A comprehensive framework for building, optimizing, and debugging production-grade Retrieval-Augmented Generation pipelines.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install rag
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 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 Pipeline Architect?
The RAG skill is a technical powerhouse designed for developers who need to implement high-fidelity Retrieval-Augmented Generation systems. It streamlines the entire lifecycle of data retrieval, from initial architecture design and vector database selection to the implementation of advanced chunking strategies. By leveraging this tool within the broader ecosystem of Openclaw Skills, developers can ensure their AI agents deliver accurate, context-aware responses while maintaining strict data integrity.
This skill goes beyond simple query-response loops by focusing on the engineering rigor required for production. It provides clear pathways for handling complex document volumes, managing latency, and ensuring that similarity search remains consistent. Using this specific skill allows for the systematic reduction of hallucinations and retrieval failures through proven industry patterns and debugging workflows.
RAG Pipeline Architect Use Cases
- Implementing scalable vector search architectures for enterprise document sets.
- Optimizing existing RAG systems to reduce hallucinations and improve context relevance.
- Building secure ingestion pipelines that require PII detection and retrieval-time access control.
- Troubleshooting retrieval failures using query expansion and hybrid search techniques.
- Monitoring production RAG quality to detect similarity drift and performance degradation.
How RAG Pipeline Architect Works
- Requirement Analysis: Evaluate document volume, update frequency, and latency needs to define the pipeline architecture.
- Ingestion Strategy: Configure chunking parameters with a 10-20% overlap to prevent context loss during embedding.
- Vector Management: Select and integrate embedding models and vector databases, ensuring query and document models remain identical.
- Retrieval Logic: Implement top-k selection, hybrid search, and reranking to surface the most relevant context.
- Evaluation Framework: Build test datasets to measure recall and precision before the system moves to production.
- Production Monitoring: Set up alerts for similarity score drops and quality drift to maintain long-term system health.
RAG Pipeline Architect Setup
To integrate this capability into your project, ensure your environment is configured for vector operations.
# Install the necessary RAG utilities via Openclaw Skills
openclaw install skill-rag
# Initialize your local architecture configuration
openclaw rag init --model text-embedding-3-small
# Validate your ingestion pipeline setup
openclaw rag check-chunks --overlap 0.15 ./docs
RAG Pipeline Architect Data Schema & Taxonomy
The skill organizes RAG components into a structured taxonomy for easier debugging and scaling:
| Component | Organization Method | Key Metadata |
|---|---|---|
| Pipelines | architecture.md |
Embedding models, Vector DB specs, Latency goals |
| Ingestion | implementation.md |
Chunk size, Overlap %, Document IDs |
| Eval Sets | evaluation.md |
Ground truth, Similarity thresholds, Recall metrics |
| Security | security.md |
Access control lists, PII masks, Compliance tags |
RAG Pipeline Architect Advanced Features
- Retrieval-time access control filtering to ensure users only see authorized data.
- Hybrid search optimization combining semantic similarity with keyword-based retrieval.
- Automated similarity score monitoring to identify and alert on quality drift in production.
- Re-embedding workflows specifically designed for GDPR compliance and document deletion.
- Advanced reranking support to prioritize context relevance beyond simple vector distance.
SKILL.md
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