Hi — I curate StackMap, a hand-curated knowledge graph of open-source AI/agent tools. Every entry is human-reviewed: a summary, an opinionated note on when to use it (and when not), and typed edges to what it pairs with or competes against — with the why written down.
xberg earned a place on the map:
How we mapped it:
- alternative to
mineru — Both turn documents (PDFs, Office) into LLM-ready text/JSON for RAG. MinerU is a heavyweight Python/VLM parser tuned for max-fidelity layout + OCR; Xberg is a lightweight polyglot engine spanning 96 formats and 15 language bindings with pluggable OCR. Pick MinerU for the hardest scanned/complex PDFs, Xberg for breadth and multi-language embedding.
- alternative to
olmocr — Same end goal — clean, ordered Markdown from PDFs for RAG/training ingestion. olmocr is a dedicated self-hosted vision-language model that excels on scans, tables and handwriting; Xberg is a general extraction framework where OCR is one pluggable backend. Use olmocr when OCR quality is the bottleneck, Xberg when you need many formats and language bindings.
- pairs with
chroma — Natural RAG pairing: Xberg does the front half (parse → clean text → syntax-aware chunks → local or hosted embeddings) and Chroma stores and retrieves those vectors. Xberg feeds the index; Chroma serves the queries.
- pairs with
llamaindex — Xberg is a stronger document reader/extraction layer than LlamaIndex's built-in loaders; plug it in as the ingestion front-end (96 formats, OCR, transcription, chunking) and let LlamaIndex handle indexing, retrieval and query orchestration.
- alternative to
unlimited-ocr — Engine vs model: xberg parses 96 formats deterministically; Unlimited-OCR throws a VLM at whole documents. Same slot in a RAG ingestion pipeline.
If any of this misrepresents the project — an edge you'd dispute, a sharper "when NOT to use" — reply here and a human fixes it. And feel free to just close this issue; it's only a heads-up.
Hi — I curate StackMap, a hand-curated knowledge graph of open-source AI/agent tools. Every entry is human-reviewed: a summary, an opinionated note on when to use it (and when not), and typed edges to what it pairs with or competes against — with the why written down.
xberg earned a place on the map:
How we mapped it:
mineru— Both turn documents (PDFs, Office) into LLM-ready text/JSON for RAG. MinerU is a heavyweight Python/VLM parser tuned for max-fidelity layout + OCR; Xberg is a lightweight polyglot engine spanning 96 formats and 15 language bindings with pluggable OCR. Pick MinerU for the hardest scanned/complex PDFs, Xberg for breadth and multi-language embedding.olmocr— Same end goal — clean, ordered Markdown from PDFs for RAG/training ingestion. olmocr is a dedicated self-hosted vision-language model that excels on scans, tables and handwriting; Xberg is a general extraction framework where OCR is one pluggable backend. Use olmocr when OCR quality is the bottleneck, Xberg when you need many formats and language bindings.chroma— Natural RAG pairing: Xberg does the front half (parse → clean text → syntax-aware chunks → local or hosted embeddings) and Chroma stores and retrieves those vectors. Xberg feeds the index; Chroma serves the queries.llamaindex— Xberg is a stronger document reader/extraction layer than LlamaIndex's built-in loaders; plug it in as the ingestion front-end (96 formats, OCR, transcription, chunking) and let LlamaIndex handle indexing, retrieval and query orchestration.unlimited-ocr— Engine vs model: xberg parses 96 formats deterministically; Unlimited-OCR throws a VLM at whole documents. Same slot in a RAG ingestion pipeline.If any of this misrepresents the project — an edge you'd dispute, a sharper "when NOT to use" — reply here and a human fixes it. And feel free to just close this issue; it's only a heads-up.