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reader and node parser for Xberg — extract text, tables, metadata, and structure-aware nodes from 107 document formats with optional OCR.","maintainers":[{"name":"nhirschfeld","email":"nhirschfeld@gmail.com"}],"readme":"<p align=\"center\">\n  <picture>\n    <source media=\"(prefers-color-scheme: dark)\" srcset=\"https://cdn.jsdelivr.net/gh/xberg-io/assets@v1/banner/readme-banner-dark.svg\">\n    <img alt=\"Xberg\" width=\"420\" src=\"https://cdn.jsdelivr.net/gh/xberg-io/assets@v1/banner/readme-banner-light.svg\">\n  </picture>\n</p>\n\n# @xberg-io/llamaindex-xberg\n\n[![npm](https://img.shields.io/npm/v/@xberg-io/llamaindex-xberg)](https://www.npmjs.com/package/@xberg-io/llamaindex-xberg)\n\nA [LlamaIndex.TS](https://ts.llamaindex.ai) reader and node parser for [Xberg](https://github.com/xberg-io/xberg).\n`XbergReader` turns a file, a directory, or raw bytes into LlamaIndex `Document`s with the extracted\ntext, tables, and rich metadata from 107 formats — with optional OCR for scans and images.\n`XbergNodeParser` then splits those documents into `TextNode`s along Xberg's own semantic boundaries\n(native chunks, or structural elements) instead of a blind character window.\n\nExtraction runs locally in-process through the `@xberg-io/xberg` native binding. No API key, no cloud\ncall, no data leaves your machine.\n\n## Installation\n\n`@llamaindex/core` is a peer dependency — install it alongside the reader:\n\n```bash\nnpm install @xberg-io/llamaindex-xberg @llamaindex/core\n```\n\nNode.js 20.15+ is required on a platform for which `@xberg-io/xberg` ships a prebuilt binary (Linux\nx64/arm64 glibc or musl, macOS arm64, Windows x64/arm64).\n\n## Quick start\n\n```ts\nimport { XbergReader } from \"@xberg-io/llamaindex-xberg\";\n\n// Single file — one Document (or one per page with pages.extractPages)\nconst reader = new XbergReader();\nconst docs = await reader.loadData(\"report.pdf\");\n\n// Multiple files — one batched extraction\nconst many = await reader.loadData([\"a.pdf\", \"b.docx\"]);\n\n// Raw bytes — mimeType is required\nconst fromBytes = await reader.loadData({ data: fileBytes, mimeType: \"application/pdf\" });\n```\n\nBy default the reader requests Xberg's `element_based` result format so each document carries a\nstructural element stream for the node parser. Errors are logged and the failed input is skipped; pass\n`{ raiseOnError: true }` to propagate them instead.\n\n## Structure-aware node parsing\n\nPair the reader with `XbergNodeParser` to split documents into nodes along Xberg's boundaries. It\nprefers native chunks (`ExtractionConfig.chunking`) and falls back to structural elements:\n\n```ts\nimport { XbergReader, XbergNodeParser } from \"@xberg-io/llamaindex-xberg\";\n\nconst reader = new XbergReader({ extractionConfig: { chunking: { maxCharacters: 1000, overlap: 200 } } });\nconst documents = await reader.loadData(\"report.pdf\");\n\nconst parser = new XbergNodeParser();\nconst nodes = parser.getNodesFromDocuments(documents);\n```\n\nThe reader forwards the chunk/element payload on private metadata keys that are excluded from LLM and\nembedding input; the parser consumes them and strips them from the emitted nodes.\n\n## Supported formats\n\nXberg extracts from 107 formats including PDF, DOCX, PPTX, XLSX, HTML, EPUB, images, and more. See the\n[Xberg documentation](https://docs.xberg.io) for the full list and the extraction configuration\nreference.\n\n## Part of Xberg.io\n\n- [Xberg](https://github.com/xberg-io/xberg) — document intelligence: text, tables, metadata from 107 formats with optional OCR.\n- [Xberg Enterprise](https://github.com/xberg-io/xberg-enterprise) — managed extraction API with SDKs, dashboards, and observability.\n\n## License\n\n[MIT](./LICENSE)\n","readmeFilename":"README.md"}