A 100-line minimalist LLM framework for Agents, Task Decomposition, RAG, etc. It models the LLM workflow as a Graph + Shared Store with nodes handling simple tasks, connected through actions for agents, and orchestrated by flows for task decomposition.
Llama Stack v0.1.0 introduces a stable API release enabling developers to build RAG applications and agents, integrate with various tools, and use telemetry for monitoring and evaluation. This release provides a comprehensive interface, rich provider ecosystem, and multiple developer interfaces, along with sample applications for Python, iOS, and Android.
This article introduces PersonaRAG, a new AI method that enhances Retrieval-Augmented Generation (RAG) systems by incorporating user-centric agents for personalized information retrieval. It addresses the limitations of traditional RAG systems by dynamically adapting to user profiles and information needs, improving accuracy and relevance of responses.
