aigoofusion 0.1.8

`AIGooFusion` is a framework for developing applications by large language models (LLMs)

AIGooFusion

AIGooFusion is a framework for developing applications by large language models (LLMs). AIGooFusion has AIGooChat and AIGooFlow.

• AIGooChat is llm abstraction to use various llm on one module.
• AIGooFlow is llm apps workflow.

How to install

• Prerequisites:
  • Install pydantic [required],
  • Install openai to use OpenAI models [optional].
  • Install boto3 to use AWS Bedrock models [optional].

Using pip

pip install aigoofusion

using requirements.txt

• Add into requirements.txt

aigoofusion

• Then install

pip install -r requirements.txt

Example

AIGooChat Example

from aigoofusion import (
    OpenAIModel,
    OpenAIConfig,
    AIGooChat,
    Message,
    Role,
    openai_usage_tracker,
    AIGooException,
)

def sample_prompt():
    info = """
    Irufano adalah seorang software engineer.
    Dia berasal dari Indonesia.
    Kamu bisa mengunjungi websitenya di https:://irufano.github.io
	"""

    # Configuration
    config = OpenAIConfig(temperature=0.7)
    llm = OpenAIModel(model="gpt-4o-mini", config=config)

    SYSTEM_PROMPT = """Answer any user questions based solely on the data below:
    <data>
    {info}
    </data>
    
    DO NOT response outside context."""

    # Initialize framework
    framework = AIGooChat(llm, system_message=SYSTEM_PROMPT, input_variables=["info"])

    try:
        messages = [Message(role=Role.USER, content="apa ibukota china")]
        with openai_usage_tracker() as usage:
            response = framework.generate(messages, info=info)
            print(f"\n>> {response.result.content}\n")
            print(f"\nUsage:\n{usage}\n")

    except AIGooException as e:
        print(f"{e}")


if __name__ == "__main__":
    sample_prompt()

AIGooFlow Example

from aigoofusion import (
    OpenAIModel,
    OpenAIConfig,
    AIGooChat,
    ToolRegistry,
    Tool,
    Message,
    Role,
    openai_usage_tracker,
    AIGooFlow,
    WorkflowState,
    START,
    END,
    tools_node,
)

async def sample_flow():
    # Configuration
    config = OpenAIConfig(temperature=0.7)

    llm = OpenAIModel("gpt-4o-mini", config)

    # Define a sample tool
    @Tool()
    def get_current_weather(location: str, unit: str = "celsius") -> str:
        return f"The weather in {location} is 22 degrees {unit}"

    @Tool()
    def get_current_time(location: str) -> str:
        return f"The time in {location} is 09:00 AM"

    tool_list = [get_current_weather, get_current_time]

    # Initialize framework
    fmk = AIGooChat(llm, system_message="You are a helpful assistant.")

    # Register tool
    fmk.register_tool(tool_list)

    # Register to ToolRegistry
    tl_registry = ToolRegistry(tool_list)

    # Workflow
    workflow = AIGooFlow(
        {
            "messages": [],
        }
    )

    async def main_agent(state: WorkflowState) -> dict:
        messages = state.get("messages", [])
        response = fmk.generate(messages)
        messages.append(response.process[-1])
        return {"messages": messages, "system": response.process[0]}

    async def tools(state: WorkflowState) -> dict:
        messages = tools_node(messages=state.get("messages", []), registry=tl_registry)
        return {"messages": messages}

    def should_continue(state: WorkflowState) -> str:
        messages = state.get("messages", [])
        last_message = messages[-1]
        if last_message.tool_calls:
            return "tools"
        return END

