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Framework

We can use LLMfy to use large language models to interpret language, have conversations, and perform tasks.

Intialize LLMfy framework

We can integrate LLM with LLMfy

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

framework = LLMfy(llm, system_message="You are helpful assistant.")

Generate

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messages = [
    Message(
        role=Role.USER, 
        content="What is the capital city of Indonesia?",
    )
]
response = framework.chat(messages)

print(f"\n>> {response.result.content}\n")

Output: 

>> The capital city of Indonesia is Jakarta.

LLMfy with Tools

To add tools to LLMfy:

Define LLMfy

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

framework = LLMfy(llm, system_message="You are helpful assistant.")

Define tools

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

tools = [get_current_weather]

Register tool

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framework.register_tool(tools)

Invoke agent

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# Example conversation with tool use
messages = [
    Message(
        role=Role.USER,
        content="what is the weather in London?",
    )
]

response = framework.chat_with_tools(messages)
print(f"\n>> {response.result.content}\n")

output: 

>> The weather in London is 22 degrees celcius.