This article explains Retrieval Augmented Generation (RAG), a method to reduce the risk of hallucinations in Large Language Models (LLMs) by limiting the context in which they generate answers. RAG is demonstrated using txtai, an open-source embeddings database for semantic search, LLM orchestration, and language model workflows.
DSPy provides composable and declarative modules for instructing LMs in a familiar Pythonic syntax. It upgrades "prompting techniques" like chain-of-thought and self-reflection from hand-adapted string manipulation tricks into truly modular generalized operations that learn to adapt to your task.
Mule uses components to conduct backend processes for specific business logic (like checking the customer and inventory databases). Then, the components route messages to the correct application (such as an order fulfillment system). This use is called a Staged Event-Driven Architecture (SEDA) for core asynchronous message processing in flows. Importantly, components don't have to have any Mule-specific code; they can simply be POJOs, Spring beans, Java beans, Groovy scripts, or web services containing the business logic for processing data. Components can even be developed in other languages such as Python, JavaScript, Ruby, and PHP. Mule’s catalog of building blocks includes the most commonly used Enterprise Integration Patterns.
A flow, therefore, is the construct within which you link together several individual components (i.e. building blocks) to handle the receipt, processing, and eventual routing of a message. You can connect many flows together to build a complete application which you can then deploy on premise, on Mule, on another application server, or in the cloud. Practically speaking, you could build, deploy, and run a Mule application in a matter of hours, rather than spending weeks or months building point-to-point connections between systems. Studio, Mule’s Eclipse-based graphical IDE, makes it even easier to model, then configure, test, and deploy your applications. Mule is so named because it “carries the heavy development load” of connecting systems.
A flow, therefore, is the construct within which you link together several individual components (i.e. building blocks) to handle the receipt, processing, and eventual routing of a message. You can connect many flows together to build a complete application which you can then deploy on premise, on Mule, on another application server, or in the cloud. Practically speaking, you could build, deploy, and run a Mule application in a matter of hours, rather than spending weeks or months building point-to-point connections between systems. Studio, Mule’s Eclipse-based graphical IDE, makes it even easier to model, then configure, test, and deploy your applications. Mule is so named because it “carries the heavy development load” of connecting systems.
