REPOGEO REPORT · LITE

stanford-futuredata/ColBERT

Default branch main · commit cc4f3dc9 · scanned 5/19/2026, 7:52:05 PM

GitHub: 3,866 stars · 470 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 stanford-futuredata/ColBERT, 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

hightopics#1

Add relevant topics to the repository

Why:

COPY-PASTE FIX

neural-search, information-retrieval, semantic-search, bert, deep-learning, nlp, retrieval-model, late-interaction, vector-search

highreadme#2

Clarify ColBERT's role as a model/technique in the README's opening

Why:

CURRENT

ColBERT is a _fast_ and _accurate_ retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds.

COPY-PASTE FIX

ColBERT is a _fast_ and _accurate_ neural retrieval **model and technique**, providing the core **late-interaction mechanism** for building scalable BERT-based search systems over large text collections in tens of milliseconds. It serves as a foundational technique for advanced semantic search.

mediumhomepage#3
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://arxiv.org/abs/2004.12832
```

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 stanford-futuredata/ColBERT

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

elastic/elasticsearch

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

elastic/elasticsearch · recommended 2×
huggingface/transformers · recommended 2×
Pinecone · recommended 1×
weaviate/weaviate · recommended 1×
facebookresearch/faiss · recommended 1×

CATEGORY QUERY
How to implement fast and accurate neural search over large text corpora?
you: not recommended
AI recommended (in order):
1. Elasticsearch (elastic/elasticsearch)
2. Pinecone
3. Weaviate (weaviate/weaviate)
4. Faiss (facebookresearch/faiss)
5. Milvus (milvus-io/milvus)
6. Qdrant (qdrant/qdrant)
AI recommended 6 alternatives but never named stanford-futuredata/ColBERT. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are effective techniques for fine-grained semantic retrieval beyond single vector embeddings?
you: not recommended
AI recommended (in order):
1. Elasticsearch (elastic/elasticsearch)
2. OpenSearch (opensearch-project/OpenSearch)
3. Vespa (vespa-engine/vespa)
4. Hugging Face Transformers (huggingface/transformers)
5. Sentence Transformers library (UKPLab/sentence-transformers)
6. Haystack (deepset-ai/haystack)
7. LangChain (langchain-ai/langchain)
8. LlamaIndex (run-llama/llama_index)
9. Neo4j (neo4j/neo4j)
10. Graph Data Science Library (neo4j/graph-data-science)
11. Amazon Neptune
12. OpenAI API
13. Hugging Face Transformers (huggingface/transformers)
AI recommended 13 alternatives but never named stanford-futuredata/ColBERT. 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 stanford-futuredata/ColBERT?
pass
AI named stanford-futuredata/ColBERT explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts stanford-futuredata/ColBERT in production, what risks or prerequisites should they evaluate first?
pass
AI named stanford-futuredata/ColBERT 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 stanford-futuredata/ColBERT solve, and who is the primary audience?
pass
AI named stanford-futuredata/ColBERT 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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stanford-futuredata/ColBERT — 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