What is Chroma?
An open-source embedding database (vector database) designed to make it easier to build LLM applications.
Chroma is an open-source vector database, released under the Apache 2.0 license, that stores embeddings along with their documents and metadata. It is popular for retrieval-augmented generation (RAG) because it can run embedded inside a Python or JavaScript application for prototyping, as a standalone server, or as a hosted service (Chroma Cloud). It integrates with common LLM frameworks such as LangChain and LlamaIndex.
Core Architecture and Mechanics
To understand how Chroma is used in practice, it helps to look at its core behaviors:
- Stores high-dimensional numerical vectors (embeddings) generated by machine learning models, rather than traditional relational rows. Each record in a collection pairs an embedding with an ID, the source document text, and optional metadata.
- Runs approximate nearest neighbor search using an HNSW index to find the vectors closest to a query. This returns semantically similar results quickly without comparing the query against every stored vector.
- Combines vector similarity with metadata filters and document text search. This lets applications narrow results (for example, to one customer or date range) or match exact keywords alongside semantic similarity.
In a lakehouse architecture, Chroma usually holds embeddings derived from governed source data, such as documents or tables in Apache Iceberg prepared with tools like dbt, while the source of truth stays in the lakehouse.
Why Chroma Matters in the Modern Data Stack
Vector databases act as a retrieval layer for AI applications, letting Large Language Models search an organization’s own documents and ground their answers through RAG.
For teams starting with RAG, Chroma’s simple API and ability to run locally lower the barrier to building a working prototype, and the same code can later point at a server or hosted deployment. Teams still need to manage how embeddings are refreshed when source data changes.
Key Benefits
- Simple Developer Experience: A small API for adding documents, embedding them, and querying by similarity.
- Vendor Neutrality: Open source under Apache 2.0, with self-hosted and hosted options.
- Flexible Deployment: Runs in-process, as a client-server deployment, or as a managed cloud service.
Frequently Asked Questions
How is a vector database different from a relational database?
Instead of searching for exact matches, vector databases search for conceptual similarity based on how close vectors are in high-dimensional space. Many applications combine both: a relational or lakehouse store for facts and a vector store for semantic retrieval.
What is an embedding?
An embedding is a representation of text, images, or audio as a list of numbers that captures its semantic meaning, so that similar items end up close together in vector space.
How does Chroma impact data governance and security?
Chroma stores copies of content derived from source systems, so governance has to cover it too. Teams should control which data is embedded, secure access to the Chroma server or cloud workspace, and use metadata filters to keep each user’s queries scoped to data they are allowed to see.
Learn More
To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.