Membangun Alur RAG Sederhana
Implementasikan alur kerja RAG dasar, mulai dari pemasukan data hingga pembuatan respons menggunakan LLM pilihan Anda.
Membangun Alur RAG Sederhana adalah pelajaran LLM Apps in Production (RAG + Vector DB + Caching) gratis di CoddyKit. Ini adalah pelajaran 3 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar LLM Apps in Production (RAG + Vector DB + Caching), dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus LLM Apps in Production (RAG + Vector DB + Caching) mencakup 4 pelajaran total.
Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.
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!
Belajar LLM Apps in Production (RAG + Vector DB + Caching) dengan tutor AI — gratis
Tulis dan jalankan kode asli di browser kamu, dapatkan bantuan instan dari tutor AI 24/7, dan lanjutkan di mana kamu tinggalkan di web atau aplikasi.
- Kursus
- 12
- Pelajaran
- 48
Pertanyaan yang Sering Diajukan
Apakah pelajaran “Membangun Alur RAG Sederhana” gratis?
Ya — teks lengkap “Membangun Alur RAG Sederhana” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus LLM Apps in Production (RAG + Vector DB + Caching), upgrade ke CoddyKit PRO. Kursus LLM Apps in Production (RAG + Vector DB + Caching) mencakup 4 pelajaran total.
Apa yang akan aku pelajari di “Membangun Alur RAG Sederhana”?
Implementasikan alur kerja RAG dasar, mulai dari pemasukan data hingga pembuatan respons menggunakan LLM pilihan Anda. Kamu berlatih LLM Apps in Production (RAG + Vector DB + Caching) dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.
Apakah aku perlu pengalaman untuk memulai LLM Apps in Production (RAG + Vector DB + Caching)?
Tidak diperlukan pengalaman sebelumnya. LLM Apps in Production (RAG + Vector DB + Caching) di CoddyKit dirancang untuk pemula hingga pelajar tingkat lanjut, jadi kamu bisa memulai di sini atau dari awal dan belajar sesuai kecepatan kamu sendiri. Ini adalah pelajaran 3 dari 4.
Berapa lama pelajaran “Membangun Alur RAG Sederhana” memakan waktu?
Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.
Bisakah aku menulis dan menjalankan kode dalam pelajaran LLM Apps in Production (RAG + Vector DB + Caching) ini?
Ya. Setiap pelajaran LLM Apps in Production (RAG + Vector DB + Caching) menyertakan editor kode bawaan, jadi kamu menulis dan menjalankan kode nyata langsung di browser dan mendapatkan umpan balik AI instan — tidak diperlukan penyiapan lokal.
Semua pelajaran dalam kursus ini
- Memilih Penyedia LLM
- Dasar-Dasar Pemuatan Data dan Pemenggalan Teks
- Membangun Alur RAG Sederhana
- Menguji dan Mengevaluasi Aplikasi RAG Anda