Pinecone Examples is an MIT-licensed collection of Jupyter Notebooks and sample applications that lets developers get hands-on with Pinecone vector databases and common AI patterns, tools, and algorithms.

What it is

Pinecone Examples lives in the AI and machine learning infrastructure ecosystem, specifically the vector database corner of it, and it is written as Jupyter Notebooks in Python. The repository is a working set of notebooks and small applications that a reader can run, download, study, and modify rather than a library that is imported into a project. Its subject matter is the Pinecone vector database and the techniques that are built on top of it, including retrieval-augmented generation, semantic search, vector search, and large language model patterns.

The concrete problem it solves is the blank starting point. A developer who wants to learn how a vector search or retrieval-augmented generation pipeline fits together normally has to assemble that starting point alone, out of scattered documentation, community posts, and personal experimentation, before any real work begins. This repository supplies readable notebooks that show the whole flow, so the first hour of a project is spent reading and running working code rather than reconstructing it. It replaces the hand-rolled starter notebook, and it does so under a permissive licence with published contribution rules.

Key capabilities

• Two explicit tiers of content: production-ready examples in ./docs that receive regular review and support from the Pinecone engineering team, and learning-focused examples in ./learn maintained by the Pinecone Developer Advocacy team.
• Notebooks that cover retrieval-augmented generation, semantic search, vector search, and large language model workflows, as reflected in the repository topics.
• Every example can be run, downloaded, studied, and modified, per the repository description of its intended use.
• A dedicated getting started walkthrough at ./learn/README.md#getting-started that covers setting up and running a Jupyter Notebook in Google Colab.
• Contribution process documented in CONTRIBUTING.md, which is where the project asks contributors to look before submitting changes.
• Support and feedback routed through the issue tracker for bugs and confusing examples, plus the Pinecone support forums at community.pinecone.io.
• Official documentation hosted at docs.pinecone.io for the underlying vector database.

Who uses it and how

• Developers evaluating vector databases who want to run a retrieval-augmented generation or semantic search pipeline end to end before committing to an architecture.
• Engineers who need a reference implementation rather than a tutorial, and who therefore work from the ./docs tier that the Pinecone engineering team reviews.
• Learners and data scientists working in Google Colab, where the documented getting started path removes the need for a local Python environment.
• Contributors who fork the repository, adapt an example, and open a pull request following CONTRIBUTING.md. The project shows 3,043 stars, 1,073 forks, and 64 open issues on GitHub.
• Teams that want their engineers reading the same worked examples instead of each writing separate throwaway notebooks.

Getting started

There is no package to install and no Docker image or compose file in the repository; the documented path is the getting started guide at ./learn/README.md#getting-started, which walks through setting up and running a Jupyter Notebook in Google Colab.

How it compares

No list of paid products that this project replaces is provided in the facts, and no similar tools are named in the facts either, so Pinecone Examples stands alone in this registry. Readers looking for comparisons should treat it as an example collection specific to Pinecone rather than an interchangeable item in a category of rivals.

When to use it — and when not to

Choose it when the goal is to learn or demonstrate a pattern on Pinecone and a notebook is an acceptable delivery format. A self-hoster should note that this is a set of examples rather than a service: it ships no server, database, or deployment artifact, so anyone adopting the code is responsible for their own notebook environment, credentials, and dependencies. It is a poor fit for someone who needs a supported client library, an SDK, or a stable API surface, and the README is short, with the real detail pushed into the linked getting started guide and documentation site.