Retrieving Relevant Information with Similarity Search in Go
Retrieving Relevant Information with Similarity Search
Welcome back! In the previous lesson, we explored how to generate embeddings for document chunks using Go-compatible APIs. Today, we will build on that knowledge by diving into vector databases and how they enable the efficient retrieval of relevant information through similarity search.
Vector databases 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 use a custom in-memory vector store to understand the core concepts. While this implementation is designed for learning purposes, it follows idiomatic patterns that match production-ready vector stores like Weaviate, Pinecone, or Chroma—meaning you can easily swap the backend when you're ready to scale.
Preparing Documents with LangChain
Before we can perform a similarity search, we need to load and chunk our document using LangChain's document loaders and text splitters.
Here's how to do it in Go:
This code demonstrates how to load a document and split it into chunks using LangChain's built-in tools. The LoadAndSplit method combines loading and chunking in a single operation, making our code more concise and maintainable.
Creating Embeddings and Vector Store
With our document chunks ready, the next step is to set up our embedding model 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 a custom in-memory vector store that follows the same idiomatic patterns as production vector databases. This means the code you write here will be nearly identical when you switch to a real vector database backend.
Let's break down what's happening in this code:
- We initialize an OpenAI LLM client configured to use the
text-embedding-3-smallmodel. - We wrap the LLM in an
Embedder, which provides a consistent interface for generating embeddings. - We create our custom in-memory vector store, passing the embedder as a configuration option.
- We add all document chunks to the store using
AddDocuments, which automatically generates embeddings and stores them.
The memstore is a simple in-memory implementation perfect for learning and development. However, the API signatures (New, WithEmbedder, AddDocuments) follow the same patterns used by production vector stores in the LangChain ecosystem, making it trivial to swap backends later.
Performing Similarity Search
Now that we have our vector store populated with embedded document chunks, we can perform a similarity search to retrieve relevant documents. Similarity search finds document chunks whose embeddings are closest to a given query's embedding, allowing us to extract information that is semantically similar to the query.
Here's the complete example with similarity search:
This code demonstrates how to perform a similarity search and display the results. The SimilaritySearch method takes three parameters:
- A context for managing the request lifecycle
- The query string to search for
- The number of top results to return (k)
The method returns a slice of documents ranked by similarity to the query. Each document contains the PageContent field with the actual text chunk. Your exact results will vary slightly depending on chunking boundaries and embedding behavior, but the program will print the query followed by the top k most similar chunks:
If no chunks are similar enough (or if the store is empty), you may see:
Swapping Vector Store Backends
One of the key advantages of using idiomatic patterns is that switching from our in-memory store to a production vector database is straightforward. For example, if you wanted to use Weaviate instead, you would simply replace:
with:
The rest of your code—including AddDocuments and SimilaritySearch—remains unchanged because all vector stores in the LangChain ecosystem implement the same interface.
Summary and Next Steps
In this lesson, you learned how to create an in-memory vector store and perform similarity search to retrieve relevant information from documents using LangChain in Go. We built on your knowledge of document loading, splitting, and embedding to enable efficient document retrieval.
Key takeaways:
- Vector stores are specialized databases for high-dimensional vector data
- The in-memory store we used follows idiomatic patterns that match production databases
SimilaritySearchfinds semantically similar documents based on embedding distance- You can easily swap vector store backends thanks to consistent interface design
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!
