REPOGEO REPORT · LITE

deepsense-ai/ragbits

Default branch main · commit 32074a71 · scanned 5/19/2026, 11:31:59 AM

GitHub: 1,648 stars · 136 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 deepsense-ai/ragbits, 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

Add a clear introductory paragraph to the README

Why:

COPY-PASTE FIX

Ragbits provides modular, independent, and interchangeable building blocks designed for each stage of a Retrieval Augmented Generation (RAG) pipeline. Unlike monolithic frameworks, Ragbits emphasizes granular experimentation, evaluation, and flexible integration, allowing developers to build reliable and scalable GenAI applications with precise control.

mediumabout#2

Update the repository description to highlight modularity

Why:

CURRENT

Building blocks for rapid development of GenAI applications

COPY-PASTE FIX

Modular, independent building blocks for rapid development, evaluation, and experimentation of GenAI applications, specifically for RAG pipelines, emphasizing granular control over each stage.

lowtopics#3

Add 'toolkit' to repository topics

Why:

CURRENT

agents, document-search, evaluation, guardrails, llms, optimization, prompts, rag, vector-stores

COPY-PASTE FIX

agents, document-search, evaluation, guardrails, llms, optimization, prompts, rag, vector-stores, toolkit

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 deepsense-ai/ragbits

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

LangChain

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

LangChain · recommended 2×
LlamaIndex · recommended 2×
Haystack · recommended 2×
Rasa · recommended 1×
OpenAI API · recommended 1×

CATEGORY QUERY
How can I quickly build reliable GenAI applications with flexible RAG processing and diverse data ingestion?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Haystack
4. Rasa
5. OpenAI API
6. Claude
7. FastAPI
8. pandas
9. requests
10. BeautifulSoup
11. Pinecone
12. Weaviate
13. Chroma
14. Qdrant
AI recommended 14 alternatives but never named deepsense-ai/ragbits. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools help integrate various LLMs and vector stores for scalable, type-safe AI applications?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Haystack
4. Semantic Kernel
5. LiteLLM
6. OpenAI Python Library
7. anthropic library
8. pinecone-client
9. weaviate-client
10. chromadb
11. Hugging Face `transformers`
12. Hugging Face `datasets`
13. `faiss-cpu`
AI recommended 13 alternatives but never named deepsense-ai/ragbits. 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 deepsense-ai/ragbits?
pass
AI did not name deepsense-ai/ragbits — 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 deepsense-ai/ragbits in production, what risks or prerequisites should they evaluate first?
pass
AI named deepsense-ai/ragbits 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 deepsense-ai/ragbits solve, and who is the primary audience?
pass
AI named deepsense-ai/ragbits 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

<a href="https://repogeo.com/en/r/deepsense-ai/ragbits"><img src="https://repogeo.com/badge/deepsense-ai/ragbits.svg" alt="RepoGEO" /></a>

Pro

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deepsense-ai/ragbits — 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