RRepoGEO

REPOGEO REPORT · LITE

ruoccofabrizio/azure-open-ai-embeddings-qna

Default branch main · commit 96ce23ac · scanned 6/8/2026, 2:03:23 PM

GitHub: 847 stars · 500 forks

AI VISIBILITY SCORE
27 /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
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 ruoccofabrizio/azure-open-ai-embeddings-qna, 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 README opening to clarify it's a reference application/architecture

    Why:

    CURRENT
    A simple web application for a OpenAI-enabled document search. This repo uses Azure OpenAI Service for creating embeddings vectors from documents. For answering the question of a user, it retrieves the most relevant document and then uses GPT-3 to extract the matching answer for the question.
    COPY-PASTE FIX
    This repository provides a complete, deployable reference architecture and web application for Retrieval-Augmented Generation (RAG) on Azure. It demonstrates an end-to-end solution for OpenAI-enabled document search and question answering, leveraging Azure OpenAI Service for embeddings and GPT models to extract answers from retrieved documents.
  • hightopics#2
    Expand repository topics to improve categorization

    Why:

    CURRENT
    azureopenai
    COPY-PASTE FIX
    azure-openai, rag, retrieval-augmented-generation, qna, document-search, web-application, gpt, embeddings, azure-ai-search, langchain, redis, reference-architecture
  • mediumreadme#3
    Add a 'Key Features' section to highlight core offerings

    Why:

    COPY-PASTE FIX
    ## Key Features

- **End-to-end RAG Web Application:** A fully functional web application demonstrating Retrieval-Augmented Generation.
- **Azure Reference Architecture:** Provides a best-practices template for deploying RAG solutions on Azure.
- **Managed AI Services Integration:** Seamlessly integrates Azure OpenAI Service, Azure AI Search, and other Azure components.
- **Flexible Orchestration:** Utilizes Langchain for robust prompt engineering and document retrieval.
- **Scalable Document Search:** Implements vector embeddings and semantic search for efficient QnA over custom documents.

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 ruoccofabrizio/azure-open-ai-embeddings-qna
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. elastic/elasticsearch · recommended 1×
  5. weaviate/weaviate · recommended 1×
  • CATEGORY QUERY
    How to implement a web application for AI-driven document search and question answering?
    you: not recommended
    AI recommended (in order):
    1. Haystack (deepset-ai/haystack)
    2. LlamaIndex (run-llama/llama_index)
    3. LangChain (langchain-ai/langchain)
    4. Elasticsearch (elastic/elasticsearch)
    5. Weaviate (weaviate/weaviate)
    6. Hugging Face Transformers (huggingface/transformers)
    7. Hugging Face Datasets (huggingface/datasets)
    8. Streamlit (streamlit/streamlit)
    9. Gradio (gradio-app/gradio)
    10. React (facebook/react)
    11. FastAPI (tiangolo/fastapi)
    12. Flask (pallets/flask)

    AI recommended 12 alternatives but never named ruoccofabrizio/azure-open-ai-embeddings-qna. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a reference architecture for building RAG applications with managed cloud AI services.
    you: not recommended
    AI recommended (in order):
    1. Azure AI Services
    2. Azure Cosmos DB
    3. Azure OpenAI Service
    4. Azure AI Search
    5. Azure Cognitive Search
    6. LangChain
    7. LlamaIndex
    8. Azure Blob Storage
    9. Azure App Service
    10. Azure Kubernetes Service (AKS)
    11. Azure Functions
    12. AWS
    13. Amazon Bedrock
    14. Amazon OpenSearch Service
    15. Amazon S3
    16. AWS Lambda
    17. Amazon EC2
    18. Amazon Aurora PostgreSQL
    19. Amazon ECS
    20. Amazon EKS
    21. Google Cloud
    22. Vertex AI
    23. Google Cloud Search
    24. Cloud Storage
    25. Cloud Run
    26. Google Kubernetes Engine (GKE)
    27. Vertex AI Search
    28. Vertex AI Vector Search
    29. Matching Engine
    30. Cloud SQL for PostgreSQL
    31. Hugging Face Inference Endpoints
    32. Pinecone
    33. Weaviate
    34. Qdrant
    35. Databricks Lakehouse AI
    36. Databricks Vector Search
    37. Delta Lake
    38. MLflow
    39. Unity Catalog
    40. Databricks Foundation Model APIs

    AI recommended 40 alternatives but never named ruoccofabrizio/azure-open-ai-embeddings-qna. 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 ruoccofabrizio/azure-open-ai-embeddings-qna?
    pass
    AI did not name ruoccofabrizio/azure-open-ai-embeddings-qna — 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 ruoccofabrizio/azure-open-ai-embeddings-qna in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ruoccofabrizio/azure-open-ai-embeddings-qna 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 ruoccofabrizio/azure-open-ai-embeddings-qna solve, and who is the primary audience?
    pass
    AI did not name ruoccofabrizio/azure-open-ai-embeddings-qna — 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?

Embed your GEO score

Drop this badge into the README of ruoccofabrizio/azure-open-ai-embeddings-qna. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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