The supported file types for ingestion are PDF, DOCX, and TXT. These formats are commonly used for document processing, and each is handled through specific parsing methods to extract text and metadata. Support for these formats ensures flexibility when working with structured or unstructured content from diverse sources. Below is a detailed breakdown of how each format is processed and considerations developers should keep in mind.
PDF files are widely supported but require specialized libraries due to their complex layout and encoding. For example, tools like PyPDF2 (Python) or PDFMiner are often used to extract text, though scanned PDFs (image-based) may require OCR (optical character recognition) to convert images to text. DOCX files, which are based on XML, can be parsed using libraries like python-docx or Apache POI (Java) to access text, tables, and styles. TXT files are the simplest to process since they contain raw text, though encoding issues (e.g., UTF-8 vs. ASCII) must be handled to avoid errors. All three formats may require preprocessing steps like removing headers/footers or normalizing whitespace.
Developers should note that support depends on implementation. For instance, a basic ingestion pipeline might handle TXT and DOCX easily but struggle with PDFs containing non-standard fonts or embedded media. Libraries like Tika (Apache) or commercial APIs (e.g., Adobe PDF Services) can simplify parsing but add dependencies. Scenarios like password-protected PDFs or DOCX files with macros may require additional steps (decryption, sandboxing). Always validate extracted text for accuracy, especially with PDFs, where line breaks or hyphenated words can disrupt readability. Testing with sample files from your target use case is critical to ensure compatibility.