Cadenas secuenciales

Desarrollo de aplicaciones LLM con LangChain

Jonathan Bennion

AI Engineer & LangChain Contributor

Una guía de viaje con IA.

Una persona que pregunta a un guía turístico de IA por actividades en París.

La IA ofrece a la persona varias sugerencias de actividades en París.

La persona le dice al guía de IA que quiere ver el Louvre y el Arco del Triunfo.

La guía de viaje con IA genera un itinerario para las actividades que hemos elegido.

Este flujo de trabajo de itinerario de viaje es un ejemplo de un problema secuencial.

Cadenas secuenciales

Salida → entrada

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.

¡Vamos a practicar!

Desarrollo de aplicaciones LLM con LangChain