The RAG Engine is equipped with state-of-the-art PDF Parser capability, which seamlessly processes and extracts content from PDF documents. This capability addresses the critical need for more sophisticated document analysis tools. It enables users to tap into the vast reservoirs of knowledge locked away in PDF files.

At its core, the PDF Parser is designed to intelligently navigate the complex structure of PDF documents, which can range from simple text-based files to more complex documents containing complex layouts, rich tables, and other elements. The parser not only extracts text but also understands the layout and structure, effectively maintaining the context and meaning of the information. This capability ensures that the extracted content is not just a stream of text but a coherent set of data that reflects the original document's intent and format.

The PDF Parser is adept at processing a broad spectrum of document types across various domains, including financial reports, product specifications, insurance paperwork, technical manuals, and many more, accommodating the diverse needs of users and industries.

The PDF Parser is integrated with the RAG Engine's built-in mechanisms, including Text Segmentation for semantic chunking, embeddings, and retrieval processes, ensuring highly precise information retrieval.

Uploading PDF documents into the RAG Engine is possible both through the API and in the AI21 Studio. For direct integration with your data sources, such as Google Drive, or AWS S3, contact us.

Features

Preserves meaning and hierarchy of documents

The PDF Parser is designed to maintain the original meaning and hierarchical structure of documents during the parsing process. This involves recognizing and respecting the relationships and order among various elements such as headings, paragraphs, lists, and other structural components. By preserving the logical flow and organization, the parser ensures that the extracted content retains the context and coherence intended by the original document creators, which is critical for accurate information retrieval and understanding.

Handles complex tables

The PDF parser is optimized to extract tabular information as accurately as possible. We use HTML to represent tables. Utilizing HTML for representing tables in PDF parsing offers significant advantages due to its robust structure and versatility, allowing for more intricate formatting and layout options compared to simpler formats like Markdown or plain text with line breaks and tabs.

Adapts to complex layouts

The parser utilizes sophisticated algorithms to precisely interpret and navigate diverse document layouts, thereby equipping the RAG Engine to effortlessly process a wide range of document designs. This capability extends to documents featuring complex configurations of text, tables, and various elements, typical in business-related materials. Such proficiency ensures a faithful reconstruction of the document's original format in the extracted output.

Current limitations

• OCR functionality is not available at this time. Only PDF documents that contain a text layer are supported.
• Some PDFs may take longer to process, depending on their content.