RRepoGEO

REPOGEO REPORT · LITE

nlmatics/nlm-ingestor

Default branch main · commit 5f0c1b92 · scanned 6/30/2026, 4:07:07 PM

GitHub: 1,283 stars · 197 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
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 nlmatics/nlm-ingestor, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README H1 and opening sentence to emphasize RAG-optimized parsing

    Why:

    CURRENT
    # About

This repo provides the service code for llmsherpa API to connect.
    COPY-PASTE FIX
    # nlm-ingestor: RAG-Optimized Document Parsers for LLMs

This repository offers custom, RAG (retrieval augmented generation) friendly parsers for various file formats (PDF, HTML, Text, DOCX, PPTX), designed to transform unstructured content into a semantically rich, structured format ideal for Large Language Models (LLMs). It also provides the service code for the llmsherpa API.
  • mediumabout#2
    Update the repository description to highlight RAG-friendly parsing

    Why:

    CURRENT
    This repo provides the server side code for llmsherpa API to connect. It includes parsers for various file formats.
    COPY-PASTE FIX
    RAG-optimized document parsers for LLMs, transforming PDFs, HTML, and other formats into structured, chunked content. Features advanced OCR, layout-aware extraction, and serves as the backend for the llmsherpa API.

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 nlmatics/nlm-ingestor
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud Document AI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Document AI · recommended 2×
  2. Unstructured-IO/unstructured · recommended 1×
  3. LlamaParse · recommended 1×
  4. facebookresearch/nougat · recommended 1×
  5. Apache Tika · recommended 1×
  • CATEGORY QUERY
    How to extract structured content from PDFs and HTML for RAG applications?
    you: not recommended
    AI recommended (in order):
    1. Unstructured.io (Unstructured-IO/unstructured)
    2. LlamaParse
    3. Nougat (facebookresearch/nougat)
    4. Apache Tika
    5. PDFMiner.six (pdfminer/pdfminer.six)
    6. BeautifulSoup (crummy/BeautifulSoup)
    7. Playwright (microsoft/playwright)
    8. Selenium (SeleniumHQ/selenium)
    9. Azure AI Document Intelligence
    10. Google Cloud Document AI
    11. AWS Textract

    AI recommended 11 alternatives but never named nlmatics/nlm-ingestor. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a tool to parse complex PDFs, including OCR and layout-aware text extraction.
    you: not recommended
    AI recommended (in order):
    1. Adobe Acrobat Pro DC
    2. Parsr (axa-group/Parsr)
    3. Rossum
    4. Amazon Textract
    5. Google Cloud Document AI
    6. PDFTron SDK

    AI recommended 6 alternatives but never named nlmatics/nlm-ingestor. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • 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 nlmatics/nlm-ingestor?
    pass
    AI named nlmatics/nlm-ingestor explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts nlmatics/nlm-ingestor in production, what risks or prerequisites should they evaluate first?
    pass
    AI named nlmatics/nlm-ingestor 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 nlmatics/nlm-ingestor solve, and who is the primary audience?
    pass
    AI named nlmatics/nlm-ingestor explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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nlmatics/nlm-ingestor — 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