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Database / ChromaDB Interview Questions

What is ChromaDB and what problem does it solve?

ChromaDB is an open-source, AI-native vector database designed to store, index, and query high-dimensional embedding vectors efficiently. It was created specifically to make building LLM-powered applications easy — particularly for retrieval-augmented generation (RAG), semantic search, and recommendation systems.

Traditional databases store and search data using exact matches or SQL-style predicates. ChromaDB instead answers the question: "which stored items are most semantically similar to this query?" It does this by storing numerical vectors (embeddings) that represent the meaning of text, images, or other data, and finding nearest neighbours using vector similarity search.

ChromaDB at a glance
FeatureDetail
LanguagePython-first (also JS/TS client)
LicenseApache 2.0 open source
Storage modesIn-memory (ephemeral) or persistent (disk-backed)
Default embedding modelall-MiniLM-L6-v2 via sentence-transformers
Distance metricscosine, l2 (Euclidean), ip (inner product)
Primary use casesRAG pipelines, semantic search, duplicate detection, recommendation
pip install chromadb

import chromadb

# Quickstart — in-memory client
client = chromadb.Client()
collection = client.create_collection("my_docs")
collection.add(
    documents=["ChromaDB is a vector database", "Python is great"],
    ids=["doc1", "doc2"],
)
results = collection.query(query_texts=["vector store for AI"], n_results=1)
print(results["documents"])  # [["ChromaDB is a vector database"]]

What type of database is ChromaDB?
What is the primary query type that ChromaDB is designed for?

More Related questions...

What is ChromaDB and what problem does it solve? What are embeddings and why are they central to how ChromaDB works? What distance metrics does ChromaDB support and how do you choose between them? What is a ChromaDB collection and how do you create, list, get, and delete collections? How do you add documents to a ChromaDB collection? How do you query a ChromaDB collection for similar documents? How do you retrieve, update, and delete specific documents in ChromaDB? How do you filter query results using metadata in ChromaDB? What is the difference between ChromaDB's in-memory and persistent storage modes? What is ChromaDB's default embedding function and how does it work? How do you use the OpenAI embedding function with ChromaDB? How do you use HuggingFace models as embedding functions in ChromaDB? How do you create a custom embedding function for ChromaDB? How does ChromaDB's PersistentClient store data on disk, and what are its limitations? What is the HNSW index in ChromaDB and what parameters can you tune? How do you efficiently add large numbers of documents to ChromaDB using batching? What is the where_document filter in ChromaDB and how does it differ from where? How do you control what data ChromaDB returns in query and get results using include? How do you design metadata schemas for effective filtering in ChromaDB? How do you inspect a ChromaDB collection's contents and configuration? How do you build a basic RAG (Retrieval-Augmented Generation) pipeline with ChromaDB? What are effective document chunking strategies when indexing documents into ChromaDB for RAG? How do you use ChromaDB as a vector store with LangChain? How do you implement multi-tenancy or data isolation in ChromaDB? What is embedding consistency and why is it critical in ChromaDB applications? How do you run ChromaDB as a standalone HTTP server and connect to it from multiple clients? When should you use upsert() instead of add() in ChromaDB, and what are common patterns? What are best practices for structuring ChromaDB collection metadata for production use? How does ChromaDB compare to FAISS, and when should you choose one over the other? What are common ChromaDB errors and how do you handle them in production code? How do you back up and restore a ChromaDB persistent database? How do you ensure the correct embedding function is used when reopening a persistent ChromaDB collection? How do you interpret ChromaDB query distances and convert them into meaningful relevance scores? What are ChromaDB's practical size limits and performance characteristics at scale? How do you use ChromaDB to detect and remove near-duplicate or semantically similar documents? How do you reset or clear a ChromaDB collection without deleting and recreating it? What configuration settings does ChromaDB support and how do you disable telemetry? What is a production readiness checklist for a ChromaDB-based application?
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