# pinecone-io/canopy

Retrieval Augmented Generation (RAG) framework and context engine powered by Pinecone

Repository: https://github.com/pinecone-io/canopy
Canonical: https://ross.abutalabs.com/products/pinecone-io-canopy
Homepage: https://www.pinecone.io/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: generative-ai, llm, rag, vector-database
Archived: true
Last push: 2024-11-13T06:32:28+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 80
- inputs: {"age_days": 1121, "days_push": 658, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1031, forks 128 (observed 2026-08-28T04:03:18.009475+00:00)

## What it is
Canopy is an open-source Retrieval Augmented Generation (RAG) framework and context engine built on top of the Pinecone vector database, providing chunking, embedding, query optimization, context retrieval, and chat history management. It ships as a Python library, a configurable RAG server, and a CLI chat tool for comparing RAG vs non-RAG workflows.

## Use cases
- build a rag chatbot over my documents
- chat with my text data using an llm
- experiment with retrieval augmented generation pipelines
- deploy a rag-powered chat server behind my chat ui
- evaluate rag vs non-rag workflows side by side
- chunk and embed documents into a vector database
- build a context engine for llm question answering

## When to choose
- you already use Pinecone and want a ready-made RAG pipeline for experimentation
- you want a quick way to chat with your documents via CLI or a built-in server
- you need a reference implementation of chunking, retrieval, and prompt construction for RAG

## When to avoid
- you need an actively maintained project - the team has explicitly stopped maintaining this repository
- you want a managed RAG solution - Pinecone points users to Pinecone Assistant instead
- you use a vector database other than Pinecone, since Canopy is tightly coupled to it
- you need production support or bug fixes going forward

## Facets
- artifact type: framework
- maturity: abandoned
- function: rag, llm-inference, vector-database, chatbot, prompt-engineering, cli, http-server
- domain: large-language-models, artificial-intelligence, chatbots, databases
- platform: python, cli, self-hosted
- tags: context-engine, pinecone, chat-engine, knowledge-base, embedding, document-chat, unmaintained, retrieval-augmented-generation, docker

## Member repositories
- pinecone-io/canopy (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:18.009475+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:07:29.024740+00:00, confidence not recorded.
  - readme: https://github.com/pinecone-io/canopy (fetched 2026-08-28T04:03:18.009475+00:00, sha 708f35e9c611)
  - homepage: https://www.pinecone.io/ (fetched 2026-08-29T13:07:17.052437+00:00, sha cde46239b446)
  - site_page: http://docs.pinecone.io/integrations/claude-code (fetched 2026-08-29T13:07:17.061689+00:00, sha acda6ff062c4)
  - site_page: https://docs.pinecone.io/ (fetched 2026-08-29T13:07:17.065541+00:00, sha 5d42c06f240c)
  - site_page: https://docs.pinecone.io/integrations/overview (fetched 2026-08-29T13:07:17.067290+00:00, sha dd7e007f0f68)
  - site_page: https://www.pinecone.io/pricing (fetched 2026-08-29T13:07:17.063576+00:00, sha 4bdfc68b2ff9)
  - site_page: https://www.pinecone.io/company (fetched 2026-08-29T13:07:17.069263+00:00, sha 7b3d2ccfef75)
- Data as of 2026-08-30T08:39:29.467469+00:00.