RRepoGEO

REPOGEO REPORT · LITE

mongodb-developer/GenAI-Showcase

Default branch main · commit 14b5274c · scanned 5/29/2026, 4:17:43 PM

GitHub: 4,230 stars · 737 forks

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 mongodb-developer/GenAI-Showcase, 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 to clearly state its purpose as a practical guide/cookbook for integrating MongoDB with GenAI

    Why:

    CURRENT
    ## MongoDB's GenAI Showcase

Welcome to MongoDB's Generative AI Showcase Repository!

Whether you are just starting out on your Generative AI journey, or looking to build advanced GenAI applications, we've got you covered. This repository has an exhaustive list of examples and sample applications that cover Retrieval-Augmented Generation (RAG), AI Agents, and industry-specific use cases. Discover how MongoDB integrates into RAG pipelines and AI Agents, serving as a vector database, operational database, and memory provider.
    COPY-PASTE FIX
    ## MongoDB's GenAI Showcase: A Practical Cookbook for Integration

Welcome to MongoDB's Generative AI Showcase Repository! This repository serves as a comprehensive cookbook, providing practical examples and sample applications for building advanced Generative AI applications. Discover exactly how to integrate MongoDB into RAG pipelines and AI Agents, leveraging it as a vector database, operational database, and memory provider, with detailed use cases covering Retrieval-Augmented Generation (RAG), AI Agents, and industry-specific scenarios.
  • mediumhomepage#2
    Update the homepage URL to a relevant product or project page

    Why:

    CURRENT
    https://www.mongodb.com/cloud/atlas/register
    COPY-PASTE FIX
    https://www.mongodb.com/products/platform/atlas-vector-search
  • lowtopics#3
    Add 'cookbook' and 'examples' to the repository topics

    Why:

    CURRENT
    agents, artificial-intelligence, generative-ai, llms, rag
    COPY-PASTE FIX
    agents, artificial-intelligence, cookbook, examples, generative-ai, llms, rag

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 mongodb-developer/GenAI-Showcase
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. Haystack · recommended 1×
  4. Microsoft Semantic Kernel · recommended 1×
  5. OpenAI Assistants API · recommended 1×
  • CATEGORY QUERY
    How to build generative AI applications using RAG patterns and AI agents?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Microsoft Semantic Kernel
    5. OpenAI Assistants API
    6. AutoGen
    7. Instructor

    AI recommended 7 alternatives but never named mongodb-developer/GenAI-Showcase. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good examples for integrating a database into RAG pipelines or AI agents?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate
    3. Chroma
    4. PostgreSQL (with pgvector extension)
    5. Elasticsearch
    6. Milvus
    7. Redis (with Redis Stack and RediSearch)

    AI recommended 7 alternatives but never named mongodb-developer/GenAI-Showcase. 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 mongodb-developer/GenAI-Showcase?
    pass
    AI named mongodb-developer/GenAI-Showcase explicitly

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

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