🎉 Releasing our Open Source LLM Framework + Playground!

William Zeng - November 22th, 2023

Since the early days, we've used our own LLM framework at Sweep (opens in a new tab). Its helped us ship way faster, and we're excited to branch off this framework as it's own sdk!

SweepAgent is our building block for having an LLM handle any task.
We’re using to thinking of LLMs as text-in -> text-out. Prompt engineering is commonly understood as “change your vocabulary and grammar until the LLM works”.

Prompt Engineering

However, prompt engineering is more about optimizing your input (format and content), output, and parser such that you reliably achieve a given task.

Our SweepAgent class allows us to separate the concerns of a LLM task handler. Rather than a text-text interface, a SweepAgent takes in variables (like any other function) and transforms it to a Pydantic BaseModel.

This takes the burden of LLM unreliability away from your main code and encapsulates it with the LLM request.
For example, if we want GPT to generate a list of cities, we can ask it to format it using these xml tags (XML is a lot more robust and costs fewer tokens):

Here's an example prompt:

Format the cities using these tags:
<locations>
City1
City2
City3
City4
</locations>

Say GPT generates the following response:

Sure, I can help with that. Here are the cities:
<locations>
Topeka
Omaha
</locations>

We can then match the inner file string using this pattern:

r"<locations>(?P<locations_string>.*?)</locations>”

The issues don't end there though.

GPT might mistakenly format the cities in any of the following ways:

* Topeka
- Topeka
Topeka
file: Topeka

Our RegexExtractModel (opens in a new tab) parses this into a string, but we also need custom logic to handle the above edge cases.
To do this, we can instantiate a RegexExtractModel with a @property decorator:

class Locations(RegexExtractModel):
    locations_string: str
    _regex = r"<locations>(?P<locations_string>.*?)</locations>"
 
    @property
    def locations(self):
        result = []
        for line in self.locations_string.split("\n"):
            line = line.strip()
            if " " in line:
                line = line.split(" ")[-1]
            result.append(line)
        return result

This allows us to use the locations property as a list of locations, while still having the original string available for debugging.
We can then use this to define our FindLocationsAgent:

class FindLocationsAgent(SweepAgent):
    regex_extract_model = Locations
    system_prompt = "You are a geographer."
    user_prompt = "Tell me the midpoint between {first_location} and {second_location}. Then provide four cities near that midpoint formatted using <locations>\nCity1\nCity2\nCity3\nCity4\n</locations> tags."
 
find_locations_agent = FindLocationsAgent()
location_obj = find_locations_agent.handle_task(
    system_prompt_args=dict(),
    user_prompt_args={"first_location": "NYC", "second_location": "LA"},
)

Our main logic just has to treat FindLocationsAgent as a function that takes in two locations and returns a list of cities. We can also test the prompt, parser, and external logic separately.

Try it out

Try it out by copying the code from https://github.com/sweepai/sweep/blob/main/docs/sdk/src/agent.py (opens in a new tab) or installing the package with pip install sweep-sdk. We'd love to hear your feedback!

In addition, we released the first version of our corresponding prompt playground at https://playground.sweep.dev (opens in a new tab)!