How to handle documents containing table data in the RAG knowledge base?
Efficient method for processing PDF table data in RAG system
Core content:
1. Use PyMuPDF and other tools to parse the table data in PDF
2. Use OCR technology to convert the table in image format into text
3. Apply semi-structured data processing methods to maintain the integrity of the table structure
When we develop the RAG system, the data formats in the knowledge base may be varied, and most of them are unstructured data content. For example, the PDF documents in the knowledge base are likely to contain table data. At this time, our processing method needs special attention to ensure that the table information can be correctly extracted and used.
Table parsing and structured storage :
It is recommended to use specialized tools or libraries to parse the table content in PDF. For example, the PyMuPDF library can extract the table data in PDF and convert it into a format suitable for retrieval, such as Markdown or Pandas DataFrame format. This method can effectively structure the table data, which is convenient for subsequent retrieval and generation tasks.
For complex tables, you can use more advanced tools such as ColPali , which combines visual Transformer technology to not only extract text information but also process table content in images.
OCR technology and image conversion :
If the table exists in the form of an image, you can use OCR (Optical Character Recognition) technology 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.
When a page is identified to contain a table, the PDF page can be converted into an image , and then the table content can be extracted through OCR technology, and then stored in a structured data format.
Semi-structured data processing :
When processing PDFs containing text, tables, and images, semi-structured data processing methods can be used. For example, the Unstructured parser can be used to split the text, tables, and icons in a PDF document and create a multi-vector database to store the original data and summary information.
This approach helps maintain the structural integrity of the table while supporting chain processing and improving retrieval efficiency.
Document slicing and index building :
When building a knowledge base, PDF documents are usually split into multiple small pieces for easy retrieval and generation. For PDFs containing tables, special attention should be paid to the integrity of the tables during the splitting process.
In addition, building an efficient index structure is the key. Tools such as LangChain can be used to achieve efficient retrieval of PDF documents and their table contents.
Combining multiple tools and techniques :
For document knowledge bases with more complex content, such as tender documents and bidding 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. You can consider combining NLP models, OCR technology, and table parsing tools to extract and process table information in PDF.
If the table data and structure itself are relatively complex, you can consider using a specialized table parsing framework, such as Tabula, pdfplumber , etc. These tools can extract table content from unstructured documents with high precision. The specific effect still requires you to try it yourself.
In short, when processing data tables in PDF documents in the RAG system, you need to make more attempts based on specific needs, and finally choose the appropriate tools and technologies to ensure that the table information can be correctly extracted, stored and retrieved, thereby improving the overall performance and accuracy of the system.
