Hi and welcome to DevXplaining channel! Todays I've got a long-form video of a Retrieval Augmented Generation (RAG) using Ollama, ChromaDB, and a little bit of Python. We'll be using RSS feed as our source, and create a program that's able to query that source and use generative AI model to answer questions. Cool thing in todays video is that it's all open source, it's all local model, so this is something you can run on your own computer.
I wanted to take my time and write the code line by line, explaining as we go, so there was no way to do a short video - this is going to be rather long one. But I think this topic and knowledge is a must-have for any developer in 2024 - and I hope this will be clear and useful for you too.
As always, code and references in the video can be found from links below. Happy coding!
And remember to like, subscribe, comment and feedback as you see fit, you know the drill by now :)
Timecodes:
0:00 - Blah blah blah
1:35 - Explaining the setup
4:00 - Prerequisites & How to Hello World with a local model
5:13 - Add necessary libraries
7:02 - Grab the NYT RSS feed with SpeedParser
14:00 - Put the data in ChromaDB Vector database
20:22 - Similarity search into vector database
25:50 - Use Ollama and Phi3 local model to ask questions about the data
36:40 - The Good Stuff
Links:
- github.com/crystoll/ollama-rag
- github.com/ollama/ollama
- github.com/chroma-core/chroma
- github.com/ronikeuru/local-ra...
1 авг 2024
