Context: Building the vector store in the last entry was only half the job — the actual point of embeddings is being able to ask a question and get back the right stored document, not just any document. Chroma does this by comparing distances: turn the question into a vector the same way you turned each document into one, then measure how numerically close the question's vector is to each stored vector. Lower distance means more similar in meaning. The real test isn't whether it returns something — it always will — it's whether the distance numbers actually mean anything, i.e. whether relevant matches consistently score lower than irrelevant ones.
Ran: Reconnected to the collection built in Entry 05 (collection.count() confirmed all 3 entries were still there), then ran two queries through the same embed-then-search pattern:
>>> q = ollama.embeddings(model="nomic-embed-text", prompt="how do I check pod status with oc")
>>> results = collection.query(query_embeddings=[q["embedding"]], n_results=2)
>>> results["ids"]