    # Add nodes
    workflow.add_node("main_agent", main_agent)
    workflow.add_node("tools", tools)

    # Define workflow structure
    workflow.add_edge(START, "main_agent")
    workflow.add_conditional_edge("main_agent", ["tools", END], should_continue)
    workflow.add_edge("tools", "main_agent")

    async def call_sql_agent(question: str):
        try:
            with openai_usage_tracker() as usage:
                res = await workflow.execute(
                    {
                        "messages": [
                            Message(role=Role.USER, content=question)
                        ]
                    }
                )

            return res, usage
        except Exception as e:
            raise e

    quest = "What's the weather like in London and what time is it?"
    res, usage = await call_sql_agent(quest)
    print("---\nResponse content:\n")
    print(res["messages"][-1].content)
    print("---\nRaw usages:")
    for usg in usage.raw_usages:
        print(f"{usg}")
    print(f"---\nCallback:\n {usage}")

async def run():
	await sample_flow()

asyncio.run(run())

In-memory Messages Example

import asyncio
import pprint
import random
import time

from aigoo_fusion.chat.messages.message import Message
from aigoo_fusion.chat.messages.role import Role
from aigoo_fusion.flow.aigoo_flow import AIGooFlow
from aigoo_fusion.flow.node.node import END, START
from aigoo_fusion.flow.state.memory_manager import MemoryManager
from aigoo_fusion.flow.state.workflow_state import WorkflowState

# Initialize memory manager
memory_manager = MemoryManager(extend_list=True)

# Create workflow with memory manager
state = {
    "messages": [],
    "skill": {"programming": []},
    "auth": {
        "name": "irufano",
        "company": "gokil",
    },
}
workflow = AIGooFlow(state, memory=memory_manager)


async def main(state: WorkflowState) -> dict:
    messages = state.get("messages", [])
    responses = [
        "Hello",
        "Wowww",
        "Amazing",
        "Gokil",
        "Good game well played",
        "Selamat pagi",
        "Maaf aku tidak tahu",
    ]
    random_answer = random.choice(responses)
    ai_message = Message(role=Role.ASSISTANT, content=random_answer)
    messages.append(ai_message)
    return {"messages": messages}


# Add nodes
workflow.add_node("main", main)
workflow.add_edge(START, "main")
workflow.add_edge("main", END)


async def call_workflow(
    question: str,
    thread_id: str,
    name: str,
    company: str,
    coding: str,
):
    try:
        message = Message(role=Role.USER, content=question)
        messages = [message]
        auth = {"name": name, "company": company}
        programming = {"programming": [{"name": coding}]}
        res = await workflow.execute(
            { 
                "messages": messages, 
                "auth": auth, 
                "skill": programming,
            }, 
            thread_id,
        )

        return res
    except Exception as e:
        raise e


async def chat_terminal():
    print("Welcome to the Chat Terminal! Type 'exit' to quit.")
    print(
        "Use one digit number on thread id for simplicity testing, i.e: thread_id: 1\n"
    )

    while True:
        thread_id = input("thread_id: ")
        name = input("name: ")
        company = input("company: ")
        coding = input("coding: ")
        user_input = input("You: ")

        if user_input.lower() == "exit":
            print("Chatbot: Goodbye!")
            break

        response = await call_workflow(
            user_input.lower(), thread_id, name, company, coding
        )
        time.sleep(0.5)  # Simulate a small delay for realism
        print(f"\nChatbot: {response['messages'][-1].content}\n")
        pprint.pp(response)
        # print("History: ")
        # for msg in history:
        #     print(f"\t{msg}")


if __name__ == "__main__":
    asyncio.run(chat_terminal())

Develop as Contributor

Build the container

docker-compose build

Run the container

docker-compose up -d aigoofusion

Stop the container

docker-compose stop aigoofusion

Access the container shell

docker exec -it aigoofusion bash

Run test

python aigoo_fusion/test/test_chat.py 
python aigoo_fusion/test/test_flow.py 

or

python aigoo_fusion.test.test_chat.py 
python aigoo_fusion.test.test_flow.py 

Build package

python setup.py sdist bdist_wheel

Upload package

twine upload dist/*