RRepoGEO

REPOGEO REPORT · LITE

qdrant/fastembed

Default branch main · commit a499c313 · scanned 5/22/2026, 7:42:33 AM

GitHub: 2,967 stars · 200 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 qdrant/fastembed, 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 highlight core differentiators

    Why:

    CURRENT
    # ⚡️ What is FastEmbed?

FastEmbed is a lightweight, fast, Python library built for embedding generation. We support popular text models. Please open a GitHub issue if you want us to add a new model.
    COPY-PASTE FIX
    # ⚡️ FastEmbed: Lightweight, Fast, and Serverless-Ready Text Embeddings

FastEmbed is a Python library designed for high-performance, resource-efficient text embedding generation, leveraging ONNX Runtime for speed and minimal dependencies. It's ideal for applications requiring fast, on-device, or serverless text embeddings, supporting popular state-of-the-art models.
  • mediumreadme#2
    Add an explicit comparison section in the README

    Why:

    COPY-PASTE FIX
    ## 🆚 FastEmbed vs. Alternatives

FastEmbed is engineered to be significantly lighter and faster than many popular embedding libraries, including `sentence-transformers` and `Hugging Face Transformers`. By utilizing ONNX Runtime and minimizing dependencies, FastEmbed avoids the heavy overhead of frameworks like PyTorch, making it particularly suitable for resource-constrained environments and serverless deployments. While other libraries offer broad NLP capabilities, FastEmbed focuses on optimized, high-accuracy embedding generation with a smaller footprint.
  • lowtopics#3
    Add 'serverless' to the repository topics

    Why:

    CURRENT
    embeddings, openai, rag, retrieval, retrieval-augmented-generation, vector-search
    COPY-PASTE FIX
    embeddings, openai, rag, retrieval, retrieval-augmented-generation, vector-search, serverless

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 qdrant/fastembed
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
UKP-LABS/sentence-transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. UKP-LABS/sentence-transformers · recommended 1×
  2. huggingface/transformers · recommended 1×
  3. facebookresearch/fastText · recommended 1×
  4. RaRe-Technologies/gensim · recommended 1×
  5. explosion/spaCy · recommended 1×
  • CATEGORY QUERY
    What are fast, lightweight Python libraries for text embeddings, suitable for serverless environments?
    you: not recommended
    AI recommended (in order):
    1. sentence-transformers (UKP-LABS/sentence-transformers)
    2. Hugging Face Transformers (huggingface/transformers)
    3. FastText (facebookresearch/fastText)
    4. Gensim (RaRe-Technologies/gensim)
    5. spaCy (explosion/spaCy)
    6. TensorFlow Lite (tensorflow/tensorflow)
    7. PyTorch Mobile (pytorch/pytorch)

    AI recommended 7 alternatives but never named qdrant/fastembed. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a highly accurate Python library for generating text embeddings for RAG applications.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. sentence-transformers
    3. OpenAI Embeddings API
    4. openai Python library
    5. Cohere Embeddings API
    6. cohere Python library
    7. Google Generative AI
    8. google-generativeai Python library
    9. Voyage AI Embeddings API
    10. voyageai Python library
    11. GTE (General Text Embeddings)

    AI recommended 11 alternatives but never named qdrant/fastembed. 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 qdrant/fastembed?
    pass
    AI did not name qdrant/fastembed — 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 qdrant/fastembed in production, what risks or prerequisites should they evaluate first?
    pass
    AI named qdrant/fastembed 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 qdrant/fastembed solve, and who is the primary audience?
    pass
    AI named qdrant/fastembed 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 qdrant/fastembed. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/qdrant/fastembed.svg)](https://repogeo.com/en/r/qdrant/fastembed)
HTML
<a href="https://repogeo.com/en/r/qdrant/fastembed"><img src="https://repogeo.com/badge/qdrant/fastembed.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

qdrant/fastembed — 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