RRepoGEO

REPOGEO REPORT · LITE

Azure-Samples/graphrag-accelerator

Default branch main · commit c660c84c · scanned 5/14/2026, 5:41:59 AM

GitHub: 2,413 stars · 421 forks

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

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 improve categorization

    Why:

    COPY-PASTE FIX
    azure, graphrag, rag, knowledge-graph, deployment, accelerator, llm, sample
  • highreadme#2
    Reposition core definition before maintenance warning in README

    Why:

    CURRENT
    # GraphRAG Accelerator

## ⚠️ ATTENTION
This repository is no longer maintained. We sincerely appreciate the interest and all contributors to the Graphrag Solution Accelerator.

🚀 Future development and updates - Please visit the graphrag library for future updates and continued collaboration with the graphrag community at Microsoft.

[](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/Azure-Samples/graphrag-accelerator)

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
    # GraphRAG Accelerator

This repository provides a solution accelerator for one-click deployment of a Knowledge Graph powered RAG (GraphRAG) system in Azure. It builds on the `graphrag` Python package to expose API endpoints for indexing pipelines and querying a knowledge graph.

## ⚠️ ATTENTION
This repository is no longer maintained. We sincerely appreciate the interest and all contributors to the Graphrag Solution Accelerator.

🚀 Future development and updates - Please visit the `graphrag` library for future updates and continued collaboration with the `graphrag` community at Microsoft.
  • mediumreadme#3
    Emphasize 'sample' and 'demonstration' nature in README's opening

    Why:

    CURRENT
    This repository provides a solution accelerator for one-click deployment of a Knowledge Graph powered RAG (GraphRAG) system in Azure. It builds on the `graphrag` Python package to expose API endpoints for indexing pipelines and querying a knowledge graph.
    COPY-PASTE FIX
    This repository provides a solution accelerator for one-click deployment of a Knowledge Graph powered RAG (GraphRAG) system in Azure. It serves as a demonstration and sample implementation, building on the `graphrag` Python package to expose API endpoints for indexing pipelines and querying a knowledge graph.

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
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. Neo4j AuraDB · recommended 2×
  4. Amazon Neptune · recommended 2×
  5. BigQuery · recommended 2×
  • CATEGORY QUERY
    How can I quickly deploy a knowledge graph-powered RAG system for LLM applications?
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. LangChain
    3. LlamaIndex
    4. Neo4j AuraDB
    5. Grakn
    6. Vaticle's TypeDB
    7. Amazon Neptune
    8. BigQuery
    9. Dataproc
    10. Apache Spark GraphFrames
    11. RDFox
    12. Memgraph

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

    Show full AI answer
  • CATEGORY QUERY
    What are the best hosted services for building RAG pipelines with graph capabilities?
    you: not recommended
    AI recommended (in order):
    1. Neo4j AuraDB
    2. LangChain
    3. LlamaIndex
    4. Amazon Neptune
    5. Google Cloud Knowledge Graph
    6. BigQuery
    7. Dataproc
    8. GraphFrames
    9. Azure Cosmos DB
    10. Memgraph Cloud
    11. TigerGraph Cloud

    AI recommended 11 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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  • Brand-free category queries5 vs 2 in Lite
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