RRepoGEO

REPOGEO REPORT · LITE

peterw/Chat-with-Github-Repo

Default branch main · commit 446c4143 · scanned 5/9/2026, 3:12:40 AM

GitHub: 1,146 stars · 163 forks

AI VISIBILITY SCORE
22 /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
1 / 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 peterw/Chat-with-Github-Repo, 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
  • hightopics#1
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    chatbot, streamlit, openai, gpt-3-5-turbo, deep-lake, github-repository, conversational-ai, code-qa, llm-application, python
  • highreadme#2
    Reposition README opening to clarify its purpose as a GitHub repo chatbot application

    Why:

    CURRENT
    # Chat-with-Github-Repo

This repository contains Python scripts that demonstrate how to create a chatbot using Streamlit, OpenAI GPT-3.5-turbo, and Activeloop's Deep Lake.
    COPY-PASTE FIX
    # Chat-with-Github-Repo: Ask Questions to Any GitHub Repository

This project provides a ready-to-use Streamlit chatbot application that allows you to converse with the content of any public GitHub repository. It leverages OpenAI GPT-3.5-turbo and Activeloop's Deep Lake to process repository files and generate intelligent responses.
  • mediumabout#3
    Refine repository description for clarity and benefit

    Why:

    CURRENT
    This repository contains two Python scripts that demonstrate how to create a chatbot using Streamlit, OpenAI GPT-3.5-turbo, and Activeloop's Deep Lake.
    COPY-PASTE FIX
    A Streamlit chatbot application that lets you ask questions and get answers from any public GitHub repository's content, powered by OpenAI GPT-3.5-turbo and Activeloop Deep Lake.

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 peterw/Chat-with-Github-Repo
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. GitPython · recommended 1×
  4. Tree-sitter · recommended 1×
  5. Universal Sentence Encoder (USE) · recommended 1×
  • CATEGORY QUERY
    How can I build a chatbot to answer questions about a specific Git repository?
    you: not recommended
    AI recommended (in order):
    1. GitPython
    2. Tree-sitter
    3. Universal Sentence Encoder (USE)
    4. Sentence-BERT
    5. Chroma
    6. Pinecone
    7. Faiss
    8. LangChain
    9. LlamaIndex
    10. OpenAI GPT-4
    11. GPT-3.5 Turbo
    12. Anthropic Claude
    13. Google Gemini
    14. Google Cloud Vertex AI

    AI recommended 14 alternatives but never named peterw/Chat-with-Github-Repo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best frameworks for creating a conversational AI over code documentation?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Rasa
    5. OpenAI API
    6. Anthropic
    7. Cohere
    8. transformers
    9. sentence-transformers
    10. faiss-cpu
    11. chromadb

    AI recommended 11 alternatives but never named peterw/Chat-with-Github-Repo. 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 peterw/Chat-with-Github-Repo?
    pass
    AI did not name peterw/Chat-with-Github-Repo — likely talking about a different project

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

  • If a team adopts peterw/Chat-with-Github-Repo in production, what risks or prerequisites should they evaluate first?
    pass
    AI named peterw/Chat-with-Github-Repo 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 peterw/Chat-with-Github-Repo solve, and who is the primary audience?
    pass
    AI did not name peterw/Chat-with-Github-Repo — likely talking about a different project

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite