REPOGEO REPORT · LITE

pixegami/rag-tutorial-v2

Default branch main · commit 5e71164a · scanned 6/8/2026, 5:22:56 PM

GitHub: 952 stars · 606 forks

AI VISIBILITY SCORE

18 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

0 pass · 1 warn · 1 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 pixegami/rag-tutorial-v2, 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

Expand README to clearly state it's a RAG tutorial

Why:

CURRENT

# rag-tutorial-v2

COPY-PASTE FIX

# RAG Tutorial v2: Building Retrieval Augmented Generation Systems

This repository provides an improved, hands-on tutorial for building Retrieval Augmented Generation (RAG) systems. It focuses on practical implementation using local LLMs, managing database updates, and incorporating robust testing practices. This guide is designed for developers and AI enthusiasts looking to understand and implement RAG from scratch.

hightopics#2
Add relevant topics to improve categorization
Why:
COPY-PASTE FIX
```
rag, tutorial, langchain, local-llm, llm, database, testing, ai
```
highlicense#3
Add a LICENSE file to clarify usage terms
Why:
COPY-PASTE FIX
```
Create a LICENSE file (e.g., MIT or Apache-2.0) in the root of the repository.
```

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 pixegami/rag-tutorial-v2

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

LlamaIndex

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

LlamaIndex · recommended 1×
LangChain · recommended 1×
Haystack · recommended 1×
Sentence-Transformers · recommended 1×
Faiss · recommended 1×

CATEGORY QUERY
How to build a retrieval-augmented generation system using local language models?
you: not recommended
AI recommended (in order):
1. LlamaIndex
2. LangChain
3. Haystack
4. Sentence-Transformers
5. Faiss
6. Hugging Face `transformers`
7. Ollama
AI recommended 7 alternatives but never named pixegami/rag-tutorial-v2. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a comprehensive guide on implementing RAG with data updates and testing practices.
you: not recommended
AI recommended (in order):
1. LangChain (langchain-ai/langchain)
2. LlamaIndex (run-llama/llama_index)
3. Haystack (deepset AI) (deepset-ai/haystack)
4. Pinecone
5. Weaviate (weaviate/weaviate)
6. Chroma (chroma-core/chroma)
7. FAISS (Facebook AI Similarity Search) (facebookresearch/faiss)
8. OpenAI API (GPT-3.5, GPT-4)
9. Anthropic Claude (Claude 2, Claude 3)
10. Hugging Face Transformers (huggingface/transformers)
11. OpenAI Embeddings (text-embedding-ada-002)
12. Hugging Face SentenceTransformers (UKP-SQuARE/sentence-transformers)
13. Kafka (apache/kafka)
14. Airflow (apache/airflow)
15. Prefect (PrefectHQ/prefect)
16. Dagster (dagster-io/dagster)
17. Elasticsearch (elastic/elasticsearch)
18. RAGAS (RAG Assessment) (explodinggradients/ragas)
19. LangChain Tracing (LangSmith)
20. OpenTelemetry (open-telemetry/opentelemetry-python)
AI recommended 20 alternatives but never named pixegami/rag-tutorial-v2. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
fail
Suggestion:
README presence
warn
Suggestion:

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 pixegami/rag-tutorial-v2?
pass
AI did not name pixegami/rag-tutorial-v2 — 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 pixegami/rag-tutorial-v2 in production, what risks or prerequisites should they evaluate first?
pass
AI named pixegami/rag-tutorial-v2 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 pixegami/rag-tutorial-v2 solve, and who is the primary audience?
pass
AI named pixegami/rag-tutorial-v2 explicitly
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 pixegami/rag-tutorial-v2. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/pixegami/rag-tutorial-v2"><img src="https://repogeo.com/badge/pixegami/rag-tutorial-v2.svg" alt="RepoGEO" /></a>

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pixegami/rag-tutorial-v2 — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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