REPOGEO REPORT · LITE

ammaarreshi/Gemini-Search

Default branch main · commit 4e054902 · scanned 6/21/2026, 7:37:42 PM

GitHub: 2,062 stars · 408 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

35 /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

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 ammaarreshi/Gemini-Search, 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 the repository

Why:

COPY-PASTE FIX

ai-search, gemini-api, perplexity-clone, fullstack-app, react, express, typescript, web-search, generative-ai, google-gemini

highreadme#2

Clarify README opening to emphasize full-stack application example

Why:

CURRENT

# Gemini Search

A Perplexity-style search engine powered by Google's Gemini 2.0 Flash model with grounding through Google Search. Get AI-powered answers to your questions with real-time web sources and citations.

COPY-PASTE FIX

# Gemini Search: A Full-Stack AI Search Engine Example

A Perplexity-style search engine *clone and full-stack application example* for developers, powered by Google's Gemini 2.0 Flash model with grounding through Google Search. This project demonstrates how to build an AI-powered search experience with real-time web sources and citations using React, Express, and TypeScript.

mediumhomepage#3
Add a homepage URL to the repository
Why:
COPY-PASTE FIX
```
[Your live demo URL here, e.g., https://gemini-search.vercel.app]
```

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 ammaarreshi/Gemini-Search

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Elasticsearch

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Elasticsearch · recommended 2×
Kibana · recommended 1×
Logstash · recommended 1×
Apache Kafka · recommended 1×
Hugging Face Transformers · recommended 1×

CATEGORY QUERY
How to build a search engine clone with AI summarization and real-time web sources?
you: not recommended
AI recommended (in order):
1. Elasticsearch
2. Kibana
3. Logstash
4. Apache Kafka
5. Hugging Face Transformers
6. PyTorch
7. TensorFlow
8. Scrapy
9. FastAPI
10. Flask
11. Django
12. Next.js
13. React
14. Vue
15. Angular
16. Docker
17. Kubernetes
AI recommended 17 alternatives but never named ammaarreshi/Gemini-Search. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What framework to use for creating an AI-powered search experience with source citations?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Haystack
4. OpenAI API
5. Anthropic's Claude
6. Google's Gemini
7. faiss-cpu
8. hnswlib
9. Elasticsearch
10. Weaviate
11. Pinecone
12. Qdrant
AI recommended 12 alternatives but never named ammaarreshi/Gemini-Search. 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 ammaarreshi/Gemini-Search?
pass
AI named ammaarreshi/Gemini-Search explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts ammaarreshi/Gemini-Search in production, what risks or prerequisites should they evaluate first?
pass
AI named ammaarreshi/Gemini-Search 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 ammaarreshi/Gemini-Search solve, and who is the primary audience?
pass
AI named ammaarreshi/Gemini-Search explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of ammaarreshi/Gemini-Search. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/ammaarreshi/Gemini-Search.svg)](https://repogeo.com/en/r/ammaarreshi/Gemini-Search)

HTML

<a href="https://repogeo.com/en/r/ammaarreshi/Gemini-Search"><img src="https://repogeo.com/badge/ammaarreshi/Gemini-Search.svg" alt="RepoGEO" /></a>

Pro

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ammaarreshi/Gemini-Search — 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