User (to Cerebras Assistant):
Provide code to count the number of prime numbers from 1 to 10000.
--------------------------------------------------------------------------------
Cerebras Assistant (to User):
To count the number of prime numbers from 1 to 10000, we will utilize a simple algorithm that checks each number in the range to see if it is prime. A prime number is a natural number greater than 1 that has no positive divisors other than 1 and itself.
Here's how we can do it using a Python script:
```python
def count_primes(n):
primes = 0
for possiblePrime in range(2, n + 1):
# Assume number is prime until shown it is not.
isPrime = True
for num in range(2, int(possiblePrime ** 0.5) + 1):
if possiblePrime % num == 0:
isPrime = False
break
if isPrime:
primes += 1
return primes
# Counting prime numbers from 1 to 10000
count = count_primes(10000)
print(count)
```
Please execute this code. I will respond with "FINISH" after you provide the result.
--------------------------------------------------------------------------------
Replying as User. Provide feedback to Cerebras Assistant. Press enter to skip and use auto-reply, or type 'exit' to end the conversation:
>>>>>>>> NO HUMAN INPUT RECEIVED.
```
## Tool Call Example
In this example, instead of writing code, we will show how Meta's Llama-3.1-70B model can perform parallel tool calling, where it recommends calling more than one tool at a time.
We'll use a simple travel agent assistant program where we have a couple of tools for weather and currency conversion.
We start by importing libraries and setting up our configuration to use Llama-3.1-70B and the `cerebras` client class.
```python
import json
import os
from typing import Literal
from typing_extensions import Annotated
import autogen
llm_config = autogen.LLMConfig(
model="llama-3.3-70b",
api_key=os.environ.get("CEREBRAS_API_KEY"),
api_type="cerebras",
)
# Create the agent for tool calling
with llm_config:
chatbot = autogen.AssistantAgent(
name="chatbot",
system_message="""
For currency exchange and weather forecasting tasks,
only use the functions you have been provided with.
When you summarize, make sure you've considered ALL previous instructions.
Output 'HAVE FUN!' when an answer has been provided.
""",
)
# 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,
)
# Create the two functions, annotating them so that those descriptions can be passed through to the LLM.
# We associate them with the agents using `register_for_execution` for the user_proxy so it can execute the function and `register_for_llm` for the chatbot (powered by the LLM) so it can pass the function definitions to the LLM.
# 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"],
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']}"
import time
start_time = time.time()
# 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",
)
end_time = time.time()
print(f"LLM SUMMARY: {res.summary['content']}\n\nDuration: {(end_time - start_time) * 1000}ms")
```
```console
user_proxy (to chatbot):
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.
--------------------------------------------------------------------------------
chatbot (to user_proxy):
***** Suggested tool call (210f6ac6d): weather_forecast *****
Arguments:
{"location": "New York"}
*************************************************************
***** Suggested tool call (3c00ac7d5): currency_calculator *****
Arguments:
{"base_amount": 123.45, "base_currency": "EUR", "quote_currency": "USD"}
****************************************************************
--------------------------------------------------------------------------------
>>>>>>>> EXECUTING FUNCTION weather_forecast...
>>>>>>>> EXECUTING FUNCTION currency_calculator...
user_proxy (to chatbot):
user_proxy (to chatbot):
***** Response from calling tool (210f6ac6d) *****
New York will be 11 degrees fahrenheit
**************************************************
--------------------------------------------------------------------------------
user_proxy (to chatbot):
***** Response from calling tool (3c00ac7d5) *****
135.80 USD
**************************************************
--------------------------------------------------------------------------------
chatbot (to user_proxy):
New York will be 11 degrees fahrenheit.
123.45 EUR is equivalent to 135.80 USD.
For a great holiday, explore the Statue of Liberty, take a walk through Central Park, or visit one of the many world-class museums. Also, you'll find great food ranging from bagels to fine dining experiences. HAVE FUN!
--------------------------------------------------------------------------------
LLM SUMMARY: New York will be 11 degrees fahrenheit. 123.45 EUR is equivalent to 135.80 USD. Explore the Statue of Liberty, walk through Central Park, or visit one of the many world-class museums for a great holiday in New York.
Duration: 73.97937774658203ms
