This is a workflow for coming up with a product idea and illustrating it with an image.
This workflow has 9 steps, starting with brainstorming ideas, and ending with saving an HTML file containing the product name, description, and image.
It also has two variables, {market} for the target market, and {price_point} for the price point.
To run this workflow, you simply enter this in the command line:
python -m run --flow=example_with_variables --variables 'market=college students' 'price_point=$50'
You don't need to write any code with LangChain.
You simply specify your workflow in a JSON file, and execute it.
In Agentflow, you write functions by inheriting from the BaseFunction class. You need to provide the definition in JSON that GPT-3.5/4 uses to understand how to call a function, and also the function logic itself. This just means creating a get_definition() function that returns a JSON Schema object, and an execute() function that performs your logic and returns a string. Once you have those, you can then just use the function in your workflow by adding "function_call": "your_function". The application does the rest. Here's the create_image function, for example, which uses the Dall-e API: https://github.com/simonmesmith/agentflow/blob/main/agentflo...
What I mean by "you don't need to write any code with LangChain" is that you don't need to write any Python at all to use Agentflow, unless you want to create a new function. Creating workflows just involves creating JSON files. It's not like LangChain, for which you'd have to chain together multiple prompts in Python.
Does that help clarify?
PS: You'll notice heavy documentation in the link above. I want to experiment with automatically generating documentation using Sphinx, so I documented everything with Sphinx formatting. It might be overkill.
Comments
For sure.
Let's use an example, like this: https://github.com/simonmesmith/agentflow/blob/main/agentflo...
This is a workflow for coming up with a product idea and illustrating it with an image.
This workflow has 9 steps, starting with brainstorming ideas, and ending with saving an HTML file containing the product name, description, and image.
It also has two variables, {market} for the target market, and {price_point} for the price point.
To run this workflow, you simply enter this in the command line: python -m run --flow=example_with_variables --variables 'market=college students' 'price_point=$50'
You don't need to write any code with LangChain.
You simply specify your workflow in a JSON file, and execute it.
Does that help to clarify?
Thanks - this does help. Curious about the function calls, especially around image generation.
Also, can you clarify what you mean by "You don't need to write any code with LangChain."?
Sure!
In Agentflow, you write functions by inheriting from the BaseFunction class. You need to provide the definition in JSON that GPT-3.5/4 uses to understand how to call a function, and also the function logic itself. This just means creating a get_definition() function that returns a JSON Schema object, and an execute() function that performs your logic and returns a string. Once you have those, you can then just use the function in your workflow by adding "function_call": "your_function". The application does the rest. Here's the create_image function, for example, which uses the Dall-e API: https://github.com/simonmesmith/agentflow/blob/main/agentflo...
What I mean by "you don't need to write any code with LangChain" is that you don't need to write any Python at all to use Agentflow, unless you want to create a new function. Creating workflows just involves creating JSON files. It's not like LangChain, for which you'd have to chain together multiple prompts in Python.
Does that help clarify?
PS: You'll notice heavy documentation in the link above. I want to experiment with automatically generating documentation using Sphinx, so I documented everything with Sphinx formatting. It might be overkill.
Thanks! I'll check this out!