Menyesuaikan Pemisahan Dokumen
Terapkan teknik pemisahan teks tingkat lanjut, termasuk pemotongan semantik serta penanganan kode atau struktur data tertentu.
Menyesuaikan Pemisahan Dokumen adalah pelajaran LangChain / RAG / Vector DBs gratis di CoddyKit. Ini adalah pelajaran 3 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 Customize Text Splitting?
When preparing documents for Retrieval Augmented Generation (RAG), how you split them into chunks is crucial. Basic text splitters are a good start, but they often fall short for complex or highly structured content.
Customizing your text splitting strategy allows you to maintain better contextual integrity, leading to more accurate retrievals and better LLM responses.
Tailoring Character Splitters
LangChain's CharacterTextSplitter is simple but powerful. You can customize it by providing specific separator characters. This is useful when your documents have unique delimiters you want to respect, like a specific tag or a unique line break pattern.
By defining your own separators, you can ensure logical breaks rather than arbitrary character counts.
from langchain.text_splitter import CharacterTextSplitter
class Main:
def run(self):
text = "Chapter 1: Intro.Section 1.1: Basics.Section 1.2: Advanced."
# Custom separator is "."
splitter = CharacterTextSplitter(
separator=".",
chunk_size=20,
chunk_overlap=0
)
chunks = splitter.split_text(text)
for i, chunk in enumerate(chunks):
print(f"Chunk {i+1}: {chunk}")
if __name__ == "__main__":
Main().run()Refining Recursive Splitters
The RecursiveCharacterTextSplitter attempts to split text using a list of separators in order, trying to keep chunks as large as possible. You can customize this list to match your document's inherent structure.
For example, you might prioritize splitting by double newlines, then single newlines, then spaces, and finally characters.
from langchain.text_splitter import RecursiveCharacterTextSplitter
class Main:
def run(self):
text = "Hello there!\n\nThis is a paragraph.\nAnd this is another sentence."
# Custom list of separators
splitter = RecursiveCharacterTextSplitter(
separators=["\n\n", "\n", " ", ""],
chunk_size=40,
chunk_overlap=0
)
chunks = splitter.split_text(text)
for i, chunk in enumerate(chunks):
print(f"Chunk {i+1}: {chunk}")
if __name__ == "__main__":
Main().run()Intro to Semantic Chunking
Instead of relying solely on character counts or delimiters, what if we could split text based on its meaning?
Semantic chunking aims to create chunks that represent complete, coherent ideas or topics. This method helps prevent important concepts from being arbitrarily split across different chunks, which often happens with fixed-size or simple character splitters.
How Semantic Chunking Works
Semantic chunking typically involves a few steps:
- Embed Sentences: Each sentence or a small unit of text is converted into a vector embedding.
- Measure Similarity: The semantic similarity between adjacent sentences or units is measured using their embeddings.
- Identify Breakpoints: Chunks are formed where semantic similarity drops significantly, indicating a topic change or a shift in discussion.
While LangChain doesn't have a single 'semantic splitter' out-of-the-box, it's a pattern you can build using embedding models and custom logic.
Specialized Code Splitters
Code has a unique structure, with functions, classes, comments, and specific syntax. Generic text splitters often break code in awkward places, making the resulting chunks hard to understand or use as context for an LLM.
LangChain provides specialized splitters for different programming languages. These splitters understand the syntax of a language and ensure that chunks are syntactically meaningful, like keeping a whole function or class together.
Python Code Splitter Demo
The RecursiveCharacterTextSplitter.from_language method allows you to specify a programming language. It then uses language-specific separators (like class definitions, function definitions, etc.) to create more intelligent chunks.
This ensures that code snippets passed to an LLM are more coherent.
from langchain.text_splitter import RecursiveCharacterTextSplitter, Language
class Main:
def run(self):
python_code = """
def calculate_sum(a, b):
# This function adds two numbers
return a + b
class MyCalculator:
def __init__(self):
self.result = 0
def add(self, num):
self.result += num
if __name__ == "__main__":
total = calculate_sum(5, 3)
print(f"Sum: {total}")
calc = MyCalculator()
calc.add(10)
print(f"Calc result: {calc.result}")
"""
# Initialize splitter for Python code
python_splitter = RecursiveCharacterTextSplitter.from_language(
language=Language.PYTHON,
chunk_size=100, # Adjust chunk size to see more splits
chunk_overlap=0
)
docs = python_splitter.create_documents([python_code])
for i, doc in enumerate(docs):
print(f"--- Chunk {i+1} ---")
print(doc.page_content)
if __name__ == "__main__":
Main().run()Beyond Code: Other Structures
LangChain also offers specialized splitters for other structured formats, not just code:
MarkdownTextSplitter: Understands Markdown syntax (headings, code blocks, lists) to create logically grouped chunks.LatexTextSplitter: Recognizes LaTeX sections, chapters, and environments, preserving the document's academic structure.
These specialized splitters are invaluable for processing documents where the formatting itself conveys important structural information.
Creating Custom Splitter Logic
For truly unique document structures or proprietary data formats, you might need to implement your own splitting logic. LangChain allows you to:
- Subclass
TextSplitter: Create a new class that inherits fromTextSplitterand overrides its methods to define custom splitting rules. - Write a custom function: Develop a function that takes your text and returns a list of chunks based on your specific parsing requirements.
This approach offers maximum flexibility to handle complex regex patterns, custom delimiters, or nested structures unique to your dataset.
Check Your Understanding
You've learned about various ways to customize text splitting for different document types. Let's test your knowledge.
Custom Splitting Recap
In this lesson, we explored how to go beyond basic text splitting to handle diverse and complex document types more effectively:
- We customized Character and Recursive Character splitters with specific lists of separators.
- We introduced the concept of Semantic Chunking for meaning-based splits.
- We learned about Language-specific splitters for code (e.g., Python, Java) and other structured formats like Markdown and LaTeX.
- Finally, we discussed the power and flexibility of creating entirely custom splitting logic for unique data.
Mastering customized splitting strategies is a critical step in building accurate and robust RAG applications.
Belajar LangChain / RAG / Vector DBs dengan tutor AI — gratis
Tulis dan jalankan kode asli di browser kamu, dapatkan bantuan instan dari tutor AI 24/7, dan lanjutkan di mana kamu tinggalkan di web atau aplikasi.
- Kursus
- 12
- Pelajaran
- 48
Pertanyaan yang Sering Diajukan
Apakah pelajaran “Menyesuaikan Pemisahan Dokumen” gratis?
Ya — teks lengkap “Menyesuaikan Pemisahan Dokumen” 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 “Menyesuaikan Pemisahan Dokumen”?
Terapkan teknik pemisahan teks tingkat lanjut, termasuk pemotongan semantik serta penanganan kode atau struktur data tertentu. 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 3 dari 4.
Berapa lama pelajaran “Menyesuaikan Pemisahan Dokumen” 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
- Memuat Berbagai Jenis Dokumen
- Memahami Strategi Pemisahan Teks
- Menyesuaikan Pemisahan Dokumen
- Menangani Metadata Dokumen dan Penyaringan