RRepoGEO

REPOGEO REPORT · LITE

hwchase17/chat-your-data

Default branch master · commit fd195b0e · scanned 6/10/2026, 5:58:09 PM

GitHub: 970 stars · 286 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
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 hwchase17/chat-your-data, 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 H1 and first sentence to clarify it's an example RAG app

    Why:

    CURRENT
    # Chat-Your-Data

Create a ChatGPT like experience over your custom docs using LangChain.
    COPY-PASTE FIX
    # Chat-Your-Data: A Minimalist RAG Example with LangChain

This repository provides a simple, self-contained example for creating a ChatGPT-like experience over your custom documents using LangChain and OpenAI Embeddings.
  • highabout#2
    Add a concise description to the repository's About section

    Why:

    COPY-PASTE FIX
    A minimalist, self-contained example of a Retrieval Augmented Generation (RAG) application for chatting with custom data, built with LangChain.
  • mediumtopics#3
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    langchain, rag, retrieval-augmented-generation, chatbot, q-and-a, custom-data, openai, embeddings, faiss, python-example

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 hwchase17/chat-your-data
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. Pinecone · recommended 2×
  3. Chroma · recommended 2×
  4. Weaviate · recommended 2×
  5. LlamaIndex · recommended 2×
  • CATEGORY QUERY
    How to create a ChatGPT-like interface for querying personal data?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. OpenAI API
    3. Pinecone
    4. Chroma
    5. Weaviate
    6. GPT-3.5 Turbo
    7. GPT-4
    8. Llama 2
    9. Mistral
    10. Hugging Face Transformers
    11. Anyscale Endpoints
    12. LlamaIndex
    13. Haystack
    14. Elasticsearch
    15. BERT
    16. RoBERTa
    17. SQLite
    18. PostgreSQL
    19. Voiceflow
    20. Botpress
    21. Python/Flask
    22. Node.js/Express
    23. Streamlit
    24. Gradio
    25. React

    AI recommended 25 alternatives but never named hwchase17/chat-your-data. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python tools exist for building Q&A chatbots from custom document collections?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Rasa
    5. Faiss
    6. Weaviate
    7. Pinecone
    8. Chroma
    9. Qdrant

    AI recommended 9 alternatives but never named hwchase17/chat-your-data. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 hwchase17/chat-your-data?
    pass
    AI named hwchase17/chat-your-data explicitly

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

  • If a team adopts hwchase17/chat-your-data in production, what risks or prerequisites should they evaluate first?
    pass
    AI named hwchase17/chat-your-data 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 hwchase17/chat-your-data solve, and who is the primary audience?
    pass
    AI did not name hwchase17/chat-your-data — 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?

Embed your GEO score

Drop this badge into the README of hwchase17/chat-your-data. 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/hwchase17/chat-your-data.svg)](https://repogeo.com/en/r/hwchase17/chat-your-data)
HTML
<a href="https://repogeo.com/en/r/hwchase17/chat-your-data"><img src="https://repogeo.com/badge/hwchase17/chat-your-data.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

hwchase17/chat-your-data — 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