BitAgent/bfcl_shuffle_full huggingface dataset:
user: What is the angle between the hour and minute hands of a clock at 3:15, rounded to 2 decimal places, and how does this compare to the angle at 8:20 and 11:50, both also rounded to 2 decimal places?

Available functions:
name: calculate_clock_angle
description: Calculate the angle between the hour and minute hands of a clock at a given time.
parameters: type: dict
properties: hours: type: integer
description: The hour on the clock face.
minutes: type: integer
description: The minutes on the clock face.
round_to: type: integer
description: The number of decimal places to round the result to, default is 2.
required: hours
minutes
user: "Can you plot two sine waves for me? The first one should have a frequency of 5 Hz, starting from 0 radians and ending at 10 radians, with an amplitude of 2 and a phase shift of 1 radian. The second one should have a frequency of 10 Hz, starting from 0 radians and ending at 20 radians, with an amplitude of 3 and a phase shift of 2 radians."

Available functions:
name: plot_sine_wave
description: Plot a sine wave for a given frequency in a given range.
parameters: type: dict
properties: start_range: type: integer
description: Start of the range in radians.
end_range: type: integer
description: End of the range in radians.
frequency: type: integer
description: Frequency of the sine wave in Hz.
amplitude: type: integer
description: Amplitude of the sine wave. Default is 1.
phase_shift: type: integer
description: Phase shift of the sine wave in radians. Default is 0.
required: start_range
end_range
frequency
user: "Can you calculate the time it would take for light to travel from Earth to a newly discovered exoplanet that is 4.22 light years away, then to another exoplanet that is 6.1 light years from the first one, and finally back to Earth which is 5.88 light years from the second exoplanet? Assume the speed of light in vacuum is 299792458 m/s."

Available functions:
name: light_travel_time
description: Calculate the time taken for light to travel from a celestial body to another.
parameters: type: dict
properties: distance_in_light_years: type: float
description: The distance between the two celestial bodies in light years.
speed_of_light: type: integer
description: The speed of light in vacuum, in m/s. Default value is 299792458 m/s.
required: distance_in_light_years
user: "Can you calculate the speed of a car that traveled a distance of 500 meters in 25 seconds and provide the answer in km/h? Also, can you calculate the speed of a bicycle that traveled a distance of 1000 meters in 200 seconds and provide the answer in m/s? Lastly, can you calculate the speed of a train that traveled a distance of 10000 meters in 600 seconds and provide the answer in km/h?"

Available functions:
name: calculate_speed
description: Calculate the speed of an object based on the distance travelled and the time taken.
parameters: type: dict
properties: distance: type: integer
description: The distance the object travelled in meters.
time: type: integer
description: The time it took for the object to travel in seconds.
to_unit: type: string
description: The unit in which the speed should be calculated, default is m/s.
required: distance
time
user: What is the distance in miles between the celestial bodies Mars and Venus, and then between Mars and Jupiter, given that the function 'calculate_distance' requires the names of the two celestial bodies and the unit of measurement?

Available functions:
name: calculate_distance
description: Calculate the distance between two celestial bodies.
parameters: type: dict
properties: body1: type: string
description: The first celestial body.
body2: type: string
description: The second celestial body.
unit: type: string
description: The unit of measurement, default is 'kilometers'.
required: body1
body2
user: "Can you calculate the area under the curve for the polynomial function with coefficients [3, -2, 1] (meaning the function is 3x^2 - 2x + 1) within the interval [-1, 2], and then do the same for the polynomial function with coefficients [1, 0, -1] (meaning the function is x^2 - 1) within the interval [0, 3]? Please provide both results."

Available functions:
name: mathematics.calculate_area_under_curve
description: Calculate the area under the curve for a given polynomial function within a specified interval.
parameters: type: dict
properties: polynomial: type: array
items: type: integer
description: The coefficients of the polynomial, in decreasing order of exponent, where the first element is the coefficient for x^n, the second element is the coefficient for x^(n-1), and so on. The last element is the constant term.
limits: type: array
items: type: integer
description: A list of two numbers specifying the lower and upper limit for the integration interval.
required: polynomial
limits
user: "Can you help me calculate the total area of three different triangles? The first triangle has a base of 15 meters and a height of 20 meters. The second triangle has a base of 25 feet and a height of 30 feet. And the third triangle has a base of 35 inches and a height of 40 inches. I would like the area of each triangle in their respective units."

