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Retrieval Augmented Generation (RAG) depends on one crucial step finding the right information at the right time. This requires fast accurate search across billions of dense embeddings. Traditional databases cannot do this. So a new class of storage systems emerged. Vector databases..

If embeddings give RAG understanding, vector databases give it memory. They enable high speed similarity search, manage billions of vectors efficiently and support indexing structures designed for modern AI workloads.

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This article explores what vector databases are, how they differ from traditional systems, how popular vector DBs compare, and what indexing really means in practice.

What Is a Vector Database?

A vector database is a specialized storage engine designed to store embeddings, numerical arrays representing text, images, audio, code, or any other content and retrieve them based on semantic similarity.

Traditional databases store structured rows and retrieve data by matching exact filters, WHERE name = ‘Alice’ AND age > 20

Vector databases don’t look for exact matches. They look for similarity. Find the top 10 vectors closest to this new vector query.

This shift from exact search to similarity search is what makes vector databases essential for RAG.

How Vector Databases Differ from Traditional Databases

Traditional Databases

• Designed for exact match queries
• Use B-trees, hash maps, indexes
• Optimized for structured data
• Cannot efficiently handle high-dimensional vectors
• Query example: “Find all orders from last week”

Vector Databases

• Designed for approximate similarity search
• Use HNSW, IVF, PQ, and ANN algorithms
• Optimized for embeddings (dense 256–4096 dimensions)
• Support millions to billions of vectors
• Query example: “Find documents semantically similar to this question”

In RAG, similarity search replaces keyword matching, enabling responses based on meaning rather than literal text.

What Is ANN Search?

Before we talk about ANN, we need to understand an important idea behind vector retrieval,

Nearest Neighbors

When text is converted into embeddings, every piece of text becomes a point in a high dimensional space. To answer a user query, the system must find the vectors that are closest to the query vector, called nearest neighbors.

Closeness means semantic similarity.

• If your query is “best places to visit in Sri Lanka”, the nearest neighbors might be chunks about tourism, Colombo, Jaffna, Sigiriya, etc.
• If your query is “diabetes symptoms”, the nearest neighbors should be medical chunks about health.

So the job of retrieval is simple in theory, Find the vectors closest to the query vector. But in practice, this becomes extremely difficult…

Why Nearest Neighbor Search Is Hard

Vector databases often contain millions or billions of embeddings. Each embedding may have hundreds to thousands of dimensions (e.g., 768D, 1024D).

To find the nearest neighbors exactly, we’d have to,

• Compare your query vector to every single vector,
• Across hundreds of dimensions,
• Millions of times.

This brute force approach is extremely slow, far too slow for real-time AI or RAG systems.

Enter ANN, Approximate Nearest Neighbor Search

ANN solves the performance problem by not searching the entire database. Instead, it uses intelligent shortcuts to inspect only a tiny fraction of the vectors, often less than 1% and still returns results that are extremely close to the true nearest neighbors.

When a query comes in, the algorithm first identifies the closest cluster or graph node instead of scanning the entire database. It then inspects only the vectors inside that region, drastically reducing the number of comparisons. Finally, it returns vectors that are very close to the true nearest neighbors.

This process, narrowing the search, focusing only on promising regions, and skipping irrelevant vectors, is what allows ANN to achieve millisecond-level search speeds while maintaining almost the same accuracy as a full search.

ANN is built on one simple idea

Trade a tiny bit of accuracy for a massive speed boost.

Exact search = perfect but slow ANN search = almost perfect but insanely fast

This speed difference is what makes ANN essential.

Understanding Vector Indexing

When you store millions of embeddings, you can’t just dump them into a giant list and hope to find similar vectors quickly. If you did, every search would require scanning every vector, which is painfully slow.

Vector indexing is the technique used to organize these vectors so that the system can find nearest neighbors fast without looking at everything.

Think of indexing like:

• A smart library system that lets you find the right book instantly
• A map that groups similar locations together
• A shortcut for the vector database to skip irrelevant regions

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Vector Indexing — Image by Author

Vector indexing is the technique used to organize vectors so that ANN search becomes efficient. The index determines how vectors are stored, how search is performed, and how accurate the results are.

We’ll look at the three major indexing approaches used in modern vector databases.

Flat Search

This is the simplest and most exact indexing approach. Every query vector is compared against every stored vector. While it guarantees perfect accuracy, it becomes impractical beyond a few million vectors. Flat search is still used in situations where precision is more important than speed, or where the dataset is small.

IVF Inverted File Indexing

IVF improves search speed by grouping vectors into clusters. When a query comes in, the system identifies the closest cluster instead of scanning the entire set. Only vectors within the nearest clusters are compared in detail.

