複雑なドキュメント構造への対応
表や入れ子構造のセクションなど、複雑なドキュメントから情報を効果的に分割・取得するための戦略を実装します。
「複雑なドキュメント構造への対応」はCoddyKit上の無料LLM Apps in Production (RAG + Vector DB + Caching)レッスンです。 これはレッスン3/4です。 下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。 これはLLM Apps in Production (RAG + Vector DB + Caching)学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 LLM Apps in Production (RAG + Vector DB + Caching)コースには全4レッスンが含まれています。
このレッスンの一部はまだ翻訳されておらず、英語で表示されています。
Beyond Simple Text: Complex Documents
When building RAG systems, we often deal with documents that aren't just plain, flowing text. Think about financial reports, scientific papers, or legal contracts.
These documents frequently contain tables, nested sections (like chapters and sub-chapters), and other intricate structures. Standard text chunking methods often struggle with these, breaking context and making retrieval less effective.
Tables: A Challenge for RAG
Tables are a prime example of complex structures. They present data in a structured, grid-like format where relationships between rows and columns are crucial.
- Lost Context: A simple character-based chunker might split a table row, separating a value from its header, making the chunk meaningless.
- Poor Embeddings: Without proper context, the generated embeddings for table fragments might not accurately represent the data.
Table-Aware Chunking Strategies
To effectively handle tables, we need specific strategies:
- Extraction: Identify and extract tables as distinct entities.
- Serialization: Convert tables into a more LLM-friendly text format, like Markdown or structured JSON, preserving their relationships.
- Summarization: For very large tables, generate a concise summary to be embedded, linking back to the full table.
This ensures the LLM receives the full, meaningful context of the table.
Extracting Table Data (Python)
Here's a simple Python example showing how you might process a table represented as a string, converting it into a more structured list of rows.
import csv
import io
def process_table_string(table_str):
# Use StringIO to treat the string as a file
f = io.StringIO(table_str)
reader = csv.reader(f, delimiter='|')
rows = []
for i, row in enumerate(reader):
# Strip whitespace and filter empty strings
cleaned_row = [item.strip() for item in row if item.strip()]
if cleaned_row and i > 0: # Skip header line
rows.append(cleaned_row)
return rows
if __name__ == "__main__":
data = """
Name | Age | City
-------|-----|--------
Alice | 30 | New York
Bob | 24 | London
Charlie| 35 | Paris
"""
processed_data = process_table_string(data)
for row in processed_data:
print(row)Understanding Nested Documents
Documents often have a natural hierarchy. Think of a book with chapters, sections, and subsections. Each part builds on the previous one, and its meaning is often tied to its parent context.
Standard chunking might split a subsection from its main section's heading, making the retrieved chunk less informative or even confusing without the proper context.
Preserving Document Hierarchy
To handle nested structures effectively, we use hierarchical chunking:
- Semantic Boundaries: Instead of fixed character counts, chunk based on logical divisions like headings (H1, H2, H3).
- Parent Context: Include the title of the parent section in the child chunk. For example, a chunk from 'Section 2.1' might start with 'Chapter 2: Introduction - Section 2.1: Subtopic'.
- Metadata: Store the full path or hierarchy level in the chunk's metadata.
Chunking by Sections (Python)
This Python example demonstrates a simple way to split a document into chunks based on markdown-style headings. Each chunk will contain a section's content.
def chunk_by_headings(document_text):
lines = document_text.split('\n')
chunks = []
current_chunk = []
current_heading = ""
for line in lines:
if line.startswith('# '): # Main heading
if current_chunk:
chunks.append({'heading': current_heading, 'content': '\n'.join(current_chunk).strip()})
current_heading = line.strip()
current_chunk = [line]
elif line.startswith('## '):
# Sub-heading, can be part of the current chunk, or signal a new sub-chunk
# For simplicity, we'll just add it to the current chunk content here
# More advanced logic might create nested chunks or separate entries
current_chunk.append(line)
else:
current_chunk.append(line)
if current_chunk:
chunks.append({'heading': current_heading, 'content': '\n'.join(current_chunk).strip()})
return chunks
if __name__ == "__main__":
doc = """
# Chapter 1: Introduction
This is the introduction text.
## Section 1.1: Background
More details about the background.
# Chapter 2: Methods
Here we describe the methods used.
## Section 2.1: Data Collection
How data was collected.
"""
document_chunks = chunk_by_headings(doc)
for i, chunk in enumerate(document_chunks):
print(f"--- Chunk {i+1} ---")
print(f"Heading: {chunk['heading']}")
print(f"Content snippet: {chunk['content'][:50]}...")
print()Metadata for Richer Context
Metadata is extra information attached to a chunk that describes it without being part of the chunk's main text. It's incredibly powerful for complex documents.
- Document Title: Which source document does this chunk come from?
- Page Number: Where in the original document was this found?
- Parent Section/Chapter: What larger context does this chunk belong to?
- Table ID: If it's a table, which table is it?
Metadata allows for targeted filtering during retrieval and provides valuable context to the LLM.
Beyond Single-Vector Retrieval
For highly complex content, simple text chunks might not be enough. Multi-vector retrieval is an advanced technique where you create different types of embeddings for the same content.
For example, you could have a small, concise summary of a table embedded for quick retrieval, and the full, detailed table content stored separately. The RAG system retrieves the summary, and if relevant, then fetches the full table to pass to the LLM.
Complex Document Check
You've learned about various strategies for handling complex document structures in RAG. Let's test your understanding.
Recap: Mastering Complex Docs
Congratulations! You've explored critical strategies for handling complex document structures in RAG.
- We saw how tables can lose context with standard chunking and learned to extract and serialize them.
- We discussed nested documents and the importance of hierarchical chunking to preserve relationships.
- Finally, we highlighted the power of metadata to enrich chunks and enable more precise retrieval.
By applying these techniques, your RAG system can deliver more accurate and contextually relevant responses, even from the most intricate documents!
よくある質問
「複雑なドキュメント構造への対応」レッスンは無料ですか？
はい。「複雑なドキュメント構造への対応」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、LLM Apps in Production (RAG + Vector DB + Caching)コースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 LLM Apps in Production (RAG + Vector DB + Caching)コースには全4レッスンが含まれています。
「複雑なドキュメント構造への対応」で何を学びますか？
表や入れ子構造のセクションなど、複雑なドキュメントから情報を効果的に分割・取得するための戦略を実装します。 ブラウザで直接実行するハンズオンコードでLLM Apps in Production (RAG + Vector DB + Caching)を演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。
LLM Apps in Production (RAG + Vector DB + Caching)を始めるのに経験は必要ですか？
事前経験は必要ありません。CoddyKitのLLM Apps in Production (RAG + Vector DB + Caching)は初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。 これはレッスン3/4です。
「複雑なドキュメント構造への対応」レッスンにはどのくらい時間がかかりますか？
ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。
このLLM Apps in Production (RAG + Vector DB + Caching)レッスンでコードを書いて実行できますか？
はい。すべてのLLM Apps in Production (RAG + Vector DB + Caching)レッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。
このコースのすべてのレッスン
- クエリの書き換えと再ランキング
- 多段階RAGとエージェント型RAGのパターン
- 複雑なドキュメント構造への対応
- 自己クエリと引用