Engineering library / 8 resources
Detailed, implementation-oriented explanations of the parsing decisions that affect retrieval, automation, cost, security, and production reliability.
A production document parsing API should normalize mixed files into readable text and structured document data, expose route and failure state, support synchronous and asynchronous use, and enforce access, cost, retention, and egress controls. Text extraction alone is only one stage of that contract.
Read guide →Converting PDF to Markdown for an LLM is useful only when reading order, headings, lists, tables, and page context survive the transformation. Keep a structured companion record beside Markdown so chunks and citations remain traceable to source pages and parser evidence.
Read guide →A reliable RAG ingestion pipeline records each transformation from source to active index: identify and hash the file, parse it into a structured document, validate output, chunk by structure, embed versioned chunks, switch the active index, and evaluate retrieval with traceable citations.
Read guide →Evaluate a document parsing API on representative document cohorts, label the source evidence you care about, score structure separately from text, record failures and latency, and measure the downstream task. Do not generalize from a single showcase PDF or an unsegmented average.
Read guide →A parser can recover every visible word and still produce a wrong document if it interleaves columns, disconnects captions, or scrambles table cells. Reading order and structural relationships must be evaluated as first-class output, with page geometry retained for audit and correction.
Read guide →Deterministic parsing is fast, repeatable, and effective when a document contains reliable native structure. OCR is necessary for pixels, and vision-language parsing can help with difficult layout or visual semantics. A production system should route by document evidence and policy instead of choosing one method for every file.
Read guide →A document parsing service accepts untrusted files and often reaches storage, queues, containers, models, URLs, and webhooks. Secure it with strict input bounds, isolated processing, server-owned routing, scoped credentials, outbound host policy, durable cost controls, minimal logs, tenant isolation, and explicit retention and purge.
Read guide →A structured intermediate model decouples downstream applications from parser-specific payloads. DocIR represents source metadata, pages, ordered typed blocks, optional geometry, assets, parser provenance, and warnings under a versioned schema while preserving Markdown as a readable projection.
Read guide →From answer to evidence
Create a free workspace, parse up to 20 text-based documents, inspect Markdown and DocIR, then use the API when the output meets your bar.