RRepoGEO

REPOGEO REPORT · LITE

opea-project/GenAIExamples

Default branch main · commit f5642267 · scanned 6/16/2026, 12:17:26 AM

GitHub: 737 stars · 342 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 opea-project/GenAIExamples, 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 introduction to clarify its role within OPEA and its microservice nature

    Why:

    CURRENT
    Generative AI Examples are designed to give developers an easy entry into generative AI, featuring microservice-based samples that simplify the processes of deploying, testing, and scaling GenAI applications. All examples are fully compatible with both Docker and Kubernetes, supporting a wide range of hardware platforms such as Gaudi, Xeon, AMD EPYC CPUs, AMD Instinct GPUs, and other hardwares including NVIDIA GPUs, ensuring flexibility and efficiency for your GenAI adoption.
    COPY-PASTE FIX
    The `opea-project/GenAIExamples` repository provides practical, runnable examples for implementing common Generative AI patterns within the Open Platform for Enterprise AI (OPEA) framework. These microservice-based samples simplify the processes of deploying, testing, and scaling GenAI applications, offering an easy entry into enterprise-grade generative AI.
  • mediumtopics#2
    Add more specific topics to improve category visibility

    Why:

    CURRENT
    chatqna, codegen, copilot, gaudi2, genai, llms, rag, summarization, tgi, xeon
    COPY-PASTE FIX
    chatqna, codegen, copilot, gaudi2, genai, llms, rag, summarization, tgi, xeon, enterprise-ai, microservices, ai-platform, genai-examples, kubernetes, docker
  • lowabout#3
    Refine the repository description for clearer positioning

    Why:

    CURRENT
    Generative AI Examples is a collection of GenAI examples such as ChatQnA, Copilot, which illustrate the pipeline capabilities of the Open Platform for Enterprise AI (OPEA) project.
    COPY-PASTE FIX
    Generative AI Examples is a collection of microservice-based GenAI examples (e.g., ChatQnA, Copilot) that illustrate the robust pipeline capabilities of the Open Platform for Enterprise AI (OPEA) project, designed for easy deployment and scaling.

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 opea-project/GenAIExamples
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. FastAPI · recommended 1×
  3. Docker · recommended 1×
  4. Hugging Face Transformers · recommended 1×
  5. Flask · recommended 1×
  • CATEGORY QUERY
    How to quickly start building generative AI applications with microservices?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. FastAPI
    3. Docker
    4. Hugging Face Transformers
    5. Flask
    6. Kubernetes
    7. Google Cloud Vertex AI
    8. Google Cloud Functions
    9. Cloud Run
    10. AWS Lambda
    11. Amazon SageMaker Endpoints
    12. Azure Functions
    13. Azure Machine Learning Endpoints
    14. Ray Serve
    15. Ray

    AI recommended 15 alternatives but never named opea-project/GenAIExamples. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for deploying and evaluating generative AI models on various hardware platforms?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Triton Inference Server
    2. OpenVINO Toolkit
    3. ONNX Runtime
    4. TensorRT
    5. MLflow
    6. AWS SageMaker
    7. Azure Machine Learning

    AI recommended 7 alternatives but never named opea-project/GenAIExamples. 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 opea-project/GenAIExamples?
    pass
    AI did not name opea-project/GenAIExamples — 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 opea-project/GenAIExamples in production, what risks or prerequisites should they evaluate first?
    pass
    AI named opea-project/GenAIExamples 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 opea-project/GenAIExamples solve, and who is the primary audience?
    pass
    AI named opea-project/GenAIExamples explicitly

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

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