RRepoGEO

REPOGEO REPORT · LITE

leptonai/search_with_lepton

Default branch main · commit 0a2fe371 · scanned 6/23/2026, 4:23:33 PM

GitHub: 8,095 stars · 1,005 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
33 /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
2 / 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 leptonai/search_with_lepton, 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 opening sentence to clarify its type and platform

    Why:

    CURRENT
    Build your own conversational search engine using less than 500 lines of code.
    COPY-PASTE FIX
    This repository provides a concise, end-to-end **Retrieval Augmented Generation (RAG)** example for building your own conversational search engine, powered by the Lepton AI platform and requiring less than 500 lines of code.
  • mediumtopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    ai, ai-applications, leptonai, llm
    COPY-PASTE FIX
    ai, ai-applications, leptonai, llm, conversational-ai, search-engine, rag, retrieval-augmented-generation, demo, example
  • lowabout#3
    Enhance the repository description to mention 'web application'

    Why:

    CURRENT
    Building a quick conversation-based search demo with Lepton AI.
    COPY-PASTE FIX
    Building a quick, full-stack conversational search web application demo with Lepton AI.

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 leptonai/search_with_lepton
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. deepset-ai/haystack · recommended 1×
  2. run-llama/llama_index · recommended 1×
  3. langchain-ai/langchain · recommended 1×
  4. OpenAI API · recommended 1×
  5. Pinecone · recommended 1×
  • CATEGORY QUERY
    How to quickly build a conversational search engine with LLM integration?
    you: not recommended
    AI recommended (in order):
    1. Haystack (deepset-ai/haystack)
    2. LlamaIndex (run-llama/llama_index)
    3. LangChain (langchain-ai/langchain)
    4. OpenAI API
    5. Pinecone
    6. Rasa (RasaHQ/rasa)

    AI recommended 6 alternatives but never named leptonai/search_with_lepton. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework simplifies integrating external search capabilities and large language models for web applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Next.js
    5. Vercel AI SDK
    6. FastAPI
    7. Django
    8. Django REST Framework

    AI recommended 8 alternatives but never named leptonai/search_with_lepton. 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 leptonai/search_with_lepton?
    pass
    AI named leptonai/search_with_lepton explicitly

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

  • If a team adopts leptonai/search_with_lepton in production, what risks or prerequisites should they evaluate first?
    pass
    AI named leptonai/search_with_lepton 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 leptonai/search_with_lepton solve, and who is the primary audience?
    pass
    AI did not name leptonai/search_with_lepton — 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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leptonai/search_with_lepton — 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