Have you ever asked a support chatbot a simple question and received a completely irrelevant answer?

It usually happens because traditional search systems rely heavily on keyword matching.

If your question doesn’t contain the exact words expected by the system, it may fail to find the right information, even when the answer already exists.

For instance, “How do I reset my password?” and “What’s the process for resetting my account?” mean the exact same thing, but a basic search engine might treat them differently.

I am going to fix that, we build a smart, semantic FAQ assistant using Retrieval-Augmented Generation (RAG). By combining Amazon S3, S3 Vector Buckets, and Amazon Bedrock Knowledge Bases, we will create a system that actually understands natural language.

User Question
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Amazon Bedrock Knowledge Base
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       v
Semantic Vector Search
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Amazon S3 Vector Bucket
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Embeddings (Titan V2)
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FAQ Documents in Amazon S3

The Scenario:

Imagine you’re working for a global customer support platform. Your users are asking questions like:

• “How do I reset my password?”
• “Can you explain your refund policy?”
• “What are the rules for getting a refund?”

Our mission is to move away from rigid keyword matching and build a vector-based search that groups these questions by their true intent and retrieves the correct, structured FAQ answers.

Your 4 Step Simple

Step 1: Set Up Your Data Source (S3 Bucket)

First, we need a place to store your raw FAQ documents.

1. Create a standard, general-purpose S3 bucket.
2. Name it data-bucket-faq-prem123 with
3. Upload the provided FAQ dataset into this bucket in its extracted format.

Step 2: Create Your Vector Store (S3 Vector Bucket & Index)

Now, let’s build the “brain” of the operation where the mathematical representations (embeddings) of your text will live.

1. Create an Amazon S3 Vector Bucket and name it exactly: faq-vector-bucket.
2. Inside that bucket, create a Vector Index named exactly: faq-vector-index.
3. Crucial detail: Set the embedding dimension to 1024.

Step 3: Wire it Up in Amazon Bedrock

It’s time to connect your data to your vector store using Amazon Bedrock Knowledge Bases.

1. Create a new self-managed Knowledge Base and name it FAQKnowledgeBase.
2. Assign the pre-created AmazonBedrock_KB_Role to it.
3. Point the Data Source to your S3 bucket data-bucket-faq-prem123.
4. Point the Vector Store to your new index (faq-vector-index).
5. Choose Amazon Titan Text Embeddings V2 as your embedding model, and ensure the dimension is set to 1024.

Step 4: Sync and Index Your Data

The plumbing is connected, but the knowledge base is empty. Let’s feed it.

1. Start a Sync (Ingestion) Job on your FAQKnowledgeBase.
2. Grab a coffee or stretch your legs,Bedrock will read your S3 files, chunk them, pass them through the Titan model to create 1024-dimensional embeddings, and store them in your S3 Vector Index.
3. Wait until the job status shows as Complete.

Testing Assistant

Now for the fun part. Let’s see if your RAG system actually understands human intent. Head over to the Amazon Bedrock Console and open up the playground to test your FAQKnowledgeBase.

Try asking it these natural language questions:

• “How do I reset my password?”
• “What is your refund policy?”
• “How can I contact support?”

How to test: You have two ways to play with this:

1. Standard Retrieval Only: This will just show you the raw chunks of text the system pulled from your S3 bucket. It’s great for debugging to ensure the right FAQ document was found.
2. Standard Retrieval with Answer Generation: This is the full RAG experience. It retrieves the right text chunks and hands them to a Large Language Model to write a friendly, conversational answer. (If you choose this, select Nova Pro as your foundation model).

What does success look like? If you ask about resetting a password, the system shouldn’t just look for the word “reset”. It should pull the specific FAQ entry detailing the password reset process and, if using generation, give you a clear, accurate summary based only on your uploaded documents.

If your assistant is consistently pulling the right context and generating accurate answers, congratulations! You’ve successfully built a serverless, semantic search engine using Amazon Bedrock and S3 Vector Buckets.

Stuck on a step? Don’t sweat it, RAG and vector databases are complex topics.

If this challenge felt a bit too steep, check out some of the guided, step by step labs to build up your muscle memory, and then come back and crush this challenge!

Building a Serverless RAG Solution Assistant with Amazon S3 Vector Buckets was originally published in NextGenAI on Medium, where people are continuing the conversation by highlighting and responding to this story.