Material for the omonymous talk.
📝 Abstract
The rise of ChatGPT and Large Language Models has revolutionized the tech landscape, leaving developers overwhelmed by the infinite opportunities and intrigued by the technical challenges posed by their complex nature.This session provides a developer-centric introduction to LLMs, focused on practical applications.No pre-existing knowledge of LLMs and NLP is required.
You will gain insights into: using closed and open-source models, how to effectively prompt LLMs, vector databases, implementing Retrieval Augmented Generation applications (answer generation based on your data), building more complex applications.
Through a hands-on approach, I will show code examples using open-source tools: Haystack LLM framework, Hugging Face Transformers, Ollama, and more. I will also show how you can switch from proprietary to open models.
- Haystack LLM framework
- Start from a proprietary model
- Switch to local open LLMs with Ollama
- Prompt Engineering
- RAG
- Retrieval
- Choose a LLM inference solution
- Deployment
- Beyond RAG...
- There is much more!
- Ollama-Haystack integration
- Code snippets: run Ollama; use Ollama in Haystack
- Introduction to keyword-based retrieval: Bag of Words and TF-IDF; BM25
- BM25 Indexing Pipeline - code snippet
- BM25 RAG Pipeline - code snippet
- Introduction to vector retrieval: From sparse representations to Language Models; Dense Passage Retrieval; Sentence Transformers for Dense Retrieval
- Embedding Indexing Pipeline - code snippet
- Embedding RAG Pipeline - code snippet
- Haystack cookbook
- Multilingual RAG from a podcast
- Hacker News summarizer
- Information extraction via LLMs
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent