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Function Calling with KerasHub models
Author: Laxmareddy Patlolla, Divyashree Sreepathihalli
Date created: 2025/07/08
Last modified: 2025/07/10
Description: A guide to using the function calling feature in KerasHub with Gemma 3 and Mistral.
Introduction
Tool calling is a powerful new feature in modern large language models that allows them to use external tools, such as Python functions, to answer questions and perform actions. Instead of just generating text, a tool-calling model can generate code that calls a function you've provided, allowing it to interact with the real world, access live data, and perform complex calculations.
In this guide, we'll walk you through a simple example of tool calling with the Gemma 3 and Mistral models and KerasHub. We'll show you how to:
- Define a tool (a Python function).
- Tell the models about the tool.
- Use the model to generate code that calls the tool.
- Execute the code and feed the result back to the model.
- Get a final, natural-language response from the model.
Let's get started!
Setup
First, let's import the necessary libraries and configure our environment. We'll be using KerasHub to download and run the language models, and we'll need to authenticate with Kaggle to access the model weights.
import os
import json
import random
import string
import re
import ast
import io
import sys
import contextlib
# Set backend before importing Keras
os.environ["KERAS_BACKEND"] = "jax"
import keras
import keras_hub
import kagglehub
import numpy as np
# Constants
USD_TO_EUR_RATE = 0.85
# Set the default dtype policy to bfloat16 for improved performance and reduced memory usage on supported hardware (e.g., TPUs, some GPUs)
keras.config.set_dtype_policy("bfloat16")
# Authenticate with Kaggle
# In Google Colab, you can set KAGGLE_USERNAME and KAGGLE_KEY as secrets,
# and kagglehub.login() will automatically detect and use them:
# kagglehub.login()
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1752300506.075074 4066 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1752300506.079536 4066 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1752300506.090992 4066 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1752300506.091002 4066 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1752300506.091004 4066 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1752300506.091005 4066 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
Loading the Model
Next, we'll load the Gemma 3 model from KerasHub. We're using the gemma3_instruct_4b preset, which is a version of the model that has been specifically fine-tuned for instruction following and tool calling.
try:
gemma = keras_hub.models.Gemma3CausalLM.from_preset("gemma3_instruct_4b")
print("✅ Gemma 3 model loaded successfully")
except Exception as e:
print(f"❌ Error loading Gemma 3 model: {e}")
print("Please ensure you have the correct model preset and sufficient resources.")
raise
normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.
✅ Gemma 3 model loaded successfully
Defining a Tool
Now, let's define a simple tool that we want our model to be able to use. For this example, we'll create a Python function called convert that can convert one currency to another.
def convert(amount, currency, new_currency):
"""Convert the currency with the latest exchange rate
Args:
amount: The amount of currency to convert
currency: The currency to convert from
new_currency: The currency to convert to
"""
# Input validation
if amount < 0:
raise ValueError("Amount cannot be negative")
if not isinstance(currency, str) or not isinstance(new_currency, str):
raise ValueError("Currency codes must be strings")
# Normalize currency codes to uppercase to handle model-generated lowercase codes
currency = currency.upper().strip()
new_currency = new_currency.upper().strip()
# In a real application, this function would call an API to get the latest
# exchange rate. For this example, we'll just use a fixed rate.
if currency == "USD" and new_currency == "EUR":
return amount * USD_TO_EUR_RATE
elif currency == "EUR" and new_currency == "USD":
return amount / USD_TO_EUR_RATE
else:
raise NotImplementedError(
f"Currency conversion from {currency} to {new_currency} is not supported."
)
Telling the Model About the Tool
Now that we have a tool, we need to tell the Gemma 3 model about it. We do this by providing a carefully crafted prompt that includes:
- A description of the tool calling process.
- The Python code for the tool, including its function signature and docstring.
- The user's question.
Here's the prompt we'll use:
message = '''
<start_of_turn>user
At each turn, if you decide to invoke any of the function(s), it should be wrapped with ```tool_code```. The python methods described below are imported and available, you can only use defined methods and must not reimplement them. The generated code should be readable and efficient. I will provide the response wrapped in ```tool_output```, use it to call more tools or generate a helpful, friendly response. When using a ```tool_call``` think step by step why and how it should be used.
