Ollama

[
    {
        "model": "llama3.1",
        "api_type": "ollama"
    },
    {
        "model": "llama3.1:8b-instruct-q6_K",
        "api_type": "ollama"
    },
    {
        "model": "mistral-nemo",
        "api_type": "ollama"
    }
]

[
    {
        "model": "llama3.1",
        "api_type": "ollama",
        "client_host": "http://192.168.0.1:11434"
    }
]

[
    {
        "model": "llama3.1:instruct",
        "api_type": "ollama",
        "num_predict": -1,
        "repeat_penalty": 1.1,
        "seed": 42,
        "stream": False,
        "temperature": 1,
        "top_k": 50,
        "top_p": 0.8
    }
]

config_list = [
    {
        # Let's choose the Meta's Llama 3.1 model (model names must match Ollama exactly)
        "model": "llama3.1:8b",
        # We specify the API Type as 'ollama' so it uses the Ollama client class
        "api_type": "ollama",
        "stream": False,
        "client_host": "http://192.168.0.1:11434",
    }
]

from pathlib import Path

from autogen import AssistantAgent, UserProxyAgent
from autogen.coding import LocalCommandLineCodeExecutor

# Setting up the code executor
workdir = Path("coding")
workdir.mkdir(exist_ok=True)
code_executor = LocalCommandLineCodeExecutor(work_dir=workdir)

# Setting up the agents

# The UserProxyAgent will execute the code that the AssistantAgent provides
user_proxy_agent = UserProxyAgent(
    name="User",
    code_execution_config={"executor": code_executor},
    is_termination_msg=lambda msg: "FINISH" in msg.get("content"),
)

system_message = """You are a helpful AI assistant who writes code and the user
executes it. Solve tasks using your python coding skills.
In the following cases, suggest python code (in a python coding block) for the
user to execute. When using code, you must indicate the script type in the code block.
You only need to create one working sample.
Do not suggest incomplete code which requires users to modify it.
Don't use a code block if it's not intended to be executed by the user. Don't
include multiple code blocks in one response. Do not ask users to copy and
paste the result. Instead, use 'print' function for the output when relevant.
Check the execution result returned by the user.

If the result indicates there is an error, fix the error.

IMPORTANT: If it has executed successfully, ONLY output 'FINISH'."""

# The AssistantAgent, using the Ollama config, will take the coding request and return code
assistant_agent = AssistantAgent(
    name="Ollama Assistant",
    system_message=system_message,
    llm_config={"config_list": config_list},
)

# Start the chat, with the UserProxyAgent asking the AssistantAgent the message
chat_result = user_proxy_agent.initiate_chat(
    assistant_agent,
    message="Provide code to count the number of prime numbers from 1 to 10000.",
)

[
    {
        "model": "llama3.1",
        "api_type": "ollama",
        "client_host": "http://192.168.0.1:11434",
        "native_tool_calls": True # Use Ollama's native tool calling, False for manual
    }
]

[
    {
        "model": "llama3.1",
        "api_type": "ollama",
        "client_host": "http://192.168.0.1:11434",
        "native_tool_calls": True,
        "hide_tools": "if_any_run" # Hide tools once any tool has been called
    }
]

import json
from typing import Literal

from typing_extensions import Annotated

import autogen

config_list = [
    {
        # Let's choose the Meta's Llama 3.1 model (model names must match Ollama exactly)
        "model": "llama3.1:8b",
        "api_type": "ollama",
        "stream": False,
        "client_host": "http://192.168.0.1:11434",
        "hide_tools": "if_any_run",
    }
]

# Create the agent for tool calling
chatbot = autogen.AssistantAgent(
    name="chatbot",
    system_message="""For currency exchange and weather forecasting tasks,
        only use the functions you have been provided with.
        Example of the return JSON is:
        {
            "parameter_1_name": 100.00,
            "parameter_2_name": "ABC",
            "parameter_3_name": "DEF",
        }.
        Another example of the return JSON is:
        {
            "parameter_1_name": "GHI",
            "parameter_2_name": "ABC",
            "parameter_3_name": "DEF",
            "parameter_4_name": 123.00,
        }.
        Output 'HAVE FUN!' when an answer has been provided.""",
    llm_config={"config_list": config_list},
)

# Note that we have changed the termination string to be "HAVE FUN!"
user_proxy = autogen.UserProxyAgent(
    name="user_proxy",
    is_termination_msg=lambda x: x.get("content", "") and "HAVE FUN!" in x.get("content", ""),
    human_input_mode="NEVER",
    max_consecutive_auto_reply=1,
)

# Currency Exchange function

CurrencySymbol = Literal["USD", "EUR"]

# Define our function that we expect to call


def exchange_rate(base_currency: CurrencySymbol, quote_currency: CurrencySymbol) -> float:
    if base_currency == quote_currency:
        return 1.0
    elif base_currency == "USD" and quote_currency == "EUR":
        return 1 / 1.1
    elif base_currency == "EUR" and quote_currency == "USD":
        return 1.1
    else:
        raise ValueError(f"Unknown currencies {base_currency}, {quote_currency}")


# Register the function with the agent


@user_proxy.register_for_execution()
@chatbot.register_for_llm(description="Currency exchange calculator.")
def currency_calculator(
    base_amount: Annotated[
        float,
        "Amount of currency in base_currency. Type is float, not string, return value should be a number only, e.g. 987.65.",
    ],
    base_currency: Annotated[CurrencySymbol, "Base currency"] = "USD",
    quote_currency: Annotated[CurrencySymbol, "Quote currency"] = "EUR",
) -> str:
    quote_amount = exchange_rate(base_currency, quote_currency) * base_amount
    return f"{format(quote_amount, '.2f')} {quote_currency}"


# Weather function


# Example function to make available to model
def get_current_weather(location, unit="fahrenheit"):
    """Get the weather for some location"""
    if "chicago" in location.lower():
        return json.dumps({"location": "Chicago", "temperature": "13", "unit": unit})
    elif "san francisco" in location.lower():
        return json.dumps({"location": "San Francisco", "temperature": "55", "unit": unit})
    elif "new york" in location.lower():
        return json.dumps({"location": "New York", "temperature": "11", "unit": unit})
    else:
        return json.dumps({"location": location, "temperature": "unknown"})


# Register the function with the agent


@user_proxy.register_for_execution()
@chatbot.register_for_llm(description="Weather forecast for US cities.")
def weather_forecast(
    location: Annotated[str, "City name"],
) -> str:
    weather_details = get_current_weather(location=location)
    weather = json.loads(weather_details)
    return f"{weather['location']} will be {weather['temperature']} degrees {weather['unit']}"

# start the conversation
res = user_proxy.initiate_chat(
    chatbot,
    message="What's the weather in New York and can you tell me how much is 123.45 EUR in USD so I can spend it on my holiday? Throw a few holiday tips in as well.",
    summary_method="reflection_with_llm",
)

print(f"LLM SUMMARY: {res.summary['content']}")