This is an enormous improvement over flat search, and it works well when the dataset is large but the accuracy requirements allow slight approximation. IVF requires tuning too many clusters and accuracy drops, too few and speed suffers. But when tuned properly, it gives an excellent balance.

HNSW Hierarchical Navigable Small World Graphs

HNSW is widely considered the gold standard for vector search today. Instead of clusters, it builds a multi layer graph where each vector is connected to its nearest neighbors. Searching becomes a matter of walking the graph from the top-level downwards, approaching closer and closer nodes until you land near the best matches.

HNSW offers remarkable accuracy, low latency, and supports fast insertions, which is essential for dynamic RAG systems that continuously ingest new content. The drawback is higher memory usage and longer index-building times but most modern vector DBs optimize around this.

The Speed Accuracy Trade-Off

Every vector search system must navigate the tension between speed and accuracy. If your use case demands exactness such as scientific research, compliance, or medical search, you would tune indexes for high recall and allow slightly slower responses.

On the other hand, if your use case requires instantaneous responses, such as conversational RAG or real time recommendations, speed becomes the priority, and you accept a lower recall for faster results.

This balance is not theoretical, it is a practical engineering decision in every RAG deployment.

Exploring the Major Vector Databases

Modern RAG systems rely on specialized vector databases rather than building search infrastructure from scratch. Let’s examine the major players in the space and how they differ in philosophy and design.

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Vector Databases — Image by Author

FAISS: The Research Powerhouse

FAISS, developed by Meta AI, is the engine behind much of the academic work on similarity search. It offers exceptional performance, supports nearly every indexing algorithm, and is highly customizable.

What it lacks is the ecosystem of a full database, FAISS is a library, not a service. We must handle storage, replication, sharding, and metadata ourself. We need to decide where to store our vectors, make copies for fault tolerance (replication), split large datasets across machines (sharding), and keep track of extra information like IDs or tags (metadata). Essentially, FAISS gives us the engine, but we build the rest of the car.

This makes FAISS ideal for research labs, experimentation, or building a custom in-house solution.

Pinecone: The Enterprise-Grade Managed Solution

Pinecone is the most mature cloud-based vector database. It hides all the complexities of indexing and scaling behind a simple API. For teams that need reliable, real-time vector search without operational headaches, Pinecone is often the default choice.

It integrates naturally with LangChain, LlamaIndex, OpenAI, Bedrock, and most major RAG frameworks. The trade-off, as with most managed services, is cost and less control over low-level behavior.

Chroma: The Developer’s Best Friend

Chroma exploded in popularity due to its simplicity. It is open-source, lightweight, easy to embed into applications, and perfect for prototypes or small scale RAG deployments. While it lacks the advanced scaling capabilities of Pinecone, its developer experience is excellent. For personal projects or mid-sized apps, Chroma is often all we need.

Here are some more,

Weaviate: Modular, Versatile, and Hybrid Search Capable

Weaviate combines the features of a vector database with rich metadata search and modular ML components. It excels at hybrid search mixing BM25 keyword search with vector similarity which improves retrieval in noisy or mixed datasets. It is more complex to deploy but extremely powerful for advanced, metadata-rich use cases.

Milvus: The Heavyweight for Large-Scale AI Systems

Milvus is designed for scale. It supports billions of vectors, distributed deployments, and handles high-throughput ingestion making it a top choice for enterprise applications, especially in recommendation systems and large RAG deployments. Its architecture is more complex but reflects the needs of organizations that require massive, fault-tolerant vector search.

Qdrant: The Rust-Based Performance Engine

Qdrant is built in Rust and optimized for performance, especially around metadata filtering and real-time inserts. It has gained popularity for being open-source, fast, and intuitive to work with. Its balance between performance and ease-of-use makes it highly appealing to teams building high-speed RAG systems that rely heavily on metadata during search.

Choosing the Right Vector Database

There is no universal best vector database. Your choice depends on scale, performance requirements, infrastructure preferences, and whether you want to manage everything yourself or rely on a managed service.

• For prototypes, Chroma is usually perfect.
• For enterprise cloud deployments, Pinecone is the smoother choice.
• For massive self-hosted architectures, Milvus or Weaviate often prevail.
• If performance and metadata filtering matter, Qdrant is hard to beat.
• For research or highly custom implementations, FAISS provides unmatched control.

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Final Thoughts

Vector databases are not just another technological trend. They represent a fundamental shift in how we store and retrieve information. In a world where meaning matters more than keywords, vector databases enable machines to search the way humans think.

They are the silent engine behind every RAG system. They determine how fast retrieval can happen, how accurate results are, and how well a model can reason with context. As AI systems continue to expand, vector databases will become as essential as relational databases were for the internet era.