What are the key components of a production RAG pipeline?
A production RAG pipeline consists of several critical components. The document ingestion layer handles parsing various file formats including PDFs, DOCX, HTML, and structured data. The chunking strategy splits documents into appropriately sized pieces, typically 256-1024 tokens, with metadata preservation. The embedding model converts text chunks into dense vector representations. The vector database stores and indexes these embeddings for efficient similarity search. The retrieval layer handles query processing, embedding, and similarity matching with optional reranking. The prompt construction layer combines retrieved context with the user query and system instructions. The LLM generates the final response. A feedback loop captures user satisfaction and retrieval quality metrics. Production systems also require caching, rate limiting, access control, and monitoring infrastructure.