Building RAG Applications with DeepSeek V3

End-to-end guide to building Retrieval-Augmented Generation systems with DeepSeek V3, covering vector databases, embedding strategies, chunking, and retrieval optimization.

Build production RAG systems with DeepSeek V3. This guide covers document ingestion and chunking strategies, embedding model selection, vector database setup (Pinecone, Weaviate, pgvector), retrieval pipeline architecture, re-ranking with cross-encoders, prompt construction for grounded generation, evaluation with RAGAS, and handling hallucination prevention.