Recent advancements in digital signal processing (DSP) technology, specifically DSPy, have enabled the development of more sophisticated chatbots and long-form article generation systems. The STORM framework, developed by Stanford, exemplifies the use of a 'multi-node' pipeline in generating long-form content, requiring multiple calls and interactions with language models (LMs), with each step contingent on the previous one's outcome. Additionally, a new demonstration showcases the integration of Retrieval-Augmented Generation (RAG) with Persona into chatbots, utilizing DSPy in conjunction with Cohere and Weaviate.io. This approach aims to personalize chatbot responses by incorporating personas, thereby enhancing the interaction quality and relevance.
Some more examples for working LLMs using DSPy and Weaviate for retrieval, in this case a chatbot with personas 🔥 https://t.co/c90V3jxL8o
Adding Personas to RAG and LLM systems is one of these ideas that have fascinated me for a while, but I've never quite gotten around to it! 🎭 I am SUPER excited to present this demo with @ecardenas300 illustrating how to add a Persona to a chatbot! For example imagine prompting… https://t.co/Lkmh5fjOQK
RAG with Persona 🎭 If you ask the same question to multiple people, there is a strong chance each person will have a different response. Here’s a new demo on RAG with Persona using DSPy, @cohere, and @weaviate_io! When building or using chatbots, it’s important to get a… https://t.co/qXq3FCIOGW
RAG with Persona 🎭 If you ask the same question to multiple people, there is a strong chance each person will have a different response. Here's a new demo on RAG with Persona using DSPy, @cohere, and @weaviate_io! When building or using chatbots, it's important to get a… https://t.co/IIaqxr5b9p
DSPy gets interesting when your LM app workflow needs a ‘multi-node’ pipeline. The recently published STORM framework for long-form article generation from Stanford is a good example. Multiple calls and interactions with LMs, each step dependent on the outcome of the previous.