Meta AI File Upload and Reading: supported formats, interfaces, and developer workflows

Meta AI now integrates file handling and multimodal document interpretation across its web interface, mobile apps, and connected messaging platforms. While early versions focused only on image queries, newer releases add file uploads and document reading features for users, with expanded multimodal processing available through the Llama API for developers. As of late 2025, the system supports images universally and continues rolling out document ingestion, including PDF reading, in selected regions.

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How file upload works in Meta AI web and app.

The Meta.ai web interface and the Meta AI app both include a feature labeled “Add media or files” inside the chat composer. Users can upload an image, screenshot, or document and ask questions directly about the file. The assistant can describe, interpret, or edit visual content, and—in newer rollouts—analyze uploaded documents.

When a file is added, it becomes part of the session’s multimodal context. The model processes text, layout, and visual structure, then allows follow-up questions such as “Summarize this document,” “List all the sections mentioning marketing strategy,” or “Explain the chart on page 2.”

The web version behaves similarly to the app, enabling uploads from desktop environments. This is particularly useful for users who need to analyze reports, contracts, or long PDFs. If the upload option is not visible, it typically indicates that document support has not yet been enabled in that user’s region or account.

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Image upload and editing across Meta platforms.

Meta AI supports full image handling across its entire ecosystem—Meta.ai, WhatsApp, Messenger, and Instagram DMs.

• On Meta.ai, users can upload photos, infographics, or screenshots and ask descriptive or analytical questions about them.

• In WhatsApp, Meta AI can receive an image through the camera icon or gallery attachment. The user can then ask questions about it, such as identifying elements, describing locations, or generating related visuals.

• On Messenger and Instagram, Meta AI chats allow users to share images and ask the assistant to interpret or edit them, including background removal or stylistic modification.

These image-based capabilities are stable and fully deployed, unlike document uploads, which are still in partial rollout.

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Document import and PDF reading in testing phase.

Meta confirmed in 2025 that it is testing document import and reading features within the Meta AI app and web platform. The announcement described a new document editor that can read, interpret, and export text files, including PDF.

Under this feature, users can upload a file and prompt Meta AI to summarize, identify sections, or highlight insights. The model processes text directly, enabling responses like “Create a table of the budget assumptions,” or “Explain the key findings on pages 3 to 5.”

The feature is still being deployed regionally. In some accounts, the “Add media or files” button supports only images, while others already allow document uploads. Meta indicated that global rollout would continue throughout 2025, expanding the supported formats and improving performance on long documents.

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File upload in WhatsApp, Messenger, and Instagram.

Each of Meta’s messaging platforms now integrates basic file-based interaction with the assistant:

• WhatsApp: Meta AI can interpret and generate images in chat. Users can attach photos, ask questions about them, and apply edits directly in the conversation.

• Messenger: Meta AI supports photo-based prompts and visual content creation within direct messages.

• Instagram DMs: Similar to Messenger, the assistant can analyze shared photos and generate new ones in chat.

Although none of these platforms yet provide full document ingestion, they serve as the entry point for multimodal understanding, where image-based reasoning and description are already available to all users.

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Developer workflows through the Llama API.

For developers building custom tools, file and document reading depend on the Llama API, which powers Meta AI’s underlying model family.

Supported input types.

The Llama API supports text and images as primary inputs. Files such as PDFs can be processed indirectly through either:

1. Text-based route: Extract text from the PDF client-side, then send it as plain text in the API request. This approach is efficient and ideal for long documents or data-heavy files.

2. Vision-based route: Render each PDF page as an image (PNG or JPEG) and upload it as part of a multimodal request. This allows the model to interpret layout, charts, tables, and scanned text that a text parser might miss.

Both methods allow accurate document interpretation, combining textual and visual understanding depending on the content structure.

Practical limits.

There is currently no native PDF ingestion endpoint in the Llama API. Developers must preprocess documents before sending them. Image uploads can be submitted as base64 strings or URLs. For large or multi-page PDFs, batching by page or section ensures better performance and token efficiency.

Safety integration.

Meta provides optional safety layers such as Llama Guard and Prompt Guard, which developers can enable to filter uploaded content and detect malicious or unsafe inputs. These models help maintain compliance and prevent prompt injection when working with user-generated files.

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Privacy and data handling.

Meta’s documentation on Private Processing describes how uploaded content is managed within its chat ecosystem, particularly on WhatsApp. Files sent to Meta AI are handled separately from end-to-end encrypted personal messages. Uploaded files are analyzed for the purpose of generating responses, but users can control and delete their sessions or uploaded media from the app’s history.

Developers using the Llama API are responsible for managing storage, anonymization, and compliance for uploaded data, as the API does not retain documents once requests are processed.

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Table — Meta AI file upload and reading capabilities by environment.

Platform	File or Media Support	Capabilities	Status
Meta.ai (web)	Images and files (PDF, DOC, under rollout)	Ask questions, summarize, edit visuals	Rolling out globally
Meta AI app	Images and documents	Analyze and interpret files; create summaries	In testing phase
WhatsApp	Images	Describe, generate, and edit photos	Fully available
Messenger	Images	Visual questions and edits	Fully available
Instagram DMs	Images	Analyze or modify photos in chat	Fully available
Llama API (developer)	Text + image inputs	Custom document ingestion (via text or page images)	Stable

This table shows how file uploads range from public chat interfaces to developer APIs, reflecting Meta’s gradual rollout toward full multimodal document handling.

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Operational recommendations for users and developers.

• Users: Upload images or documents through Meta.ai or the Meta AI app, then ask structured questions like “List key figures” or “Summarize this file section by section.” If the upload option isn’t visible, the feature is likely not yet available in your region.

• Developers: Use Llama API multimodal inputs to handle PDF or document reading. Extract text for long-form data or render page images for layout-dependent content.

• Organizations: Implement Llama Guard or Prompt Guard to monitor incoming files before processing. Ensure data retention policies are clear for uploaded materials.

These workflows ensure consistent document interpretation across both consumer and enterprise contexts while respecting privacy and safety requirements.

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Summary of Meta AI’s file reading evolution.

Meta AI’s ecosystem now supports a wide range of file interactions—from image-based questions in WhatsApp and Instagram to experimental document analysis in Meta.ai and the Meta AI app. For developers, the Llama API already offers the necessary multimodal capabilities to interpret both text and image data, allowing custom applications to process and reason over complex documents.

As document import and PDF reading expand globally, Meta AI is positioning itself as a versatile multimodal assistant capable of reading, summarizing, and visually interpreting information across personal, professional, and development environments.

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