Getting Started with ChromaDB Install Build a Streamlit App Document Search | Python | Vector DB

Показать описание

This video delves deep into ChromaDB, an open-source embedding database designed for efficient vector storage and retrieval. Whether you're new to ChromaDB or just looking to enhance your knowledge, this video covers!

You'll learn:
✅ How to install and set up ChromaDB.
✅ How to use ChromaDB to store and query embeddings.
✅ How to create a persistent database that stores your documents.
✅ How to build a Streamlit app that takes a user query and finds the closest matching document using embeddings.

By the end of the video, you'll have a working example of an app that efficiently searches and retrieves the most relevant document based on user input!

Perfect for data enthusiasts, machine learning practitioners, and anyone looking to incorporate embedding-based search into their projects.

Don't forget to like, subscribe, and hit the bell icon to stay updated on more Python and machine learning tutorials!

00:00 Introduction
03:10 Install Chroma
04:40 Create Chroma client
08:30 Check Embeddings of stored documents
10:04 Query search in chromadb
15:40 Persist Chroma Client Instance
19:23 Setup different embedding models
25:33 Filter where
27:00 Add metadata in documents
28:00 Filter query using metadata
29:40 Operators in chromadb
32:20 Streamlit app using Chromadb