ChromaDB

AI & Machine Learning

Definitions

  1. An open-source embedding database (vector database) designed to be the easiest way to build AI applications that need to search over documents, images, or other data using semantic similarity. ChromaDB stores embeddings alongside their metadata and original documents, providing a simple API for adding data, querying by similarity, and filtering by metadata. It can run embedded in a Python process (great for prototyping) or as a standalone server. ChromaDB integrates directly with LangChain, LlamaIndex, and other LLM frameworks, making it the default vector store for many RAG (Retrieval-Augmented Generation) applications. Its focus on simplicity and developer experience positioned it as the 'SQLite of vector databases,' prioritizing ease of use over distributed scale.

    In plain English: A simple database for storing and searching AI embeddings. Feed it your documents, and it helps your AI app find the most relevant ones when answering questions. Easy to set up and use.

    Example: Our support chatbot uses ChromaDB to store documentation embeddings. When a user asks a question, it finds the three most relevant docs and feeds them to the LLM.