RRepoGEO

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

Scan history for this repo

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.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

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.

OVERALL DIRECTION
  • highreadme#1
    Reposition 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#2
    Add specific RAG and LLM-related topics

    Why:

    CURRENT
    document-parser, document-structure, layout-parsing, table-detection
    COPY-PASTE FIX
    document-parser, document-structure, layout-parsing, table-detection, rag, llm, document-chunking, semantic-chunking
  • mediumreadme#3
    Make 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.

Recall
0 / 2
0% of queries surface Filimoa/open-parse
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. NLTK · recommended 1×
  4. SpaCy · recommended 1×
  5. Unstructured.io · recommended 1×
  • CATEGORY QUERY
    How to effectively chunk complex documents for RAG systems, preserving semantic structure?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. NLTK
    4. SpaCy
    5. Unstructured.io
    6. Haystack

    AI recommended 6 alternatives but never named Filimoa/open-parse. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good libraries for parsing document layouts and grouping related content visually?
    you: not recommended
    AI recommended (in order):
    1. LayoutParser (layout-parser/layout-parser)
    2. DeepDoctection (deepdoctection/deepdoctection)
    3. OpenCV (opencv/opencv)
    4. Tesseract OCR (tesseract-ocr/tesseract)
    5. pdfminer.six (pdfminer/pdfminer.six)
    6. 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 completeness
    pass

  • README presence
    pass

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?
    pass
    AI 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?
    pass
    AI 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?
    pass
    AI 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

Drop this badge into the README of Filimoa/open-parse. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/Filimoa/open-parse.svg)](https://repogeo.com/en/r/Filimoa/open-parse)
HTML
<a href="https://repogeo.com/en/r/Filimoa/open-parse"><img src="https://repogeo.com/badge/Filimoa/open-parse.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

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