Chain sequenziali con LCEL

Ora che hai creato i tuoi template di prompt, è il momento di mettere tutto insieme, incluso l'LLM, usando le chain e LCEL. È già stato definito per te un llm che utilizza il modello gpt-4o-mini di OpenAI.

Per l'ultimo passaggio, quando chiami la chain, inserisci pure qualsiasi attività tu voglia! Se ti mancano le idee, prova a inserire "play the harmonica".

Questo esercizio fa parte del corso

Sviluppare applicazioni LLM con LangChain

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Istruzioni dell'esercizio

Crea una chain sequenziale con LCEL che passi learning_prompt al llm e invii l'output a time_prompt per poi rimandarlo al llm.
La prima parte dovrebbe creare un dizionario con "learning_plan" come chiave e la prima chain come valore.
Chiama la chain usando il metodo .invoke() con l'attività che preferisci!

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

learning_prompt = PromptTemplate(
    input_variables=["activity"],
    template="I want to learn how to {activity}. Can you suggest how I can learn this step-by-step?"
)

time_prompt = PromptTemplate(
    input_variables=["learning_plan"],
    template="I only have one week. Can you create a concise plan to help me hit this goal: {learning_plan}."
)

# Complete the sequential chain with LCEL
seq_chain = ({"learning_plan": ____ | ____ | ____}
    | ____
    | ____
    | StrOutputParser())

# Call the chain
print(seq_chain.____({"____": "____"}))

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Sviluppare applicazioni LLM con LangChain

IntermediárioNível de habilidade

4.8+

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Welcome to the LangChain framework for building applications on LLMs! You'll learn about the main components of LangChain, including models, chains, agents, prompts, and parsers. You'll create chatbots using both open-source models from Hugging Face and proprietary models from OpenAI, create prompt templates, and integrate different chatbot memory strategies to manage context and resources during conversations.

Exercise 1: The LangChain ecosystem Exercise 2: OpenAI models in LangChain!Exercise 3: Hugging Face models in LangChain!Exercise 4: Prompt templates Exercise 5: Prompt templates and chaining Exercise 6: Chat prompt templates Exercise 7: Few-shot prompting Exercise 8: Creating the few-shot example set Exercise 9: Building the few-shot prompt template Exercise 10: Implementing few-shot prompting

Time to level up your LangChain chains! You'll learn to use the LangChain Expression Language (LCEL) for defining chains with greater flexibility. You'll create sequential chains, where inputs are passed between components to create more advanced applications. You'll also begin to integrate agents, which use LLMs for decision-making.

Exercise 1: Catene sequenziali Exercise 2: Costruire suggerimenti per catene sequenziali Exercise 3: Chain sequenziali con LCEL

Esercizio in corso

Exercise 4: Introduzione agli agenti LangChain Exercise 5: Cos'è un agent?Exercise 6: Agenti ReAct Exercise 7: Strumenti personalizzati per gli agenti Exercise 8: Definizione di una funzione per l'utilizzo degli strumenti Exercise 9: Creare tool personalizzati Exercise 10: Integrare strumenti personalizzati con gli agenti

One limitation of LLMs is that they have a knowledge cut-off due to being trained on data up to a certain point. In this chapter, you'll learn to create applications that use Retrieval Augmented Generation (RAG) to integrate external data with LLMs. The RAG workflow contains a few different processes, including splitting data, creating and storing the embeddings using a vector database, and retrieving the most relevant information for use in the application. You'll learn to master the entire workflow!

Exercise 1: Integrating document loaders Exercise 2: PDF document loaders Exercise 3: CSV document loaders Exercise 4: HTML document loaders Exercise 5: Splitting external data for retrieval Exercise 6: Splitting by character Exercise 7: Recursively splitting by character Exercise 8: Splitting HTML Exercise 9: RAG storage and retrieval using vector databases Exercise 10: Preparing the documents and vector database Exercise 11: Building a retrieval prompt template Exercise 12: Creating a RAG chain Exercise 13: Wrap-up!