Hello there, welcome to the second lesson of our "Scaling Up RAG with Vector Databases" course! In the previous unit, you explored how to break large documents into smaller chunks and attach useful metadata (like doc_id, chunk_id, and labels such as category). These chunks are essential for structuring data in a way that makes retrieval easier. In this lesson, we'll build on that groundwork by showing you how to store them in a vector database. Vector databases are specialized systems designed for high-speed, semantic querying of vectors. By switching from keyword-based searches to semantic searches, your RAG system will retrieve relevant information more efficiently. Let's dive in!

A vector database stores data in the form of numerical vectors that capture the semantic essence of texts (or other data). The database then uses similarity metrics — rather than literal word matches — so that conceptually similar items are stored close together. This means searches on vector databases can retrieve contextually relevant results even when keywords are absent. By leveraging approximate or exact nearest-neighbor strategies for similarity, vector databases can scale to handle millions or billions of vectors while still providing quick query responses. This makes them especially suitable for RAG systems, which rely on fast semantic lookups across large collections of text.

Now, let's jump into coding with a vector database in Java. Here's how to set up a vector database client:

How It Works:

• Embedding Setup: We use a BERT model to generate vectors for the text chunks. The model maps sentences to a dense vector space, capturing semantic meaning.
• Client and Collection: We create a Client instance to interact with the ChromaDB and manage collections.
• Embedding Function: A custom BertSentenceTransformerEmbedding is implemented to handle the conversion between text and vector representations.

After setting up your client and embedding function, the next step is to prepare your chunks for insertion and add them to the database:

Key Points:

• Data Grouping: Each chunk is mapped to its text, a unique ID, and metadata. These are used during retrieval and future reference.
• Seamless Insertion: The prepared data can be inserted into the vector database using Java-compatible methods.

Vector databases allow you to keep your collection up to date with new or modified information. Below is an example of adding and then deleting a "document" (or chunk) after the collection has already been created:

Key Points:

• chunks is our initial list of text chunks with their metadata.
• id is a unique identifier string created by combining document ID and chunk ID (e.g., "chunk_2_0").
• Why Unique IDs Matter: Each chunk needs a unique identifier so the vector database can reference it later for updates, deletions, or retrieval. By combining doc_id and chunk_id into a string like "chunk_2_0", we ensure each chunk has a distinct ID while maintaining its relationship to the source document.
• Adding and Deleting: The prepared data can be added or removed from the vector database using Java-compatible methods. By sending null as the embeddings, they are automatically calculated using the EmbeddingFunction of the collection.

By storing text chunks in a vector database, you've laid the foundation for faster, more semantically aware retrieval. You know how to create, update, and manage a vector database collection — crucial skills for any large-scale RAG system.

In the next lesson, you'll learn how to query the vector database to fetch the most relevant chunks and feed them into a language model. That's where the real magic of producing context-rich, accurate responses shines! For now, feel free to explore different embedding models or try adding and deleting a variety of chunks. When you're ready, proceed to the practice exercises to cement these concepts and further refine your RAG workflow.