The following code was developed for this article on Mastering RAG.
In the article I've walked through the nitty-gritty of leveraging Large Language Models (LLMs) for practical, business use cases. We started with understanding the limitations of LLMs and how fine-tuning and Retrieval Augmented Generation (RAG) can address these issues. Then, we dived into the nitty-gritty of building a RAG application using open-source tools.
It's clear that while LLMs are powerful, they aren't without their shortcomings, especially when it comes to accessing current or proprietary data. But fear not, because with a bit of ingenuity and the right tools, you can turn these challenges into opportunities. The combination of fine-tuning and RAG, supported by open-source models and frameworks like Langchain, ChromaDB, Ollama, and Streamlit, offers a robust solution to making LLMs work for you.
- Ensure poetry is installed for dependency management
- CD to application directory
- Run
poetry installto install all dependencies - Run
poetry run streamlit run .\langchain-rag-bot\app.py