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Memahami Strategi Pemisahan Teks

Pelajari alasan dan cara membagi dokumen besar menjadi potongan-potongan kecil yang bermakna untuk mengoptimalkan pengambilan dan penggunaan jendela konteks.

Memahami Strategi Pemisahan Teks adalah pelajaran LangChain / RAG / Vector DBs gratis di CoddyKit. Ini adalah pelajaran 2 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar LangChain / RAG / Vector DBs, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus LangChain / RAG / Vector DBs mencakup 4 pelajaran total.

Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.

Why Split Documents?

Large Language Models (LLMs) have a 'context window' – a limit on how much text they can process at once. If you feed them a document that's too long, they simply can't handle it all.

Text splitting is the process of breaking down large documents into smaller, manageable chunks. This makes them suitable for LLMs and helps retrieval systems find more precise information.

The Context Window Limit

Imagine an LLM as a very smart person with a short-term memory limit. The context window is like that limit. If you give it too much information, it might forget the beginning or get confused.

  • LLMs can only process a certain number of tokens (words or sub-words).
  • Going over this limit means information is truncated or ignored.
  • Smaller chunks ensure all relevant information fits and is processed effectively.

Basic Splitting: By Character

One of the most straightforward ways to split text is using a CharacterTextSplitter. It simply breaks text based on a specified separator, usually a newline character (\n).

It's like cutting a long rope into smaller pieces at every knot you find. This method is easy to understand but can sometimes break sentences or paragraphs in awkward places.

Code: Simple Character Split

Try running this example. Notice how the CharacterTextSplitter breaks the text primarily at each newline character.

from langchain_text_splitters import CharacterTextSplitter

text = "Hello world.\nThis is a test.\nAnother line here." 

# Initialize the splitter
text_splitter = CharacterTextSplitter(
    separator="\n",
    chunk_size=20, # Max characters per chunk
    chunk_overlap=0, # No overlap for simplicity
    length_function=len # How to measure chunk length
)

# Split the text
chunks = text_splitter.split_text(text)

# Print the resulting chunks
for i, chunk in enumerate(chunks):
    print(f"Chunk {i+1}: '{chunk}'")

Understanding Chunk Size

The chunk_size parameter determines the maximum length of each piece of text. If a piece of text (before splitting by a separator) exceeds this size, the splitter will try to break it further.

Choosing the right size is crucial:

  • Too small: Context might be lost across multiple chunks, making it harder for the LLM to understand the full picture.
  • Too large: Might still exceed the LLM's context window or contain too much irrelevant information, diluting the focus.

The Role of Overlap

chunk_overlap specifies how many characters (or tokens) each chunk shares with the previous one. This is vital to maintain continuity and prevent loss of context at the boundaries of chunks.

Imagine a sentence that gets split perfectly in half across two chunks. Without overlap, the LLM might miss the connection between the two halves. Overlap ensures that key phrases or ideas aren't cut off abruptly, providing a smoother flow of information.

Recursive Character Splitting

The RecursiveCharacterTextSplitter is often preferred for general-purpose documents. Instead of just one separator, it tries a list of separators in order of preference (e.g., ["\n\n", "\n", " ", ""]).

It first tries to split by the largest, most semantically meaningful separator (like a double newline for paragraphs). If a chunk is still too big, it then tries the next smaller separator (like a single newline), and so on. This creates more semantically coherent chunks.

Code: Recursive Split in Action

This example uses a recursive splitter. Notice how it prioritizes paragraph breaks (double newlines) to keep related sentences together.

from langchain_text_splitters import RecursiveCharacterTextSplitter

text = """
LangChain is a framework for developing applications powered by language models.
It enables applications that are:
1. Data-aware: connect a language model to other sources of data.
2. Agentic: allow a language model to interact with its environment.

This framework provides tools and components to build complex LLM workflows.
"""

# Initialize the recursive splitter
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=100, # Max characters per chunk
    chunk_overlap=20, # Overlap to maintain context
    length_function=len # How to measure chunk length
)

# Split the text
chunks = text_splitter.split_text(text)

# Print the resulting chunks
for i, chunk in enumerate(chunks):
    print(f"Chunk {i+1}: '{chunk}'")

Choosing the Right Strategy

When should you use which splitter?

  • CharacterTextSplitter: Good for simple, highly structured text where you know the exact delimiters (e.g., CSV files, specific log formats).
  • RecursiveCharacterTextSplitter: Generally the default and best choice for most general-purpose documents (like articles, reports), as it aims for more logical and semantically coherent breaks.
  • Other splitters: LangChain offers specialized splitters for code, Markdown, and even semantic content. We'll touch on these in future lessons!

Text Splitting Challenge

You have a long article and need to split it into smaller chunks for an LLM. You decide to use a chunk_size of 500 and a chunk_overlap of 50.

Recap: Text Splitting Fundamentals

Great job! You've learned the fundamental concepts behind text splitting, a crucial step for preparing documents for LLMs.

  • We split text due to LLM context window limits and for more effective retrieval.
  • The CharacterTextSplitter provides basic splitting using a single separator.
  • The RecursiveCharacterTextSplitter offers a smarter, hierarchical approach for general text.
  • chunk_size controls the maximum length of your chunks.
  • chunk_overlap preserves context by sharing text between adjacent chunks.

Next, we'll dive deeper into customizing splitting strategies for specific content types!

Pertanyaan yang Sering Diajukan

Apakah pelajaran “Memahami Strategi Pemisahan Teks” gratis?

Ya — teks lengkap “Memahami Strategi Pemisahan Teks” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus LangChain / RAG / Vector DBs, upgrade ke CoddyKit PRO. Kursus LangChain / RAG / Vector DBs mencakup 4 pelajaran total.

Apa yang akan aku pelajari di “Memahami Strategi Pemisahan Teks”?

Pelajari alasan dan cara membagi dokumen besar menjadi potongan-potongan kecil yang bermakna untuk mengoptimalkan pengambilan dan penggunaan jendela konteks. Kamu berlatih LangChain / RAG / Vector DBs dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.

Apakah aku perlu pengalaman untuk memulai LangChain / RAG / Vector DBs?

Tidak diperlukan pengalaman sebelumnya. LangChain / RAG / Vector DBs di CoddyKit dirancang untuk pemula hingga pelajar tingkat lanjut, jadi kamu bisa memulai di sini atau dari awal dan belajar sesuai kecepatan kamu sendiri. Ini adalah pelajaran 2 dari 4.

Berapa lama pelajaran “Memahami Strategi Pemisahan Teks” memakan waktu?

Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.

Bisakah aku menulis dan menjalankan kode dalam pelajaran LangChain / RAG / Vector DBs ini?

Ya. Setiap pelajaran LangChain / RAG / Vector DBs menyertakan editor kode bawaan, jadi kamu menulis dan menjalankan kode nyata langsung di browser dan mendapatkan umpan balik AI instan — tidak diperlukan penyiapan lokal.

Semua pelajaran dalam kursus ini

  1. Memuat Berbagai Jenis Dokumen
  2. Memahami Strategi Pemisahan Teks
  3. Menyesuaikan Pemisahan Dokumen
  4. Menangani Metadata Dokumen dan Penyaringan
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