RAG

Vector databases for AI applications have become essential infrastructure for RAG (Retrieval-Augmented Generation), semantic search, and recommendation systems in 2026. The best vector databases—Pinecone, Milvus, Qdrant, Weaviate, Chroma, pgvector, and Elasticsearch—provide efficient similarity search over high-dimensional embeddings at scale. Choosing vector databases requires evaluating query latency, index types (HNSW, IVF), deployment models (managed vs self-hosted), and cost structures. Pinecone excels as a fully managed solution with minimal operations, while Milvus provides maximum control for self-hosted deployments. Qdrant offers Rust-based performance with Docker simplicity, and pgvector extends PostgreSQL with vector capabilities. Vector database performance directly impacts RAG application quality—slow retrieval degrades LLM response times and increases costs. For teams building LLM applications, vector database selection is as critical as model choice. ...

RAG okviri (Retrieval-Augmented Generation frameworks) postali su bitni za izgradnju AI aplikacija proizvodne razine u 2026. Najbolji RAG okviri—LangChain, LlamaIndex, Haystack, DSPy i LangGraph—omogućuju programerima kombiniranje velikih jezičnih modela s dohvaćanjem znanja specifičnim za domenu. Kada se uspoređuju LangChain s LlamaIndexom i Haystackom, ključni čimbenici uključuju učinkovitost tokena, troškove orkestracije i mogućnosti obrade dokumenata. Referentne vrijednosti performansi otkrivaju da Haystack postiže najnižu upotrebu tokena (~1570 tokena), dok DSPy nudi minimalno opterećenje (~3,53 ms). LlamaIndex je odličan za aplikacije usmjerene na dokumente, LangChain pruža maksimalnu fleksibilnost, a Haystack nudi cjevovode spremne za proizvodnju. Razumijevanje arhitekture RAG okvira ključno je za programere koji grade baze znanja, chatbotove i sustave za generiranje s proširenim pretraživanjem. ...