The following Python methods are available:
```python
def convert(amount, currency, new_currency):
"""Convert the currency with the latest exchange rate
Args:
amount: The amount of currency to convert
currency: The currency to convert from
new_currency: The currency to convert to
"""
```
User: What is $200,000 in EUR?<end_of_turn>
<start_of_turn>model
'''
Generating the Tool Call
Now, let's pass this prompt to the model and see what it generates.
print(gemma.generate(message))
<start_of_turn>user
At each turn, if you decide to invoke any of the function(s), it should be wrapped with ```tool_code```. The python methods described below are imported and available, you can only use defined methods and must not reimplement them. The generated code should be readable and efficient. I will provide the response wrapped in ```tool_output```, use it to call more tools or generate a helpful, friendly response. When using a ```tool_call``` think step by step why and how it should be used.
The following Python methods are available:
```python
def convert(amount, currency, new_currency):
"""Convert the currency with the latest exchange rate
Args:
amount: The amount of currency to convert
currency: The currency to convert from
new_currency: The currency to convert to
"""
```
User: What is $200,000 in EUR?<end_of_turn>
<start_of_turn>model
```tool_code
convert(amount=200000, currency="USD", new_currency="EUR")
```<end_of_turn>
As you can see, the model has correctly identified that it can use the convert function to answer the question, and it has generated the corresponding Python code.
Executing the Tool Call and Getting a Final Answer
In a real application, you would now take this generated code, execute it, and feed the result back to the model. Let's create a practical example that shows how to do this:
# First, let's get the model's response
response = gemma.generate(message)
print("Model's response:")
print(response)
# Extract the tool call from the response
def extract_tool_call(response_text):
"""Extract tool call from the model's response."""
tool_call_pattern = r"```tool_code\s*\n(.*?)\n```"
match = re.search(tool_call_pattern, response_text, re.DOTALL)
if match:
return match.group(1).strip()
return None
def capture_code_output(code_string, globals_dict=None, locals_dict=None):
"""
Executes Python code and captures any stdout output.
⚠️ SECURITY WARNING ⚠️
This function uses eval() and exec() which can execute arbitrary code.
NEVER use this function with untrusted code in production environments.
Always validate and sanitize code from LLMs before execution.
Consider using a sandboxed environment or code analysis tools.
Args:
code_string (str): The code to execute (expression or statements).
globals_dict (dict, optional): Global variables for execution.
locals_dict (dict, optional): Local variables for execution.
Returns:
The captured stdout output if any, otherwise the return value of the expression,
or None if neither.
"""
if globals_dict is None:
globals_dict = {}
if locals_dict is None:
locals_dict = globals_dict
output = io.StringIO()
try:
with contextlib.redirect_stdout(output):
try:
# Try to evaluate as an expression
result = eval(code_string, globals_dict, locals_dict)
except SyntaxError:
# If not an expression, execute as statements
exec(code_string, globals_dict, locals_dict)
result = None
except Exception as e:
return f"Error during code execution: {e}"
stdout_output = output.getvalue()
if stdout_output.strip():
return stdout_output
return result
# Extract and execute the tool call
tool_code = extract_tool_call(response)
if tool_code:
print(f"\nExtracted tool call: {tool_code}")
try:
local_vars = {"convert": convert}
tool_result = capture_code_output(tool_code, globals_dict=local_vars)
print(f"Tool execution result: {tool_result}")
# Create the next message with the tool result
message_with_result = f'''
<start_of_turn>user
At each turn, if you decide to invoke any of the function(s), it should be wrapped with ```tool_code```. The python methods described below are imported and available, you can only use defined methods and must not reimplement them. The generated code should be readable and efficient. I will provide the response wrapped in ```tool_output```, use it to call more tools or generate a helpful, friendly response. When using a ```tool_call``` think step by step why and how it should be used.
