Pinecone Examples is a collection of Jupyter Notebooks and sample applications for learning and building with Pinecone vector databases.
The repository addresses the need to understand how vector databases work in practice and how to apply them to real AI problems. It provides two categories of material: production-ready examples maintained by the Pinecone engineering team, and learning-focused notebooks created by the Developer Advocacy team that explore AI techniques, retrieval-augmented generation, semantic search, and patterns for building different kinds of applications. Users can run the notebooks directly, study the code, and modify examples for their own use cases.
The project suits developers new to vector databases who want hands-on experience before building production systems, as well as those exploring specific AI patterns like RAG and semantic search. The learning-focused examples are designed for experimentation, while the production examples in the docs folder offer vetted implementations suitable for reference. The repository includes a getting started guide with instructions for running notebooks in Google Colab, lowering the barrier to entry for exploration.
The project maintains two distinct tracks with different maintenance models: production examples receive regular review and support from the engineering team, while learning examples are maintained by the Developer Advocacy team and optimized for educational value. The repository actively solicits feedback through its issue tracker and welcomes community contributions via a documented contribution process.
