Sequential chains

Developing LLM Applications with LangChain

Jonathan Bennion

AI Engineer & LangChain Contributor

An AI travel guide.

A person asking an AI travel guide for activities in Paris.

The AI provides the person several suggestions for activities in Paris.

The person tells the AI guide that they want to see the Louvre and the Arc de Triomphe.

The AI travel guide generates an itinerary for our chosen activities.

This travel itinerary workflow is an example of a sequential problem.

Sequential chains

Output → input

destination_prompt = PromptTemplate(
    input_variables=["destination"],
    template="I am planning a trip to {destination}. Can you suggest some activities to do there?"
)

activities_prompt = PromptTemplate(
    input_variables=["activities"],
    template="I only have one day, so can you create an itinerary from your top three activities: {activities}."
)


llm = ChatOpenAI(model="gpt-4o-mini", api_key=openai_api_key)


seq_chain = ({"activities": destination_prompt | llm | StrOutputParser()}

    | activities_prompt
    | llm
    | StrOutputParser())

print(seq_chain.invoke({"destination": "Rome"}))

- Morning:
1. Start your day early with a visit to the Colosseum. Take a guided tour to learn about its history and significance.
2. After exploring the Colosseum, head to the Roman Forum and Palatine Hill to see more of ancient Rome's ruins.

- Lunch:
3. Enjoy a delicious Italian lunch at a local restaurant near the historic center.

- Afternoon:
4. Visit the Vatican City and explore St. Peter's Basilica, the Vatican Museums, and the Sistine Chapel.
5. Take some time to wander through the charming streets of Rome, stopping at landmarks like the Pantheon, Trevi
Fountain, and Piazza Navona.

- Evening:
6. Relax in one of Rome's beautiful parks, such as Villa Borghese or the Orange Garden, for a peaceful escape from the
bustling city.
7. End your day with a leisurely dinner at a local restaurant, indulging in more Italian cuisine and maybe some gelato.

Let's practice!

Developing LLM Applications with LangChain