RRepoGEO

REPOGEO REPORT · LITE

chonkie-inc/chonkie

Default branch main · commit 8b4a0702 · scanned 5/16/2026, 8:07:05 AM

GitHub: 4,017 stars · 271 forks

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 chonkie-inc/chonkie, 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 H1 to explicitly state its purpose as a Python RAG ingestion library

    Why:

    CURRENT
    # 🦛 Chonkie ✨

_The lightweight ingestion library for fast, efficient and robust RAG pipelines_
    COPY-PASTE FIX
    # 🦛 Chonkie ✨: The lightweight Python ingestion library for fast, efficient and robust RAG pipelines
  • mediumreadme#2
    Add a dedicated 'Key Capabilities' section to highlight end-to-end RAG pipeline steps

    Why:

    COPY-PASTE FIX
    ## ✨ Key Capabilities

Chonkie provides an end-to-end solution for your RAG pipeline needs, enabling you to:

*   **Fetch**: Seamlessly retrieve data from various sources.
*   **CHONK**: Efficiently split and process text using advanced chunking algorithms.
*   **Refine**: Optimize your data for retrieval.
*   **Embed**: Generate high-quality embeddings for semantic search.
*   **Ship**: Directly integrate with your favorite vector databases for storage and retrieval.
  • lowtopics#3
    Add more specific RAG-related topics to improve category visibility

    Why:

    CURRENT
    ai, chonkie, chunker, chunking-algorithm, llms, rag, retrieval-systems, semantic-chunker, similarity-search, splitting-algorithms, text-splitter
    COPY-PASTE FIX
    ai, chonkie, chunker, chunking-algorithm, llms, rag, retrieval-systems, semantic-chunker, similarity-search, splitting-algorithms, text-splitter, vector-databases, embedding, data-ingestion, python-library

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 chonkie-inc/chonkie
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
deepset-ai/haystack
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. deepset-ai/haystack · recommended 2×
  2. LangChain.R · recommended 1×
  3. textshaping · recommended 1×
  4. stringr · recommended 1×
  5. tokenizers · recommended 1×
  • CATEGORY QUERY
    What's a lightweight library for efficient text splitting and ingestion in RAG pipelines?
    you: not recommended
    AI recommended (in order):
    1. LangChain.R
    2. textshaping
    3. stringr
    4. tokenizers
    5. reticulate

    AI recommended 5 alternatives but never named chonkie-inc/chonkie. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to quickly process documents and embed them for vector database storage?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Haystack (deepset-ai/haystack)
    4. SpaCy (explosion/spaCy)
    5. Sentence-Transformers (UKPLab/sentence-transformers)
    6. NLTK (nltk/nltk)
    7. Hugging Face Transformers (huggingface/transformers)
    8. Unstructured.io (Unstructured-IO/unstructured)
    9. Deepset's Haystack (deepset-ai/haystack)

    AI recommended 9 alternatives but never named chonkie-inc/chonkie. 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 chonkie-inc/chonkie?
    pass
    AI named chonkie-inc/chonkie explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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