This article walks through a complete, production‑ready Retrieval‑Augmented Generation pipeline that lets AI answer a company’s private documents, covering chunking strategies, embedding model choices, vector‑database selection, retrieval methods, full LangChain chain assembly, and common pitfalls to avoid.
This guide explains how to create a proprietary knowledge base for DeepSeek by leveraging Baidu Cloud's VectorDB, offering both code‑first and no‑code approaches to integrate internal data securely and enable accurate AI‑driven Q&A for business applications.
The article walks through preparing a GPU‑enabled environment, downloading and LoRA‑fine‑tuning a DeepSeek model with LLaMA‑Factory, merging the adapter, then wrapping the model in a web UI that queries a ChromaDB vector store via crawled web data, illustrating security‑focused use cases and forecasting domain‑specific LLM adoption.
This article explains the concept, architecture, and step‑by‑step implementation of Retrieval Augmented Generation (RAG), covering indexing, retrieval & generation processes, a practical LangChain‑Redis‑llama.cpp example on Kubernetes, code snippets, test results, challenges, and references.
This weekly digest curates top industry technical articles, featuring MySQL XA transaction crash safety, SQL optimization case study, MySQL 9.0 new features, database sharding origins, vector database workings, and a Chinese graph database product guide, offering readers concise insights into current database developments.
