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Amadeus

This notebook walks you through connecting LangChain to the Amadeus travel APIs.

This Amadeus toolkit allows agents to make decision when it comes to travel, especially searching and booking trips with flights.

To use this toolkit, you will need to have your Amadeus API keys ready, explained in the Get started Amadeus Self-Service APIs. Once you’ve received a AMADEUS_CLIENT_ID and AMADEUS_CLIENT_SECRET, you can input them as environmental variables below.

Note: Amadeus Self-Service APIs offers a test enviornment with free limited data. This allows developers to build and test their applications before deploying them to production. To access real-time data, you will need to move to the production environment.

%pip install --upgrade --quiet  amadeus > /dev/null

Assign Environmental Variables​

The toolkit will read the AMADEUS_CLIENT_ID and AMADEUS_CLIENT_SECRET environmental variables to authenticate the user, so you need to set them here.

# Set environmental variables here
import os

os.environ["AMADEUS_CLIENT_ID"] = "CLIENT_ID"
os.environ["AMADEUS_CLIENT_SECRET"] = "CLIENT_SECRET"
# os.environ["AMADEUS_HOSTNAME"] = "production" or "test"

Create the Amadeus Toolkit and Get Tools​

To start, you need to create the toolkit, so you can access its tools later.

By default, AmadeusToolkit uses ChatOpenAI to identify airports closest to a given location. To use it, just set OPENAI_API_KEY.

os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_KEY"
from langchain_community.agent_toolkits.amadeus.toolkit import AmadeusToolkit

toolkit = AmadeusToolkit()
tools = toolkit.get_tools()

Alternatively, you can use any LLM supported by langchain, e.g. HuggingFaceHub.

from langchain_community.llms import HuggingFaceHub

os.environ["HUGGINGFACEHUB_API_TOKEN"] = "YOUR_HF_API_TOKEN"

llm = HuggingFaceHub(
    repo_id="tiiuae/falcon-7b-instruct",
    model_kwargs={"temperature": 0.5, "max_length": 64},
)

toolkit_hf = AmadeusToolkit(llm=llm)

Use Amadeus Toolkit within an Agent​

from langchain import hub
from langchain.agents import AgentExecutor, create_react_agent
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(temperature=0)

prompt = hub.pull("hwchase17/react")
agent = create_react_agent(llm, tools, prompt)

agent_executor = AgentExecutor(
    agent=agent,
    tools=tools,
    verbose=True,
)
agent_executor.invoke({"input": "What is the name of the airport in Cali, Colombia?"})


> Entering new AgentExecutor chain...
I should use the closest_airport tool to find the airport in Cali, Colombia.
Action: closest_airport
Action Input: location= "Cali, Colombia"content='{\n  "iataCode": "CLO"\n}'The airport in Cali, Colombia is called CLO.
Final Answer: CLO

> Finished chain.
{'input': 'What is the name of the airport in Cali, Colombia?',
 'output': 'CLO'}
agent_executor.invoke(
    {
        "input": "What is the departure time of the cheapest flight on August 23, 2023 leaving Dallas, Texas before noon to Lincoln, Nebraska?"
    }
)
agent_executor.invoke(
    {
        "input": "At what time does earliest flight on August 23, 2023 leaving Dallas, Texas to Lincoln, Nebraska land in Nebraska?"
    }
)
agent_executor.invoke(
    {
        "input": "What is the full travel time for the cheapest flight between Portland, Oregon to Dallas, TX on October 3, 2023?"
    }
)
agent_executor.invoke(
    {
        "input": "Please draft a concise email from Santiago to Paul, Santiago's travel agent, asking him to book the earliest flight from DFW to DCA on Aug 28, 2023. Include all flight details in the email."
    }
)