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 — 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]] — 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 — adds response size