How to use Chroma to store and query vector embeddings

• example-chroma-vector-embeddings

  1 0 2.5 Python

  Example project for using chroma to store and query vector embeddings

Chroma is an open-source embedding database designed to store and query vector embeddings efficiently, enhancing Large Language Models (LLMs) by providing relevant context to user inquiries. In this tutorial, I will explain how to use Chroma in persistent server mode using a custom embedding model within an example Python project. The companion code repository for this blog post is available on GitHub.

• chroma

  32 12,877 9.8 Rust

  the AI-native open-source embedding database

  

Create a new project directory for our example project. Next, we need to clone the Chroma repository to get started. At the root of your project directory let's clone Chroma into it:

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• uvicorn

  58 8,019 8.6 Python

  An ASGI web server, for Python. 🦄

  

This will set up Chroma and run it as a server with uvicorn, making port 8000 accessible outside the net docker network. The command also mounts a persistent docker volume for Chroma's database, found at chroma/chroma from your project's root.

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