AI Agents Day 5.1: Gorilla LLM, xLAM & SWE Agent

In this main session of the "AI Agents" course, we cover the following:
-AI agents using tools, with emphasis on function calling and API interaction.
-LLM API Interaction: Discusses how LLMs, like GPT-4, use APIs to perform tasks such as generating images (e.g., "dancing cats").
-Challenges: Highlights the gap between commercial LLMs and open-source models like LLaMA 3 in effectively calling APIs.
-API Function Calling: LLMs translating user prompts into API calls using frameworks like Hugging Face Diffusion.
-Gorilla LLM: Introduces Gorilla LLM, trained on multiple APIs (Hugging Face, PyTorch) to improve function-calling accuracy and reduce hallucinations.
-Open Functions Benchmark: Discusses Berkeley’s benchmark for measuring LLM performance in translating prompts into correct API calls.
-Retrieval-Augmented Generation (RAG): Gorilla’s use of RAG for API updates and reducing errors in API calls.
-Commercial Use: Ethics of companies using open-source tools, with examples like Amazon's use of Elasticsearch.
-Multi-Agent Systems: Emphasizes the growing complexity and importance of multi-agent systems and API management in LLMs.