# Supported file types & limits GLBNXT Workspace supports a range of common file formats for document upload and querying. This section provides an overview of what is supported, what limits apply, and what to consider when working with different types of content. *** ### Supported File Types #### Documents | Format | Extension | | ---------------------- | --------- | | PDF | .pdf | | Microsoft Word | .docx | | Plain text | .txt | | Markdown | .md | | Microsoft PowerPoint | .pptx | | Microsoft Excel | .xlsx | | Comma-separated values | .csv | #### Images Image uploads are supported for models with vision capabilities. When an image is uploaded to a vision-enabled model, the AI can describe, analyse, and reason over the visual content, including text within images. | Format | Extension | | ------ | ------------ | | JPEG | .jpg / .jpeg | | PNG | .png | | GIF | .gif | | WebP | .webp | #### Audio Audio file uploads are supported in some interface configurations, enabling transcription and content analysis of spoken recordings. | Format | Extension | | --------- | --------- | | MP3 | .mp3 | | MP4 audio | .m4a | | WAV | .wav | | WebM | .webm | Support for audio uploads depends on the interface and model configuration enabled for your account. If audio upload is not available in your interface, contact your administrator. *** ### File Size Limits Individual file uploads are subject to a maximum file size limit. This limit is set at the platform level and may be further defined by your administrator based on your organisation's configuration. As a general guideline: * Standard document uploads are supported up to a maximum of **10MB per file** in most configurations * Image uploads are typically supported up to **10MB per file** * Audio files may be subject to different limits depending on the transcription model in use If a file exceeds the applicable limit, the interface will notify you at the point of upload and the file will not be processed. In this case, consider reducing the file size, splitting the content across multiple smaller files, or extracting the relevant sections into a new document before uploading. *** ### How File Type Affects Processing Quality Not all files are processed with equal accuracy. The format and structure of a document have a direct bearing on how well the AI can read and reason over its content. **Plain text and Markdown files** produce the most reliable results. The content is clean, structured, and directly readable without any conversion required. **Word documents** are generally processed well, with text content and basic formatting preserved during extraction. **PDFs** vary significantly depending on how they were created. PDFs generated from digital sources, such as exported Word documents or typeset reports, are typically processed accurately. Scanned PDFs, where the content exists as an image rather than selectable text, are processed less reliably and may produce incomplete or inaccurate results. If you are working with scanned documents, consider running them through an OCR tool before uploading to improve the quality of the extracted text. **Spreadsheets and CSV files** are supported for tabular data, but complex formatting, merged cells, or heavily structured layouts may affect how accurately the content is interpreted. For best results, ensure the data is organised in a clean, flat structure with clear column headers. **PowerPoint presentations** are supported for text content extraction, but visual elements such as diagrams, charts, and embedded images within slides are not processed as part of the text extraction. If the key information in a presentation is contained in charts or visuals rather than text, consider exporting the relevant content to a document format before uploading. **Images** are processed by vision-capable models and can include text recognition, scene description, and visual analysis. The accuracy of text recognition within images depends on the clarity and resolution of the original file. *** ### Working Within Context Window Limits Regardless of file size, the amount of content that can be actively referenced by the AI in any single interaction is constrained by the context window of the model you are using. Different models have different context window sizes, and very long documents may only have their most relevant sections retrieved and passed to the model at any one time. This means that for very large documents, the AI may not have simultaneous visibility of the entire file. If you are working with lengthy documents and need the AI to reason over content from multiple distant sections, breaking your queries into focused, section-specific prompts will generally produce more accurate results than broad queries across the whole document. *** ### Recommendations for Best Results **Use digital source documents where possible.** Natively digital files produce significantly more accurate results than scanned or photographed documents. **Keep files focused.** A document containing only the content relevant to your task will produce better results than uploading a large, multi-topic file and asking the AI to navigate it. **Split large files where practical.** If you are working with a document that exceeds the file size limit or is very long, splitting it into logical sections and uploading the most relevant portion for each query is more effective than attempting to work with the full file. **Check file formatting before uploading.** Heavily formatted documents with complex layouts, multiple columns, embedded tables within tables, or non-standard structures may not extract cleanly. Where possible, simplify the formatting of a document before uploading if you expect extraction quality to be an issue. *** ### Limitations and When to Consider GLBNXT Platform The file upload and processing capabilities within Workspace are designed for individual and team-level document work within a session-based environment. They are well-suited to the majority of professional document tasks. For organisations that require support for a broader range of file types, higher volume document ingestion, persistent knowledge bases that index content across users and sessions, or custom processing pipelines for complex or proprietary document formats, these capabilities are available through GLBNXT Platform. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.glbnxt.com/workspace/knowledge-and-data/supported-file-types-and-limits.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.