鐵人檔案
本系列將用 30 天從零打造一套完整的 AI Engineering 系統。首先使用 vLLM 部署開源大型語言模型，逐步加入 Embedding、Vector Database、RAG、Re-ranking 與 Recommendation System，建立具備知識檢索與個人化推薦能力的 AI 應用；接著使用 LangChain 實作 Tool Calling、Memory 與 AI Agent 工作流程，最後進一步探索 Codex Coding Agent，讓 AI 從「回答問題」進化到能理解專案、使用工具、修改程式與完成工程任務，完整呈現從 LLM Inference 到 Agent
Large Language Models such as ChatGPT, Claude, and Gemini have made AI more acce...
while using a cloud API is convenient, it also made me curious about another que...
LLM 為什麼不用一直重新計算？ In Day 2, we saw that Large Language Models generate text one t...
Recap from Day 3: KV Cache avoids recomputing previous K/V states, but every ac...
RAG retrieves the information; vLLM generates the answer. Over the past few da...
讓 Local LLM 真正讀懂自己的文件 In Day 5, we successfully served Qwen through vLLM and con...
In Day 6, we built a working RAG pipeline and saw FAISS retrieve the most releva...
From Embeddings to FAISS and Nearest-Neighbor Search In Day 7, our embedding exp...
In the previous days, we built a RAG pipeline from the ground up using embedding...
In the previous days, we built a RAG system step by step by working directely wi...