officeParser v7.0 is a TypeScript document processing library developed by harshankur and project contributors, released on May 12, 2026. It parses files with varying structures and representations—such as Office documents, PDFs, RTF, CSV, HTML, and Markdown—into a unified Abstract Syntax Tree (AST), then converts the results back into Markdown, HTML, CSV, RTF, or plain text. Similar to how a translator maps multiple languages into a common semantic representation before outputting in a target language, officeParser normalizes disparate document formats into a common structure, enabling reuse across retrieval, transformation, and generation stages. Its core identity lies in preserving document structure as input for downstream processing, rather than merely extracting strings. Existing document-based AI pipelines often require selecting separate parsers for DOCX, PDF extractors, HTML cleaners, and text splitters, then manually connecting their disparate output formats with custom code. During this process, contextual information such as hierarchy between headings and body text, table rows and columns, or paragraph boundaries may be lost, leading to retrieval results that deviate from the original meaning or unclear grounds for answers. officeParser differentiates itself by handling document parsing, multi-format generation, structure preservation, and chunking for vector databases within a single TypeScript library. It functions less like a workspace of interchangeable tools and more like a unified document processing pipeline that decomposes documents into common components and reassembles them according to purpose. Chunking for RAG (Retrieval-Augmented Generation) is documented as supporting fixed-size, document-structure, and semantic approaches. The fixed-size method provides a reproducible baseline by dividing documents into constant ranges specified by the researcher, while the document-structure approach suits configurations that leverage boundaries such as headings or sections. The semantic approach can be chosen when retrieval units need to consider content flow. By combining this with metadata-aware processing, developers can manage metadata assigned during implementation—such as filenames, document segments, or original locations—alongside chunks, enabling pipelines that trace retrieval results back to the source documents. However, exact option names, defaults, and supported metadata fields for each strategy should not be finalized without consulting the official API documentation. Biotech researchers can normalize experimental protocols, equipment reports, analysis result tables, and manuscript drafts accumulated in various formats into a common AST, then generate Markdown review copies and RAG search chunks within the same processing flow. For example, researchers can divide 100 documents using a fixed size of 1,000 tokens with a 100-token overlap condition to load them into a vector database, or maintain inclusion/exclusion criteria for clinical trial documents as independent retrieval units through section-level structure chunking. When processing CSV experimental results alongside narrative reports, format-specific preprocessing results can be managed at a common application layer, facilitating the construction of reproducible Node.js workflows for evidence document retrieval and review-oriented format conversion. The figures presented are illustrative examples to explain usage design and do not represent official performance metrics or default settings.

officeParser v7.0 is a TypeScript document processing library developed by harshankur and project contributors, released on May 12, 2026. It parses files with varying structures and representations—such as Office documents, PDFs, RTF, CSV, HTML, and Markdown—into a unified Abstract Syntax Tree (AST), and converts the results back into Markdown, HTML, CSV, RTF, or plain text. Much like a translator maps multiple languages to a common semantic representation before outputting in a target language,