Splitsen op teken

Een belangrijk proces bij het implementeren van Retrieval Augmented Generation (RAG) is het opsplitsen van documenten in chunks om ze op te slaan in een vectordatabase.

Er zijn verschillende splitstrategieën beschikbaar in LangChain, sommige met complexere routines dan andere. In deze oefening implementeer je een character text splitter, die documenten splitst op basis van tekens en de chunk-lengte meet aan de hand van het aantal tekens.

Onthoud dat er geen ideale splitstrategie is; je zult mogelijk met een paar moeten experimenteren om te vinden wat het beste werkt voor jouw usecase.

Deze oefening maakt deel uit van de cursus

LLM-toepassingen ontwikkelen met LangChain

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Oefeninstructies

Importeer de klasse CharacterTextSplitter uit langchain_text_splitters.
Maak een CharacterTextSplitter-instance met separator="\n", chunk_size=24 en chunk_overlap=10.
Gebruik de methode .split_text() om de quote te splitsen en print de chunks en chunklengtes.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import the character splitter
from langchain_text_splitters import ____

quote = 'Words are flowing out like endless rain into a paper cup,\nthey slither while they pass,\nthey slip away across the universe.'
chunk_size = 24
chunk_overlap = 10

# Create an instance of the splitter class
splitter = CharacterTextSplitter(
    separator=____,
    chunk_size=____,
    chunk_overlap=____)

# Split the string and print the chunks
docs = splitter.____(quote)
print(docs)
print([len(doc) for doc in docs])

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

LLM-toepassingen ontwikkelen met LangChain

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

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: Documentloaders integreren Exercise 2: PDF-documentloaders Exercise 3: CSV-documentloaders Exercise 4: HTML-documentloaders Exercise 5: Externe data splitsen voor retrieval Exercise 6: Splitsen op teken

Huidige oefening

Exercise 7: Recursief splitsen op teken Exercise 8: HTML splitsen Exercise 9: RAG-opslag en -opvraging met vectordatabases Exercise 10: De documenten en de vector database voorbereiden Exercise 11: Een retrieval-prompt-template bouwen Exercise 12: Een RAG-chain maken Exercise 13: Afronding!