RRepoGEO

REPOGEO REPORT · LITE

stanford-futuredata/ColBERT

Default branch main · commit cc4f3dc9 · scanned 7/1/2026, 5:32:10 AM

GitHub: 3,891 stars · 468 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, nlp, deep-learning, retrieval-model, late-interaction
  • highhomepage#2
    Add the official project homepage URL

    Why:

    COPY-PASTE FIX
    https://colbert-ir.com/
  • highreadme#3
    Reposition the README's opening sentence to emphasize 'system' or 'framework'

    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 **framework for building** fast and accurate neural search systems, leveraging BERT-based late interaction for scalable retrieval over large text collections in tens of milliseconds.

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
0%
Of all named tools, what % are you?
Top rival
weaviate/weaviate
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. weaviate/weaviate · recommended 1×
  2. Pinecone · recommended 1×
  3. elastic/elasticsearch · recommended 1×
  4. qdrant/qdrant · recommended 1×
  5. huggingface/transformers · recommended 1×
  • CATEGORY QUERY
    How to build a scalable and accurate neural search system for large text corpora?
    you: not recommended
    AI recommended (in order):
    1. Weaviate (weaviate/weaviate)
    2. Pinecone
    3. Elasticsearch (elastic/elasticsearch)
    4. Qdrant (qdrant/qdrant)
    5. Hugging Face Transformers (huggingface/transformers)
    6. Sentence Transformers (UKPLab/sentence-transformers)
    7. OpenAI Embeddings API
    8. Cohere Embeddings
    9. LangChain (langchain-ai/langchain)
    10. LlamaIndex (run-llama/llama_index)
    11. Apache Spark (apache/spark)
    12. Pandas (pandas-dev/pandas)

    AI recommended 12 alternatives but never named stanford-futuredata/ColBERT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best BERT-based models for efficient semantic search with fine-grained relevance?
    you: not recommended
    AI recommended (in order):
    1. Sentence-BERT (SBERT)
    2. MiniLM
    3. MPNet
    4. DistilBERT
    5. RoBERTa
    6. E5
    7. BERT

    AI recommended 7 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?

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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