Introduction

ChromaDB is a powerful vector database designed for semantic search and similarity retrieval. One of its core features is the ability to store and query fixed-dimensional embeddings. In this article, we will explore how ChromaDB handles fixed-dimensional embeddings, why dimensionality matters, and how to ensure compatibility when using different embedding models.

Understanding Fixed-Dimensional Embeddings

Embeddings are numerical representations of text, images, or other data types in a high-dimensional space. The dimensionality of an embedding refers to the number of elements in its vector representation. For example, the popular all-MiniLM-L6-v2 model generates embeddings with 384 dimensions.

In ChromaDB, every collection enforces that all embeddings stored within it have a fixed dimensionality. This ensures consistency and makes similarity search operations more efficient.

Why Fixed Dimensionality Matters

1. Consistency: Fixed dimensionality ensures that all vectors in a collection are compatible when performing similarity calculations (e.g., cosine similarity or Euclidean distance).
2. Performance:：ChromaDB uses indexing algorithms like HNSW, which rely on fixed-dimensional vectors for efficient nearest-neighbor searches.
3. Model Compatibility:：Different embedding models produce vectors of varying dimensions. ChromaDB requires all embeddings in a collection to match the dimensionality of the model used when the collection was created.

How ChromaDB Enforces Fixed Dimensionality

When creating a collection in ChromaDB, the dimensionality is determined by the embedding model you specify. For example:

import chromadb
from chromadb.utils.embedding_functions import SentenceTransformerEmbeddingFunction

# Initialize an embedding function with a 384-dimensional model  
embedding_fn = SentenceTransformerEmbeddingFunction()

# Create a collection with fixed dimensionality  
client = chromadb.Client()
collection = client.create_collection(
    name="my_collection",
    embedding_function=embedding_fn
)