# Filling up Qdrant collection with the embeddings generated by Cohere co.embed API
qdrant_client.upsert(
    collection_name="pubmed_qa",
    points=rest.Batch(
        ids=ids,
        vectors=vectors,
        payloads=list(dataset["train"]),
    )
)

```

And that’s it. Without even setting up a single server on our own, we created a system that might be easily asked a question. I don’t want to call
it serverless, as this term is already taken, but co.embed API with Qdrant Cloud makes everything way easier to maintain.

### [Anchor](https://qdrant.tech/articles/qa-with-cohere-and-qdrant/\#answering-the-questions-with-semantic-search--the-quality) Answering the questions with semantic search — the quality

It’s high time to query our database with some questions. It might be interesting to somehow measure the quality of the system in general.
In those kinds of problems we typically use _top-k accuracy_. We assume the prediction of the system was correct if the correct answer
was present in the first _k_ results.

```python