The following Python methods are available:
```python
def convert(amount, currency, new_currency):
"""Convert the currency with the latest exchange rate
Args:
amount: The amount of currency to convert
currency: The currency to convert from
new_currency: The currency to convert to
"""
```
User: What is $200,000 in EUR?<end_of_turn>
<start_of_turn>model
```tool_code
print(convert(200000, "USD", "EUR"))
```<end_of_turn>
<start_of_turn>user
```tool_output
{tool_result}
```
<end_of_turn>
<start_of_turn>model
'''
# Get the final response
final_response = gemma.generate(message_with_result)
print("\nFinal response:")
print(final_response)
except Exception as e:
print(f"Error executing tool call: {e}")
else:
print("No tool call found in the response")
Model's response:
<start_of_turn>user
At each turn, if you decide to invoke any of the function(s), it should be wrapped with ```tool_code```. The python methods described below are imported and available, you can only use defined methods and must not reimplement them. The generated code should be readable and efficient. I will provide the response wrapped in ```tool_output```, use it to call more tools or generate a helpful, friendly response. When using a ```tool_call``` think step by step why and how it should be used.
The following Python methods are available:
```python
def convert(amount, currency, new_currency):
"""Convert the currency with the latest exchange rate
Args:
amount: The amount of currency to convert
currency: The currency to convert from
new_currency: The currency to convert to
"""
```
User: What is $200,000 in EUR?<end_of_turn>
<start_of_turn>model
```tool_code
convert(amount=200000, currency="USD", new_currency="EUR")
```<end_of_turn>
Extracted tool call: convert(amount=200000, currency="USD", new_currency="EUR")
Tool execution result: 170000.0
Final response:
<start_of_turn>user
At each turn, if you decide to invoke any of the function(s), it should be wrapped with ```tool_code```. The python methods described below are imported and available, you can only use defined methods and must not reimplement them. The generated code should be readable and efficient. I will provide the response wrapped in ```tool_output```, use it to call more tools or generate a helpful, friendly response. When using a ```tool_call``` think step by step why and how it should be used.
The following Python methods are available:
```python
def convert(amount, currency, new_currency):
"""Convert the currency with the latest exchange rate
Args:
amount: The amount of currency to convert
currency: The currency to convert from
new_currency: The currency to convert to
"""
```
User: What is $200,000 in EUR?<end_of_turn>
<start_of_turn>model
```tool_code
print(convert(200000, "USD", "EUR"))
```<end_of_turn>
<start_of_turn>user
```tool_output
170000.0
```
<end_of_turn>
<start_of_turn>model
Okay, $200,000 is equivalent to 170,000 EUR.<end_of_turn>
Automated Tool Call Execution Loop
Let's create a more sophisticated example that shows how to automatically handle multiple tool calls in a conversation:
def automated_tool_calling_example():
"""Demonstrate automated tool calling with a conversation loop."""
conversation_history = []
max_turns = 5
# Initial user message
user_message = "What is $500 in EUR, and then what is that amount in USD?"
# Define base prompt outside the loop for better performance
base_prompt = f'''
<start_of_turn>user
At each turn, if you decide to invoke any of the function(s), it should be wrapped with ```tool_code```. The python methods described below are imported and available, you can only use defined methods and must not reimplement them. The generated code should be readable and efficient. I will provide the response wrapped in ```tool_output```, use it to call more tools or generate a helpful, friendly response. When using a ```tool_call``` think step by step why and how it should be used.
The following Python methods are available:
```python
def convert(amount, currency, new_currency):
"""Convert the currency with the latest exchange rate
Args:
amount: The amount of currency to convert
currency: The currency to convert from
new_currency: The currency to convert to
"""
```
User: {user_message}<end_of_turn>
<start_of_turn>model
'''
for turn in range(max_turns):
print(f"\n--- Turn {turn + 1} ---")
# Build conversation context by appending history to base prompt
context = base_prompt
for hist in conversation_history:
context += hist + "\n"
# Get model response
response = gemma.generate(context, strip_prompt=True)
print(f"Model response: {response}")
# Extract tool call
tool_code = extract_tool_call(response)
if tool_code:
print(f"Executing: {tool_code}")
try:
local_vars = {"convert": convert}
tool_result = capture_code_output(tool_code, globals_dict=local_vars)
conversation_history.append(
f"```tool_code\n{tool_code}\n```<end_of_turn>"
)
conversation_history.append(
f"<start_of_turn>user\n```tool_output\n{tool_result}\n```<end_of_turn>"
)
conversation_history.append(f"<start_of_turn>model\n")
print(f"Tool result: {tool_result}")
except Exception as e:
print(f"Error executing tool: {e}")
break
else:
print("No tool call found - conversation complete")
conversation_history.append(response)
break
print("\n--- Final Conversation ---")
print(context)
for hist in conversation_history:
print(hist)
# Run the automated example
print("Running automated tool calling example:")
automated_tool_calling_example()
Running automated tool calling example:
--- Turn 1 ---
Model response: ```tool_code
print(convert(500, 'USD', 'EUR'))
```<end_of_turn>
Executing: print(convert(500, 'USD', 'EUR'))
Tool result: 425.0
--- Turn 2 ---
Model response: Okay, $500 is 425.0 EUR. Now, let's convert that EUR amount to USD.
