A specialized tool for analyzing document corpora and designing optimal chunking strategies to enhance RAG pipeline performance.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install rag-chunking-optimizer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install rag-chunking-optimizer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is RAG Chunking Optimizer?
The RAG Chunking Optimizer is a sophisticated tool designed to bridge the gap between raw data and high-quality AI retrieval. By evaluating document structures, this skill provides data-driven recommendations for chunk sizes, splitting methods, and overlap settings. It ensures that your Retrieval-Augmented Generation systems operate with maximum precision by tailoring the data preparation process to the specific needs of your embedding models and document types.
Leveraging Openclaw Skills, this optimizer moves beyond simple fixed-size splitting. It analyzes the semantic coherence of your content, whether it be technical API documentation, narrative blog posts, or complex codebases. By implementing the right strategy, developers can significantly reduce noise in their vector databases and improve the accuracy of LLM-generated responses.
RAG Chunking Optimizer Use Cases
- Optimizing existing RAG pipelines that suffer from low retrieval precision or fragmented context.
- Determining the best chunking strategy for mixed document types including Markdown, PDF, and HTML.
- Selecting the ideal chunk size and overlap based on specific embedding models like text-embedding-3-small or voyage-3.
- Designing A/B tests to compare recursive character splitting against semantic or heading-based chunking.
- Enhancing metadata enrichment to improve filtering and relational retrieval in vector stores.
How RAG Chunking Optimizer Works
- Document Profiling: The skill scans the corpus to identify document types, heading hierarchies, and code block density using Openclaw Skills.
- Strategy Mapping: It classifies documents into categories (e.g., structured, narrative, or code-heavy) and maps them to the most effective splitting algorithm.
- Parameter Calibration: It calculates optimal chunk sizes and overlap percentages based on the target embedding model's requirements.
- Metadata Generation: The tool recommends specific metadata tags, such as structural hierarchy and semantic entities, to be attached to each chunk.
- Impact Simulation: It provides projected improvements for metrics like MRR@5 and Recall@10 based on the suggested configuration.
RAG Chunking Optimizer Setup
To begin optimizing your RAG pipeline, ensure your documents are accessible in a local directory. You can use the following commands to analyze your corpus structure:
# Analyze document types and distribution
find docs/ -type f \( -name "*.md" -o -name "*.txt" -o -name "*.pdf" \) -exec wc -l {} + | sort -rn
# Profile internal structure for splitting boundaries
for f in docs/*.md; do
echo "Processing $f"
grep -c "^#" "$f"
done
Integrate the generated Python configuration into your ingestion script using standard libraries like LangChain or LlamaIndex.
RAG Chunking Optimizer Data Schema & Taxonomy
The skill organizes recommendations based on a document-to-strategy taxonomy. The resulting data schema helps you visualize the distribution of your chunks:
| Attribute | Description |
|---|---|
| Document Type | Classification of the source (API, Tutorial, Legal, etc.) |
| Split Method | The recommended algorithm (Heading-based, Semantic, Recursive) |
| Target Size | Optimal token count per chunk |
| Overlap | Recommended token overlap to maintain context (typically 10-20%) |
| Metadata | Proposed tags for source, structure, and relational tracking |
RAG Chunking Optimizer Advanced Features
- Hybrid Splitting Logic: Combine heading-based splitting for structure with recursive splitting for unstructured sections.
- Model-Specific Optimization: Custom configurations tailored for specific embedding models' token limits and sweet spots.
- AST-Aware Code Chunking: Language-specific splitting that respects function and class boundaries.
- A/B Test Matrix Design: Automatically generates a testing framework to compare multiple chunking variables and models.
- Contextual Windowing: Advanced overlap strategies that preserve parent/sibling relationships for complex retrieval tasks.
SKILL.md
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