Available functions:
name: geometry.area_triangle
description: Calculate the area of a triangle.
parameters: type: dict
properties: base: type: integer
description: The length of the base of the triangle.
height: type: integer
description: The height of the triangle from the base.
unit: type: string
description: The measurement unit for the area. Defaults to square meters.
required: base
height
user: "Can you calculate the result of the following mathematical operation: first, raise the number 3 to the power of 5, then raise the number 2 to the power of 3."

Available functions:
name: math.power
description: Calculate the power of one number raised to another.
parameters: type: dict
properties: base: type: integer
description: The base number.
exponent: type: integer
description: The exponent.
mod: type: float
description: The modulus. Default is None. Calculates pow(base, exponent) % mod when provided.
required: base
exponent
user: You are given a task to train a Random Forest classifier on two different datasets, 'dataset1' and 'dataset2'. For the first run, you are asked to set the maximum depth of the trees in the forest to 10 and the number of trees in the forest to 100. For the second run, you are asked to set the maximum depth of the trees in the forest to 20 and the number of trees in the forest to 200. How would you invoke the 'train_random_forest_classifier' function to accomplish this task?

Available functions:
name: train_random_forest_classifier
description: Train a Random Forest classifier with the specified parameters.
parameters: type: dict
properties: dataset: type: string
description: The dataset to train the classifier on.
max_depth: type: integer
description: The maximum depth of the trees in the forest.
n_estimators: type: integer
description: The number of trees in the forest.
required: dataset
max_depth
n_estimators
user: "Could you calculate the Body Mass Index (BMI) for four individuals? The first person weighs 75 kilograms and is 180 centimeters tall, the second person weighs 60 kilograms and is 165 centimeters tall, the third person weighs 80 kilograms and is 175 centimeters tall, and the fourth person weighs 90 kilograms and is 185 centimeters tall. Please use the metric system for all calculations."

Available functions:
name: calculate_bmi
description: Calculate the Body Mass Index (BMI) for a person based on their weight and height.
parameters: type: dict
properties: weight: type: integer
description: The weight of the person in kilograms.
height: type: integer
description: The height of the person in centimeters.
system: type: string
description: The system of units to be used, 'metric' or 'imperial'. Default is 'metric'.
required: weight
height
user: You are given a dataset with various variables including 'Age', 'Income', 'Education', 'Gender', 'Marital Status', and 'Spending Score'. You want to predict 'Spending Score' based on the other variables. Could you please use the 'run_linear_regression' function to build a linear regression model using 'Age', 'Income', and 'Education' as predictor variables and 'Spending Score' as the target variable without applying standardization on the predictors? Then, could you please run the same function again but this time with standardization applied on the predictors?

Available functions:
name: run_linear_regression
description: Build a linear regression model using given predictor variables and a target variable.
parameters: type: dict
properties: predictors: type: array
items: type: string
description: Array containing the names of predictor variables.
target: type: string
description: The name of target variable.
standardize: type: boolean
description: Option to apply standardization on the predictors. Defaults to False.
required: predictors
target
user: You are given a dataset "data_random_forest" in the form of a dataframe and you want to train a Random Forest Model on this data. You decide to experiment with different numbers of trees in the forest and different maximum depths of the trees to see how these parameters affect the model's performance. 

First, you train a model with 100 trees and a maximum depth of 10. Then, you train another model with 200 trees and a maximum depth of 20. After that, you train a third model with 300 trees and a maximum depth of 30. Finally, you train a fourth model with 400 trees and a maximum depth of 40. 

Can you invoke the 'random_forest.train' function four times with these different parameters and compare the performance of the four models?

Available functions:
name: random_forest.train
description: Train a Random Forest Model on given data
parameters: type: dict
properties: n_estimators: type: integer
description: The number of trees in the forest.
max_depth: type: integer
description: The maximum depth of the tree.
data: type: string
description: The training data for the model.
required: n_estimators
max_depth
data
user: "Could you use the 'predict_house_price' function to compare the estimated prices of four different houses? The first house is located in New York, has 3 bedrooms, 2 bathrooms, and an area of 1500 square feet. The second house is in Los Angeles, with 4 bedrooms, 3 bathrooms, and an area of 2000 square feet. The third house is in Chicago, with 2 bedrooms, 1 bathroom, and an area of 1200 square feet. The fourth house is in Miami, with 3 bedrooms, 2 bathrooms, and an area of 1800 square feet."

Available functions:
name: predict_house_price
description: Predict the price of a house in a given area based on number of bedrooms, bathrooms and area.
parameters: type: dict
properties: bedrooms: type: integer
description: The number of bedrooms in the house.
bathrooms: type: integer
description: The number of bathrooms in the house.
area: type: integer
description: The area of the house in square feet.
location: type: string
description: The location of the house.
required: bedrooms
bathrooms
area
location
user: You are a data scientist working on a project that requires you to generate random numbers from a normal distribution. You need to generate four random numbers: two from a normal distribution with a mean of 5 and a standard deviation of 2, and two from a normal distribution with a mean of 10 and a standard deviation of 3. How can you use the 'random.normalvariate' function to achieve this?