```tool_code
print(convert(425.0, 'EUR', 'USD'))
```<end_of_turn>
Executing: print(convert(425.0, 'EUR', 'USD'))
Tool result: 500.0
--- Turn 3 ---
Model response: Okay, $500 is 425 EUR, and 425 EUR is equal to $500 USD.<end_of_turn>
No tool call found - conversation complete
--- Final Conversation ---
<start_of_turn>user
At each turn, if you decide to invoke any of the function(s), it should be wrapped with ```tool_code```. The python methods described below are imported and available, you can only use defined methods and must not reimplement them. The generated code should be readable and efficient. I will provide the response wrapped in ```tool_output```, use it to call more tools or generate a helpful, friendly response. When using a ```tool_call``` think step by step why and how it should be used.
The following Python methods are available:
```python
def convert(amount, currency, new_currency):
"""Convert the currency with the latest exchange rate
Args:
amount: The amount of currency to convert
currency: The currency to convert from
new_currency: The currency to convert to
"""
```
User: What is $500 in EUR, and then what is that amount in USD?<end_of_turn>
<start_of_turn>model
```tool_code
print(convert(500, 'USD', 'EUR'))
```<end_of_turn>
<start_of_turn>user
```tool_output
425.0
```<end_of_turn>
<start_of_turn>model
```tool_code
print(convert(425.0, 'EUR', 'USD'))
```<end_of_turn>
<start_of_turn>user
```tool_output
500.0
```<end_of_turn>
<start_of_turn>model
```tool_code
print(convert(500, 'USD', 'EUR'))
```<end_of_turn>
<start_of_turn>user
```tool_output
425.0
```<end_of_turn>
<start_of_turn>model
```tool_code
print(convert(425.0, 'EUR', 'USD'))
```<end_of_turn>
<start_of_turn>user
```tool_output
500.0
```<end_of_turn>
<start_of_turn>model
Okay, $500 is 425 EUR, and 425 EUR is equal to $500 USD.<end_of_turn>
Mistral
Mistral differs from Gemma in its approach to tool calling, as it requires a specific format and defines special control tokens for this purpose. This JSON-based syntax for tool calling is also adopted by other models, such as Qwen and Llama.
We will now extend the example to a more exciting use case: building a flight booking agent. This agent will be able to search for appropriate flights and book them automatically.
To do this, we will first download the Mistral model using KerasHub. For agentic AI with Mistral, low-level access to tokenization is necessary due to the use of control tokens. Therefore, we will instantiate the tokenizer and model separately, and disable the preprocessor for the model.
tokenizer = keras_hub.tokenizers.MistralTokenizer.from_preset(
"kaggle://keras/mistral/keras/mistral_0.3_instruct_7b_en"
)
try:
mistral = keras_hub.models.MistralCausalLM.from_preset(
"kaggle://keras/mistral/keras/mistral_0.3_instruct_7b_en", preprocessor=None
)
print("✅ Mistral model loaded successfully")
except Exception as e:
print(f"❌ Error loading Mistral model: {e}")
print("Please ensure you have the correct model preset and sufficient resources.")
raise
normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.
✅ Mistral model loaded successfully
Next, we'll define functions for tokenization. The preprocess function will take a tokenized conversation in list form and format it correctly for the model. We'll also create an additional function, encode_instruction, for tokenizing text and adding instruction control tokens.
def preprocess(messages, sequence_length=8192):
"""Preprocess tokenized messages for the Mistral model.
