Criando um Pipeline RAG Simples
Implemente um fluxo de trabalho básico de RAG, desde a ingestão de dados até a geração de respostas usando um LLM escolhido.
Criando um Pipeline RAG Simples é uma aula grátis de LLM Apps in Production (RAG + Vector DB + Caching) no CoddyKit. Esta é a aula 3 de 4. Você pode ler a aula completa abaixo gratuitamente — depois pratica ao vivo no navegador com um editor de código integrado e um tutor de IA 24/7. Faz parte do caminho de aprendizado de LLM Apps in Production (RAG + Vector DB + Caching), e seu progresso é sincronizado entre a web e o app CoddyKit. O curso de LLM Apps in Production (RAG + Vector DB + Caching) inclui 4 aulas no total.
Partes desta aula ainda não foram traduzidas e aparecem em inglês.
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!
Perguntas Frequentes
A aula “Criando um Pipeline RAG Simples” é grátis?
Sim — o texto completo de “Criando um Pipeline RAG Simples” é grátis para ler aqui na web. Para praticá-la interativamente (um editor de código integrado e um tutor de IA 24/7) e desbloquear o restante do curso de LLM Apps in Production (RAG + Vector DB + Caching), atualize para CoddyKit PRO. O curso de LLM Apps in Production (RAG + Vector DB + Caching) inclui 4 aulas no total.
O que vou aprender em “Criando um Pipeline RAG Simples”?
Implemente um fluxo de trabalho básico de RAG, desde a ingestão de dados até a geração de respostas usando um LLM escolhido. Você pratica LLM Apps in Production (RAG + Vector DB + Caching) com código prático que executa diretamente no navegador, e um tutor de IA 24/7 responde suas dúvidas enquanto trabalha na aula.
Preciso ter experiência prévia para começar LLM Apps in Production (RAG + Vector DB + Caching)?
Nenhuma experiência prévia é necessária. LLM Apps in Production (RAG + Vector DB + Caching) no CoddyKit é estruturado para alunos iniciantes até avançados, então você pode começar aqui ou desde o início e aprender no seu ritmo. Esta é a aula 3 de 4.
Quanto tempo leva a aula “Criando um Pipeline RAG Simples”?
A maioria das aulas CoddyKit leva cerca de 5–10 minutos. Cada uma é compacta e interativa, então você faz progresso constante e retoma exatamente de onde parou entre web e app.
Posso escrever e executar código nesta aula de LLM Apps in Production (RAG + Vector DB + Caching)?
Sim. Cada aula de LLM Apps in Production (RAG + Vector DB + Caching) inclui um editor de código integrado, então você escreve e executa código real direto no navegador e recebe feedback de IA instantaneamente — nenhuma configuração local necessária.
Todas as aulas deste curso
- Escolhendo um Provedor de LLM
- Fundamentos do Carregamento de Dados e da Divisão de Texto
- Criando um Pipeline RAG Simples
- Testando e avaliando seu aplicativo RAG