Available functions:
name: random.normalvariate
description: Generates a random number from a normal distribution given the mean and standard deviation.
parameters: type: dict
properties: mu: type: integer
description: Mean of the normal distribution.
sigma: type: integer
description: Standard deviation of the normal distribution.
required: mu
sigma
user: "In a board game, you have a six-sided die. You are curious about the probability of rolling a 4 three times in a row. After that, you want to know the probability of rolling a 2 twice in a row. Finally, you wonder what the probability would be if the die had 8 sides and you wanted to roll a 7 two times in a row. Can you calculate these probabilities?"

Available functions:
name: probability.dice_roll
description: Calculate the probability of rolling a certain number on a six-sided die a certain number of times in a row.
parameters: type: dict
properties: desired_number: type: integer
description: The number you want to roll.
number_of_rolls: type: integer
description: How many times you want to roll that number in a row.
die_sides: type: integer
description: The number of sides on the die (optional; default is 6).
required: desired_number
number_of_rolls
user: "In a game of chance, you have a 0.3 probability of winning any given round. If you play this game 20 times, what is the probability of winning exactly 5 times? Also, if you play the game 50 times, what is the probability of winning exactly 15 times? Lastly, if you play the game 100 times, what is the probability of winning exactly 30 times? Use the function 'prob_dist.binomial' to compute these probabilities."

Available functions:
name: prob_dist.binomial
description: Compute the probability of having 'success' outcome from binomial distribution.
parameters: type: dict
properties: trials: type: integer
description: The number of independent experiments.
successes: type: integer
description: The number of success events.
p: type: float
description: The probability of success on any given trial, defaults to 0.5
required: trials
successes
user: "In a game of basketball, a player has a 60% chance of making any given shot. In a series of 10 shots, what is the probability that the player makes exactly 7 shots? Also, in another series of 15 shots, what is the probability that the player makes exactly 10 shots? Finally, in a series of 20 shots, what is the probability that the player makes exactly 15 shots?"

Available functions:
name: calculate_binomial_probability
description: Calculates the binomial probability given the number of trials, successes and the probability of success on an individual trial.
parameters: type: dict
properties: number_of_trials: type: integer
description: The total number of trials.
number_of_successes: type: integer
description: The desired number of successful outcomes.
probability_of_success: type: float
description: The probability of a successful outcome on any given trial.
default: 0.5
required: number_of_trials
number_of_successes
user: You are a teacher preparing a probability lesson for your students. You have a deck of 52 playing cards and you want to explain the probability of drawing certain cards. 

1. What is the probability of drawing an Ace (4 successful outcomes) from the deck (52 total outcomes)? Please provide this as a decimal. 

2. Then, what is the probability of drawing a heart (13 successful outcomes) from the deck (52 total outcomes)? Please provide this as a decimal. 

3. Finally, what is the probability of drawing a red card (26 successful outcomes) from the deck (52 total outcomes)? But this time, please provide the answer as a ratio.

Available functions:
name: probability_of_event
description: Calculates the probability of an event.
parameters: type: dict
properties: success_outcomes: type: integer
description: The number of successful outcomes.
total_outcomes: type: integer
description: The total number of possible outcomes.
format_as_ratio: type: boolean
description: When true, formats the output as a ratio instead of a decimal. Default is false.
required: success_outcomes
total_outcomes
user: "In a game of basketball, a player has a 60% chance of making a successful shot. In a particular match, the player attempts 10 shots. What is the probability that the player makes exactly 6 successful shots? Now, consider a different scenario where the player's success rate drops to 50% but the number of attempts remains the same. What is the probability of making exactly 6 successful shots in this scenario? Finally, consider a third scenario where the player's success rate remains at 50% but the number of attempts increases to 15. What is the probability of making exactly 6 successful shots in this third scenario?"

Available functions:
name: calc_binomial_prob
description: Calculates the probability of an outcome based on the binomial distribution
parameters: type: dict
properties: num_trials: type: integer
description: Number of independent experiments.
num_success: type: integer
description: Number of times the event of interest has occurred.
prob_success: type: float
description: Probability of the event of interest on any single experiment.
required: num_trials
num_success
prob_success
user: You are a data analyst and you have been given two 2x2 contingency tables representing the results of a survey conducted in two different cities. The first table is [45, 55, 35, 65] and the second table is [30, 70, 50, 50]. You are asked to perform a Chi-Squared test for independence on both tables to determine if there is a significant relationship between the variables in each city. Use a significance level of 0.05 for both tests. Can you tell if there is a significant relationship in each city based on the Chi-Squared test results?

