Embedding Strategy Skill

name: embedding-strategy version: 1.0 last_updated: 2025-12-04 description: PROTECTED - Chunking strategy and embedding dimension management license: MIT priority: critical triggers: - "chunking", "embedding", "dimension", "overlap", "splitting" dependencies: - docs/protected-schemas.md - templates/rag-checklist.md

📐 Embedding Strategy SKILL

Purpose

PROTECTED: Manage chunking strategies and embedding dimensions. Changes require user approval + full impact analysis.

Chunking Strategies

1. RecursiveCharacterTextSplitter (Recommended)

from langchain.text_splitter import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(
    chunk_size=512,          # PROTECTED
    chunk_overlap=50,        # PROTECTED
    separators=["\n\n", "\n", " ", ""],
    length_function=len
)

chunks = splitter.split_text(document)

When to use: General purpose (paragraphs, sentences)

Chunk Size Guidelines:

• 256 tokens: Very focused Q&A
• 512 tokens: Balanced (recommended for most cases)
• 1024 tokens: Broad context (summarization)
• 2048 tokens: Document understanding

2. TokenTextSplitter

from langchain.text_splitter import TokenTextSplitter

splitter = TokenTextSplitter(
    chunk_size=512,
    chunk_overlap=50
)

When to use: LLM context window management (token-based limits)

3. MarkdownTextSplitter

from langchain.text_splitter import MarkdownTextSplitter

splitter = MarkdownTextSplitter(
    chunk_size=512,
    chunk_overlap=50
)

When to use: Markdown documents (preserves structure)

Chunk Overlap Strategy

Why Overlap Matters

Without overlap (BAD):
Chunk 1: "...end of sentence A."
Chunk 2: "Start of sentence B..."
→ Context lost between chunks

With overlap (GOOD):
Chunk 1: "...end of sentence A. Start of sentence B..."
Chunk 2: "...end of sentence A. Start of sentence B. More context..."
→ Continuity preserved

Overlap Guidelines

• Small chunks (256): 20-30 tokens overlap
• Medium chunks (512): 50-100 tokens overlap
• Large chunks (1024): 100-200 tokens overlap

Rule of thumb: 10-20% of chunk_size

Embedding Dimensions (PROTECTED)

Common Models

Protection Rule

NEVER change dimension without:

1. User approval
2. Full re-indexing plan
3. Backup of existing vectors

See: templates/rag-checklist.md Q1

Chunk Size Optimization

A/B Testing

from ragas.metrics import ContextRelevance

# Test different chunk sizes
chunk_sizes = [256, 512, 1024]
results = {}

for size in chunk_sizes:
    splitter = RecursiveCharacterTextSplitter(chunk_size=size)
    # Re-index with new chunk size
    # Run evaluation
    score = evaluate_retrieval(splitter)
    results[size] = score

# Choose best
best_size = max(results, key=results.get)
print(f"Best chunk size: {best_size}")

Pre-Processing Best Practices

1. Clean Text

import re

def clean_text(text: str) -> str:
    # Remove extra whitespace
    text = re.sub(r'\s+', ' ', text)

    # Remove special characters
    text = re.sub(r'[^\w\s.,!?-]', '', text)

    # Normalize line breaks
    text = text.replace('\r\n', '\n')

    return text.strip()

2. Remove Boilerplate

def remove_boilerplate(text: str) -> str:
    # Remove headers/footers
    text = re.sub(r'Page \d+ of \d+', '', text)

    # Remove navigation
    text = re.sub(r'Home \| About \| Contact', '', text)

    return text

Integration

See: templates/rag-checklist.md for pre-modification checklist

Last Updated: 2025-12-04