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.

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"]]