Available functions:
name: chi_squared_test
description: Performs a Chi-Squared test for independence on a 2x2 contingency table.
parameters: type: dict
properties: table: type: array
items: type: integer
description: A 2x2 contingency table presented in array form.
alpha: type: float
description: Significance level for the Chi-Squared test. Default is 0.05.
required: table
user: "Could you please perform a statistical t-test to check if the means of two independent datasets are statistically different? The first dataset, Dataset A, includes the following integers: 12, 15, 18, 20, 22, 25, 28, 30, 32, 35. The second dataset, Dataset B, includes these integers: 14, 17, 19, 21, 23, 26, 29, 31, 33, 36. Please perform the test twice, once with a significance level of 0.05 and once with a significance level of 0.01."

Available functions:
name: t_test
description: Perform a statistical t-test to check if the means of two independent datasets are statistically different.
parameters: type: dict
properties: dataset_A: type: array
items: type: integer
description: Dataset A for comparison.
dataset_B: type: array
items: type: integer
description: Dataset B for comparison.
alpha: type: float
description: Significance level for the test. Default is 0.05.
required: dataset_A
dataset_B
user: Can you predict the price of a house with an area of 2500 square feet, 3 rooms, constructed in the year 2000, and located in New York, and then compare it with the price of a similar house but with an area of 3000 square feet, constructed in the year 2005, and located in Los Angeles? Finally, predict the price of a third house with an area of 2000 square feet, 2 rooms, constructed in the year 1995, and located in Chicago.

Available functions:
name: predict_house_price
description: Predict house price based on area, number of rooms and year of construction.
parameters: type: dict
properties: area: type: integer
description: Area of the house in square feet.
rooms: type: integer
description: Number of rooms in the house.
year: type: integer
description: Year when the house was constructed.
location: type: string
description: The location or city of the house.
required: area
rooms
year
location
user: What is the coefficient of determination (R squared) of a regression model if we use the dataset located at "/user/home/datasets/finance.csv", with 'income', 'age' and 'education' as the independent variables and 'credit_score' as the dependent variable, and then repeat the same process with 'income', 'age' and 'credit_score' as the independent variables and 'education' as the dependent variable?

Available functions:
name: linear_regression.get_r_squared
description: Calculate the coefficient of determination of a regression model.
parameters: type: dict
properties: dataset_path: type: string
description: Path to the CSV dataset file.
independent_variables: type: array
items: type: string
description: The independent variables to use in the regression model.
dependent_variable: type: string
description: The dependent variable to predict in the regression model.
required: dataset_path
independent_variables
dependent_variable
user: "Can you help me calculate the quarterly dividend per share for my company? We have just paid out a total of $5,000,000 in dividends and currently have 2,000,000 outstanding shares. Also, I am considering a scenario where we might increase our total payout to $6,000,000 while keeping the same number of outstanding shares. What would be the quarterly dividend per share in that case? And what if we also increase our outstanding shares to 2,500,000 while keeping the total payout at $6,000,000?"

Available functions:
name: finance.calculate_quarterly_dividend_per_share
description: Calculate quarterly dividend per share for a company given total dividend payout and outstanding shares
parameters: type: dict
properties: total_payout: type: integer
description: The total amount of dividends paid out in USD
outstanding_shares: type: integer
description: Total number of outstanding shares
required: total_payout
outstanding_shares
user: "Can you help me calculate the discounted cash flow of a bond? I have a bond with an annual coupon payment of $50, a time frame of 5 years, and a discount rate of 5%. Also, the face value of the bond is $1000. I would like to know the discounted cash flow for this bond. After that, I want to compare it with another bond that has an annual coupon payment of $60, a time frame of 7 years, and a discount rate of 4%, with the same face value of $1000. Can you calculate the discounted cash flow for this second bond as well?"

Available functions:
name: calculate_discounted_cash_flow
description: Calculate the discounted cash flow of a bond for a given annual coupon payment, time frame and discount rate.
parameters: type: dict
properties: coupon_payment: type: integer
description: The annual coupon payment.
period: type: integer
description: The time frame in years for which coupon payment is made.
discount_rate: type: float
description: The discount rate.
face_value: type: integer
description: The face value of the bond, default is $1000.
required: coupon_payment
period
discount_rate
user: "Can you help me calculate the compound interest for my savings? I initially invested $5000 as the principal amount. The bank offers an annual interest rate of 2.5% (or 0.025 in decimal form). I plan to keep my money in the bank for 10 years. Also, the interest is compounded quarterly, so it's compounded 4 times in a year. Can you calculate the compound interest for the first 2 years, then for the next 3 years and finally for the remaining 5 years?"

