Introducing liteparse-server: Self-Hosted Document Parsing and OCR for AI Workflows

LiteParse offers an efficient solution for document parsing challenges in AI and data workflows by providing a fast, local, and accurate tool that maintains spatial layout fidelity, essential for tasks like table extraction and citation grounding. Unlike naive extraction methods and cloud parsing APIs, LiteParse ensures precise text extraction with bounding boxes and supports a wide range of document formats, including PDFs, Word documents, spreadsheets, and images, using open-source tools like LibreOffice and ImageMagick. The liteparse-server wraps LiteParse in an HTTP API, allowing easy integration into any service while offering robust features such as mixed-format batch processing, two main endpoints for parsing documents and rendering page images, and optional deployment modes through Docker or direct Node/Bun setups. For scalable and production-ready environments, the full stack deployment supports Redis caching and rate limiting, distributed tracing with OpenTelemetry and Jaeger, and metrics collection via Prometheus and Grafana, ensuring efficient handling of document parsing with infrastructure-level optimizations. The tool is accessible via GitHub, offering comprehensive documentation and a pre-built Docker image for easy implementation.