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Database / ChromaDB Interview Questions

What are embeddings and why are they central to how ChromaDB works?

An embedding is a dense numerical vector — a list of floating-point numbers — that represents the semantic meaning of a piece of data. Text, images, audio, and code can all be converted into embeddings by a neural network (embedding model). Items with similar meanings produce vectors that are close together in the high-dimensional vector space.

ChromaDB stores these vectors alongside the original data and metadata. When you query ChromaDB with a new piece of text, the same embedding model converts it to a vector, and ChromaDB uses an approximate nearest-neighbour (ANN) algorithm to find the stored vectors that are geometrically closest — these correspond to the most semantically relevant stored documents.

# Conceptual illustration
# "ChromaDB is a vector database"  → [0.12, -0.45, 0.89, ..., 0.03]  (384 numbers)
# "Vector stores for AI apps"      → [0.14, -0.41, 0.91, ..., 0.01]  (close!)
# "My cat loves tuna fish"         → [-0.55, 0.72, -0.11, ..., 0.88] (far away)

import chromadb
from chromadb.utils import embedding_functions

# You can inspect the raw embedding vectors ChromaDB generates
client = chromadb.Client()
collection = client.create_collection("demo")
collection.add(documents=["Hello world"], ids=["1"])

# Get the stored embedding
result = collection.get(ids=["1"], include=["embeddings"])
print(len(result["embeddings"][0]))   # 384 — length of the default model's vector
print(result["embeddings"][0][:5])    # first 5 of 384 floats

Embedding dimensions by model
ModelDimensionsNotes
all-MiniLM-L6-v2 (default)384Fast, small, good for English
text-embedding-ada-002 (OpenAI)1536High quality, API call required
text-embedding-3-small (OpenAI)1536Newer, cheaper than ada-002
all-mpnet-base-v2768Higher quality than MiniLM, slower
CLIP (images)512Multimodal — text and images same space
What is an embedding in the context of ChromaDB?
Why do semantically similar texts produce vectors that are close together?

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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