การสร้างไปป์ไลน์ RAG อย่างง่าย

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.

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

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.

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.

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.

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.

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.

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.

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.

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!

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!