RRepoGEO

REPOGEO REPORT · LITE

adithya-s-k/omniparse

Default branch main · commit 9d1ae83c · scanned 6/27/2026, 1:23:07 PM

GitHub: 7,618 stars · 650 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)

3 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 adithya-s-k/omniparse, 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 'IMPORTANT' section to emphasize local, all-in-one capabilities

    Why:

    CURRENT
    > [!IMPORTANT]
> OmniParse is a platform that ingests and parses any unstructured data into structured, actionable data optimized for GenAI (LLM) applications. Whether you are working with documents, tables, images, videos, audio files, or web pages, OmniParse prepares your data to be clean, structured, and ready for AI applications such as RAG, fine-tuning, and more
    COPY-PASTE FIX
    > [!IMPORTANT]
> OmniParse is a **completely local, all-in-one platform** for ingesting and parsing any unstructured data (documents, multimedia, web pages) into structured, actionable data. It's specifically optimized for GenAI (LLM) applications like RAG and fine-tuning, designed to run efficiently on a T4 GPU without external APIs.
  • mediumtopics#2
    Expand repository topics to include more specific GenAI data preparation keywords

    Why:

    CURRENT
    ingestion-api, ocr, omniparser, parse-server, parser-library, vision-transformer, web-crawler, whisper-api
    COPY-PASTE FIX
    ingestion-api, ocr, omniparser, parse-server, parser-library, vision-transformer, web-crawler, whisper-api, rag-pipeline, llm-data-preparation, local-ai, multimodal-parsing, data-ingestion-platform, unstructured-data, data-extraction, ai-data-prep
  • lowreadme#3
    Add a dedicated comparison section in the README

    Why:

    COPY-PASTE FIX
    ## OmniParse vs. Alternatives

Unlike general-purpose LLM frameworks (e.g., LangChain, LlamaIndex) or single-function parsing tools (e.g., Unstructured.io, Tesseract OCR), OmniParse offers a completely local, all-in-one solution for ingesting and structuring diverse data types. It provides a unified API for processing documents, images, audio, video, and web pages, specifically optimized for GenAI applications like RAG and fine-tuning, designed to run efficiently on a single T4 GPU without reliance on external services.

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 adithya-s-k/omniparse
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
opencv/opencv
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. opencv/opencv · recommended 2×
  2. langchain-ai/langchain · recommended 1×
  3. run-llama/llama_index · recommended 1×
  4. Unstructured-IO/unstructured · recommended 1×
  5. FFmpeg/FFmpeg · recommended 1×
  • CATEGORY QUERY
    How to process various unstructured data types like documents and multimedia for LLM applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Unstructured.io (Unstructured-IO/unstructured)
    4. OpenCV (opencv/opencv)
    5. FFmpeg (FFmpeg/FFmpeg)
    6. PyPDF2 (py-pdf/pypdf)
    7. pdfminer.six (pdfminer/pdfminer.six)
    8. Hugging Face transformers library (huggingface/transformers)
    9. Hugging Face datasets library (huggingface/datasets)

    AI recommended 9 alternatives but never named adithya-s-k/omniparse. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a local solution to extract structured data from documents, images, and audio for RAG.
    you: not recommended
    AI recommended (in order):
    1. LlamaParse
    2. Unstructured.io (unstructured-io/unstructured)
    3. Nougat (facebookresearch/nougat)
    4. Tesseract OCR (tesseract-ocr/tesseract)
    5. pytesseract (madmaze/pytesseract)
    6. OpenCV (opencv/opencv)
    7. Whisper (openai/whisper)
    8. Pillow (python-pillow/Pillow)
    9. Apache Tika (apache/tika)

    AI recommended 9 alternatives but never named adithya-s-k/omniparse. 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 adithya-s-k/omniparse?
    pass
    AI named adithya-s-k/omniparse explicitly

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

  • If a team adopts adithya-s-k/omniparse in production, what risks or prerequisites should they evaluate first?
    pass
    AI named adithya-s-k/omniparse 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 adithya-s-k/omniparse solve, and who is the primary audience?
    pass
    AI named adithya-s-k/omniparse 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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adithya-s-k/omniparse — 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