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Rag Pipeline Setup

Build end-to-end Retrieval-Augmented Generation pipeline including document ingestion, chunking, embedding, vector store, retrieval, and LLM integration.

Complexity: Complex | Duration: 30m+ | Category: Devops

Tags: ml rag llm vector-search nlp

Workflow Diagram​

Steps​

Step 1: Document ingestion​

Agent: ml

engineer - Load PDFs, docs, web pages

Step 2: Text chunking​

Agent: ml

engineer - Split into overlapping chunks (512-1024 tokens)

Step 3: Embedding generation​

Agent: ml

engineer - Use OpenAI/Cohere/sentence-transformers

Step 4: Vector store setup​

Agent: backend

architect - Configure Pinecone/Weaviate/ChromaDB

Step 5: Indexing​

Agent: ml

engineer - Store embeddings with metadata in vector DB

Step 6: Retrieval testing​

Agent: ml

engineer - Test semantic search with sample queries

Step 7: LLM integration​

Agent: ml

engineer - Combine retrieved context with LLM (GPT-4, Claude)

Step 8: End​

Agent: to

end testing - testing-specialist - Verify accuracy of generated answers

Usage​

To execute this workflow:

/workflow devops/rag-pipeline-setup.workflow

See other workflows in this category for related automation patterns.