Perplexity AI PDF Reading: supported formats, limits, and long-document processing

Perplexity AI has introduced a robust document-reading framework that allows users to upload, analyze, and summarize PDFs directly through the chat interface or API. Designed for both individual researchers and enterprise teams, the system supports file ingestion, semantic retrieval, and targeted question answering across large, multi-page documents. By 2025, Perplexity’s platform includes consistent file size limits, long-context routing, and workspace-level repositories for persistent document access.

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How PDF reading works in Perplexity.

Perplexity allows users to upload PDFs directly into a chat session from both desktop and mobile interfaces. Each uploaded file is indexed for semantic search, enabling conversational questions such as “Summarize this document,” “List all tables with revenue data,” or “Explain the findings on pages 10 to 15.”

The platform supports multiple files per session, allowing cross-document queries and comparisons. When several PDFs are uploaded, Perplexity automatically links and interprets them together, generating aggregated summaries or multi-source answers.

Internally, the system performs text extraction and segmentation, breaking each PDF into manageable blocks before embedding them into its retrieval layer. This process allows the model to recall relevant sections without requiring the full document to fit within the context window.

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Supported formats and file limits.

Perplexity’s file ingestion engine accommodates a wide range of document formats used for text-based content and structured data. These include PDF, DOCX, XLSX, CSV, MD, JSON, and TXT files, as well as direct imports from Google Docs, Sheets, and Slides through connected accounts.

The general limits for consumer and professional users are as follows:

• Up to 10 files per upload in a single chat.

• Maximum size of 40 MB per file in the app interface.

• Up to 50 MB per file through the API.

• Text-only paste limit of approximately 8,000 tokens; longer pastes are automatically converted into file uploads.

If a file exceeds the limit, users can segment it into smaller parts or convert lengthy text into structured inputs such as CSVs. The automatic conversion feature ensures that even lengthy text entries are efficiently processed without token overflow.

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Token windows and long-document routing.

Perplexity AI operates on dynamic context routing rather than a fixed prompt window for large documents. Uploaded PDFs are not simply “fed” into a single model window. Instead, the system uses chunked retrieval to interpret documents in parts while maintaining coherence.

For direct uploads:

• Short PDFs are handled within the active 128k–200k token range, depending on the model used (e.g., Sonar or Sonar Pro).

• Longer PDFs are processed through retrieval-based reconstruction, allowing users to query sections by headings or page numbers.

This hybrid architecture enables the application to answer questions about multi-hundred-page PDFs without hitting a single-window limit. Users can ask, “Summarize section 3,” “Extract all financial ratios,” or “Compare the methods section across all documents,” and Perplexity will automatically recall relevant passages.

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Using the API for PDF analysis.

The Perplexity API, built around the Sonar model family, extends document reading to developers and enterprise teams. PDFs can be uploaded as public URLs or base64-encoded files via the API. Each request supports:

• PDF, DOC, DOCX, TXT, and RTF file formats.

• Up to 50 MB per file.

• JSON-based responses for structured outputs.

• Model-specific context windows of 128k or 200k tokens depending on the selected Sonar version.

For large documents, developers can design multi-pass pipelines that split files into sections (e.g., by table of contents), process each part independently, and then combine summaries into one final report. This modular approach maximizes accuracy while controlling token usage and latency.

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Table — Perplexity AI PDF reading capabilities by environment.

Environment	Supported Formats	Per-File Limit	Uploads per Session	Key Features
Web / Mobile App	PDF, DOCX, XLSX, CSV, MD, JSON, TXT	40 MB	10	Real-time summaries, cross-file Q&A
API (Sonar Models)	PDF, DOC, DOCX, TXT, RTF	50 MB	Unlimited via endpoints	Base64 or URL upload, structured JSON output
Enterprise Repository	PDF and other internal docs	50 MB typical	Up to 500 files org-wide	Persistent storage for knowledge base queries
Text Paste	Plain text	~8,000 tokens	—	Auto-converts long inputs to files

This table summarizes file support, limits, and use cases across Perplexity’s main surfaces, showing how the same model infrastructure scales from casual document reading to enterprise-level retrieval.

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What Perplexity can do with PDFs.

Perplexity’s file analysis is not limited to simple summaries. The AI can perform a wide range of text and table-oriented tasks:

• Summarization and outlining: Create executive overviews, abstracts, or key point lists from long reports.

• Targeted Q&A: Retrieve answers from specific sections or page ranges.

• Table extraction: Convert tabular data into plain text or CSV-style outputs for reuse in spreadsheets.

• Citation-aware explanations: For source-based documents, Perplexity identifies cited portions and indicates page-level references.

• Cross-document synthesis: When multiple PDFs are uploaded, the system merges insights across files to build unified responses.

For scanned or image-based PDFs, text extraction depends on the presence of an OCR layer. If OCR text is missing, preprocessing the file with a text recognition tool significantly improves result accuracy.

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Enterprise repositories and shared document analysis.

Perplexity’s Enterprise tier introduces persistent document repositories where organizations can store internal files for team-wide use. Each workspace can upload up to 500 documents, including PDFs, policies, or training manuals. These are stored securely in encrypted form and used for internal knowledge retrieval through the same conversational interface.

Files in enterprise repositories remain accessible across sessions, eliminating the need for repeated uploads. Administrators can apply retention and deletion policies at any time through the workspace console, ensuring compliance with corporate data governance standards.

This makes Perplexity particularly attractive to organizations building internal research assistants or compliance chatbots that need to reference static documents safely and repeatedly.

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Best practices for PDF prompting and workflow.

1. Start broad, then narrow: Begin with “Summarize this document in 5 sections,” then move into targeted queries like “Extract methods from section 2.”

2. Use page ranges: Specify ranges to help Perplexity localize its retrieval scope, improving accuracy and speed.

3. Preprocess large PDFs: For documents above 40 MB, split by chapters or compress redundant pages before upload.

4. For enterprise setups: Keep internal PDFs versioned, label them with metadata, and restrict visibility to authorized users.

5. Combine with structured outputs: Request JSON or bullet-based summaries when data needs to be exported downstream.

These practices balance model performance and interpretability while minimizing latency and token consumption.

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Troubleshooting common PDF issues.

• Upload failure: Check file type and ensure the size is below the stated limit (40 MB for app, 50 MB for API).

• Partial or incomplete reading: Break long PDFs into multiple uploads or query them by section headers.

• OCR problems: For scanned documents, run text recognition externally before uploading.

• Slow responses on large files: Use Sonar Pro with a 200k-token window and batch queries by section.

Following these adjustments ensures consistent and accurate document analysis, especially for research, legal, and financial use cases.

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Summary of Perplexity’s PDF reading ecosystem.

By 2025, Perplexity AI has refined its PDF reading system into a complete document-intelligence layer. Users can upload, query, and summarize multiple PDFs simultaneously, with clear file-size limits and long-context processing through Sonar models. Enterprise users gain persistent repositories for internal documents, while developers access scalable ingestion through the API.

Whether through ad-hoc research chats or structured automation pipelines, Perplexity AI’s document features combine semantic retrieval, citation awareness, and large-context comprehension, positioning it as a reliable solution for anyone working with complex PDF datasets.

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