PDF/OCR/Office and other formats
- Native text retention: Maximize the preservation of the original document structure and semantic information
- Rapid Integration: a few lines of code to embed into an existing RAG preprocessing pipeline
Specific methods of implementation
- architectural design::
- Using Kreuzberg as a Document Preprocessing Microservice
- Output of standardized text for subsequent vectorization
- Code Integration Examples::
# RAG预处理环节 def preprocess_document(file_path): extractor = Kreuzberg() # 自动识别并处理各种格式 text = extractor.extract_text(file_path) # 执行必要的文本清洗 cleaned_text = clean_text(text) return cleaned_text - Performance Tuning::
- Enable Parallel Processing for High Volume Documents
- Cache intermediate results of processed documents
Effectiveness evaluation
Compared to traditional solutions, the use of Kreuzberg can be:
- Reduction of format compatibility codes above 50%
- Increase document processing throughput above 30%
- Reduce the cost of invoking OCR services
This answer comes from the articleKreuzberg: open source tool to extract text from any documentThe
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