RRepoGEO

REPOGEO REPORT · LITE

pinecone-io/canopy

Default branch main · commit fabb7d7c · scanned 5/29/2026, 3:31:54 PM

GitHub: 1,032 stars · 129 forks

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 pinecone-io/canopy, 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
  • highabout#1
    Update repository description to reflect deprecation

    Why:

    CURRENT
    Retrieval Augmented Generation (RAG) framework and context engine powered by Pinecone
    COPY-PASTE FIX
    DEPRECATED: Retrieval Augmented Generation (RAG) framework and context engine powered by Pinecone. Please see Pinecone Assistant for a maintained solution.
  • mediumreadme#2
    Integrate deprecation status into README's main introduction

    Why:

    CURRENT
    **Canopy** is an open-source Retrieval Augmented Generation (RAG) framework and context engine built on top of the Pinecone vector database. Canopy enables you to quickly and easily experiment with and build applications using RAG. Start chatting with your documents or text data with a few simple commands.
    COPY-PASTE FIX
    **Canopy** was an open-source Retrieval Augmented Generation (RAG) framework and context engine built on top of the Pinecone vector database. This repository is no longer maintained; for a high-quality managed RAG solution with continued updates and improvements, please check out the Pinecone Assistant.
  • lowtopics#3
    Add 'deprecated' topic to repository

    Why:

    CURRENT
    generative-ai, llm, rag, vector-database
    COPY-PASTE FIX
    generative-ai, llm, rag, vector-database, deprecated

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 pinecone-io/canopy
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 2×
  2. LangChain · recommended 2×
  3. Streamlit · recommended 1×
  4. Vercel · recommended 1×
  5. Gradio · recommended 1×
  • CATEGORY QUERY
    How to quickly build and deploy a Retrieval Augmented Generation (RAG) chat application?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. Streamlit
    3. Vercel
    4. LangChain
    5. Gradio
    6. Hugging Face Spaces
    7. Haystack (deepset/haystack)
    8. FastAPI
    9. Render
    10. OpenAI Assistants API
    11. Weaviate
    12. Pinecone
    13. LiteLLM

    AI recommended 13 alternatives but never named pinecone-io/canopy. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source frameworks simplify building RAG applications with document-based context retrieval?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack (deepset/Haystack)
    4. RAGatouille
    5. DSPy
    6. Sentence-Transformers

    AI recommended 6 alternatives but never named pinecone-io/canopy. 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 pinecone-io/canopy?
    pass
    AI named pinecone-io/canopy explicitly

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

  • If a team adopts pinecone-io/canopy in production, what risks or prerequisites should they evaluate first?
    pass
    AI named pinecone-io/canopy 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 pinecone-io/canopy solve, and who is the primary audience?
    pass
    AI named pinecone-io/canopy 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 pinecone-io/canopy. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/pinecone-io/canopy.svg)](https://repogeo.com/en/r/pinecone-io/canopy)
HTML
<a href="https://repogeo.com/en/r/pinecone-io/canopy"><img src="https://repogeo.com/badge/pinecone-io/canopy.svg" alt="RepoGEO" /></a>
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pinecone-io/canopy — 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