Database / ChromaDB Interview Questions
How do you query a ChromaDB collection for similar documents?
The primary query method is collection.query(). You pass either query_texts (raw strings that ChromaDB embeds automatically) or query_embeddings (pre-computed vectors). ChromaDB returns the n_results nearest neighbours for each query.
import chromadb client = chromadb.Client() collection = client.create_collection("knowledge_base") collection.add( documents=[ "Python is great for data science and machine learning.", "JavaScript is used for web development.", "ChromaDB stores and retrieves vector embeddings.", "Docker containers package applications with dependencies.", ], ids=["d1", "d2", "d3", "d4"], ) # Basic query â returns top 2 most similar documents results = collection.query( query_texts=["vector database for AI"], n_results=2, ) print(results["documents"]) # [[most_similar, second_most_similar]] print(results["ids"]) # [["d3", "d1"]] print(results["distances"]) # [[0.18, 0.74]] â lower = more similar # Query multiple texts at once (batch query) results = collection.query( query_texts=["machine learning", "web frameworks"], n_results=2, ) # results["documents"][0] = top 2 for "machine learning" # results["documents"][1] = top 2 for "web frameworks" # Control what is returned with include= results = collection.query( query_texts=["Python programming"], n_results=3, include=["documents", "metadatas", "distances", "embeddings"], ) # Default include: ["documents", "metadatas", "distances"] # "embeddings" must be explicitly requested â adds response size
| Field | Type | Description |
|---|---|---|
| ids | list[list[str]] | IDs of matching documents, outer list = per query |
| documents | list[list[str]] | Original text of matching documents |
| metadatas | list[list[dict]] | Metadata dicts of matching documents |
| distances | list[list[float]] | Similarity distances (lower = more similar for l2/cosine) |
| embeddings | list[list[list[float]]] | Raw vectors — only if include=['embeddings'] |
What does n_results=5 mean in a ChromaDB query?
In a ChromaDB query result, what do the distance values represent?
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