Vector databases are the core infrastructure for RAG and AI applications. This guide compares Pinecone, Milvus, Qdrant, and Weaviate across positioning, deployment, indexing, and retrieval, with selection advice by scale and scenario.
RAG augments large language models with external knowledge retrieval, ideal for enterprise knowledge-base Q&A. This guide walks through loading, splitting, embedding, storing, retrieval, reranking, generation, and evaluation.
A hands-on guide to the Google Gemini API covering Gemini 3 model selection, multimodal and long-context input, the google-genai SDK, the Interactions API, Google AI Studio vs Vertex AI, and pricing.
A deep dive into Azure OpenAI covering resource and model deployment, the GPT and embedding model lineup, model version policies, enterprise security and compliance, and RAG on Azure AI Search.