REPOGEO REPORT · LITE

bragai/bRAG-langchain

Default branch main · commit a3e5c7b0 · scanned 5/10/2026, 11:43:23 AM

GitHub: 4,101 stars · 495 forks

AI VISIBILITY SCORE

33 /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

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 bragai/bRAG-langchain, 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 sentence to clarify its 'guide' nature

Why:

CURRENT

This repository contains a comprehensive exploration of Retrieval-Augmented Generation (RAG) for various applications.

COPY-PASTE FIX

This repository serves as a comprehensive, hands-on guide and practical example collection for building Retrieval-Augmented Generation (RAG) applications from introductory to advanced levels, leveraging Python and LangChain.

mediumtopics#2

Add more specific topics to highlight its educational and LangChain focus

Why:

CURRENT

agentic-rag, ai, chatbot, llm, machine-learning, python, rag, retrieval-augmented-generation

COPY-PASTE FIX

agentic-rag, ai, chatbot, llm, machine-learning, python, rag, retrieval-augmented-generation, langchain, rag-tutorial, rag-examples, hands-on-guide

mediumlicense#3

Clarify the existing license in the README

Why:

COPY-PASTE FIX

This project is licensed under [Specify License Name(s) here, e.g., 'a custom license combining MIT and Apache-2.0 principles']. Please see the `LICENSE` file for full details.

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 bragai/bRAG-langchain

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Pinecone

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Pinecone · recommended 2×
LlamaIndex · recommended 1×
LangChain · recommended 1×
deepset/haystack · recommended 1×
Faiss · recommended 1×

CATEGORY QUERY
How can I build a retrieval-augmented generation application with Python for a custom chatbot?
you: not recommended
AI recommended (in order):
1. LlamaIndex
2. LangChain
3. Haystack (deepset/haystack)
4. Faiss
5. Sentence-Transformers
6. OpenAI Python Library
7. Hugging Face Transformers
8. Chroma
9. Pinecone
10. Weaviate
AI recommended 10 alternatives but never named bragai/bRAG-langchain. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Where can I find a detailed, hands-on guide to implement advanced RAG techniques?
you: not recommended
AI recommended (in order):
1. LangChain (langchain-ai/langchain)
2. Cohere Rerank
3. BGE-M3
4. LlamaIndex (run-llama/llama_index)
5. Ragas (explodinggradients/ragas)
6. LangSmith
7. Pinecone
8. Weaviate (weaviate/weaviate)
9. Chroma (chroma-core/chroma)
10. Qdrant (qdrant/qdrant)
11. DeepLearning.AI
12. ChatGPT API
13. OpenAI Embeddings
14. Cohere Embeddings
15. Hugging Face Transformers (huggingface/transformers)
16. Hugging Face Datasets (huggingface/datasets)
17. T5
18. BART
19. evaluate (huggingface/evaluate)
20. qna-openai
21. reranker-cohere
AI recommended 21 alternatives but never named bragai/bRAG-langchain. 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 bragai/bRAG-langchain?
pass
AI did not name bragai/bRAG-langchain — 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 bragai/bRAG-langchain in production, what risks or prerequisites should they evaluate first?
pass
AI named bragai/bRAG-langchain 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 bragai/bRAG-langchain solve, and who is the primary audience?
pass
AI named bragai/bRAG-langchain explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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HTML

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bragai/bRAG-langchain — 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