Args:
messages: List of tokenized message sequences
sequence_length: Maximum sequence length for the model
Returns:
Dictionary containing token_ids and padding_mask
"""
concatd = np.expand_dims(np.concatenate(messages), 0)
# Truncate if the sequence is too long
if concatd.shape[1] > sequence_length:
concatd = concatd[:, :sequence_length]
# Calculate padding needed
padding_needed = max(0, sequence_length - concatd.shape[1])
return {
"token_ids": np.pad(concatd, ((0, 0), (0, padding_needed))),
"padding_mask": np.expand_dims(
np.arange(sequence_length) < concatd.shape[1], 0
).astype(int),
}
def encode_instruction(text):
"""Encode instruction text with Mistral control tokens.
Args:
text: The instruction text to encode
Returns:
List of tokenized sequences with instruction control tokens
"""
return [
[tokenizer.token_to_id("[INST]")],
tokenizer(text),
[tokenizer.token_to_id("[/INST]")],
]
Now, we'll define a function, try_parse_funccall, to handle the model's function calls. These calls are identified by the [TOOL_CALLS] control token. The function will parse the subsequent data, which is in JSON format. Mistral also requires us to add a random call ID to each function call. Finally, the function will call the matching tool and encode its results using the [TOOL_RESULTS] control token.
def try_parse_funccall(response):
"""Parse function calls from Mistral model response and execute tools.
Args:
response: Tokenized model response
Returns:
List of tokenized sequences including tool results
"""
# find the tool call in the response, if any
tool_call_id = tokenizer.token_to_id("[TOOL_CALLS]")
pos = np.where(response == tool_call_id)[0]
if not len(pos):
return [response]
pos = pos[0]
try:
decoder = json.JSONDecoder()
tool_calls, _ = decoder.raw_decode(tokenizer.detokenize(response[pos + 1 :]))
if not isinstance(tool_calls, list) or not all(
isinstance(item, dict) for item in tool_calls
):
return [response]
res = [] # Initialize result list
# assign a random call ID
for call in tool_calls:
call["id"] = "".join(
random.choices(string.ascii_letters + string.digits, k=9)
)
if call["name"] not in tools:
continue # Skip unknown tools
res.append([tokenizer.token_to_id("[TOOL_RESULTS]")])
res.append(
tokenizer(
json.dumps(
{
"content": tools[call["name"]](**call["arguments"]),
"call_id": call["id"],
}
)
)
)
res.append([tokenizer.token_to_id("[/TOOL_RESULTS]")])
return res
except (json.JSONDecodeError, KeyError, TypeError, ValueError) as e:
# Log the error for debugging
print(f"Error parsing tool call: {e}")
return [response]
We will extend our set of tools to include functions for currency conversion, finding flights, and booking flights. For this example, we'll use mock implementations for these functions, meaning they will return dummy data instead of interacting with real services.
tools = {
"convert_currency": lambda amount, currency, new_currency: (
f"{amount*USD_TO_EUR_RATE:.2f}"
if currency == "USD" and new_currency == "EUR"
else (
f"{amount/USD_TO_EUR_RATE:.2f}"
if currency == "EUR" and new_currency == "USD"
else f"Error: Unsupported conversion from {currency} to {new_currency}"
)
),
"find_flights": lambda origin, destination, date: [
{"id": 1, "price": "USD 220", "stops": 2, "duration": 4.5},
{"id": 2, "price": "USD 22", "stops": 1, "duration": 2.0},
{"id": 3, "price": "USD 240", "stops": 2, "duration": 13.2},
],
"book_flight": lambda id: {
"status": "success",
"message": f"Flight {id} booked successfully",
},
}
It's crucial to inform the model about these available functions at the very beginning of the conversation. To do this, we will define the available tools in a specific JSON format, as shown in the following code block.
