LLM Apps in Production (RAG + Vector DB + Caching) · 课时

构建简单的 RAG 流水线

使用选定的 LLM，实现从数据摄取到生成响应的基础 RAG 工作流。

构建简单的 RAG 流水线 是 CoddyKit 上的免费 LLM Apps in Production (RAG + Vector DB + Caching) 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 LLM Apps in Production (RAG + Vector DB + Caching) 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 LLM Apps in Production (RAG + Vector DB + Caching) 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。

Intro to RAG Pipelines

You've learned what RAG is and why it's powerful. Now, let's build one! A Retrieval Augmented Generation (RAG) pipeline is a sequence of steps that combine an LLM with external data.

Its main goal is to give LLMs up-to-date, factual information, reducing "hallucinations" and improving response quality.

Understanding the RAG Flow

Think of a RAG pipeline as having two main phases: preparation and querying. First, you get your data ready. Then, when a user asks a question, your system finds relevant info and uses it to help the LLM answer.

Preparation: Ingest & Index Data
Querying: Retrieve & Generate Response

Step 1: Prepare Your Knowledge Base

Before an LLM can use your data, it needs to be processed. This involves:

Loading: Getting data from various sources (PDFs, websites, databases).
Chunking: Breaking large documents into smaller, manageable pieces (chunks). A chunk might be a few sentences or a paragraph.

Smaller chunks are easier to search and fit into an LLM's context window.

Step 2: Turn Chunks into Embeddings

How do we "search" text semantically? We turn it into numbers! An embedding model converts each text chunk into a list of numbers called a vector embedding.

These vectors capture the meaning of the text. Chunks with similar meanings will have vectors that are "close" to each other in a mathematical sense.

Step 3: Store for Fast Retrieval

Once you have vector embeddings for all your chunks, you need to store them efficiently. A vector store (or vector database) is specialized for this.

It allows for very fast "similarity search" – finding vectors that are closest to a given query vector. This is key for quickly retrieving relevant information.

Processing a User Query

When a user types a question, your RAG pipeline springs into action. The first thing that happens is that the user's query itself is converted into a vector embedding.

This query embedding will then be used to search your stored data for relevant information.

Step 4: Find the Best Matches

With the user query's embedding, the RAG system performs a similarity search in your vector store. It looks for data chunks whose embeddings are most similar to the query's embedding.

The most similar chunks are considered the most relevant "context" for answering the user's question.

Step 5: Enhance the LLM's Prompt

Now, we combine the user's original question with the retrieved context. This creates an augmented prompt.

Instead of just asking, "What is X?", the prompt becomes something like: "Given this information: [retrieved chunks], what is X?"

This guides the LLM to use the provided facts.

Step 6: LLM Generates the Answer

Finally, the augmented prompt is sent to the Large Language Model. The LLM processes both the user's question and the retrieved context.

It then generates a response that is grounded in the factual information provided by your data, rather than relying solely on its pre-trained knowledge.

Visualize the RAG Steps

Here's a conceptual Python example showing the flow. Imagine load_data, chunk_text, create_embeddings, index_embeddings, search_vector_store, and generate_llm_response are functions you'd implement.

Try running this example to see the sequence!

public class Main {
  public static void main(String[] args) {
    System.out.println("1. User query received: What is RAG?");

    // Simulate embedding the query
    String queryEmbedding = "Embedding for 'What is RAG?'";
    System.out.println("2. Query embedded: " + queryEmbedding);

    // Simulate retrieving relevant chunks from a vector store
    String[] retrievedChunks = {
      "Chunk 1: RAG helps LLMs use external facts.",
      "Chunk 2: Vector databases store embeddings."
    };
    System.out.println("3. Retrieved relevant chunks: " + String.join(", ", retrievedChunks));

    // Simulate augmenting the LLM prompt
    String augmentedPrompt = (
      "Based on the following context:\n"
      + String.join(" ", retrievedChunks) + "\n\n"
      + "Answer the question: What is RAG?"
    );
    System.out.println("4. Augmented LLM prompt created.");

    // Simulate LLM response generation
    String llmResponse = (
      "RAG pipelines enhance LLMs by providing external, "
      + "factual context from stored documents, which helps "
      + "reduce hallucinations and improve accuracy."
    );
    System.out.println("5. LLM generated response.");

    System.out.println("\nFinal Answer: " + llmResponse);
  }
}

RAG Pipeline Quiz

Which of the following accurately describes the correct order of steps when a user submits a query in a RAG pipeline?

Recap: Building RAG

Great job! You've now grasped the full flow of a basic RAG pipeline. We covered:

The preparation steps: ingesting, chunking, embedding, and indexing your data.
The querying steps: embedding the user query, retrieving context, augmenting the prompt, and generating a response with the LLM.

This foundational understanding will help you build more robust LLM applications!

常见问题解答

「构建简单的 RAG 流水线」课时是免费的吗？

是的 — 「构建简单的 RAG 流水线」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 LLM Apps in Production (RAG + Vector DB + Caching) 课程的其余内容，请升级到 CoddyKit PRO。 LLM Apps in Production (RAG + Vector DB + Caching) 课程共包含 4 节课。

「构建简单的 RAG 流水线」这节课中我会学到什么？

使用选定的 LLM，实现从数据摄取到生成响应的基础 RAG 工作流。你通过在浏览器中直接运行的动手代码来练习 LLM Apps in Production (RAG + Vector DB + Caching)，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 LLM Apps in Production (RAG + Vector DB + Caching) 需要有经验吗？

无需任何先前经验。CoddyKit 上的 LLM Apps in Production (RAG + Vector DB + Caching) 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。

「构建简单的 RAG 流水线」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 LLM Apps in Production (RAG + Vector DB + Caching) 课中编写并运行代码吗？

能。每节 LLM Apps in Production (RAG + Vector DB + Caching) 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。

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