RRepoGEO

REPOGEO REPORT · LITE

Azure-Samples/graphrag-accelerator

Default branch main · commit c660c84c · scanned 6/24/2026, 5:56:47 PM

GitHub: 2,413 stars · 418 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /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
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 Azure-Samples/graphrag-accelerator, 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

2 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 emphasize 'solution accelerator' and 'deployment'

    Why:

    CURRENT
    Welcome to the GraphRAG solution accelerator! This accelerator builds on top of the graphrag python package and exposes API endpoints hosted on Azure, which can be used to trigger indexing pipelines and enable querying of the graphrag knowledge graph.
    COPY-PASTE FIX
    This repository provides a **GraphRAG solution accelerator** for **one-click deployment** of a Knowledge Graph powered RAG system on Azure. It builds on the `graphrag` Python package, exposing API endpoints for indexing pipelines and querying the knowledge graph.
  • mediumreadme#2
    Add a sentence to the README clarifying its unique value as a complete, deployable solution

    Why:

    COPY-PASTE FIX
    Unlike individual components or frameworks, this accelerator offers a complete, opinionated, and ready-to-deploy infrastructure and code setup for hosting GraphRAG functionality on Azure.

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 Azure-Samples/graphrag-accelerator
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Azure Cosmos DB
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Azure Cosmos DB · recommended 2×
  2. Azure Functions · recommended 2×
  3. Neo4j AuraDS · recommended 2×
  4. Azure AI Search · recommended 1×
  5. Knowledge Store · recommended 1×
  • CATEGORY QUERY
    Looking for a way to quickly deploy knowledge graph RAG for LLM applications on Azure.
    you: not recommended
    AI recommended (in order):
    1. Azure AI Search
    2. Knowledge Store
    3. Graph Projection
    4. Azure Cosmos DB
    5. Azure Functions
    6. Azure Data Factory
    7. Neo4j AuraDS
    8. Azure Marketplace
    9. Microsoft Graph Data Connect
    10. Microsoft 365
    11. Azure Data Lake Storage
    12. Azure SQL Database

    AI recommended 12 alternatives but never named Azure-Samples/graphrag-accelerator. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking solutions to host and expose knowledge graph RAG functionality via scalable APIs.
    you: not recommended
    AI recommended (in order):
    1. Neo4j AuraDS
    2. Neo4j Graph Data Science Library
    3. FastAPI (tiangolo/fastapi)
    4. Flask (pallets/flask)
    5. Gunicorn (benoitc/gunicorn)
    6. Uvicorn (encode/uvicorn)
    7. AWS ALB
    8. Nginx
    9. Docker (docker/docker)
    10. Kubernetes (kubernetes/kubernetes)
    11. Amazon Neptune
    12. Amazon SageMaker
    13. AWS Lambda
    14. API Gateway
    15. BigQuery
    16. Dataproc
    17. Apache Spark (apache/spark)
    18. GraphFrames (graphframes/graphframes)
    19. Vertex AI
    20. Cloud Functions
    21. Cloud Run
    22. Azure Cosmos DB
    23. Azure Machine Learning
    24. Azure Functions
    25. Azure API Management
    26. Dgraph (dgraph-io/dgraph)
    27. Hugging Face Transformers (huggingface/transformers)
    28. Triton Inference Server (triton-inference-server/server)
    29. Nginx Ingress Controller (kubernetes/ingress-nginx)
    30. Apollo Server (apollographql/apollo-server)
    31. Grakn/TypeDB (vaticle/typedb)
    32. Spring Boot (spring-projects/spring-boot)

    AI recommended 32 alternatives but never named Azure-Samples/graphrag-accelerator. 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 Azure-Samples/graphrag-accelerator?
    pass
    AI did not name Azure-Samples/graphrag-accelerator — 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 Azure-Samples/graphrag-accelerator in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Azure-Samples/graphrag-accelerator 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 Azure-Samples/graphrag-accelerator solve, and who is the primary audience?
    pass
    AI named Azure-Samples/graphrag-accelerator explicitly

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

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Azure-Samples/graphrag-accelerator — RepoGEO report