Memisah berdasarkan karakter

Proses penting dalam menerapkan Retrieval Augmented Generation (RAG) adalah memecah dokumen menjadi potongan (chunk) untuk disimpan dalam basis data vektor.

Tersedia beberapa strategi pemecahan (splitting) di LangChain, sebagian memiliki rutinitas yang lebih kompleks daripada yang lain. Pada latihan ini, Anda akan menerapkan character text splitter, yang memecah dokumen berdasarkan karakter dan mengukur panjang chunk berdasarkan jumlah karakter.

Ingat bahwa tidak ada strategi pemecahan yang ideal; Anda mungkin perlu bereksperimen dengan beberapa pendekatan untuk menemukan yang paling tepat bagi kasus penggunaan Anda.

Latihan ini adalah bagian dari kursus

Mengembangkan Aplikasi LLM dengan LangChain

Petunjuk latihan

Impor kelas CharacterTextSplitter dari langchain_text_splitters.
Buat instance CharacterTextSplitter dengan separator="\n", chunk_size=24, dan chunk_overlap=10.
Gunakan metode .split_text() untuk memecah quote lalu cetak chunk beserta panjang chunk-nya.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# 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])

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Mengembangkan Aplikasi LLM dengan LangChain

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus 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: Mengintegrasikan pemuat dokumen Exercise 2: Pemuatan dokumen PDF Exercise 3: Pemuat dokumen CSV Exercise 4: Pemuat dokumen HTML Exercise 5: Memecah data eksternal untuk pengambilan kembali Exercise 6: Memisah berdasarkan karakter

Latihan Saat Ini

Exercise 7: Memecah secara rekursif berdasarkan karakter Exercise 8: Membagi HTML Exercise 9: Penyimpanan dan pengambilan RAG menggunakan basis data vektor Exercise 10: Menyiapkan dokumen dan basis data vektor Exercise 11: Membangun templat prompt retrieval Exercise 12: Membuat rantai RAG Exercise 13: Penutup!