REPOGEO REPORT · LITE

AnswerDotAI/byaldi

Default branch main · commit 4583c073 · scanned 6/10/2026, 1:41:59 AM

GitHub: 850 stars · 92 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

35 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 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 AnswerDotAI/byaldi, 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 H1 and opening paragraph to clarify purpose

Why:

CURRENT

# Welcome to Byaldi
_Did you know? In the movie RAGatouille, the dish Remy makes is not actually a ratatouille, but a refined version of the dish called "Confit Byaldi"._

<p align="center"></p>

⚠️ This is the pre-release version of Byaldi. Please report any issue you encounter, there will likely be quite a few quirks to iron out!

Byaldi is RAGatouille's mini sister project. It is a simple wrapper around the ColPali repository to make it easy to use late-interaction multi-modal models such as ColPALI with a familiar API.

COPY-PASTE FIX

# Byaldi: Easy Late-Interaction Multi-Modal Retrieval for RAG
_Byaldi is a simple Python library designed to make integrating late-interaction multi-modal models like ColPALI into your RAG pipelines effortless. It's RAGatouille's mini sister project, providing a familiar API for enhanced document retrieval and reranking._

⚠️ This is the pre-release version of Byaldi. Please report any issue you encounter, there will likely be quite a few quirks to iron out!

mediumhomepage#2
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://github.com/AnswerDotAI/byaldi (or a dedicated project/documentation page if one exists)
```

lowtopics#3

Add 'library' or 'framework' to the repository topics

Why:

CURRENT

colbert, colpali, multi-modal, nlp, rag, reranking, retrieval

COPY-PASTE FIX

colbert, colpali, multi-modal, nlp, rag, reranking, retrieval, library

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 AnswerDotAI/byaldi

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Haystack

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Haystack · recommended 1×
LlamaIndex · recommended 1×
Weaviate · recommended 1×
Hugging Face Transformers · recommended 1×
Sentence Transformers · recommended 1×

CATEGORY QUERY
How to easily integrate multi-modal retrieval and reranking into my RAG pipeline?
you: not recommended
AI recommended (in order):
1. Haystack
2. LlamaIndex
3. Weaviate
4. Hugging Face Transformers
5. Sentence Transformers
6. Pinecone
7. Milvus
8. Chroma
AI recommended 8 alternatives but never named AnswerDotAI/byaldi. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a library for late-interaction multi-modal models to enhance document retrieval performance.
you: not recommended
AI recommended (in order):
1. ColBERT
2. ColBERTv2
3. SPLADE
4. DeepImpact
5. Co-Condenser
6. ANCE
7. OpenMatch
AI recommended 7 alternatives but never named AnswerDotAI/byaldi. This is the gap to close.
Show full AI answer

Objective checks

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

Metadata completeness
warn
Suggestion:
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 AnswerDotAI/byaldi?
pass
AI named AnswerDotAI/byaldi explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts AnswerDotAI/byaldi in production, what risks or prerequisites should they evaluate first?
pass
AI named AnswerDotAI/byaldi 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 AnswerDotAI/byaldi solve, and who is the primary audience?
pass
AI named AnswerDotAI/byaldi 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 AnswerDotAI/byaldi. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/AnswerDotAI/byaldi.svg)](https://repogeo.com/en/r/AnswerDotAI/byaldi)

HTML

<a href="https://repogeo.com/en/r/AnswerDotAI/byaldi"><img src="https://repogeo.com/badge/AnswerDotAI/byaldi.svg" alt="RepoGEO" /></a>

Pro

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AnswerDotAI/byaldi — 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