On this tutorial, we’ll reveal allow operate calling in Mistral Brokers utilizing the usual JSON schema format. By defining your operate’s enter parameters with a transparent schema, you can also make your customized instruments seamlessly callable by the agent—enabling highly effective, dynamic interactions.
We shall be utilizing the AviationStack API to retrieve real-time flight standing knowledge, showcasing how exterior APIs will be built-in as callable capabilities inside a Mistral Agent.
Step 1: Establishing dependencies
Putting in the Mistral library
Loading the Mistral API Key
You will get an API key from https://console.mistral.ai/api-keys
from getpass import getpass
MISTRAL_API_KEY = getpass('Enter Mistral API Key: ')Loading the Aviation Stack API Key
You’ll be able to join a free API key from their dashboard to get began.
AVIATIONSTACK_API_KEY = getpass('Enter Aviation Stack API: ')Step 2: Defining the Customized Operate
Subsequent, we outline a Python operate get_flight_status() that calls the AviationStack API to retrieve the real-time standing of a flight. The operate accepts an elective flight_iata parameter and returns key particulars comparable to airline identify, flight standing, departure and arrival airports, and scheduled instances. If no matching flight is discovered, it gracefully returns an error message.
import requests
from typing import Dict
def get_flight_status(flight_iata=None):
"""
Retrieve flight standing utilizing elective filters: dep_iata, arr_iata, flight_iata.
"""
params = {
"access_key": AVIATIONSTACK_API_KEY,
"flight_iata": flight_iata
}
response = requests.get("http://api.aviationstack.com/v1/flights", params=params)
knowledge = response.json()
if "knowledge" in knowledge and knowledge["data"]:
flight = knowledge["data"][0]
return {
"airline": flight["airline"]["name"],
"flight_iata": flight["flight"]["iata"],
"standing": flight["flight_status"],
"departure_airport": flight["departure"]["airport"],
"arrival_airport": flight["arrival"]["airport"],
"scheduled_departure": flight["departure"]["scheduled"],
"scheduled_arrival": flight["arrival"]["scheduled"],
}
else:
return {"error": "No flight discovered for the supplied parameters."}Step 3: Creating the Mistral shopper and Agent
On this step, we create a Mistral Agent that makes use of tool-calling to fetch real-time flight info. The agent, named Flight Standing Agent, is configured to make use of the “mistral-medium-2505” mannequin and is supplied with a customized operate instrument named get_flight_status. This instrument is outlined utilizing a JSON schema that accepts a single required parameter: the flight’s IATA code (e.g., “AI101”). As soon as deployed, the agent can mechanically invoke this operate each time it detects a related consumer question, enabling seamless integration between pure language inputs and structured API responses.
from mistralai import Mistral
shopper = Mistral(MISTRAL_API_KEY)
flight_status_agent = shopper.beta.brokers.create(
mannequin="mistral-medium-2505",
description="Supplies real-time flight standing utilizing aviationstack API.",
identify="Flight Standing Agent",
instruments=[
{
"type": "function",
"function": {
"name": "get_flight_status",
"description": "Retrieve the current status of a flight by its IATA code (e.g. AI101).",
"parameters": {
"type": "object",
"properties": {
"flight_iata": {
"type": "string",
"description": "IATA code of the flight (e.g. AI101)"
},
},
"required": ["flight_iata"]
}
}
}
]
)Step 4: Beginning the Dialog and dealing with Operate Calling
On this step, we provoke a dialog with the Flight Standing Agent by asking a pure language query: “What’s the present standing of AI101?”. The Mistral mannequin detects that it ought to invoke the get_flight_status operate and returns a operate name request. We parse the arguments, run the operate domestically utilizing the AviationStack API, and return the end result again to the agent utilizing FunctionResultEntry. Lastly, the mannequin incorporates the API response and generates a pure language reply with the present flight standing, which we print to the console.
from mistralai import FunctionResultEntry
import json
# Person begins a dialog
response = shopper.beta.conversations.begin(
agent_id=flight_status_agent.id,
inputs=[{"role": "user", "content": "What's the current status of AI101?"}]
)
# Examine if mannequin requested a operate name
if response.outputs[-1].kind == "operate.name" and response.outputs[-1].identify == "get_flight_status":
args = json.masses(response.outputs[-1].arguments)
# Run the operate
function_result = json.dumps(get_flight_status(**args))
# Create end result entry
result_entry = FunctionResultEntry(
tool_call_id=response.outputs[-1].tool_call_id,
end result=function_result
)
# Return end result to agent
response = shopper.beta.conversations.append(
conversation_id=response.conversation_id,
inputs=[result_entry]
)
print(response.outputs[-1].content material)
else:
print(response.outputs[-1].content material)Try the Pocket book on GitHub. All credit score for this analysis goes to the researchers of this undertaking. Additionally, be at liberty to comply with us on Twitter and don’t neglect to hitch our 95k+ ML SubReddit and Subscribe to our E-newsletter.
I’m a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I’ve a eager curiosity in Knowledge Science, particularly Neural Networks and their software in numerous areas.
