构建高性能的 RAG 系统：使用 Gemini 2.5 Flash 和 FAISS

Retrieval-Augmented Generation (RAG) serves as the gold standard for minimizing hallucinations in large language models (LLMs) and enabling AI to access private data. Building a RAG system from scratch allows for complete control over the entire pipeline. This system leverages Google's Gemini API for embeddings and text generation, along with FAISS for fast vector similarity search. The tech stack includes Gemini 2.5 Flash as the LLM, gemini-embedding-001 for embeddings, FAISS as the vector database, and a Python 3.13+ environment with the uv package manager.