RRepoGEO

REPOGEO REPORT · LITE

GaoQ1/rasa_chatbot_cn

Default branch master · commit 4b7908b7 · scanned 5/31/2026, 9:23:10 AM

GitHub: 980 stars · 284 forks

AI VISIBILITY SCORE
22 /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
1 / 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 GaoQ1/rasa_chatbot_cn, 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 README introduction to highlight Chinese Rasa chatbot example

    Why:

    CURRENT
    # Rasa Core and Rasa NLU
    ## rasa对话系统系列文章
    - rasa对话系统踩坑记(一)
    ...
    ## Introduction
    rasa版本已经更新到了2.0版本,改动比较大, 等2.0版本稳定后再跟进了。现在这里的代码还是去年上半年的版本,后面rasa做了很多改动,component已经支持bert,对中文的支持也更好。所以这个之前基于1.1.x的版本就转到1.1.x分支,目前master分支的话就分享最新的基于1.10.18的一套支持中文的pipeline
    ...
    ### external link
    Body Visualizer
    AudioX
    WebNovel AI
    Video Any
    Deepseek Video
    Seedance 2.0
    Seedance Image To Video
    DeepFake AI
    VeoNano
    Veo4
    Happy Horse
    Unbound AI
    AI Product Photography
    Tale Hug
    COPY-PASTE FIX
    # GaoQ1/rasa_chatbot_cn: A Chinese Dialogue System with Rasa (v1.10.18)
    
    This repository provides a practical, runnable example of a Chinese dialogue system built using Rasa (version 1.10.18). It demonstrates a complete pipeline for conversational AI in Chinese, including intent classification, slot filling, and policy management, with support for modern NLP components like BERT.
    
    ## Introduction
    While Rasa has evolved to version 2.0+, this project focuses on a stable and robust implementation based on Rasa 1.10.18, specifically optimized for Chinese language understanding and generation. It serves as a valuable resource for developers looking to implement Rasa-based chatbots for Chinese users.
    
    ## Rasa Dialogue System Articles
    This repository is accompanied by a series of articles detailing the development process and common pitfalls:
    - rasa对话系统踩坑记(一)
    - rasa对话系统踩坑记(二)
    - rasa对话系统踩坑记(三)
    - rasa对话系统踩坑记(四)
    - rasa对话系统踩坑记(五)
    - rasa对话系统踩坑记(六)
    - rasa对话系统踩坑记(七)
    - rasa对话系统踩坑记(八)
    - rasa对话系统踩坑记(九)
    - rasa对话系统踩坑记(十)
    - rasa-nlu的究极形态
    - 闲聊模型的实践并应用到rasa
  • highlicense#2
    Add a LICENSE file to clarify usage terms

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root, choosing an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that reflects your intentions for the project.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Set the repository's homepage URL to a relevant link, such as `https://github.com/GaoQ1/rasa_chatbot_cn` (if no external project page exists) or a blog post detailing the project.

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 GaoQ1/rasa_chatbot_cn
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PaddleNLP
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PaddleNLP · recommended 2×
  2. Hugging Face Transformers · recommended 2×
  3. Jieba · recommended 2×
  4. DeepPavlov · recommended 2×
  5. Rasa · recommended 1×
  • CATEGORY QUERY
    How to build a conversational AI system with robust Chinese language understanding?
    you: not recommended
    AI recommended (in order):
    1. Rasa
    2. PaddleNLP
    3. Hugging Face Transformers
    4. Jieba
    5. DeepPavlov
    6. Microsoft Bot Framework Composer
    7. Google Dialogflow ES/CX

    AI recommended 7 alternatives but never named GaoQ1/rasa_chatbot_cn. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good Python frameworks for developing Chinese NLP-driven dialogue systems?
    you: not recommended
    AI recommended (in order):
    1. Rasa Open Source
    2. DeepPavlov
    3. Hugging Face Transformers
    4. NLTK
    5. Jieba
    6. SpaCy
    7. PaddleNLP

    AI recommended 7 alternatives but never named GaoQ1/rasa_chatbot_cn. 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 GaoQ1/rasa_chatbot_cn?
    pass
    AI did not name GaoQ1/rasa_chatbot_cn — 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 GaoQ1/rasa_chatbot_cn in production, what risks or prerequisites should they evaluate first?
    pass
    AI named GaoQ1/rasa_chatbot_cn 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 GaoQ1/rasa_chatbot_cn solve, and who is the primary audience?
    pass
    AI did not name GaoQ1/rasa_chatbot_cn — 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?

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GaoQ1/rasa_chatbot_cn — 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