Welcome! Today, we’ll learn how to build and optimize a RAG (Retrieval-Augmented Generation) collection. Why does this matter? If you want an AI assistant to answer questions using your own notes or plans, it must quickly find and use the most relevant information. A well-structured RAG collection makes this possible.

By the end, you’ll know how to break data into useful pieces, store it in a vector database, and retrieve the best information to answer questions. Let’s begin!

With chunked data from previous lesson, we now need a way to store and search it efficiently. A vector database like ChromaDB lets us store text chunks as vectors and quickly find similar ones.

Here’s how to build a collection, with detailed comments explaining each step:

• We use a pre-trained embedding model to convert text into vectors.
• Each chunk gets a unique ID and metadata.
• All chunks are added to the collection for fast retrieval.

When a user asks a question, we want to find the most relevant chunks. This is semantic search: we look for text with similar meaning, not just matching words.

Here’s how to retrieve the top relevant chunks, with detailed comments:

• The function converts the query into a vector and finds the most similar chunks in the collection.
• The results include the chunk text, its ID, and a similarity score.

To help the agent answer accurately, we build a prompt with the user’s question and the retrieved context. This way, the agent “sees” the most relevant information when generating a response.

Here’s a function to build the prompt, with comments:

• This function creates a prompt that includes the user’s question and the most relevant context chunks.
• The agent can now use this prompt to generate a more accurate answer.

You’ve learned how to build and optimize a RAG collection:

• Load and prepare your data
• Chunk documents for better retrieval
• Store chunks in a vector database
• Retrieve the most relevant information for a query
• Build prompts that combine user questions with context

These steps are key to creating AI agents that use your knowledge base to answer questions effectively.

Now it’s your turn! In the next section, you’ll practice building and optimizing your own RAG collection — loading data, chunking it, storing it in a vector database, and retrieving relevant information to answer questions. Let’s put your new skills to work!