tool_definitions = [
{
"type": "function",
"function": {
"name": "convert_currency",
"description": "Convert the currency with the latest exchange rate",
"parameters": {
"type": "object",
"properties": {
"amount": {"type": "number", "description": "The amount"},
"currency": {
"type": "string",
"description": "The currency to convert from",
},
"new_currency": {
"type": "string",
"description": "The currency to convert to",
},
},
"required": ["amount", "currency", "new_currency"],
},
},
},
{
"type": "function",
"function": {
"name": "find_flights",
"description": "Query price, time, number of stopovers and duration in hours for flights for a given date",
"parameters": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"description": "The city to depart from",
},
"destination": {
"type": "string",
"description": "The destination city",
},
"date": {
"type": "string",
"description": "The date in YYYYMMDD format",
},
},
"required": ["origin", "destination", "date"],
},
},
},
{
"type": "function",
"function": {
"name": "book_flight",
"description": "Book the flight with the given id",
"parameters": {
"type": "object",
"properties": {
"id": {
"type": "number",
"description": "The numeric id of the flight to book",
},
},
"required": ["id"],
},
},
},
]
We will define the conversation as a messages list. At the very beginning of this list, we need to include a Beginning-Of-Sequence (BOS) token. This is followed by the tool definitions, which must be wrapped in [AVAILABLE_TOOLS] and [/AVAILABLE_TOOLS] control tokens.
messages = [
[tokenizer.token_to_id("<s>")],
[tokenizer.token_to_id("[AVAILABLE_TOOLS]")],
tokenizer(json.dumps(tool_definitions)),
[tokenizer.token_to_id("[/AVAILABLE_TOOLS]")],
]
Now, let's get started! We will task the model with the following: Book the most comfortable flight from Linz to London on the 24th of July 2025, but only if it costs less than 20€ as of the latest exchange rate.
messages.extend(
encode_instruction(
"Book the most comfortable flight from Linz to London on the 24th of July 2025, but only if it costs less than 20€ as of the latest exchange rate."
)
)
In an agentic AI system, the model interacts with its tools through a sequence of messages. We will continue to handle these messages until the flight is successfully booked. For educational purposes, we will output the tool calls issued by the model; typically, a user would not see this level of detail. It's important to note that after the tool call JSON, the data must be truncated. If not, a less capable model may 'babble', outputting redundant or confused data.
flight_booked = False
max_iterations = 10 # Prevent infinite loops
iteration_count = 0
while not flight_booked and iteration_count < max_iterations:
iteration_count += 1
# query the model
res = mistral.generate(
preprocess(messages), max_length=8192, stop_token_ids=[2], strip_prompt=True
)
# output the model's response, add separator line for legibility
response_text = tokenizer.detokenize(
res["token_ids"][0, : np.argmax(~res["padding_mask"])]
)
print(response_text, f"\n\n\n{'-'*100}\n\n")
# Check for tool calls and track booking status
tool_call_id = tokenizer.token_to_id("[TOOL_CALLS]")
pos = np.where(res["token_ids"][0] == tool_call_id)[0]
if len(pos) > 0:
try:
decoder = json.JSONDecoder()
tool_calls, _ = decoder.raw_decode(
tokenizer.detokenize(res["token_ids"][0][pos[0] + 1 :])
)
if isinstance(tool_calls, list):
for call in tool_calls:
if isinstance(call, dict) and call.get("name") == "book_flight":
# Check if book_flight was called successfully
flight_booked = True
break
except (json.JSONDecodeError, KeyError, TypeError, ValueError):
pass
# perform tool calls and extend `messages`
messages.extend(try_parse_funccall(res["token_ids"][0]))
if not flight_booked:
print("Maximum iterations reached. Flight booking was not completed.")
[{"name": "find_flights", "arguments": {"origin": "Linz", "destination": "London", "date": "20250724"}}]
After finding the available flights, compare the price with the budget (20€). If the flight is affordable, proceed to book it.
If the flight is affordable, book the flight with the found id.
Here's a code snippet that does that:
[{"name": "find_flights", "arguments": {"origin": "Linz", "destination": "London", "date": "20250724"}}]
If the response from find_flights is a list with at least one flight, let's assume the first one:
* Let's call the price of this flight as `flight_price_eur`
* Convert the flight price to the base currency (let's call it `base_currency`) using convert_currency
* Compare the converted price to the specified budget (20€). If it's less than or equal to the budget, then proceed to book the flight.
If the flight is affordable, book the flight with the found id.
Here's a code snippet that implements this:
[{"name": "find_flights", "arguments": {"origin": "Linz", "destination": "London", "date": "20250724"}}]
If the response from find_flights is a list with at least one flight:
1. Let's call the price of the first flight as `flight_price_eur`
2. Convert the flight price to the base currency (let's call it `base_currency`) using `convert_currency`:
- Let's call the converted price as `flight_price_base`
3. Compare the converted price to the specified budget (20€). If it's less than or equal to the budget, then proceed to book the flight using the book_flight function.
