Common Architectures

With a large number of supported node types (full details here: Experimenting with Workflows) and few limits on how they can be connected to each other, the types of architectures/ applications you can create using Workflows is very large.

The list of architectures below is not exhaustive, we’re continuing to build it out. If you come up with an interesting architecture that you think the community might benefit from, please reach out so we can add it to the list here.

1. Create a Retrieval Augmented Generation System

LLM applications often require specific context from a Vector DB which is added into the prompt. Forget signing up for multiple systems and being stuck on various micro decisions, with Vellum you can prototype a RAG system in minutes

  • Step 1: Create a Document Index and upload your documents (follow this article for tips: Uploading Documents)
  • Step 2: Add a Search Node in your Workflow
  • Step 3: Add a Prompt Node that takes the results of your Search Node as an input variable
  • Step 4: Link to a Final Output or other downstream node (e.g., if the Prompt Node result is a certain value branch execution based on a Conditional Node)
  • Step 5: Set up variables and hit Run!

RAG Workflow

2. Route Relevant Messages Dynamically to a Human

If you’re building an agent that answers questions coming from users (e.g., a support chatbot), you may want to set up rules such that anytime the incoming message from a user is sensitive (e.g., the user is angry or in a dangerous situation) then the LLM automatically escalates it to a human. With Workflows you’d be able to build that out real quick.

  • Step 1: Create a classification prompt (Prompt Node) to filter out incoming messages
  • Step 2: Create a downstream prompt (Prompt Node) for the LLM to respond to messages that don’t need to be escalated
  • Step 3: Link outputs of the classification prompt to two separate Final Output Nodes
  • Step 4: Set up variables and hit Run!

Conditional Routing Workflow