What is a Vector Database? How Vector Search Powers RAG and GenAI

A vector database is a database that stores data as vectors, which are long lists of numbers that capture the meaning of text, images, or audio. It lets you search by meaning. Ask for “cheap flights to the beach” and it can find a document titled “budget trips to coastal cities,” even though the two share almost no words. This kind of search, called vector search or semantic search, is what lets AI chatbots answer questions using your company’s own documents.

If you are asking what is a vector database because your team is building a generative AI app, this guide covers the basics in plain language. You will learn how vector search works, how it powers retrieval-augmented generation (RAG), and which AWS services you can use to run it.

What Is a Vector Database? 

A vector database is a system built to store, index, and search vector embeddings. An embedding is a list of numbers, often hundreds or thousands long, that a machine learning model creates to capture the meaning of a piece of data. AWS explains embeddings as numerical representations that help AI systems understand relationships between real-world objects.

Keyword search matches strings. A shopper who types sofa can miss listings that say couch, and a query about staying focused at home can miss an article on remote work productivity. Embeddings close that gap because the model has learned that these phrases live in the same neighborhood of meaning.

Similar content gets similar vectors, so related items land close together in the vector space. A vector database is tuned for finding those nearby points across millions or billions of stored vectors. Almost any content type can become an embedding:

• Text passages, such as support articles, contracts, and chat transcripts
• Product photos and design images
• Audio clips from calls or podcasts
• Video scenes that a text query can find later
• Browsing histories that show which items shoppers view together

Model choice sets the vector size. Amazon Titan Text Embeddings V2, available through Amazon Bedrock, accepts up to 8,192 tokens per input and returns a 1,024-dimension vector by default, with 512 and 256 as smaller options. A model with more dimensions can hold finer detail, and it also needs more storage and compute per search.

>>> Read more: Why Businesses Need Amazon Bedrock Consulting in Vietnam

How Does a Vector Database Work?

A vector database works in four main steps. It takes in your content, organizes it for fast lookup, compares new queries against what it holds, and then filters and ranks the results.

1. Ingesting and Chunking Content

Long documents are split into smaller pieces called chunks, often a few hundred words each. Smaller chunks give more precise search results, because each one covers a single idea. Each chunk is turned into an embedding and stored along with the original text and useful labels, such as the file name, date, or department. These labels are called metadata.

2. Indexing for Speed

Comparing a query against every single vector would be far too slow at scale. So vector databases build a special index that lets them skip most of the data. The most popular method is HNSW (Hierarchical Navigable Small World), described in this research paper. It connects vectors in layers, a bit like a network of highways, main roads, and side streets, so the search can jump quickly to the right neighborhood and then look closely.

3. Similarity Search

When a query arrives, the database converts it into an embedding and finds the stored vectors closest to it. It measures closeness with math such as cosine similarity, which checks how closely two vectors point in the same direction. This is called approximate nearest neighbor (ANN) search. It trades a tiny amount of accuracy for a huge gain in speed, returning results in milliseconds.

4. Filtering and Ranking

Good vector databases let you combine meaning with rules. You can ask for the most relevant documents that are also from the HR department and updated after January 2026. Many also support hybrid search, which blends vector search with classic keyword search. Hybrid search helps with things like product codes and names, where exact words matter.

>>> Read more: The Data Journey: From Raw Data to Insights

What Are Vector Databases Used For?

Vector databases show up anywhere people need to find things by meaning. These are the most common uses.

AI Chatbots and Assistants

Customer support bots and internal help desks use RAG to answer questions from manuals, policies, and past tickets. This is the most popular use case today.

Semantic Search

Website and intranet search improves when it understands intent. A shopper searching for “warm jacket for skiing” can find insulated waterproof coats, even when those exact words never appear.

Recommendations

Streaming services and online stores turn products, songs, or articles into embeddings. The system then suggests items close to what a customer already liked.

Image, Audio, and Video Search

With multimodal embeddings, you can search a photo library by describing a scene, or find products that look like an uploaded picture.

Memory for AI Agents

AI agents that carry out multi-step tasks need to remember past conversations and results. A vector database gives them long-term memory they can search when they need context.

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Vector Database vs Traditional Database

A relational database answers exact questions. A vector database answers questions about closeness. The table shows how the two differ in daily use.

Aspect	Traditional Database	Vector Database
Main data	Rows, columns, and documents	Embeddings plus metadata
Query style	SQL filters and keyword match	Nearest-neighbor search on meaning
Match type	Exact or lexical	Approximate and semantic
Index	B-tree and inverted index	HNSW, IVF, and quantized indexes
Typical result	Records that satisfy a condition	Items ranked by similarity score
Best for	Transactions, reporting, and lookups	Semantic search, RAG, and recommendations

Keep a traditional database for anything that needs exact answers, such as invoices, inventory counts, and user accounts. Add vector search when users ask fuzzy questions, when the content is unstructured, or when an LLM needs to fetch context. Many teams keep both in one system by running pgvector inside PostgreSQL.

