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chunking-strategies
by jpoutrin
Claude Code Marketplace Plugin to dev like a pro in a multi-agent fashion
⭐ 4🍴 0📅 2026年1月22日
SKILL.md
name: chunking-strategies description: Document chunking strategies for RAG systems. Use when implementing document processing pipelines to determine optimal chunking approaches based on document type and retrieval requirements.
Chunking Strategies Skill
This skill provides chunking strategies for RAG document processing.
Chunking Methods
1. Fixed-Size Chunking
def fixed_size_chunk(text: str, chunk_size: int = 500, overlap: int = 50):
chunks = []
start = 0
while start < len(text):
end = start + chunk_size
chunks.append(text[start:end])
start = end - overlap
return chunks
2. Semantic Chunking
Split on natural boundaries (sentences, paragraphs).
def semantic_chunk(text: str, max_tokens: int = 500):
paragraphs = text.split("\n\n")
chunks = []
current_chunk = []
current_tokens = 0
for para in paragraphs:
para_tokens = count_tokens(para)
if current_tokens + para_tokens > max_tokens:
chunks.append("\n\n".join(current_chunk))
current_chunk = [para]
current_tokens = para_tokens
else:
current_chunk.append(para)
current_tokens += para_tokens
if current_chunk:
chunks.append("\n\n".join(current_chunk))
return chunks
3. Recursive Chunking
Hierarchical splitting on multiple separators.
SEPARATORS = ["\n\n", "\n", ". ", " "]
def recursive_chunk(text: str, max_size: int, separators: list[str]):
if len(text) <= max_size:
return [text]
sep = separators[0] if separators else ""
chunks = []
parts = text.split(sep)
for part in parts:
if len(part) <= max_size:
chunks.append(part)
elif len(separators) > 1:
chunks.extend(recursive_chunk(part, max_size, separators[1:]))
else:
chunks.append(part[:max_size])
return chunks
Chunking by Document Type
| Document Type | Recommended Strategy | Chunk Size |
|---|---|---|
| Technical docs | Semantic (headers) | 500-1000 tokens |
| Legal documents | Semantic (sections) | 1000-2000 tokens |
| Code | Function/class based | 200-500 tokens |
| Conversations | Message boundaries | 100-300 tokens |
| General text | Recursive | 300-500 tokens |
Chunk Enrichment
@dataclass
class EnrichedChunk:
content: str
metadata: dict
summary: str # LLM-generated
keywords: list[str]
parent_id: str # For hierarchical retrieval
Best Practices
- Add overlap between chunks (10-20%)
- Preserve semantic boundaries
- Include metadata (source, position)
- Consider hierarchical chunking for long docs
- Test retrieval quality with different sizes
スコア
総合スコア
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