Vector Databases Explained: A Developer's Practical Guide

Traditional databases find rows where column A equals value B. Fast. Precise. And completely useless when a user searches for "that article about dogs being loyal companions" and the article only contains the words "canine" and "faithful friend."

Vector databases solve this. They search by meaning instead of matching by text. And if you are building any AI feature that involves search, recommendations, or content retrieval, you need to understand how they work.

Not the math. The practical reality.

What Embeddings Actually Are

Strip away the academic language and embeddings are coordinates. Points in space. But instead of X, Y, Z coordinates in 3D space, they are 1536 coordinates in 1536-dimensional space.

Each dimension captures some aspect of meaning. Not a specific, labeled aspect. A learned, abstract aspect. The model figured out during training that certain patterns in language correspond to certain dimensions.

The result: similar things end up near each other in this space. "Dog" is close to "puppy" and "canine." "JavaScript" is close to "TypeScript" and "programming." "Happy" is close to "joyful" and "delighted."

This is not keyword matching. This is meaning matching. The embedding for "affordable accommodation in Paris" will be close to the embedding for "cheap hotels near the Eiffel Tower" even though they share almost no words.

For developers, the mental model is: embeddings convert any content into a searchable coordinate. Finding similar content means finding nearby coordinates.

How Similarity Search Works

You have a database full of embedding vectors. A user submits a query. You convert the query into an embedding vector. Now you need to find the stored vectors closest to the query vector.

Distance metrics determine what "closest" means. Cosine similarity measures the angle between vectors. Euclidean distance measures the straight-line distance. Dot product measures a combination of direction and magnitude.

For text search, cosine similarity is usually the right choice. It measures directional similarity regardless of magnitude, which means a short document and a long document about the same topic get similar scores.

The naive approach checks the query vector against every stored vector. This works fine for thousands of vectors. For millions, it is catastrophically slow.

Indexing Strategies

This is where vector databases earn their keep. They build index structures that make similarity search fast at scale.

HNSW (Hierarchical Navigable Small World) is the most common indexing algorithm. It builds a graph structure where similar vectors are connected. Searching means navigating the graph from a random starting point toward the query vector, hopping between connected nodes.

HNSW is not exact. It is approximate. It might miss the absolute closest vector in favor of a very-close vector. For most applications, this trade-off is excellent. 99.5% accuracy at 100x the speed of exact search.

IVF (Inverted File Index) partitions the vector space into regions. At search time, only the most relevant regions are searched. Faster than HNSW for very large datasets. Less accurate for small datasets.

Flat indexes search every vector exactly. Perfect accuracy. Terrible speed at scale. Use them for benchmarking and for datasets under 100K vectors where speed is not critical.

The practical decision: start with HNSW. It works well for most applications at most scales. Switch to IVF or hybrid approaches only when benchmarks show you need to.

Choosing Your Vector Database

The landscape has options. Each with trade-offs.

Pinecone is fully managed. You do not run servers. You do not tune indexes. You do not manage backups. You send vectors, you query vectors, Pinecone handles everything else. The trade-off is cost and lock-in. Good for teams that want zero operational overhead.

Weaviate is open source with a managed option. Strong hybrid search that combines vector and keyword search in one query. Good schema support with typed properties on your vectors. The right choice when you need filtering and metadata alongside vector search.

Chroma is lightweight and developer-friendly. Runs in-process or as a standalone server. Perfect for prototyping and small-to-medium applications. The Python SDK is clean and intuitive. The trade-off is scale. Chroma is not built for billion-vector datasets.

Qdrant is Rust-based and performance-focused. Excellent filtering capabilities. Strong quantization support for reducing memory usage at scale. Good for teams that need high performance and are comfortable with self-hosting.

pgvector adds vector search to PostgreSQL. If you already use Postgres, this avoids adding another database to your stack. Performance is reasonable for datasets under a million vectors. Beyond that, dedicated vector databases outperform it.

Metadata and Filtering

Vectors alone are not enough. Real applications need to filter results by metadata. Show me similar documents, but only from the last 30 days. Show me similar products, but only in the user's price range. Show me similar articles, but only in English.

Every major vector database supports metadata filtering. The implementation details matter. Some filter before the vector search (pre-filtering), which can miss relevant results. Some filter after (post-filtering), which can return too few results. The best implementations integrate filtering into the search algorithm.

Store metadata that you will need to filter on. Timestamps, categories, user IDs, language codes, status flags. Design your metadata schema like you would design a database schema. Think about what queries you will need to run.

Chunking Strategy Matters More Than You Think

Before you can search your content, you need to embed it. Before you can embed it, you need to chunk it. How you chunk directly determines search quality.

Too large and chunks contain multiple topics. A search for Topic A matches a chunk that is mostly about Topic B but mentions Topic A in passing. Irrelevant results.

Too small and chunks lose context. A sentence about "the framework" does not embed well because "the framework" could be anything. The surrounding context that gives it meaning is in a different chunk.

The sweet spot for most text content: 200-500 tokens per chunk with 50-100 tokens of overlap between consecutive chunks. This preserves context while keeping topics focused.

For structured content like documentation, chunk by section. For conversations, chunk by turn or topic change. For code, chunk by function or class. Match your chunking strategy to your content structure.

Building Your First Vector Search

Start with a small dataset. A few hundred documents. Embed them with OpenAI's text-embedding-3-small or a comparable model. Store them in Chroma or pgvector. Build a simple search endpoint.

Test with real queries. Not carefully constructed queries that you know will work. Real queries from real users. The messy, incomplete, ambiguous queries that people actually type.

Measure search quality. Are the top results relevant? Are important results missing? Do irrelevant results rank too high?

Iterate on your chunking strategy, your embedding model, and your similarity threshold based on real results. This is not a set-it-and-forget-it system. It is a search engine that needs tuning.

The fundamentals are simple. The details are where quality lives.