Available functions:
name: calculate_compound_interest
description: Calculate compound interest for an initial principal amount.
parameters: type: dict
properties: principal: type: integer
description: The principal amount that the interest is applied to.
rate: type: float
description: The annual interest rate. Enter as a decimal. E.g, 5% is 0.05
time: type: integer
description: The time the money is invested for in years.
n: type: integer
description: The number of times that interest is compounded per time period. Default is 1.
required: principal
rate
time
user: "Can you calculate the return on equity for two companies? The first company has a net income of $1,000,000, shareholder's equity of $5,000,000, and paid dividends of $200,000. The second company has a net income of $2,000,000, shareholder's equity of $10,000,000, but did not pay any dividends."

Available functions:
name: calculate_return_on_equity
description: Calculate a company's return on equity based on its net income, shareholder's equity, and dividends paid.
parameters: type: dict
properties: net_income: type: integer
description: The company's net income.
shareholder_equity: type: integer
description: The company's total shareholder's equity.
dividends_paid: type: integer
description: The total dividends paid by the company. Optional. If not given, assumes it's 0 as default.
required: net_income
shareholder_equity
user: "Imagine you have two different investment opportunities. The first one has a present value of $5000, an annual interest rate of 5%, and you plan to hold it for 10 years. The second one has a present value of $7000, an annual interest rate of 4%, and you plan to hold it for 15 years. Both investments compound interest annually. Can you calculate the future value of both investments using the finance.predict_future_value function?"

Available functions:
name: finance.predict_future_value
description: Calculate the future value of an investment given its present value, interest rate, the number of compounding periods per year, and the time horizon.
parameters: type: dict
properties: present_value: type: integer
description: The present value of the investment.
annual_interest_rate: type: float
description: The annual interest rate of the investment.
compounding_periods_per_year: type: integer
description: The number of times that interest is compounded per year. Default is 1 (annually).
time_years: type: integer
description: The investment horizon in years.
required: present_value
annual_interest_rate
time_years
user: "John has decided to invest in two different funds. He invested $5000 in Fund A which has an annual return rate of 7% and he plans to keep his money there for 5 years. On the other hand, he invested $8000 in Fund B with an annual return rate of 5% for a period of 7 years. Can you predict the profit John will make from both Fund A and Fund B?"

Available functions:
name: investment.predictProfit
description: Predict the profit for given investment after specified number of years.
parameters: type: dict
properties: investment_amount: type: integer
description: The amount invested in dollars.
annual_return: type: float
description: The annual return rate of the investment.
years: type: integer
description: The time period in years for which the investment is made.
required: investment_amount
annual_return
years
user: You are an investor who recently sold some stocks. You bought one stock at $150, another at $200, and another at $250. You sold them at $180, $210, and $300 respectively. You also received dividends of $20, $30, and $40 for each stock. Can you calculate the return on investment for each of these stocks using the 'calculate_return_on_investment' function?

Available functions:
name: calculate_return_on_investment
description: Calculate the return on investment for a given stock based on its purchase price, sale price, and any dividends received.
parameters: type: dict
properties: purchase_price: type: integer
description: The price the stock was bought at.
sale_price: type: integer
description: The price the stock was sold at.
dividend: type: integer
description: Any dividends received from the stock.
default: 0
required: purchase_price
sale_price
user: "Could you please calculate the future value of my investments? I have invested $5000 in Apple Inc. (AAPL) and expect an annual return of 7% over the next 5 years. I have also invested $8000 in Microsoft Corporation (MSFT) with an expected annual return of 6% for the next 7 years. Lastly, I have invested $10000 in Amazon.com, Inc. (AMZN) expecting an annual return of 8% for the next 10 years."

Available functions:
name: portfolio_future_value
description: Calculate the future value of an investment in a specific stock based on the invested amount, expected annual return and number of years.
parameters: type: dict
properties: stock: type: string
description: The ticker symbol of the stock.
invested_amount: type: integer
description: The invested amount in USD.
expected_annual_return: type: float
description: The expected annual return on investment as a decimal. E.g. 5% = 0.05
years: type: integer
description: The number of years for which the investment is made.
required: stock
invested_amount
expected_annual_return
years
user: "John invested $5000 in a mutual fund 5 years ago. Today, the value of his investment has grown to $7000. He wants to compare this with another investment he made 3 years ago where he invested $8000 and now it's worth $12000. Can you help John calculate the Compound Annual Growth Rate (CAGR) for both these investments?"

