REPOGEO REPORT · LITE
Filimoa/open-parse
Default branch main · commit 6c2da9b5 · scanned 5/20/2026, 11:11:52 PM
GitHub: 3,160 stars · 140 forks
Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.
2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface Filimoa/open-parse, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.
Action plan — copy-paste fixes
3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.
- highreadme#1Reposition the README's opening to explicitly state its RAG/LLM application focus
Why:
CURRENT**Easily chunk complex documents the same way a human would.** Chunking documents is a challenging task that underpins any RAG system. High quality results are critical to a sucessful AI application, yet most open-source libraries are limited in their ability to handle complex documents. Open Parse is designed to fill this gap by providing a flexible, easy-to-use library capable of visually discerning document layouts and chunking them effectively.
COPY-PASTE FIX**Open Parse: Visually intelligent document chunking for RAG and LLM applications.** Open Parse is a flexible, easy-to-use library designed to chunk complex documents the same way a human would, by visually discerning document layouts. This approach provides high-quality, semantically rich chunks critical for successful RAG systems and other LLM applications, addressing limitations of traditional text splitters and basic ML layout parsers.
- mediumtopics#2Add specific RAG and LLM-related topics
Why:
CURRENTdocument-parser, document-structure, layout-parsing, table-detection
COPY-PASTE FIXdocument-parser, document-structure, layout-parsing, table-detection, rag, llm, document-chunking, semantic-chunking
- mediumreadme#3Make the 'How is this different' comparison section immediately visible
Why:
CURRENT<details> <summary><b>How is this different from other layout parsers?</b></summary> #### ✂️ Text Splitting Text splitting converts a file to raw text and slices it up. ... </details>
COPY-PASTE FIX#### **How is this different from other layout parsers?** #### ✂️ Text Splitting Text splitting converts a file to raw text and slices it up. - You lose the ability to easily overlay the chunk on the original pdf - You ignore the underlying semantic structure of the file - headings, sections, bullets represent valuable information. - No support for tables, images or markdown. #### 🤖 ML Layout Parsers There's some of fantastic libraries like layout-parser. - While they can identify various elements like text blocks, images, and tables, but they are not built to group related content effectively. - They strictly focus on layout parsing - you will need to add another model to extract markdown from the images, parse tables, group nodes, etc. - We've found performance to be sub-optimal on many documents while also being computationally heavy. #### 💼 Commercial Solutions - Typically priced at ≈ $10 / 1k pages. See here, here and here
Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash
Category visibility — the real GEO test
Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?
Same questions for every model — switch tabs to compare answers and rankings.
- LangChain · recommended 1×
- LlamaIndex · recommended 1×
- NLTK · recommended 1×
- SpaCy · recommended 1×
- Unstructured.io · recommended 1×
- CATEGORY QUERYHow to effectively chunk complex documents for RAG systems, preserving semantic structure?you: not recommendedAI recommended (in order):
- LangChain
- LlamaIndex
- NLTK
- SpaCy
- Unstructured.io
- Haystack
AI recommended 6 alternatives but never named Filimoa/open-parse. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat are good libraries for parsing document layouts and grouping related content visually?you: not recommendedAI recommended (in order):
- LayoutParser (layout-parser/layout-parser)
- DeepDoctection (deepdoctection/deepdoctection)
- OpenCV (opencv/opencv)
- Tesseract OCR (tesseract-ocr/tesseract)
- pdfminer.six (pdfminer/pdfminer.six)
- PyMuPDF (Fitz) (pymupdf/PyMuPDF)
AI recommended 6 alternatives but never named Filimoa/open-parse. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesspass
- README presencepass
Self-mention check
Does AI even know your repo exists when asked about it directly?
- Compared to common alternatives in this category, what is the core differentiator of Filimoa/open-parse?passAI named Filimoa/open-parse explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts Filimoa/open-parse in production, what risks or prerequisites should they evaluate first?passAI named Filimoa/open-parse explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- In one sentence, what problem does the repo Filimoa/open-parse solve, and who is the primary audience?passAI named Filimoa/open-parse explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
Embed your GEO score
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Filimoa/open-parse — Lite scans stay free; this card itemizes Pro deep limits vs Lite.
- Deep reports10 / month
- Brand-free category queries5 vs 2 in Lite
- Prioritized action items8 vs 3 in Lite