RRepoGEO

REPOGEO REPORT · LITE

enoch3712/ExtractThinker

Default branch main · commit 66920c9a · scanned 6/23/2026, 11:22:21 AM

GitHub: 1,576 stars · 156 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 enoch3712/ExtractThinker, 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
    Emphasize ORM-style interaction as the core differentiator in the README's opening

    Why:

    CURRENT
    # ExtractThinker

ExtractThinker is a flexible document intelligence tool that leverages Large Language Models (LLMs) to extract and classify structured data from documents, functioning like an ORM for seamless document processing workflows.
    COPY-PASTE FIX
    # ExtractThinker

**The ORM for Document Intelligence: Extract structured data from documents with LLMs, effortlessly.**

ExtractThinker is a flexible document intelligence tool that leverages Large Language Models (LLMs) to extract and classify structured data from documents, functioning like an ORM for seamless document processing workflows.
  • mediumreadme#2
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## 💡 Why ExtractThinker? (Compared to LangChain/LlamaIndex)

[Add content here explaining how ExtractThinker's ORM-style document intelligence differs from general LLM orchestration frameworks like LangChain and LlamaIndex, focusing on its specialized approach to structured data extraction from diverse document types.]
  • mediumtopics#3
    Add more specific topics related to structured data extraction

    Why:

    CURRENT
    ai, document-image-analysis, document-intelligence, document-parsing, document-processing, langchain, llm, machine-learning, nlp, ocr, openai, pdf, pdf-to-text, python
    COPY-PASTE FIX
    ai, data-extraction, document-image-analysis, document-intelligence, document-parsing, document-processing, langchain, llm, machine-learning, nlp, ocr, openai, pdf, pdf-to-text, python, structured-data

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 enoch3712/ExtractThinker
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 2×
  2. LangChain · recommended 2×
  3. OpenAI GPT-4 / GPT-3.5 Turbo with Function Calling · recommended 1×
  4. Anthropic Claude 3 (Opus/Sonnet) with Tool Use · recommended 1×
  5. Google Gemini 1.5 Pro with Function Calling · recommended 1×
  • CATEGORY QUERY
    How to extract structured data from diverse document types using large language models?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4 / GPT-3.5 Turbo with Function Calling
    2. Anthropic Claude 3 (Opus/Sonnet) with Tool Use
    3. LlamaIndex
    4. LangChain
    5. Google Gemini 1.5 Pro with Function Calling
    6. Microsoft Azure OpenAI Service
    7. Hugging Face Transformers

    AI recommended 7 alternatives but never named enoch3712/ExtractThinker. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Python library for ORM-style document processing and data extraction with LLMs?
    you: not recommended
    AI recommended (in order):
    1. Pydantic-LLM
    2. Instructor
    3. LlamaIndex
    4. LangChain
    5. Haystack
    6. Pydantic

    AI recommended 6 alternatives but never named enoch3712/ExtractThinker. 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 enoch3712/ExtractThinker?
    pass
    AI named enoch3712/ExtractThinker explicitly

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

  • If a team adopts enoch3712/ExtractThinker in production, what risks or prerequisites should they evaluate first?
    pass
    AI named enoch3712/ExtractThinker 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 enoch3712/ExtractThinker solve, and who is the primary audience?
    pass
    AI named enoch3712/ExtractThinker explicitly

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

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enoch3712/ExtractThinker — 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