oxidize-pdf

A comprehensive PDF toolkit written in pure Rust. Generate, parse, modify, extract and validate PDFs without C bindings. Use one crate for ordinary PDF workflows or continue into structure-aware chunks for AI/RAG applications.

[dependencies]
oxidize-pdf = "5.3.0"

Structure-aware RAG remains a first-class workflow:

let chunks = PdfDocument::open("paper.pdf")?.rag_chunks()?;
// Each chunk: text, pages, bounding boxes, element types, heading context, token estimate

Why oxidize-pdf?

• One toolkit: generation, parsing, manipulation, extraction and validation.
• Pure Rust deployment: PDF processing and certificate verification use Rust implementations without a native crypto library. See the dependency gate and architecture for supported builds, platform interfaces and the optional Tesseract integration.
• AI-ready when needed: page references, bounding boxes, element types, heading context and token estimates are available from the same parser.
• Explicit scope: page rasterization is not provided; use a renderer such as Pdfium when bitmap output is required.
• Auditable capability claims: the claims inventory records the source, version, verification evidence and limits for the supported PDF/A, signature and enterprise-scope statements.

Structure-aware RAG

Most PDF libraries give you a wall of text. oxidize-pdf gives you structured, metadata-rich chunks ready for your vector store:

Measured generation: 6,311 one-page documents/sec and 732 ten-page reports/sec (medians, oxidize-pdf 5.1.5, synthetic text-only workloads on an Intel i7-3770). See the protocol, environment and comparison.

Quick Start

[dependencies]
oxidize-pdf = "5.3.0"

RAG Pipeline

use oxidize_pdf::parser::PdfDocument;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let doc = PdfDocument::open("document.pdf")?;

    // Structure-aware chunking with full metadata
    let chunks = doc.rag_chunks()?;

    for chunk in &chunks {
        println!("Chunk {}: pages {:?}, ~{} tokens",
            chunk.chunk_index, chunk.page_numbers, chunk.token_estimate);
        println!("  Types: {}", chunk.element_types.join(", "));
        if let Some(heading) = &chunk.heading_context {
            println!("  Section: {}", heading);
        }

        // Use chunk.full_text for embeddings (includes heading context)
        // Use chunk.text for display (content only)
    }

    Ok(())
}

Custom Chunk Size

use oxidize_pdf::pipeline::HybridChunkConfig;

// Smaller chunks for more precise retrieval
let config = HybridChunkConfig {
    max_tokens: 256,
    ..HybridChunkConfig::default()
};
let chunks = doc.rag_chunks_with(config)?;

Contextual Retrieval (no-ML)

Prepend a deterministic context snippet to each chunk's full_text before embedding — the no-ML analogue of Contextual Retrieval / Late Chunking, which cut retrieval failures substantially by situating each chunk in its document. No LLM, no GPU: the prefix is a pure function of the document metadata and the chunk's heading breadcrumb, so it is fully reproducible (stable chunk_id). The display text field stays context-free.

use oxidize_pdf::pipeline::{ContextFormat, ContextMode, DocumentSource, HybridChunkConfig};

let config = HybridChunkConfig {
    context_mode: ContextMode::Contextual(ContextFormat::Labeled),
    ..HybridChunkConfig::default()
};
let source = DocumentSource::with_file(Some("annual.pdf".into()), Some("sha256-…".into()));
let chunks = doc.rag_chunks_with_source_and_config(source, config)?;

// chunk.full_text now reads, e.g.:
//   Document: Annual Report — Acme Corp
//   Section: 1 Introduction › 1.2 Scope (p. 3–4)
//
//   <chunk text>

ContextFormat::Prose renders the same facts as one natural-language sentence. Modes: ContextMode::None (bare text), Heading (leaf heading only, the default), Contextual(_) (document + section prefix).