The two models also work together in a single query. Hybrid search runs a keyword query and a vector query together, then blends the scores. AWS describes hybrid search in Amazon OpenSearch Service that mixes lexical, k-NN, and neural queries, and it reports latency gains of up to four times after 2024 optimizations. Hybrid scoring helps with product codes, names, and acronyms that embeddings sometimes blur.

Vector Search Algorithms: HNSW, IVF, and Exact Search

Exact search compares a query against every vector and returns perfect results, and it gets slow as collections grow. Approximate nearest neighbor (ANN) algorithms trade a small amount of accuracy for a large gain in speed.

Recall is the share of true nearest neighbors that an approximate search actually returns.

The pgvector extension runs exact search by default. Adding an approximate index changes that, so teams track recall by comparing approximate results with exact ones on a sample of real queries.

HNSW Graphs

Hierarchical Navigable Small World (HNSW) builds a layered graph where each point links to close neighbors. Search starts in a sparse top layer and moves down toward the query, which gives logarithmic complexity scaling according to the original paper. In pgvector, HNSW offers a better speed and recall balance than IVFFlat at the price of slower builds and higher memory use.

IVF and Quantization

Inverted File (IVF) indexes divide vectors into clusters and search only the clusters nearest the query. They build faster and use less memory than HNSW, with lower query performance. Quantization compresses vectors to cut memory. The open-source FAISS library from Meta supports compact quantization codes, and OpenSearch Service supports scalar and product quantization. OpenSearch also accepts vectors of up to 16,000 dimensions, which covers most embedding models.

Index settings shift recall, latency, and cost, so test them on your own data. A short proof of concept lets you measure all three before you commit to an architecture.

>>> Read more: How to Build a Generative AI PoC on AWS with Renova Cloud

How a Vector Database Powers RAG and GenAI

Large language models (LLMs) know what they saw during training. They know nothing about your private documents or last week’s policy change. A vector database gives them a searchable memory. The pattern also lets you refresh knowledge without retraining a model, since new documents only need embedding and indexing.

Grounding LLM Answers With RAG

RAG retrieves relevant passages first and hands them to the model along with the question. The 2020 paper that introduced RAG paired a language model with a dense vector index of Wikipedia. AWS Prescriptive Guidance describes the same pattern for enterprise data, and the flow looks like this:

1. Ingest once by chunking the documents, creating embeddings, and storing them in the vector database.
2. Embed each incoming question with the same model.
3. Retrieve the closest chunks with a vector search.
4. Add those chunks to the prompt and let the LLM write the answer.

Amazon Bedrock Knowledge Bases can run that whole pipeline as a managed service, from ingestion to embeddings to vector storage. Answers come back grounded in your own sources, and the app can show which documents supported them.

Semantic Search

Employees and customers type questions in everyday language. Vector search matches intent, so a query about resetting a login finds a guide titled Account Recovery Steps despite little keyword overlap. Adding hybrid scoring keeps exact terms such as model numbers in play.

Recommendations and Similar-Item Discovery

Store an embedding for each product, article, or track. The nearest neighbors of an item a user liked become the recommendations. The OpenSearch k-NN documentation lists recommendations, image recognition, and fraud detection among its use cases.

Multimodal Search

Images, audio, and video can share a vector space with text. Amazon Bedrock Knowledge Bases supports multimodal content, so a text query can return a specific video moment or audio segment with timestamp references.

Memory for AI Agents

AI agents need context that outlives one conversation. A vector store keeps past tickets, documents, and outcomes retrievable by meaning. An agent handling a billing complaint can pull the three most similar earlier cases before it replies. Amazon S3 Vectors targets this use with affordable, large-scale storage for agent memory.

>>> Read more: How to Implement AI Agents on AWS in 2026

What Vector Database Options Does AWS Offer?

AWS offers several ways to store and search vectors, from low-cost storage to high-speed search engines. Many teams start with Amazon Bedrock Knowledge Bases, a fully managed RAG service that connects to sources like Amazon S3, SharePoint, and Confluence and handles chunking, embeddings, and vector storage for you. Behind the scenes, you still choose where the vectors live.

