RRepoGEO

REPOGEO REPORT · LITE

datawhalechina/all-in-rag

Default branch main · commit 499d6822 · scanned 6/24/2026, 9:37:56 PM

GitHub: 8,902 stars · 4,434 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)

3 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 datawhalechina/all-in-rag, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the root of the repository, specifying the intended open-source license (e.g., MIT, Apache-2.0, or CC-BY-4.0 for documentation).
  • highreadme#2
    Add an explicit English positioning statement to the README

    Why:

    CURRENT
    The current README starts with # All-in-RAG | 大模型应用开发实战一：RAG技术全栈指南
    COPY-PASTE FIX
    Add the following line directly after the main H1: 'This is a comprehensive, full-stack *guide and tutorial* for Retrieval Augmented Generation (RAG) techniques.'
  • mediumabout#3
    Refine the repository description to highlight its nature as a guide

    Why:

    CURRENT
    🔍大模型应用开发实战一：RAG 技术全栈指南，在线阅读地址：https://datawhalechina.github.io/all-in-rag/
    COPY-PASTE FIX
    A comprehensive, full-stack *guide and tutorial* for Retrieval Augmented Generation (RAG) techniques, covering theory, practice, and production-ready applications for LLM developers. Online reading: https://datawhalechina.github.io/all-in-rag/

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 datawhalechina/all-in-rag
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. Weaviate · recommended 2×
  3. LlamaIndex · recommended 2×
  4. Streamlit · recommended 1×
  5. Gradio · recommended 1×
  • CATEGORY QUERY
    Where can I find a full-stack guide for building retrieval augmented generation systems?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Streamlit
    3. Gradio
    4. Pinecone
    5. Weaviate
    6. Chroma
    7. OpenAI
    8. Anthropic
    9. LlamaIndex
    10. Hugging Face Transformers
    11. Hugging Face Datasets
    12. Qdrant

    AI recommended 12 alternatives but never named datawhalechina/all-in-rag. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to develop production-ready multimodal RAG applications with advanced retrieval techniques?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Weaviate
    5. Milvus
    6. Zilliz Cloud
    7. Elasticsearch

    AI recommended 7 alternatives but never named datawhalechina/all-in-rag. 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 datawhalechina/all-in-rag?
    pass
    AI named datawhalechina/all-in-rag explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts datawhalechina/all-in-rag in production, what risks or prerequisites should they evaluate first?
    pass
    AI named datawhalechina/all-in-rag 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 datawhalechina/all-in-rag solve, and who is the primary audience?
    pass
    AI named datawhalechina/all-in-rag explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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datawhalechina/all-in-rag — 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