RAG 系统的核心组件
了解 RAG 流程的基本构建模块，包括文档加载、嵌入、向量存储和检索
RAG 系统的核心组件 是 CoddyKit 上的免费 LangChain / RAG / Vector DBs 课时。 这是第 3 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 LangChain / RAG / Vector DBs 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 LangChain / RAG / Vector DBs 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
RAG's Building Blocks
Let us break down a RAG system’s parts. Think of RAG as a smart librarian for your LLM, fetching the right facts from your own knowledge base.
Load Your Knowledge
It starts with document loading: pulling your knowledge in from any source — PDFs, web pages, databases, plain text — so the LLM can draw on it.
Diverse Data Sources
Document loading is like scanning your books and notes in for the AI to read. Sources span PDFs, web pages, and structured databases alike.
Chunking for Context
Loaded docs are usually too big, so you split them into chunks. Smaller pieces fit the LLM’s limited context window and are faster to search.
Text into Numbers
Each chunk becomes an embedding — a numeric fingerprint of its meaning. Similar meanings get similar fingerprints, so machines can compare ideas, not just keywords.
Store Your Embeddings
Those embeddings live in a vector store: a database tuned to hold and search high-dimensional vectors fast, finding the most similar chunks for any query.
Retrieval: Finding the Needle
When a question arrives, retrieval kicks in: embed the query, compare it to the vector store, and pull the top chunks closest in meaning.
Augmentation: Smart Prompting
Next is augmentation: the retrieved chunks get attached to the user’s question, handing the LLM a factual cheat sheet drawn straight from your data.
Generation: Crafting the Answer
Finally, generation: the LLM reads the query plus retrieved context and writes a grounded, factual answer — sharply cutting hallucinations.
The RAG Flow in Action
The full RAG flow: load, split, embed, and store your docs; then embed the query, retrieve top chunks, augment the prompt, and generate the answer.
RAG Checkpoint
Which RAG component is responsible for converting text into numerical representations that capture its meaning?
RAG Components Recap
You now know RAG’s pieces: loading, splitting, embeddings, vector stores, retrieval, augmentation, and generation — working together for informed AI answers.
常见问题解答
「RAG 系统的核心组件」课时是免费的吗？
是的 — 「RAG 系统的核心组件」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 LangChain / RAG / Vector DBs 课程的其余内容，请升级到 CoddyKit PRO。 LangChain / RAG / Vector DBs 课程共包含 4 节课。
「RAG 系统的核心组件」这节课中我会学到什么？
了解 RAG 流程的基本构建模块，包括文档加载、嵌入、向量存储和检索 你通过在浏览器中直接运行的动手代码来练习 LangChain / RAG / Vector DBs，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 LangChain / RAG / Vector DBs 需要有经验吗？
无需任何先前经验。CoddyKit 上的 LangChain / RAG / Vector DBs 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 3 节课，共 4 节。
「RAG 系统的核心组件」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 LangChain / RAG / Vector DBs 课中编写并运行代码吗？
能。每节 LangChain / RAG / Vector DBs 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。
此课程中的所有课时
- 什么是大语言模型
- 检索增强生成的必要性
- RAG 系统的核心组件
- 嵌入与向量数据库