Chroma

Since testcontainers-python v4.6.0

Introduction

The Testcontainers module for Chroma.

Adding this module to your project dependencies

Please run the following command to add the Chroma module to your python dependencies:

pip install testcontainers[chroma] chromadb requests

Usage example

Creating a Chroma container

import chromadb

from chromadb.config import Settings



from testcontainers.chroma import ChromaContainer





def basic_example():

    with ChromaContainer() as chroma:

        # Get connection URL

        connection_url = chroma.get_connection_url()



        # Create Chroma client

        client = chromadb.HttpClient(host=connection_url, settings=Settings(allow_reset=True))



        # Create a collection

        collection_name = "test_collection"

        collection = client.create_collection(name=collection_name)

        print(f"Created collection: {collection_name}")



        # Add documents and embeddings

        documents = [

            "This is a test document about AI",

            "Machine learning is a subset of AI",

            "Deep learning uses neural networks",

        ]



        embeddings = [

            [0.1, 0.2, 0.3],  # Simple example embeddings

            [0.2, 0.3, 0.4],

            [0.3, 0.4, 0.5],

        ]



        ids = ["doc1", "doc2", "doc3"]

        metadatas = [

            {"source": "test1", "category": "AI"},

            {"source": "test2", "category": "ML"},

            {"source": "test3", "category": "DL"},

        ]



        collection.add(documents=documents, embeddings=embeddings, ids=ids, metadatas=metadatas)

        print("Added documents to collection")



        # Query the collection

        results = collection.query(query_embeddings=[[0.1, 0.2, 0.3]], n_results=2)



        print("\nQuery results:")

        print(f"Documents: {results['documents'][0]}")

        print(f"Distances: {results['distances'][0]}")

        print(f"Metadatas: {results['metadatas'][0]}")



        # Get collection info

        collection_info = client.get_collection(collection_name)

        print("\nCollection info:")

        print(f"Name: {collection_info.name}")

        print(f"Count: {collection_info.count()}")



        # List all collections

        collections = client.list_collections()

        print("\nAvailable collections:")

        for coll in collections:

            print(f"- {coll.name}")





if __name__ == "__main__":

    basic_example()

Features

• Vector similarity search
• Document storage and retrieval
• Metadata filtering
• Collection management
• Embedding storage
• Distance metrics
• Batch operations
• REST API support

Configuration

The Chroma container can be configured with the following parameters:

• port: Port to expose (default: 8000)
• version: Chroma version to use (default: "latest")
• persist_directory: Directory to persist data (default: None)
• allow_reset: Whether to allow collection reset (default: True)