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