Welcome back! In the previous lesson, we explored how to generate embeddings for document chunks using OpenAI and LangChain. Today, we will build on that knowledge by diving into vector stores and how they enable the efficient retrieval of relevant information through similarity search.

Vector stores are specialized storage systems designed to handle high-dimensional vector data, such as the embeddings we generated in the last lesson. They are crucial for performing similarity searches, which allow us to find document chunks that are semantically similar to a given query. In this lesson, we will focus on using InMemoryEmbeddingStore, a simple yet powerful tool provided by LangChain4j, to create a local vector storage. This will enable us to efficiently store and search through our embeddings, paving the way for advanced document retrieval tasks.

Before we can perform a similarity search, we need to prepare our document and initialize our embedding model.

Here's a quick recap of how to do it:

With our document chunks ready and embedding model initialized, the next step is to generate embeddings and create a vector store. As you learned in the previous lesson, embeddings are numerical representations of text that capture semantic meaning.

We'll use InMemoryEmbeddingStore to create a vector store. This is a simple in-memory storage solution perfect for development and testing:

The InMemoryEmbeddingStore stores vectors in RAM and uses simple linear search for similarity comparisons. While this is fast and convenient for small datasets, it is not optimized for scale. In production systems handling thousands or millions of embeddings, you would typically switch to a more efficient vector database that uses Approximate Nearest Neighbor (ANN) search for performance.

Now that we have our vector store, we can perform a similarity search to retrieve relevant documents:

It’s also important to periodically inspect retrieved chunks to ensure the results make contextual sense for your queries. Embedding-based search can sometimes return results that are mathematically similar but not contextually relevant. Regular inspection helps you refine your chunking strategy and adjust embedding settings for better retrieval quality.

In this lesson, you learned how to create a local vector storage with InMemoryEmbeddingStore and perform a similarity search to retrieve relevant information from documents. We built on your knowledge of document loading, splitting, and embedding to enable efficient document retrieval.

As you move on to the practice exercises, I encourage you to experiment with different documents and queries to solidify your understanding. This hands-on practice will prepare you for the next unit, where we will continue to build on these skills. Keep up the great work, and I look forward to seeing you in the next lesson!