Bedrock Knowledge Bases Introduction Part 1

In this lesson you’ll learn what Amazon Bedrock Knowledge Bases are and how they simplify building retrieval-augmented generation (RAG) applications. Instead of implementing ingestion, embeddings, vector storage, and retrieval yourself, Bedrock Knowledge Bases provide a managed pipeline that makes your application logic much simpler and more maintainable. We’ll cover:

The real-world problems RAG systems solve.
Why building a RAG pipeline from scratch is complex.
How Bedrock Knowledge Bases address those complexities.
The runtime request flow and a simple pseudocode example.
The configuration items required to create a knowledge base.

Why building a RAG system yourself is hard Implementing a full RAG stack involves many moving parts that become application-level responsibilities:

Challenge	What you must implement
Ingestion	Read, normalize, and keep many source documents up to date
Chunking / parsing	Split large documents into semantically coherent chunks before embedding
Embeddings	Call and manage an embeddings model for each chunk
Vector storage	Run or maintain a vector store with similarity search capabilities
Retrieval	Embed queries, search the vector store, and select relevant chunks
Prompt augmentation	Combine retrieved chunks with the user query to create a grounded prompt

Each of these components adds engineering, operational, and scaling overhead across teams and use cases. How Bedrock Knowledge Bases solve these problems Bedrock Knowledge Bases provide a managed, end-to-end RAG pipeline so you can focus on application logic rather than infrastructure. They cover:

Document ingestion and automatic chunking.
Conversion of chunks into embeddings with a configured embeddings model.
Managed vector storage for persistent vectors.
Similarity search and retrieval at query time.
Assembly of an augmented prompt that is passed to a foundation model for generation.

A slide titled "Solution: Bedrock Knowledge Base" showing six blue tiles labeled Document Ingestion, Chunking, Embeddings, Vector Storage, Retrieval, and Prompt Augmentation, each with a simple icon. The layout presents a visual workflow for building a knowledge base.

With Bedrock Knowledge Bases you simply configure the data source, select an embeddings model and a vector store (for example, Titan Text Embeddings with an S3-backed vector store), and Bedrock takes care of ingestion, embedding computation, vector persistence, and retrieval. How the request flow fits with your application A RAG system still requires pre-ingested vectors for documents. At query time your application triggers retrieval and generation. Bedrock exposes a RetrieveAndGenerate operation (via the AWS SDK or Runtime) that orchestrates these steps. High-level runtime flow:

Your application calls RetrieveAndGenerate with:
- The user prompt (question).
- The knowledge base identifier to use for retrieval.
- The foundation model name for final generation.
Bedrock Runtime sends the prompt to the configured Knowledge Base.
The Knowledge Base embeds the prompt using the aligned embeddings model and performs a vector similarity search against the configured vector store.
It selects the most relevant document chunks and constructs an augmented, grounded prompt.
The augmented prompt is returned to Bedrock Runtime, which forwards it to the specified foundation model (for example, meta.llama3-8b-instruct).
The model returns a grounded response that your application receives.

This workflow keeps application-side code concise while Bedrock handles embedding computation, retrieval logic, and prompt augmentation. Example (pseudocode)

# Example pseudocode showing a RetrieveAndGenerate call
response = retrieve_and_generate(
    input="How do I reset billing?",
    knowledgeBaseId="kb-123",
    model="meta.llama3-8b-instruct"
)
print(response.output)

Creating a knowledge base: required configuration When creating a knowledge base you must provide several configuration items. The table below outlines each item with a short description and an example.

Configuration item	Purpose	Example
Name	Friendly identifier for the knowledge base	`customer-support-kb`
`IAM role`	Grants the knowledge base permissions to access data or vector stores	See AWS - IAM
Data source	Where source documents live (e.g., S3, other connectors)	`s3://my-company-docs/`
Parsing strategy	How documents are parsed and chunked on ingestion	`semantic-split`
Embedding model	Model used to convert text chunks into vectors	`titan-text-embedding`
Vector store	Managed storage for vectors used at retrieval time	`bedrock-managed-vector-store`

Ensure the IAM role you provide follows least-privilege principles. Grant only the necessary permissions to access specific resources (for example, particular S3 buckets or encryption keys) so the knowledge base can ingest data securely. See AWS - IAM for more on roles and policies.

Quick comparison: RAG DIY vs. Bedrock Knowledge Bases

Area	DIY RAG	Bedrock Knowledge Bases
Ingestion	You implement and maintain ingestion pipelines	Managed ingestion and chunking
Embeddings	You operate embedding model calls	Managed embeddings via configured model
Vector store	You deploy or operate vector DBs	Managed vector storage with retrieval APIs
Retrieval logic	Application-level code for search & ranking	Built-in retrieval and prompt assembly
Prompt augmentation	You assemble grounded prompts	Automatic augmented prompt construction

Summary and next steps Amazon Bedrock Knowledge Bases automate the end-to-end RAG pipeline—ingestion, chunking, embeddings, vector storage, retrieval, and prompt augmentation—so you can build grounded question-answering and retrieval-enabled applications with less engineering overhead. The runtime RetrieveAndGenerate API lets you request grounded responses with a simple call. Next steps (hands-on):

Create a knowledge base and connect a data source (for example, an S3 bucket).
Choose an embeddings model and a vector store.
Ingest documents and test queries using RetrieveAndGenerate to observe retrieval and grounded generation in action.

Links and references

Introduction to Gen AI and LLMs

Introduction to Amazon Bedrock

Getting Started With Amazon Bedrock

Foundation Models in Amazon Bedrock

Basic Concepts of Prompt Engineering

Introduction to Text Generation With Amazon Bedrock

How Foundation Models Process Input

Extract Insights Using Prompt Engineering

Working With Different Task Types

Integrating Amazon Bedrock With Other AWS Services

Build a Marketing Email Generation Application

Introduction to Retrieval Augmentation With Bedrock Knowledge Bases

Best Practices and Optimization

Governance, Safety in Responsible AI

Security

Monitoring and Logging

Taking action with Bedrock Agents

Advanced Topics (Optional)

Final Project

Wrap Up

Bedrock Knowledge Bases Introduction Part 1

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