RRepoGEO

REPOGEO REPORT · LITE

zhongyu09/openchatbi

Default branch main · commit 335d7c46 · scanned 6/10/2026, 9:47:09 AM

GitHub: 576 stars · 75 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
40 /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
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 zhongyu09/openchatbi, 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's opening to emphasize 'complete platform'

    Why:

    CURRENT
    OpenChatBI is an open source, chat-based intelligent BI tool powered by large language models, designed to help users query, analyze, and visualize data through natural language conversations. Built on LangGraph and LangChain ecosystem, it provides chat agents and workflows that support natural language to SQL conversion and streamlined data analysis.
    COPY-PASTE FIX
    OpenChatBI is a complete open-source, chat-based intelligent BI platform that empowers users to query, analyze, and visualize data through natural language conversations. Leveraging the LangGraph and LangChain ecosystem, it delivers ready-to-use chat agents and workflows for natural language to SQL conversion and streamlined data analysis.
  • mediumreadme#2
    Add a 'Why OpenChatBI?' or 'Key Differentiators' section to the README

    Why:

    COPY-PASTE FIX
    ## Why OpenChatBI?
OpenChatBI stands out as an open-source, full-fledged Business Intelligence (BI) platform that deeply integrates Large Language Models (LLMs) for natural language querying, SQL generation, and direct chart visualization. Unlike proprietary solutions, OpenChatBI offers transparency, extensibility, and community-driven development, making advanced BI accessible and customizable.
  • lowreadme#3
    Expand 'Core Features' with explicit user benefits and use cases

    Why:

    CURRENT
    1. **Natural Language Interaction**: Get data analysis results by asking questions in natural language
    COPY-PASTE FIX
    1. **Natural Language Interaction**: Get data analysis results by asking questions in natural language, enabling business users and analysts to explore data without writing code.

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 zhongyu09/openchatbi
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
rasahq/rasa
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. rasahq/rasa · recommended 1×
  2. OpenAI GPT-3/GPT-4 · recommended 1×
  3. Google Cloud Natural Language API · recommended 1×
  4. sqlalchemy/sqlalchemy · recommended 1×
  5. pandas-dev/pandas · recommended 1×
  • CATEGORY QUERY
    How to build an intelligent BI tool that understands natural language queries for data analysis?
    you: not recommended
    AI recommended (in order):
    1. Rasa (rasahq/rasa)
    2. OpenAI GPT-3/GPT-4
    3. Google Cloud Natural Language API
    4. SQLAlchemy (sqlalchemy/sqlalchemy)
    5. Pandas (pandas-dev/pandas)
    6. Apache Calcite (apache/calcite)
    7. Tableau
    8. Power BI
    9. Plotly Dash (plotly/dash)
    10. Apache Superset (apache/superset)
    11. PostgreSQL
    12. Snowflake

    AI recommended 12 alternatives but never named zhongyu09/openchatbi. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool to convert natural language to SQL queries and visualize data with LLMs?
    you: not recommended
    AI recommended (in order):
    1. ThoughtSpot
    2. Microsoft Power BI with Copilot
    3. Tableau with Ask Data/Tableau Pulse
    4. Dataherald
    5. Seek AI
    6. AI2sql
    7. ChatGPT/GPT-4

    AI recommended 7 alternatives but never named zhongyu09/openchatbi. 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 zhongyu09/openchatbi?
    pass
    AI named zhongyu09/openchatbi explicitly

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

  • If a team adopts zhongyu09/openchatbi in production, what risks or prerequisites should they evaluate first?
    pass
    AI named zhongyu09/openchatbi 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 zhongyu09/openchatbi solve, and who is the primary audience?
    pass
    AI named zhongyu09/openchatbi explicitly

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

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zhongyu09/openchatbi — 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