3 posts tagged with "OpenAI"

Off-the-shelf LLMs are excellent at manipulating and generating text, but they only know general facts about the world and probably very little about your use case. Retrieval augmented generation (RAG) refers not to a single algorithm, but rather a broad approach to provide relevant context to an LLM. As industry applications mature, RAG strategies will be tailored case-by-case to optimize relevance, business outcomes, and operational concerns.

crosspost from https://blog.dagworks.io/p/retrieval-augmented-generation-reference-arch

Skip learning convoluted LLM-specific frameworks and write your first LLM application using regular Python functions and Hamilton! In this post, we’ll present a containerized PDF summarizer powered by the OpenAI API. Its flow is encoded in Hamilton, which the FastAPI backend runs and exposes as an inference endpoint. The lightweight frontend uses Streamlit and exercises the backend. (GitHub repo)

crosspost from https://blog.dagworks.io/p/containerized-pdf-summarizer-with

In this post, we’re going to share how Hamilton can help you write modular and maintainable code for your large language model (LLM) application stack. Hamilton is great for describing any type of dataflow, which is exactly what you’re doing when building an LLM powered application. With Hamilton you get strong software maintenance ergonomics, with the added benefit of being able to easily swap and evaluate different providers/implementations for components of your application.

crosspost from https://blog.dagworks.io/p/building-a-maintainable-and-modular