In developing the RAG system, the data formats in the knowledge base can be diverse, and most of them are unstructured data content. For example, PDF documents in the knowledge base are likely to contain table data, and our approach to handling this needs special attention to ensure that the table information can be correctly extracted and utilized.
Table Parsing and Structured Storage:
It is recommended to use specialized tools or libraries to parse table content in PDFs. For instance, the PyMuPDF library can extract table data from PDFs and convert it into a format suitable for retrieval, such as Markdown or Pandas DataFrame format. This method effectively structures table data, facilitating subsequent retrieval and generation tasks.
For complex tables, more advanced tools like ColPali can be used, which combines visual Transformer technology to not only extract text information but also handle table content within images.
OCR Technology and Image Conversion:
If the table exists as an image, OCR (Optical Character Recognition) technology can be used to convert the table in the image into text format. For example, PaddleOCR is a commonly used OCR tool that can recognize and extract text from tables.
Additionally, when a page is identified to contain a table, the page can be converted to an image and then the table content can be extracted using OCR technology, storing it as structured data.
Semi-Structured Data Processing:
When handling PDFs that contain text, tables, and images, semi-structured data processing methods can be employed. For instance, using an Unstructured parser can split the text, tables, and icons in the PDF document and create a multi-vector database to store the raw data and summary information.
This method helps maintain the structural integrity of the tables while supporting chain processing and improving retrieval efficiency.
Document Slicing and Index Building:
When building a knowledge base, PDFs are typically sliced into smaller chunks for easier retrieval and generation. However, special attention must be paid to the integrity of tables during the slicing process.
Moreover, building an efficient indexing structure is crucial, and tools like LangChain can be used to achieve efficient retrieval of PDF documents and their table contents.
Combining Various Tools and Technologies:
For more complex document knowledge bases, such as tender documents in the procurement field, it may be necessary to use a combination of multiple tools and technologies to optimize the extraction and processing of PDF tables. Consider combining NLP models, OCR technology, and table parsing tools to extract and process table information from PDFs.
If the table data and structure itself are still quite complex, consider using specialized table parsing frameworks, such as Tabula or pdfplumber, which can extract table content from unstructured documents with high precision, though the specific results need to be tried out by users themselves.
In summary, when handling table data in PDF documents within the RAG system, it is essential to experiment based on specific needs and ultimately choose the appropriate tools and technologies to ensure that table information can be accurately extracted, stored, and retrieved, thereby enhancing the overall performance and accuracy of the system.
Recommended Content:
1. What are Model Distillation and Model Quantization?
2. What are the mainstream technologies for deploying large models?
3. Why do large model training and inference phases require hardware acceleration like GPU, TPU, etc.?
That’s all for this issue. I hope it can help you. Thank you for reading to the end. If you find the content useful, please like and share to encourage us. See you next time.
