Production-style documentation samples for Pinecone vector database. Written to demonstrate documentation for ML-adjacent developer audiences — the same audience who would read Pinecone's actual docs.
Author: Tyra Miles · tyratech23@gmail.com · Portfolio
| File | Type | What it demonstrates |
|---|---|---|
quickstart-pinecone-python.md |
Developer quickstart | Step-by-step Pinecone onboarding — index creation, upsert, similarity search, metadata filtering |
what-are-vector-embeddings.md |
Conceptual guide | Vector embeddings explained for API developers new to ML — covers similarity metrics, embedding models, ANN search, sparse vs. dense |
rag-with-pinecone.md |
Walkthrough + sample app | End-to-end RAG pipeline using Pinecone + OpenAI — embed a knowledge base, retrieve context, generate grounded answers |
These samples were written to demonstrate documentation for vector database and AI search infrastructure — the documentation domain I'm currently focused on alongside my work at Chargebee.
My background is 7+ years of API and developer documentation for high-growth SaaS companies. See my full API documentation samples for production samples from billing API work.