Most people meet the OpenAI API through a single chat-completion snippet, then hit a wall the moment they need retrieval, evaluation, or structured output. This repo is where OpenAI itself answers those follow-up questions — a maintained library of working recipes, each one a runnable notebook rather than prose, so you copy a tested pattern instead of reverse-engineering one.
What Sets It Apart
- Roughly 93% of the content is executable Jupyter notebooks, not documentation — you run the cell, see the output, then adapt it. The gap between "reading about embeddings" and "having a working embedding pipeline" is one click.
- It is maintained by OpenAI staff alongside the community, so recipes track the current API surface (function calling, structured outputs, retrieval patterns) rather than drifting out of date like most third-party tutorials.
- Coverage spans the full applied stack — text generation, embeddings and vector search, RAG, function calling, fine-tuning, evals, and multimodal — instead of one narrow topic, so it doubles as a map of what the API can actually do.
Who It's For
Great fit if you are a developer who learns by running code and wants a vetted starting point for a concrete task — "how do I do RAG with embeddings," "how do I shape function-calling output." Look elsewhere if you want conceptual depth or model internals (this is recipes, not theory), if you are not building on the OpenAI API specifically, or if you need a packaged framework — these are standalone examples you assemble yourself, not a library you import.
