pinecone-io/examples: Jupyter Notebooks and Sample Apps for Pinecone Vector Databases

Two Tiers: Supported Notebooks and Exploratory Notebooks

The repository makes a clear structural distinction between two types of content. The docs/ folder contains production-ready examples that receive regular review and support from Pinecone's engineering team. These notebooks are the safer choice for developers building on Pinecone in a production context: they are checked against current API behavior and reflect patterns the Pinecone team recommends.

The learn/ folder contains examples created and maintained by Pinecone's Developer Advocacy team. These notebooks are optimized for learning and exploring AI techniques rather than serving as reference implementations for production code. They cover a wider range of topics and patterns but without the same review cadence as the docs/ notebooks.

This two-tier structure is worth understanding before picking a notebook to follow. A developer who wants to implement semantic search or RAG in a real application should start in docs/. Someone who wants to understand how a particular retrieval technique or AI pattern works at a conceptual level will find more variety in learn/.

What the Repository Covers

The README describes the repository as covering Pinecone vector databases and common AI patterns, tools, and algorithms. The pyproject.toml development dependencies reveal the stack the examples are built around: the pinecone SDK at 6.0.2 or later, langchain at 0.3.21 or later, openai at 1.68.0 or later, tiktoken for token counting, and python-dotenv for managing API keys. The marimo dependency (0.23.6 or later) suggests some notebooks use the Marimo reactive notebook format rather than standard Jupyter.

The top-level repository structure includes directories for .ai/, .claude/, and .cursor/ editor configuration files alongside the notebooks, indicating the repository is explicitly designed to be used with AI coding assistants that read the project context. The presence of AGENTS.md in the top-level entries suggests some notebooks or scripts are intended to be run by or with AI agents.

The repository does not include the Pinecone API itself or any self-hosted Pinecone components. Every example assumes you have a Pinecone account and an active index. The notebooks demonstrate how to use the Pinecone client library, not how to operate or extend Pinecone's infrastructure.

Setting Up the Development Environment

The README directs first-time users to the Getting Started guide in the learn/ section, which provides instructions for running notebooks in Google Colab. Google Colab is the quickest path for developers who want to run a notebook without setting up a local Python environment: open the notebook in Colab, supply your Pinecone API key as a Colab secret, and run the cells.

The repository uses uv for local development, evidenced by the uv.lock file and the [tool.uv] section in pyproject.toml. The pyproject.toml specifies Python 3.10 or later and below 3.14. Dev dependencies are declared as optional extras under [project.optional-dependencies], including pinecone, langchain, openai, tiktoken, python-dotenv, marimo, ruff, and pre-commit. The README does not document a specific local install command; it directs users to the Getting Started guide in learn/ for setup steps.

The pre-commit configuration and ruff linter are included for contributors. Ruff is configured to check for pyflakes and isort rules and formats docstrings in code blocks. The line-length setting is 88 characters.

Running Notebooks in Google Colab vs Locally

Google Colab requires no local installation and handles GPU access for notebooks that need it, but it has session timeouts and limited persistent storage. For notebooks that process large datasets or require long-running jobs, a local or cloud-hosted Jupyter environment is more practical.

Running locally against the pyproject.toml dev dependencies gives you consistent versions of pinecone, langchain, and openai as they were when the notebook was tested. This matters because AI libraries release breaking changes frequently; a notebook that ran correctly against langchain 0.3.x may fail against a newer version. The lock file (uv.lock) captures the exact resolved versions used during development.

The CONTRIBUTING.md file in the repository describes how to contribute new examples. Notebooks contributed to docs/ require the review of the Pinecone engineering team; those going into learn/ are maintained by the Developer Advocacy team. This means the review bar and turnaround time differ depending on where a contribution lands.

Limitations of the Examples Repository

The repository is a learning and reference resource, not a deployable application or a library you install into a project. Its value is in showing how Pinecone integrates with other AI libraries and what patterns work well; it does not provide abstraction layers or utilities you can import into your own code.

The docs/ and learn/ notebooks depend on external API keys for Pinecone and, in most cases, OpenAI. None of the examples demonstrate running a comparable pipeline with a fully local or open-source stack. Developers who need to avoid OpenAI for cost or privacy reasons will find that most notebook examples assume access to OpenAI's embedding or chat completion endpoints.

The examples also do not cover Pinecone's operational concerns: index configuration for production workloads, cost estimation, monitoring, or disaster recovery. The repository is explicitly a code example collection, not documentation of Pinecone's operational features. For production guidance, the README points to Pinecone's official documentation at docs.pinecone.io and the community forums at community.pinecone.io.

Maintenance, License, and How to Contribute

The last push was on 2026-09-19, nine days before the current date, which is consistent with regular maintenance. The MIT license applies to all code in the repository, meaning you can use, modify, and redistribute the notebook code in your own projects.

A broadly comparable resource for developers working with other vector databases is the ChromaDB Cookbook repository, which similarly provides Jupyter Notebooks for demonstrating ChromaDB's retrieval patterns with Python. The key difference is scope: pinecone-io/examples focuses exclusively on Pinecone's managed cloud vector database, while ChromaDB examples target a self-hosted and embeddable vector database with a different operational model. If your project requires a self-hosted vector store, the patterns in pinecone-io/examples will not transfer directly because Pinecone's index management API is specific to its managed service.

Contributions to the repository follow the guidelines in CONTRIBUTING.md. Issues and confusing or broken examples can be reported through the GitHub issue tracker. The README explicitly asks users to open issues rather than just abandoning a notebook that does not work.

Editorial conclusion

This repository is the right starting point for any developer who wants working code that demonstrates Pinecone's API in real patterns rather than minimal toy examples. The split between docs/ and learn/ is meaningful: docs/ notebooks are reviewed by the engineering team and reflect patterns Pinecone considers production-grade, while learn/ notebooks cover a wider range of AI techniques at the cost of less rigorous review. Before running any notebook, verify that the Pinecone SDK version in pyproject.toml matches your project's requirements, since the dev dependencies pin specific versions of pinecone, langchain, and openai.

Frequently asked questions

Official sources

1. Issues
2. License: MIT
3. pinecone-io/examples on GitHub
4. Project website
5. README