AWS option	What it is	Best for	Speed
Amazon S3 Vectors	Vector storage built into S3	Large datasets, cost-sensitive RAG, agent memory	Around 100 ms for frequent queries, under 1 second otherwise
Amazon OpenSearch Service	Search engine with a vector engine	Fast, busy apps and hybrid search	Very fast, in the low milliseconds
Amazon Aurora PostgreSQL with pgvector	Relational database with vector support	Teams already on PostgreSQL	Fast for small to medium datasets
Amazon MemoryDB	In-memory database with vector search	Real-time uses that need the lowest delay	Single-digit milliseconds

Amazon S3 Vectors

Amazon S3 Vectors became generally available in December 2025, and AWS says it can cut the cost of uploading, storing, and querying vectors by up to 90% compared with specialized vector databases. Each index can hold up to two billion vectors, and each vector bucket can hold up to 10,000 indexes. Frequent queries return in about 100 milliseconds. There are no servers to manage. The S3 Vectors launch post covers the details.

Amazon OpenSearch Service

OpenSearch is the pick when speed and search quality matter most, such as a busy customer-facing chatbot. It supports hybrid search, filters, and several indexing methods. S3 Vectors also connects with OpenSearch, so you can keep most vectors in low-cost S3 storage and move only the busiest data into OpenSearch.

Amazon Aurora PostgreSQL with pgvector

If your app already runs on PostgreSQL, pgvector lets you add vector search to the same database. You can combine vector search with regular SQL queries and keep everything in one place. This is a simple, practical choice for small and medium RAG projects.

>>> Read more: Generative AI on AWS

How to Choose the Right Vector Database

Six questions narrow the field quickly.

Data Volume and Growth

Count vectors after chunking, since one document often becomes dozens of them. A few hundred thousand vectors fit comfortably in PostgreSQL with pgvector. Hundreds of millions point toward a distributed engine or an object-storage index.

Query Latency

Chat assistants and recommendation widgets need answers in tens of milliseconds. Batch analysis and archive search tolerate longer waits. MemoryDB and OpenSearch fit the first group, and S3 Vectors suits workloads that accept sub-second responses.

Metadata Filtering and Hybrid Search

Real queries add conditions such as language, department, or date. Confirm that filters run during the vector search, since filtering afterward can leave too few results. Check for hybrid keyword and vector scoring as well.

Your Existing Stack

A team fluent in PostgreSQL can start with pgvector on Aurora and skip a new system. A team that already runs OpenSearch for logs or site search can add vector fields to that cluster.

Security and Access Control

Chunks and embeddings come from your documents, so apply the same encryption, IAM policies, and per-user permissions that protect the source files. A RAG app should filter retrieved chunks by the asker’s access rights before the model sees them.

Workload Type and Cost

Search, RAG, and agents create different query patterns. Agents can call retrieval many times per task, and each call adds cost. Estimate daily queries, then price storage and compute at that volume.

Whatever you shortlist, run a proof of concept with a few hundred real questions from your users. Measure recall, latency at your expected load, and monthly cost side by side. Those three numbers settle most debates quickly.

>>> Read more: When to Use an AI Agent: A Practical Guide for Businesses

FAQs

What is a vector database in simple terms?

A vector database is a system that stores the meaning of content as numbers and finds items with similar meaning. It powers AI search, chatbots, and recommendations.

What is the difference between vector search and keyword search?

Keyword search matches the exact words you type. Vector search matches the meaning behind them, so it can find relevant results that use different words.

Do I need a vector database for RAG?

Most RAG apps use one, because the vector database is what finds the right documents to send to the language model. On AWS, Bedrock Knowledge Bases can set one up for you.

Is Amazon S3 Vectors a vector database?

Amazon S3 Vectors is vector storage with built-in search, built into S3. It works like a low-cost vector database and fits large datasets where sub-second responses are fast enough.

Can PostgreSQL be used as a vector database?

Yes. With the pgvector extension, PostgreSQL, including Amazon Aurora PostgreSQL, can store embeddings and run similarity searches alongside regular SQL queries.

How much does a vector database cost on AWS?

It depends on the service and usage. S3 Vectors charges for storage, uploads, and queries with no servers. OpenSearch and Aurora charge for the compute and storage you run.

Build Vector Search on AWS With Renova Cloud

Renova Cloud is an AWS Premier Tier Services Partner and the first local partner in Vietnam to earn the AWS DevOps Competency. 

Our team helps businesses across Southeast Asia design and launch generative AI apps on AWS, from choosing the right vector store to building RAG pipelines with Amazon Bedrock, S3 Vectors, and OpenSearch. We focus on answers your users can trust, strong data security, and costs that stay predictable as you grow. 

If you are planning a knowledge assistant, semantic search, or an AI agent, contact the Renova Cloud team and book a consultation for your use case.