Suddividere HTML

In questo esercizio suddividerai un file HTML contenente un ordine esecutivo sull'AI emanato dalla Casa Bianca degli Stati Uniti nell'ottobre 2023. Per mantenere il maggior contesto possibile nei blocchi, userai valori più alti per chunk_size e chunk_overlap.

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Questo esercizio fa parte del corso

Sviluppare applicazioni LLM con LangChain

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Istruzioni dell'esercizio

Crea un UnstructuredHTMLLoader per white_house_executive_order_nov_2023.html e caricalo in memoria.
Imposta un chunk_size di 300 e un chunk_overlap di 100.
Crea un RecursiveCharacterTextSplitter che suddivide sul carattere '.' e usa il metodo .split_documents() per suddividere data e stampare i blocchi.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Load the HTML document into memory
loader = UnstructuredHTMLLoader(____)
data = loader.____()

# Define variables
chunk_size = ____
chunk_overlap = ____

# Split the HTML
splitter = RecursiveCharacterTextSplitter(
    chunk_size=chunk_size,
    chunk_overlap=chunk_overlap,
    separators=____)

docs = splitter.____(data)
print(docs)

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: 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: 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: Integrazione di caricatori di documenti Exercise 2: PDF caricatori di documenti Exercise 3: CSV caricatori di documenti Exercise 4: HTML caricatori di documenti Exercise 5: Suddivisione dei dati esterni per il recupero Exercise 6: Suddivisione per carattere Exercise 7: Divisione ricorsiva per carattere Exercise 8: Suddividere HTML

Esercizio in corso

Exercise 9: RAG archiviazione e recupero con database vettoriali Exercise 10: Preparazione dei documenti e del database vettoriale Exercise 11: Creare un prompt template per il retrieval Exercise 12: Creare una chain RAG Exercise 13: Conclusione!