That’s the direction I’m heading in but the solution isn’t quite that simple… My goal is to create a top level Agent that can select the agent to solve a particular task from a pool of thousands or even hundreds of thousands of agents. There are some intermediate steps needed to achieve that.
First step is to get a few agents working rock solid and AlphaWave is on a path towards that.
1 Like
I solved it with a Delegate-Agent as a middleman, all agents are categorized by their tasks. For example, agents which are for local tasks, like programming, file handling, folder management are categorized. And have sub-categories for example all agents which falls into the programming are chunked in agents for writing, testing, fixing code etc.
The Delegate-Agent analyze the current task and decides which category need to be used.
Their are also agents for decomposing tasks, refine the overall plan, etc.
This makes the system very solid, because separation of concerns is a powerful principle here.
But like I mentioned above, some tasks can be delegated to the SOAR architecture, which is specialized in this kind of planning and executing. Excerpt from GPT-4:
Here are a few ideas on how you might integrate SOAR and GPT to create a more effective problem-solving architecture:
-
Rule-Based Decision Making: GPT could be used to generate a set of potential operators or actions based on the current state. These could then be evaluated using SOAR’s rule-based system to select the most appropriate action. This could allow for more creative and diverse problem-solving strategies.
-
Natural Language Processing: GPT’s strength lies in its ability to understand and generate human-like text. This could be used to enhance SOAR’s ability to interact with human users. For example, users could describe a problem in natural language, and GPT could translate this into a state that SOAR could understand and act upon.
-
Learning and Adaptation: GPT could be used to generate new rules for SOAR based on past experiences. This could allow the system to learn and adapt over time, improving its problem-solving capabilities.
-
Problem Decomposition: GPT could be used to break down complex problems into smaller, more manageable subproblems. SOAR could then solve these subproblems individually, making it easier to tackle complex tasks.
-
Explanation and Justification: After SOAR has made a decision or taken an action, GPT could be used to generate a human-readable explanation or justification. This could make the system more transparent and understandable to users.
-
Simulation and Prediction: GPT could be used to simulate potential future states based on the current state and a proposed action. SOAR could then use these simulations to make more informed decisions.
2 Likes
It’s review time for us at Microsoft so I’m working on a sample agent that can help you write a performance review and thought I’d share a quick screen shot. This from about 2 turns in for the “Core Priorities” section of my review. This is gpt-3.5-turbo and I’ve turned off all the feedback reporting but there’s a couple of points in the task where the model tends to hallucinate:
Here’s the entire code for that agent:
As you can see the Agent made it all the way through the task and finished with a call to completeTask.
This will be structured as a hierarchy of agents that break the task down into smaller and smaller subtasks:
Now to very that agents can call other agents 
1 Like
Alphawave 0.1.0 and Promptrix 0.1.0 now available on pypi!
pip3 install alphawave
should install both, or if you only want promptrix: pip3 install promptrix
Alphawave 0.1.0 only includes core alphawave, no Agents. Look for that shortly.
Also, only OpenAI client support. Azure client is there, but in raw GPT-4 translation, known errors. If you need this and can’t do it yourself let me know.
An OS LLM Client with support for fastchat server api and also simple JSON/txt socket, as well as user/asst template support using the FastChat conversation.py class, coming very soon.
3 Likes
Good work rate, I’ll install the python version and test it.
1 Like
I have it installed it, but how do I use it ?
Any READ.me?
@bruce.dambrosio do you want to maybe try to port the base AlphaWave sample from JS to Python?
@jethro.adeniran the readme on the JS side of things my help get you started. I know Bruce was going to try and work up a jupyter notebook but this is still a bit of a work in progress:
Yeah, sorry. It is rather inscrutable. That’s high on the list.
@stevenic - I’ll take a look tonight. I really want to get that OSClient out, I think alphawave is really needed there.
2 Likes
@stevenic - which of these will run without the agent code?
This sample requires no agent code… It’s basically a simple ChatGPT style example. I still owe you an example of base AlphaWave with a validator that walks the model back from hallucinations but I can tell you that it works. It’s best seen in the agent demos because they make mistakes all the time but the core isolation & feedback mechanism AlphaWave employs works.
I was demoing an agent to someone today and GPT-3.5 first failed to return a JSON thought structure the agent system needs so it gave it feedback. Normally it returns the correct structure on the next call but this time the model returned a partial JSON object. AlphaWave gave it feedback as to the fields it was missing and on the next call it returned a complete and valid object. And that was GPT-3.5 which is the worst of these models.
