Vector Databases Fundamentals to Production [2026 Edition]

Editorial course preview

What the public course preview actually shows

These 3 complementary views highlight concrete, legible examples from the course presentation.

A screenshot of a VS Code editor showing Python code defining text documents in app.py, with a terminal below displaying the execution output including document IDs and distances. — Preview 1 of 3
This screenshot shows a VS Code environment where a Python script defines text documents and executes to retrieve results from a vector database, displaying calculated distances.

A diagram titled Chroma database workflow showing data flow from an App sending Queries through Gen Embedding into Chroma for Documents and Embeddings, then to an LLM ContextWindow — Preview 2 of 3
This diagram illustrates the Chroma database workflow, showing how queries are embedded and matched against stored documents before being passed to an LLM context window.

A screenshot of a Python code editor showing a list of document dictionaries and a for loop using collection.upsert to insert data into a vector database. — Preview 3 of 3
This IDE view demonstrates the Python syntax for defining document objects and using the upsert method to ingest text data into a ChromaDB collection.