JSON for Vector Store Ingestion

// Serialize all chunks to JSON (requires `semantic` feature)
let json = doc.rag_chunks_json()?;
std::fs::write("chunks.json", json)?;

Element Partitioning

For fine-grained control, access the typed element pipeline directly:

use oxidize_pdf::pipeline::ExtractionProfile;

let doc = PdfDocument::open("document.pdf")?;

// Partition into typed elements
let elements = doc.partition()?;
for el in &elements {
    println!("page {} : {}", el.page(), el.text());
}

// Or with a pre-configured profile
let elements = doc.partition_with_profile(ExtractionProfile::Academic)?;

// Build a relationship graph (parent/child sections)
let (elements, graph) = doc.partition_graph(Default::default())?;
for section in graph.top_level_sections() {
    println!("Section: {}", elements[section].text());
}

Also in the box

Beyond RAG, the same crate also handles PDF parsing (99.3 % success on 9,000+ real-world PDFs, CJK, lenient recovery), generation (see the measured workloads below), encryption (RC4-40/128, AES-128, AES-256 R5 — read and write; AES-256 R6 — read), digital signatures (detection, PKCS#7 verification, certificate validation and incremental-signature preparation), PDF/A validation (8 conformance levels), JBIG2 image decoding (pure-Rust ITU-T T.88), invoice extraction (ES/EN/DE/IT), and split/merge/rotate operations. One dependency for the full pipeline.

Signature preparation is not a completed cryptographic signature: an external signer produces CMS and finalization embeds it. PAdES is outside the scope of oxidize-pdf. Anyone can implement it as an independent external extension using the existing signing interfaces, without incorporating it into this library. The extension author is responsible for implementing and validating the chosen profile, including timestamps and long-term validation data where required. Embedding CMS alone does not establish profile compliance or certification. See the operation-level signature contract.

Version 5.2.0 rejects simulated public-key operations and malformed Flate/predictor data, adds explicit recovery diagnostics, and makes build identification optional. See the versioned inventory and migration guides for the exact behavior and remaining limits.

Certificate-based recipient encryption is unsupported. The legacy PublicKeySecurityHandler returns explicit errors for cryptographic operations; certificate verification for signatures is separate. See the support and migration boundary.

See oxidize-pdf-core/examples/ for working samples (133 examples) and docs.rs for the API surface.

Full Feature Set

AI/RAG Pipeline

• Structure-aware chunking with RagChunk metadata (pages, bboxes, types, headings)
• Element partitioning: Title, Paragraph, Table, ListItem, Image, CodeBlock, KeyValue
• ElementGraph for parent/child section relationships
• 6 extraction profiles (Standard, Academic, Form, Government, Dense, Presentation)
• Reading order strategies (Simple, XYCut)
• LLM-optimized export formats (Markdown, Contextual, JSON)
• Invoice data extraction (ES, EN, DE, IT)

PDF Processing

• Parse PDF 1.0-1.7 with 99.3% success rate (9,000+ PDFs tested)
• Generate multi-page documents with text, graphics, images
• Encryption: RC4-40/128, AES-128, AES-256 R5 -- read and write; AES-256 R6 -- read
• Digital signatures: detection, PKCS#7 verification, certificate validation, incremental-signature preparation
• PDF/A validation: 8 conformance levels (1a/b, 2a/b/u, 3a/b/u)
• JBIG2 decoder: pure Rust (ITU-T T.88)
• Split, merge, rotate operations
• CJK text support (Chinese, Japanese, Korean)
• Corruption recovery and lenient parsing
• Decompression bomb protection

Performance

Measured with oxidize-stats pdf-generation-v1 on 2026-09-28: oxidize-pdf 5.1.5, Intel Core i7-3770 @ 3.40 GHz, Linux x86_64, Rust 1.96.0 release build. Twenty samples per workload; construction and serialization to memory, without file IO.

The p95 describes batch averages, not individual-request tail latency. RSS includes runtime and warmup, not just PDF allocation. Each sample's representative PDF was checked independently for structure, page count and exact text. This shared-host measurement does not establish performance for images, embedded fonts or arbitrary real-world documents. The comparison now includes pdf_oxide 0.3.78 alongside lopdf, pdf-writer and printpdf. All four have higher median throughput in these workloads, but the one-page uncertainty intervals of oxidize-pdf and pdf_oxide overlap. Serialization defaults and output sizes differ. See the full comparison and reproducible protocol.

Testing

7,993 tests across unit, integration, and doc tests. 7-tier corpus (T0-T6) with 9,000+ PDFs.

cargo test --workspace         # Full test suite
cargo clippy -- -D warnings    # Lint check
cargo run --example rag_pipeline -- path/to/file.pdf

License

MIT -- see LICENSE.

Links

• Documentation (docs.rs)
• Crates.io
• GitHub
• Issue Tracker