A Guide to Chroma DB Installation

Chroma DB Introduction

Chroma DB is an open-source vector database that seamlessly integrates with all Artificial intelligence frameworks, making it a prime choice for developers.

Its efficient indexing and retrieval mechanisms enable rapid similarity searches, allowing users to quickly find relevant data points within large datasets.

Vectors and Vector Database:

A vector is a mathematical representation of data. Vectors are arrays of numbers, with each number representing a specific feature or attribute of the data.

A vector database is a type of database designed to store and manage vectors. They are capable of storing and analyzing unstructured data like text, images, and audio. 

Whether you are developing an AI Chatbot, image search, AI Product Recommendation etc, a vector database is essential for their working.

Why Use Chroma DB?

• Easy to use: Chroma DB is designed to be simple to use, so that anyone without prior knowledge to vector databases can use it with ease.
• In-memory storage: Chroma DB allows low latency data access by utilizing in-memory storage mechanisms.
• Seamless Integration: Chroma DB’s API provide developers with a straightforward interface to interact with the database.
• Scalability: It can seamlessly  expand to accommodate increased demands without compromising performance.
• High-throughput operations: it has high throughput rate for processing queries and retrieving vector embeddings.

Setting up Chroma DB

1) Setup Using Docker:

Prerequisites:

• Git: For cloning the repository.
• Docker: For containerizing the application.
• Docker Compose: For managing multi-container Docker applications.

Step-by-Step Instructions:

• Clone the GitHub Repository. Open your terminal and run the following command to clone the Chroma DB repository to your local machine and navigate to cloned folder.

git clone https://github.com/chroma-core/chroma.git
cd chroma

• Since we are using Chroma DB API version 1, you can switch to version by executing the following command:

git checkout 9cce6b10d444ab05e1482adf73ef4d7e3039d0c7

• Review the docker-compose.yml file that defines the services needed to run the application. Ensure the docker-compose.yml file meets your requirements or modify it as needed.
• Build and Run the Containers using command using command.

docker-compose up -d --build

2) Setup using python

Prerequisites:

• Git: For cloning the repository.
• Python/ Miniconda/ Conda: For managing Python versions>3.11 and environments.

Step-by-Step Instructions:

• Clone the GitHub Repository. Open your terminal and run the following command to clone the ChromaDB repository to your local machine and navigate to cloned folder

git clone https://github.com/chroma-core/chroma.git
cd chroma

• Since we are using Chroma DB API version 1, you can switch to version by executing the following command:

git checkout 9cce6b10d444ab05e1482adf73ef4d7e3039d0c7

• Create a Virtual environment using conda or miniconda and activate it.
• Install the requirements using requirements.txt file by executing the following command.

pip install -r requirements.txt
pip install chromadb==0.5.3

• Now run the chroma db using command

python chromadb/cli/cli.py run --path ./chroma_data --host 0.0.0.0 --port 8000

• You can verify the Setup if ChromaDB is running by visiting the exposed ports or endpoints defined in the docker-compose.yml file (e.g., http://{your_server_ip}:8000 if the service is exposed on port 8000).

Conclusion

Chroma DB’s easy-to-use interface, in-memory storage, and efficient indexing mechanisms make it an excellent choice for developers looking to integrate vector databases into their applications.

Whether you build up with Docker or Python, both options give a simple way to get started with this powerful technology.

Its scalability, high throughput, and seamless integration capabilities ensure that it can handle growing data needs while providing low-latency access to stored vectors.