Available functions:
name: calculate_cagr
description: Calculate the Compound Annual Growth Rate (CAGR) given an initial investment value, a final investment value, and the number of years.
parameters: type: dict
properties: initial_value: type: integer
description: The initial investment value.
final_value: type: integer
description: The final investment value.
period_in_years: type: integer
description: The period of the investment in years.
required: initial_value
final_value
period_in_years
user: What is the current price per ounce of gold, silver, platinum, and palladium?

Available functions:
name: get_metal_price
description: Retrieve the current price for a specified metal and measure.
parameters: type: dict
properties: metal: type: string
description: The metal whose price needs to be fetched.
measure: type: string
description: The measure unit for price, like 'ounce' or 'kg'.
required: metal
measure
user: What were the closing stock prices for Microsoft and Apple on NASDAQ on the dates 2022-01-01 and 2022-02-01?

Available functions:
name: get_stock_price
description: Get the closing stock price for a specific company on a specified date.
parameters: type: dict
properties: company_name: type: string
description: Name of the company.
date: type: string
description: Date of when to get the stock price. Format: yyyy-mm-dd.
exchange: type: string
description: Name of the stock exchange market where the company's stock is listed. Default is 'NASDAQ'
required: company_name
date
user: What were the stock prices of Apple Inc. listed on NASDAQ and Microsoft Corporation listed on NYSE for the past 10 and 15 days respectively?

Available functions:
name: get_stock_price
description: Retrieve the stock price for a specific company and time frame.
parameters: type: dict
properties: company: type: string
description: The ticker symbol of the company.
days: type: integer
description: Number of past days for which the stock price is required.
exchange: type: string
description: The stock exchange where the company is listed, default is NYSE
required: company
days
user: What were the 'Open', 'Close', 'High', and 'Low' stock prices for Microsoft and Apple over the past 30 days?

Available functions:
name: stock_price
description: Get stock price data for a given company over a specified number of days.
parameters: type: dict
properties: company: type: string
description: The company name.
days: type: integer
description: The number of previous days to retrieve data for.
data_type: type: string
description: The type of price data to retrieve (e.g., 'Open', 'Close', 'High', 'Low'). Default is 'Close'.
required: company
days
user: Can you use the get_stock_prices function to retrieve the stock prices for Apple, Microsoft, Amazon, and Tesla over the duration of 1 week, 2 weeks, 3 weeks, and 1 month respectively?

Available functions:
name: get_stock_prices
description: Retrieves stock prices for specified companies and duration.
parameters: type: dict
properties: companies: type: array
items: type: string
description: List of companies to retrieve stock prices for.
duration: type: string
description: Time duration to retrieve stock prices for. E.g., '1 week', '2 weeks', '1 month', etc.
required: companies
duration
user: "John is planning to invest in a mutual fund. He is considering two scenarios. In the first scenario, he will make an initial investment of $5000 with an annual rate of return of 7% and he will not make any additional contributions. In the second scenario, he will make an initial investment of $3000 with an annual rate of return of 6% and he will make additional regular contributions of $200 every year. He wants to compare the future value of his investment after 10 years in both scenarios. Can you help him calculate the future value of his investment in both scenarios?"

Available functions:
name: finance.calculate_future_value
description: Calculate the future value of an investment given an initial investment, annual rate of return, and a time frame.
parameters: type: dict
properties: initial_investment: type: integer
description: The initial investment amount.
rate_of_return: type: float
description: The annual rate of return.
years: type: integer
description: The time frame of the investment in years.
contribution: type: integer
description: Optional: Additional regular contributions. Default is 0.
required: initial_investment
rate_of_return
years
user: "Imagine you are a drone operator. You are currently operating a drone that is at a point (5, 7) in the sky. You are asked to move the drone to a new point (10, 15). After reaching the new point, you are again asked to move the drone to another point (20, 25). Can you calculate the total distance the drone has traveled using the Euclidean norm method?"

Available functions:
name: math.hypot
description: Calculate the Euclidean norm, sqrt(sum(squares)), the length of the vector from the origin to point (x, y) which is the hypotenuse of the right triangle.
parameters: type: dict
properties: x: type: integer
description: The x-coordinate value.
y: type: integer
description: The y-coordinate value.
z: type: integer
description: Optional. The z-coordinate value. Default is 0.
required: x
y
user: "Can you help me find the roots of two different quadratic equations? The first equation is 3x^2 + 7x + 2 = 0, where 'a' is the coefficient of x^2 (3), 'b' is the coefficient of x (7), and 'c' is the constant term (2). The second equation is 5x^2 - 4x + 1 = 0, where 'a' is the coefficient of x^2 (5), 'b' is the coefficient of x (-4), and 'c' is the constant term (1)."

