Pinecone split its examples into shipping code and sketchpads

What it does

This repository houses sample applications and Jupyter Notebooks for experimenting with Pinecone’s vector database and common AI patterns. The content is explicitly divided into two camps: production-ready examples in ./docs that receive engineering support, and learning-oriented notebooks in ./learn maintained by the developer advocacy team. The included getting started guide targets Google Colab for experimentation.

The interesting bit

Most example repos dump everything into one folder and let you guess which code is safe to cargo-cult. Pinecone labels the shelf life upfront: one directory for reviewed, supported code and another for exploratory AI techniques. That transparency is rarer than it should be.

Key highlights

• Production-ready examples live in ./docs and get regular engineering review
• Learning and exploration notebooks live in ./learn, aimed at AI technique experimentation
• Maintained by two distinct internal teams: engineering and developer advocacy
• Designed for Google Colab execution, though the README does not detail specific notebook topics
• Open to community contributions with a dedicated CONTRIBUTING.md

Caveats

• The README stays high-level; you will need to browse the directories to see which notebooks exist
• The getting started guide targets Google Colab; local or offline workflows are not discussed here

Verdict

Developers evaluating Pinecone or prototyping vector-search features should bookmark this. If you are not using Pinecone or already know the SDK by heart, there is little reason to browse.