Vector embeddings in ClickHouse with Ollama and OpenAI

ClickHouse's new `aiEmbed` function generates embeddings directly in SQL — no external pipeline needed. In this video, Mark Needham shows how to use it with both a local Ollama model and OpenAI. https://clickhouse.com/blog/clickhouse-release-26-06#aiembed 🔸 Point `aiEmbed` at a local Ollama model (Qwen3 Embedding) via a named collection with an OpenAI-compatible endpoint 🔸 Auto-generate embeddings on insert using a `DEFAULT aiEmbed(text)` column, backed by an HNSW vector similarity index 🔸 Run approximate nearest neighbor search with `L2Distance` to find phrases closest to a lookup embedding 🔸 Query a 1M-row Hugging Face dbpedia dataset pre-embedded with OpenAI's `text-embedding-3-large` 🔸 Switch `aiEmbed` to OpenAI, match embedding dimensions to the dataset, and search for "the best footballer ever"