Available functions:
name: algebra.quadratic_roots
description: Find the roots of a quadratic equation ax^2 + bx + c = 0.
parameters: type: dict
properties: a: type: integer
description: Coefficient of x^2.
b: type: integer
description: Coefficient of x.
c: type: integer
description: Constant term.
required: a
b
c
user: Can you estimate the population of Bengal Tigers in India for the year 2021, compare it with the estimated population of African Elephants in Kenya for the same year, and then estimate the population of both these species in their respective countries for year 2023?

Available functions:
name: estimate_population
description: Estimate the population of a particular species in a given country.
parameters: type: dict
properties: species: type: string
description: The species for which population needs to be estimated.
country: type: string
description: The country where the species lives.
year: type: integer
description: The year for which population estimate is sought. Default is 2020.
required: species
country
user: What are the potential greenhouse gas emissions savings if I switch to solar energy for 12 months and wind energy for 8 months in the Midwest region of the United States?

Available functions:
name: calculate_emission_savings
description: Calculate potential greenhouse gas emissions saved by switching to renewable energy sources.
parameters: type: dict
properties: energy_type: type: string
description: Type of the renewable energy source.
usage_duration: type: integer
description: Usage duration in months.
region: type: string
description: The region where you use energy.
default: West
required: energy_type
usage_duration
user: What is the air quality data for New York City, including additional data like PM2.5, PM10, ozone levels, and pollution sources, for today, yesterday, and the day before yesterday? Today is May 5, 2023

Available functions:
name: get_air_quality
description: Retrieve real-time air quality and pollution data for a specific location.
parameters: type: dict
properties: location: type: string
description: The city that you want to get the air quality data for.
detail: type: boolean
description: If true, additional data like PM2.5, PM10, ozone levels, and pollution sources will be retrieved. the value is set to false to default.
historical: type: string
description: Optional date (in 'YYYY-MM-DD' format) to retrieve historical data.
default: today
required: location
user: What are the current traffic conditions for a route from New York to Los Angeles using driving as the preferred method of transportation, then from Los Angeles to San Francisco using bicycling as the preferred method of transportation, and finally from San Francisco back to New York using transit as the preferred method of transportation?

Available functions:
name: get_traffic_info
description: Retrieve current traffic conditions for a specified route.
parameters: type: dict
properties: start_location: type: string
description: The starting point of the route.
end_location: type: string
description: The destination of the route.
mode: type: string
enum: driving
walking
bicycling
transit
description: Preferred method of transportation, default to 'driving'.
required: start_location
end_location
user: Can you find me parks in New York, USA that have a Tennis Court and a Picnic Area, then find parks in Los Angeles, USA that have a Playground and Running Track, and finally find parks in Chicago, USA that have a Tennis Court and a Playground?

Available functions:
name: parks.find_nearby
description: Locate nearby parks based on specific criteria like tennis court availability.
parameters: type: dict
properties: location: type: string
description: The city and state, e.g. London, UK
amenities: type: array
items: type: string
enum: Tennis Court
Picnic Area
Playground
Running Track
description: Preferred amenities in park.
default: Playground
required: location
user: What is the shortest driving distance from New York City to Los Angeles, and then from Los Angeles to Miami, considering both the shortest and scenic route preferences?

Available functions:
name: calculate_shortest_distance
description: Calculate the shortest driving distance between two locations.
parameters: type: dict
properties: start_location: type: string
description: The starting location for the drive.
end_location: type: string
description: The destination location for the drive.
route_preference: type: string
enum: Shortest
Scenic
description: The preferred type of route.
required: start_location
end_location
route_preference
user: Can you help me find public libraries in New York, NY that have a Reading Room and Fiction section, and then in Los Angeles, CA that offer Wi-Fi and have a Children Section, and finally in Chicago, IL that have a Cafe and a Reading Room?

Available functions:
name: public_library.find_nearby
description: Locate nearby public libraries based on specific criteria like English fiction availability and Wi-Fi.
parameters: type: dict
properties: location: type: string
description: The city and state, e.g. Boston, MA
facilities: type: array
items: type: string
enum: Wi-Fi
Reading Room
Fiction
Children Section
Cafe
description: Facilities and sections in public library.
required: location
facilities
user: Can you fetch the latest news on the topic of "Climate Change" and "Artificial Intelligence", each with 5 articles, and specifically for the region "Europe"?