Here's a code snippet that implements this:
[{"name": "find_flights", "arguments": {"origin": "Linz", "destination": "London", "date": "20250724"}}]
If the response from find_flights is a list with at least one flight:
1. Let's call the price of the first flight as `flight_price_eur`
2. Convert the flight price to the base currency (let's call it `base_currency`) using `convert_currency`:
- Let's call the converted price as `flight_price_base`
3. Compare the converted price to the specified budget (20€). If it's less than or equal to the budget, then proceed to book the flight using the book_flight function:
- Call the book_flight function with the first flight's id (assumed to be the index 0 of the flights list):
```
[{"name": "book_flight", "arguments": {"id": $flights[0].id}}]
```
Here's the complete code snippet:
[{"name": "find_flights", "arguments": {"origin": "Linz", "destination": "London", "date": "20250724"}}]
If the response from find_flights is a list with at least one flight:
1. Let's call the price of the first flight as `flight_price_eur`
2. Convert the flight price to the base currency (let's call it `base_currency`) using `convert_currency`:
- Let's call the converted price as `flight_price_base`
3. Compare the converted price to the specified budget (20€). If it's less than or equal to the budget, then proceed to book the flight using the book_flight function:
- Call the book_flight function with the first flight's id (assumed to be the index 0 of the flights list):
```
[{"name": "book_flight", "arguments": {"id": $flights[0].id}}]
```
----------------------------------------------------------------------------------------------------
Now let's convert the price from USD to EUR using the latest exchange rate:
[{"name": "convert_currency", "arguments": {"amount": 22, "currency": "USD", "new_currency": "EUR"}}]
[{"name": "convert_currency", "arguments": {"amount": 220, "currency": "USD", "new_currency": "EUR"}}]
[{"name": "convert_currency", "arguments": {"amount": 240, "currency": "USD", "new_currency": "EUR"}}]
The exchange rates: 1 USD = 0.92 EUR, 1 USD = 0.89 EUR, 1 USD = 0.92 EUR respectively.
Let's check if the most comfortable flight costs less than 20€ (19.60 EUR):
The most comfortable flight costs 22 EUR, which is above the desired limit of 19.60 EUR. Therefore, it cannot be booked as of now.
----------------------------------------------------------------------------------------------------
The price of the flight with the id 2 in EUR is 18.70. Since it is below the 20€ limit, let's book this flight:
[{"name": "book_flight", "arguments": {"id": 2}}]
----------------------------------------------------------------------------------------------------
For understandability, here's the conversation as received by the model, i.e. when truncating after the tool calling JSON:
- User:
Book the most comfortable flight from Linz to London on the 24th of July 2025, but only if it costs less than 20€ as of the latest exchange rate.
- Model:
[{"name": "find_flights", "arguments": {"origin": "Linz", "destination": "London", "date": "20250724"}}]
- Tool Output:
[{"id": 1, "price": "USD 220", "stops": 2, "duration": 4.5}, {"id": 2, "price": "USD 22", "stops": 1, "duration": 2.0}, {"id": 3, "price": "USD 240", "stops": 2, "duration": 13.2}]
- Model:
Now let's convert the price from USD to EUR using the latest exchange rate:
[{"name": "convert_currency", "arguments": {"amount": 22, "currency": "USD", "new_currency": "EUR"}}]
- Tool Output:
"18.70"
- Model:
The price of the flight with the id 2 in EUR is 18.70. Since it is below the 20€ limit, let's book this flight:
[{"name": "book_flight", "arguments": {"id": 2}}]
It's important to acknowledge that you might have to run the model a few times to obtain a good output as depicted above. As a 7-billion parameter model, Mistral may still make several mistakes, such as misinterpreting data, outputting malformed tool calls, or making incorrect decisions. However, the continued development in this field paves the way for increasingly powerful agentic AI in the future.
Conclusion
Tool calling is a powerful feature that allows large language models to interact with the real world, access live data, and perform complex calculations. By defining a set of tools and telling the model about them, you can create sophisticated applications that go far beyond simple text generation.