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While how LLMs work may be elusive to many developers, how LLM apps work is not - they essentially involve a series of calls to external services such as LLMs/databases/search engines, or intermediate data processing, all glued together.

Flows

Flex flow

You can create LLM apps using a Python function or class as the entry point, which encapsulating your app logic. You can directly test or run these with pure code experience. Or you can define a flow.flex.yaml that points to these entries, which enables testing, running, or viewing traces via the Promptflow VS Code Extension.

Our examples should also give you an idea how to write flex flows.

DAG flow

Thus LLM apps can be defined as Directed Acyclic Graphs (DAGs) of function calls. These DAGs are flows in prompt flow.

A DAG flow in prompt flow is a DAG of functions (we call them tools). These functions/tools connected via input/output dependencies and executed based on the topology by prompt flow executor.

A flow is represented as a YAML file and can be visualized with our Prompt flow for VS Code extension. Here is an example flow.dag.yaml:

flow_dag

Please refer to our examples to learn how to write a DAG flow.

When to use Flex or DAG flow

Dag flow provides a UI-friendly way to develop your LLM app, which has the following benefits:

  • Low code: user can drag-and-drop in UI to create a LLM app.
  • DAG Visualization: user can easily understand the logic structure of the app with DAG view.

Flex flow provides a code-friendly way to develop your LLM app, which has the following benefits:

  • Quick start: Users can quickly test with a simple prompt, then customize with python code with Tracing visualization UI.
  • More advanced orchestration: Users can write complex flow with Python built-in control operators (if-else, foreach) or other 3rd party / open-source library.
  • Easy onboard from other platforms: user might already onboard platforms like langchain and sematic kernel with existing code. User can easily onboard promptflow with a few code changes.

Flow types

Prompt flow examples organize flows by three categories:

  • Standard flow or Chat flow: these two are for you to develop your LLM application. The primary difference between the two lies in the additional support provided by the "Chat Flow" for chat applications. For instance, you can define chat_history, chat_input, and chat_output for your flow. The prompt flow, in turn, will offer a chat-like experience (including conversation history) during the development of the flow. Moreover, it also provides a sample chat application for deployment purposes.
  • Evaluation flow is for you to test/evaluate the quality of your LLM application (standard/chat flow). It usually run on the outputs of standard/chat flow, and compute some metrics that can be used to determine whether the standard/chat flow performs well. E.g. is the answer accurate? is the answer fact-based?

Flex flow examples:

  • basic: A basic standard flow define using function entry that calls Azure OpenAI with connection info stored in environment variables.
  • chat-basic: A basic chat flow defined using class entry. It demonstrates how to create a chatbot that can remember previous interactions and use the conversation history to generate next message.
  • eval-code-quality: A example flow defined using function entry which shows how to evaluate the quality of code snippet.

DAG flow examples:

  • basic: A basic standard flow using custom python tool that calls Azure OpenAI with connection info stored in environment variables.
  • chat-basic: This example shows how to create a basic chat flow. It demonstrates how to create a chatbot that can remember previous interactions and use the conversation history to generate next message.
  • eval-basic:

Next steps