Available functions:
name: get_news
description: Fetches the latest news on a specific topic.
parameters: type: dict
properties: topic: type: string
description: The subject for the news topic.
quantity: type: integer
description: Number of articles to fetch.
region: type: string
description: The geographical region for the news (Optional). default is 'USA'
required: topic
quantity
user: Can you send an email to my colleague at john.doe@example.com with the subject "Project Update" and the body content "Dear John, The project is progressing as planned and we are on track to meet our deadlines. Best, Alex", then carbon copy the email to my manager at manager@example.com and blind carbon copy it to the HR at hr@example.com? After that, can you send another email to my other colleague at jane.doe@example.com with the subject "Meeting Reminder" and the body content "Dear Jane, This is a reminder for our meeting scheduled for tomorrow at 10 AM. Best, Alex", and carbon copy it to my assistant at assistant@example.com and blind carbon copy it to the HR at hr@example.com?

Available functions:
name: send_email
description: Send an email to the specified email address.
parameters: type: dict
properties: to: type: string
description: The email address to send to.
subject: type: string
description: The subject of the email.
body: type: string
description: The body content of the email.
cc: type: string
description: The email address to carbon copy. default is ''.
bcc: type: string
description: The email address to blind carbon copy. the value is set to '' for default.
required: to
subject
body
user: Can you find me upcoming jazz events in Los Angeles, CA for the next 14 days and then find the same for rock events in Chicago, IL for the next 10 days and finally find upcoming classical music events in Boston, MA for the next 7 days?

Available functions:
name: event_finder.find_upcoming
description: Find upcoming events of a specific genre in a given location.
parameters: type: dict
properties: location: type: string
description: The city and state where the search will take place, e.g. New York, NY.
genre: type: string
description: The genre of events.
days_ahead: type: integer
description: The number of days from now to include in the search.
default: 7
required: location
genre
user: Can you retrieve additional information like Director, Cast, Awards etc. for the same movie "Inception" and also for the movie "The Dark Knight"?

Available functions:
name: movie_details.brief
description: This function retrieves a brief about a specified movie.
parameters: type: dict
properties: title: type: string
description: Title of the movie
extra_info: type: boolean
description: Option to get additional information like Director, Cast, Awards etc.
default: false
required: title
user: Can you please retrieve the details of two lawsuits for me? The first one has a case number of '12345' and was filed in the 'New York Supreme Court'. I would also like to know the verdict details for this case. The second lawsuit has a case number '67890' and was filed in the 'Los Angeles Superior Court'. I do not need the verdict details for this case.

Available functions:
name: get_lawsuit_details
description: Retrieve details of a lawsuit based on its case number and court location.
parameters: type: dict
properties: case_number: type: string
description: Case number of the lawsuit.
court_location: type: string
description: The location of the court where the lawsuit was filed.
with_verdict: type: boolean
description: Flag to include verdict details if available. Default is False
required: case_number
court_location
user: "Can you provide me with the details of the lawsuit case with the case number '12345ABC', which was initiated in the year 2018 and filed in the New York court jurisdiction? Also, can you retrieve the same information for another lawsuit case with the case number '67890XYZ', initiated in the year 2019 and filed in the California court jurisdiction?"

Available functions:
name: lawsuit_info
description: Retrieves details of a lawsuit given a case number
parameters: type: dict
properties: case_number: type: string
description: The unique identifier of the lawsuit case
year: type: integer
description: The year in which the lawsuit case was initiated
optional: yes
default: 2000
location: type: string
description: The location or court jurisdiction where the case was filed.
optional: yes
default: New York
required: case_number
user: Can you use the lawsuit_search function to retrieve all lawsuits involving the entity "Google" from the county of "Santa Clara" and then do the same for the entity "Facebook" in the county of "San Mateo", both in the state of California?

Available functions:
name: lawsuit_search
description: Retrieve all lawsuits involving a particular entity from specified jurisdiction.
parameters: type: dict
properties: entity: type: string
description: The entity involved in lawsuits.
county: type: string
description: The jurisdiction for the lawsuit search.
state: type: string
description: The state for the lawsuit search. Default is California.
required: entity
county
user: What is the current temperature and humidity in New York, Los Angeles, London and Tokyo, if I want to include both temperature and humidity in the results?

Available functions:
name: get_current_weather
description: Retrieves the current temperature and humidity for a specific location.
parameters: type: dict
properties: location: type: string
description: The location to get the weather for.
include_temperature: type: boolean
description: Whether to include the temperature in the result. Default is true.
include_humidity: type: boolean
description: Whether to include the humidity in the result. Default is true.
required: location