Chroma - Vector Database for LLM Applications | OpenAI integration

☕️ 𝗕𝘂𝘆 𝗺𝗲 𝗮 𝗰𝗼𝗳𝗳𝗲𝗲:
To support the channel and encourage new videos, please consider buying me a coffee here:
https://ko-fi.com/bugbytes

⭐Top resource to learn Python - https://datacamp.pxf.io/kOjKkV ⭐

In this video, we'll take a look at the ChromaDB vector database, which can be used to store embedding data and retrieve embeddings that are most similar to an input query.

We'll take a look at loading and. embedding a real-life text dataset, and then querying for similar vectors. We'll also look at different client options for in-memory databases and persistent databases with Chroma, and how to integrate with OpenAI's embeddings API.

📌 𝗖𝗵𝗮𝗽𝘁𝗲𝗿𝘀:
00:00 Intro
00:56 ChromaDB introduction
02:18 Creating a ChromaDB client and collections
03:32 Adding documents to a collection
08:45 Passing filters to collection queries
10:24 Reading in real-life dataset with Polars
13:10 Creating Embeddings with OpenAI APIs
18:11 Adding OpenAI vectors to ChromaDB
28:27 Persisting the ChromaDB database

𝗦𝗼𝗰𝗶𝗮𝗹 𝗠𝗲𝗱𝗶𝗮:
📖 Blog: https://bugbytes.io/posts/vector-databases-pgvector-and-langchain/
👾 Github: https://github.com/bugbytes-io/
🐦 Twitter: https://twitter.com/bugbytesio

📚 𝗙𝘂𝗿𝘁𝗵𝗲𝗿 𝗿𝗲𝗮𝗱𝗶𝗻𝗴 𝗮𝗻𝗱 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻:
ChromaDB: https://docs.trychroma.com/
ChromaDB Embeddings: https://docs.trychroma.com/guides/embeddings
ChromaDB Integrations: https://docs.trychroma.com/integrations
Kaggle News Articles Dataset: https://www.kaggle.com/datasets/asad1m9a9h6mood/news-articles

#python #chromadb #datascience