ai-pipeline

name	ai-pipeline
description	RAG/embedding pipeline scaffolding — delegates to de-ai-data-engineer agent. Use when building RAG pipelines, embedding workflows, feature stores, or text- to-SQL systems.

AI Pipeline Command

Scaffold RAG pipelines, embedding workflows, feature stores, and text-to-SQL

Usage

/ai-pipeline <description-or-file>

Examples

/ai-pipeline "RAG pipeline for internal docs with pgvector"
/ai-pipeline "Embedding pipeline from S3 PDFs to Pinecone"
/ai-pipeline "Feature store setup with Feast for ML models"
/ai-pipeline "Text-to-SQL agent for analytics queries"

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What This Command Does

1. Invokes the de-ai-data-engineer agent
2. Analyzes your AI/ML data requirements
3. Loads KB patterns from ai-data-engineering and streaming domains
4. Generates:
   • RAG pipeline architecture and code
   • Embedding pipeline with chunking strategies
   • Vector database setup and indexing
   • Feature store definitions
   • Text-to-SQL prompt templates

Agent Delegation

Agent	Role
de-ai-data-engineer	Primary — RAG, embeddings, vector DBs, features
de-streaming-engineer	Escalation — real-time embedding pipelines
test-data-quality-analyst	Escalation — embedding quality metrics

KB Domains Used

• ai-data-engineering — RAG pipelines, vector databases, feature stores, LLMOps
• streaming — real-time embedding ingestion
• data-quality — embedding quality, drift detection

Output

The agent generates pipeline code, configuration, and architecture documentation for your AI data workflow.