freewayai 0.1.2

A new open-standard specification for LLM-backed systems and an SDK for building on it

FreewayAI

Welcome to FreewayAI: a new open-standard specification for LLM-backed systems

Freeway is meant to provide a simple, flexible, enxtensible and lightweight standard for managing prompt templates, database query templates, user input points, and LLM-backed system diagrams.

The units of FreewayAI

The unit elements of Freeway are as follows:

• Prompt Templates
• Database Query Templates
• User Inputs

These units can then be used to construct a diagram of a system structure, to indicate the general flow of data.
We know that systems built on LLMs are full of json parsing, for loops, if statements, and regexes. We just think that you should focus on outlining the LLM-specific components of a system.

We also believe that prompt templates should be managed out of code, where they can be edited on their own lifecycle.

Installation

pip install freewayai

Usage

Project Structure

system_configs/
├─ my_system/
│  ├─ database_templates.json
│  ├─ prompt_templates.json
│  ├─ system_structure.json
│  ├─ user_inputs.json
├─ my_second_system/
│  ├─ database_templates.json
│  ├─ prompt_templates.json
│  ├─ system_structure.json
│  ├─ user_inputs.json
index.py

Example prompt_templates.json file

{
  "templates": {
    "pirate_talk": {
      "prompt_id": "pirate_talk",
      "prompt_template": "Respond to this question from the user but talk like a pirate: {question}. Answer: ",
      "variables": [
        {
          "name": "question",
          "source": "user"
        }
      ]
    },
    "shakespeare_talk": {
      "prompt_id": "shakespeare_talk",
      "prompt_template": "Respond to this question from the user but talk like Shakespeare: {question}. Answer: ",
      "variables": [
        {
          "name": "question",
          "source": "user"
        }
      ]
    },
  }
}

Example SDK Usage

from openai import OpenAI
from freewayai import LLMSystem

SYSTEM_LOCATION = "system_configs/"

my_system = LLMSystem(SYSTEM_LOCATION, "my_system")

variables = {
  "question": input()
}

pirate_prompt = my_system.get_formatted_prompt("pirate_talk", variables)
formatted_pirate_prompt = pirate_prompt.to_openai()

client = OpenAI()
completion = client.chat.completions.create(
    model="gpt-3.5-turbo-0125",
    messages = formatted_pirate_prompt,
    temperature=0.2
)

print(completion.choices[0].message.content)