Prompt template e concatenazione

In questo esercizio inizierai a usare due componenti fondamentali di LangChain: prompt template e chain!

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Sviluppare applicazioni LLM con LangChain

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Istruzioni dell'esercizio

Converte il testo template fornito in un prompt template standard (non chat).
Crea una chain per passare il prompt template al LLM.
Esegui la chain sulla variabile question fornita.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Create a prompt template from the template string
template = "You are an artificial intelligence assistant, answer the question. {question}"
prompt = PromptTemplate.____(
    template=____
)

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

# Create a chain to integrate the prompt template and LLM
llm_chain = ____ | ____

# Invoke the chain on the question
question = "How does LangChain make LLM application development easier?"
print(llm_chain.invoke({"question": ____}))

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Sviluppare applicazioni LLM con LangChain

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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: L'ecosistema LangChain Exercise 2: Modelli OpenAI in LangChain!Exercise 3: Modelli Hugging Face in LangChain!Exercise 4: Modelli di prompt Exercise 5: Prompt template e concatenazione

Esercizio in corso

Exercise 6: Template di prompt per chat Exercise 7: Suggerimento di pochi colpi Exercise 8: Creare un set di esempi di pochi scatti Exercise 9: Creare il template di few-shot prompting Exercise 10: Implementare il 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: Sequential chains Exercise 2: Building prompts for sequential chains Exercise 3: Sequential chains with LCEL Exercise 4: Introduction to LangChain agents Exercise 5: What's an agent?Exercise 6: ReAct agents Exercise 7: Custom tools for agents Exercise 8: Defining a function for tool use Exercise 9: Creating custom tools Exercise 10: Integrating custom tools with agents

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!