RRepoGEO

REPOGEO REPORT · LITE

Azure-Samples/aisearch-openai-rag-audio

Default branch main · commit 85d0b4e2 · scanned 6/9/2026, 6:38:08 AM

GitHub: 556 stars · 349 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 Azure-Samples/aisearch-openai-rag-audio, 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 emphasize "RAG application pattern for audio"

    Why:

    CURRENT
    This repo contains an example of how to implement RAG support in applications that use voice as their user interface, powered by the GPT-4o realtime API for audio.
    COPY-PASTE FIX
    This repository provides a complete application pattern and example for building interactive voice generative AI experiences. It demonstrates Retrieval Augmented Generation (RAG) specifically for audio input, leveraging Azure AI Search and Azure OpenAI's GPT-4o Realtime API to enable natural language querying of audio content.
  • mediumabout#2
    Clarify the repository description

    Why:

    CURRENT
    A simple example implementation of the VoiceRAG pattern to power interactive voice generative AI experiences using RAG with Azure AI Search and Azure OpenAI's gpt-4o-realtime-preview model.
    COPY-PASTE FIX
    An example implementation of the VoiceRAG application pattern, demonstrating how to build interactive voice generative AI experiences using Retrieval Augmented Generation (RAG) with Azure AI Search and Azure OpenAI's gpt-4o-realtime-preview model.
  • lowtopics#3
    Add `voice-rag` to repository topics

    Why:

    CURRENT
    ai-azd-templates, azd-templates, azure, azure-ai-search, generative-ai, gpt, language-model, openai, rag, retrieval-augmented-generation, search, vector-database
    COPY-PASTE FIX
    ai-azd-templates, azd-templates, azure, azure-ai-search, generative-ai, gpt, language-model, openai, rag, retrieval-augmented-generation, search, vector-database, voice-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 Azure-Samples/aisearch-openai-rag-audio
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Llama 2
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Llama 2 · recommended 2×
  2. Mistral · recommended 2×
  3. Google Cloud Speech-to-Text · recommended 2×
  4. Google Cloud Text-to-Speech · recommended 2×
  5. AWS Polly · recommended 2×
  • CATEGORY QUERY
    How to build interactive voice AI experiences using retrieval augmented generation?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. GPT-4
    3. GPT-3.5 Turbo
    4. Whisper API
    5. OpenAI Embeddings
    6. LangChain (github.com/langchain-ai/langchain)
    7. LlamaIndex (github.com/run-llama/llama_index)
    8. Llama 2
    9. Mistral
    10. Cohere Command
    11. Google Cloud Speech-to-Text
    12. AWS Transcribe
    13. Vosk (github.com/alphacep/vosk-api)
    14. NVIDIA NeMo (github.com/NVIDIA/NeMo)
    15. AWS SageMaker
    16. Google Cloud Vertex AI
    17. Hugging Face Inference Endpoints
    18. Google Cloud Text-to-Speech
    19. AWS Polly
    20. ElevenLabs
    21. Microsoft Azure Text-to-Speech
    22. Google Cloud AI Platform
    23. Vertex AI Search
    24. Vertex AI LLMs
    25. Vertex AI Vector Search
    26. AWS AI Services
    27. Amazon Kendra
    28. Amazon Bedrock
    29. Hugging Face Ecosystem
    30. Hugging Face Transformers (github.com/huggingface/transformers)
    31. Hugging Face Sentence Transformers (github.com/UKPLab/sentence-transformers)
    32. Pinecone
    33. Weaviate (github.com/weaviate/weaviate)
    34. Chroma (github.com/chroma-core/chroma)
    35. Milvus (github.com/milvus-io/milvus)
    36. Deepgram
    37. AssemblyAI

    AI recommended 37 alternatives but never named Azure-Samples/aisearch-openai-rag-audio. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking examples for real-time audio processing with LLMs for RAG applications.
    you: not recommended
    AI recommended (in order):
    1. OpenAI Whisper
    2. OpenAI GPT-4
    3. OpenAI GPT-3.5 Turbo
    4. Pinecone
    5. Weaviate
    6. ElevenLabs
    7. Google Cloud Speech-to-Text
    8. Google Gemini Pro
    9. Google PaLM 2
    10. Google Cloud Vertex AI Vector Search
    11. Google Cloud Text-to-Speech
    12. AssemblyAI
    13. Anthropic Claude
    14. Chroma
    15. Qdrant
    16. AWS Polly
    17. Hugging Face Transformers
    18. Llama 2
    19. Mistral
    20. FAISS
    21. Elasticsearch
    22. Coqui TTS
    23. Deepgram
    24. Cohere
    25. Milvus
    26. Zilliz
    27. Play.ht

    AI recommended 27 alternatives but never named Azure-Samples/aisearch-openai-rag-audio. 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 Azure-Samples/aisearch-openai-rag-audio?
    pass
    AI did not name Azure-Samples/aisearch-openai-rag-audio — 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/aisearch-openai-rag-audio in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Azure-Samples/aisearch-openai-rag-audio 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/aisearch-openai-rag-audio solve, and who is the primary audience?
    pass
    AI did not name Azure-Samples/aisearch-openai-rag-audio — 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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  • Brand-free category queries5 vs 2 in Lite
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