This stuff sounds like snake oil but I promise it works to improve the overall reliability of these models to return valid structured results.
Ah. Already have that one running, and in the tests dir on github. will post now
Well, deep apologies, I’m a reasonably experienced developer, but unexperienced in packaging and pypi.
do this to get alphawave 0.1.1:
pip3 install --ugrade alphawave
now save the below to alphachat.py, then run it:
import os
from pathlib import Path
import readline
from alphawave.AlphaWave import AlphaWave
from alphawave.OpenAIClient import OpenAIClient
import promptrix
from promptrix.Prompt import Prompt
from promptrix.SystemMessage import SystemMessage
from promptrix.ConversationHistory import ConversationHistory
from promptrix.UserMessage import UserMessage
import asyncio
# Read in .env file.
env_path = Path('..') / '.env'
#load_dotenv(dotenv_path=env_path)
# Create an OpenAI or AzureOpenAI client
client = OpenAIClient(apiKey=os.getenv("OPENAI_API_KEY"))
# Create a wave
wave = AlphaWave(
client=client,
prompt=Prompt([
SystemMessage('You are an AI assistant that is friendly, kind, and helpful', 50),
ConversationHistory('history', 1.0),
UserMessage('{{$input}}', 450)
]),
prompt_options={
'completion_type': 'chat',
'model': 'gpt-3.5-turbo',
'temperature': 0.9,
'max_input_tokens': 2000,
'max_tokens': 1000,
}
)
# Define main chat loop
async def chat(bot_message=None):
# Show the bots message
if bot_message:
print(f"\033[32m{bot_message}\033[0m")
# Prompt the user for input
user_input = input('User: ')
# Check if the user wants to exit the chat
if user_input.lower() == 'exit':
# Exit the process
exit()
else:
# Route users message to wave
result = await wave.completePrompt(user_input)
if result['status'] == 'success':
print(result)
await chat(result['message']['content'])
else:
if result['response']:
print(f"{result['status']}: {result['message']}")
else:
print(f"A result status of '{result['status']}' was returned.")
# Exit the process
exit()
# Start chat session
asyncio.run(chat("Hello, how can I help you?"))
I had a lot of issues with dependencies, pyreadline3 is the windows alternative for readline, installed pyee too or so.
But still getting different errors atm “KeyError: ‘Could not automatically map gpt-4 to a tokeniser. Please use tiktok.get_encoding to explicitly get the tokeniser you expect.’”
Ah. good to know. I run it with 3.5 for testing. Doesn’t really matter, it’s just using that to get a rough token count. I don’t think I can get a fix out tonight, but maybe, otherwise early tomorrow. with luck will have core agent code in as well by then…
pyee - ah. tnx, will add to dependencies in pypi pytoken.toml. Sorry
ps - will be offline for the night shortly
1 Like
To clarify, I didn’t change the code to use gpt4, it remains in 3.5 turbo.
Why the error writes GPT4 i’m not sure. Gm
1 Like
Basic port of promptrix AND alphawave, including some example Agents, now on pypi.
thanks to @stevenic (original promptrix and alphawave developer in typescript) for all his support and encouragement.
probably a couple of bugs still in the port, don’t blame him, DM me.
1 Like
Great job!
Updated to the new version but seems I’ll test it in the morning
1 Like
@stevenic This is amazing! Thank you so much for sharing.
I was wondering how the agent handles token limits? I’ve used “smol-ai/developer)” which is based on BabyAGI, but the OpenAI token limits the complexity of the output, simply by the 8k token limit. I’ve used Prompt.md which definitely help, but even still, I can’t get the length of response needed to get the answers I want. Thoughts?
1 Like
I’m only using gpt-3.5-turbo currently so I don’t even have an 8k token limit and I’m having success but it probably depends on the task. I have access to gpt-4 but all of our customers (Microsofts Customers) want to use turbo to save cost so my current focus is making turbo work as well as I possibly can.
What’s your specific scenario?
1 Like
It’s live. pypi alphawave
that should install both alphawave and promptrix, including version 0.2.2 of alphawave Agents. Make sure you get 0.2.2
Runs the macbeth test script! ( from github: alphawave-py/tests/macbeth)
@stevenic is planning an overhaul of agents, so that part might change in subsequent releases.
There aren’t a lot of examples of how to write prompts or validation schema yet, DM me(python) or @stevenic (js) or just discuss here.
I will be posting examples on promptrix-py and alphawave-py tonight.
2 Likes
