RRepoGEO

REPOGEO REPORT · LITE

bakrianoo/mini-rag

Default branch tut-017 · commit 77050419 · scanned 6/1/2026, 12:18:19 PM

GitHub: 617 stars · 261 forks

AI VISIBILITY SCORE
40 /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
3 / 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 bakrianoo/mini-rag, 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's opening to clearly state it's an educational course

    Why:

    CURRENT
    # mini-rag

This is a minimal implementation of the RAG model for question answering.
    COPY-PASTE FIX
    # mini-rag: A Step-by-Step Educational Course for Production-Ready RAG Applications

This repository serves as a comprehensive, step-by-step educational project designed to teach you how to build a production-ready Retrieval Augmented Generation (RAG) application from scratch.
  • mediumtopics#2
    Add more specific educational keywords to topics

    Why:

    CURRENT
    docker, education, fastapi, genai, python, rag
    COPY-PASTE FIX
    docker, education, fastapi, genai, python, rag, course, tutorial, learning, guide
  • mediumreadme#3
    Explicitly highlight FastAPI and Docker integration in the README's introductory sections

    Why:

    COPY-PASTE FIX
    Ensure the introductory section of the README (e.g., the second paragraph) explicitly mentions the use of FastAPI for API development and Docker for deployment, e.g., 'The course provides practical guidance on integrating essential tools like FastAPI for robust API development and Docker for seamless deployment within a RAG system.'

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 bakrianoo/mini-rag
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 2×
  2. run-llama/llama_index · recommended 2×
  3. Pinecone · recommended 2×
  4. weaviate/weaviate · recommended 2×
  5. qdrant/qdrant · recommended 2×
  • CATEGORY QUERY
    How to build a production-ready RAG application step-by-step with Python?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Pandas (pandas-dev/pandas)
    4. NLTK (nltk/nltk)
    5. spaCy (explosion/spaCy)
    6. Regex (re module)
    7. Hugging Face Transformers (huggingface/transformers)
    8. Sentence Transformers (UKP-LAB/sentence-transformers)
    9. OpenAI Embeddings API
    10. Pinecone
    11. Weaviate (weaviate/weaviate)
    12. Qdrant (qdrant/qdrant)
    13. Chroma (chroma-core/chroma)
    14. Faiss (Facebook AI Similarity Search) (facebookresearch/faiss)
    15. OpenAI API
    16. Anthropic Claude API
    17. Google Gemini API
    18. Ragas (explodinggradients/ragas)
    19. MLflow (mlflow/mlflow)
    20. Prometheus (prometheus/prometheus)
    21. Grafana (grafana/grafana)
    22. FastAPI (tiangolo/fastapi)
    23. Streamlit (streamlit/streamlit)
    24. Gradio (gradio-app/gradio)
    25. Docker (docker/docker-ce)
    26. Kubernetes (kubernetes/kubernetes)
    27. Google Kubernetes Engine
    28. Amazon EKS
    29. Azure Kubernetes Service
    30. AWS Lambda
    31. Google Cloud Functions
    32. Azure Functions
    33. AWS EC2
    34. Google Compute Engine
    35. Azure Virtual Machines
    36. AWS ECS
    37. Google Cloud Run
    38. Azure Container Apps

    AI recommended 38 alternatives but never named bakrianoo/mini-rag. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best practices for integrating FastAPI and Docker in a RAG system?
    you: not recommended
    AI recommended (in order):
    1. FastAPI (tiangolo/fastapi)
    2. Docker
    3. python:3.9-slim-buster
    4. python:3.10-slim-bullseye
    5. Poetry (python-poetry/poetry)
    6. Rye (mitsuhiko/rye)
    7. PDM (pdm-project/pdm)
    8. pip (pypa/pip)
    9. curl (curl/curl)
    10. Pydantic (pydantic/pydantic)
    11. Chroma (chroma-core/chroma)
    12. Pinecone
    13. Weaviate (weaviate/weaviate)
    14. Qdrant (qdrant/qdrant)
    15. LangChain (langchain-ai/langchain)
    16. LlamaIndex (run-llama/llama_index)
    17. OpenAI GPT-4
    18. Anthropic Claude
    19. Llama 3
    20. Ollama (ollama/ollama)
    21. vLLM (vllm-project/vllm)
    22. Sentence Transformers (UKPLab/sentence-transformers)
    23. Redis (redis/redis)
    24. Docker Compose (docker/compose)
    25. Kubernetes (kubernetes/kubernetes)
    26. Docker Swarm
    27. AWS ECS
    28. Google Cloud Run
    29. Azure Container Apps
    30. Nginx (nginx/nginx)
    31. Traefik (traefik/traefik)
    32. Loguru (Delgan/loguru)
    33. Prometheus (prometheus/prometheus)
    34. Grafana (grafana/grafana)
    35. Uvicorn (encode/uvicorn)
    36. Gunicorn (benoitc/gunicorn)
    37. SQLAlchemy (sqlalchemy/sqlalchemy)
    38. PgBouncer (pgbouncer/pgbouncer)

    AI recommended 38 alternatives but never named bakrianoo/mini-rag. 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 bakrianoo/mini-rag?
    pass
    AI named bakrianoo/mini-rag explicitly

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

  • If a team adopts bakrianoo/mini-rag in production, what risks or prerequisites should they evaluate first?
    pass
    AI named bakrianoo/mini-rag 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 bakrianoo/mini-rag solve, and who is the primary audience?
    pass
    AI named bakrianoo/mini-rag explicitly

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite