In the ever-evolving landscape of artificial intelligence (AI) and natural language processing (NLP), researchers and developers continue to push the boundaries of what’s possible. One such groundbreaking development is the Auto Generated Agent Chat, a cutting-edge system that employs Retrieval Augmented Generation (RAG) to transform group conversations. This technology combines the strengths of both retrieval-based and generative models, offering a unique and efficient solution for enhancing communication in group settings.
In this tutorial, I am going to keep developing multi-agent LLM applications in the AutoGen framework and using decent open-source language models with function calls to see whether they can generate and execute a function task like calculation for currency exchange.
Link
Use routes to remind agents of particular information or routes (we will do this in this notebook). Use routes to act as protective guardrails against specific types of queries.
Link
We show you how to build a simple agent that adds a retry layer on top of a RouterQueryEngine, allowing it to retry queries until the task is complete. We build this on top of both a SQL tool and a vector index query tool. Even if the tool makes an error or only answers part of the question, the agent can continue retrying the question until the task is complete.
LLMCompiler is a framework that enables an efficient and effective orchestration of parallel function calling with LLMs, including both open-source and close-source models, by automatically identifying which tasks can be performed in parallel and which ones are interdependent.
[Link]{https://github.com/SqueezeAILab/LLMCompiler}
LLM Compiler Agent Cookbook
In this article, we’ll introduce you to the innovative world of Autogen, an AI agent that’s revolutionizing how we fine-tune and customize large multimodal models. Autogen takes the complexity out of the equation by automating and simplifying the fine-tuning process, making it accessible to developers and researchers alike. We’ll explore how Autogen collaborates seamlessly with models like LLaVA, streamlining AI agent development and opening the doors to more efficient and precise AI-driven solutions.
