Integrating Knowledge Bases

Welcome back to Putting Bedrock Models to Action with Strands Agents! In our previous lessons, you've successfully built intelligent agents and enhanced them with calculation and custom tools. Now, in this third unit, we're taking a significant leap forward by connecting your agents to Amazon Bedrock Knowledge Bases, unlocking the power of enterprise-scale information retrieval and context-aware responses.

This lesson transforms your AWS Technical Assistant from a tool-equipped agent into a comprehensive knowledge system capable of searching through vast document repositories and providing detailed, citation-backed responses. We'll explore the retrieve tool from strands_tools, master advanced retrieval parameters, and implement sophisticated search configurations that allow precise targeting of specific information within your knowledge bases.

By the end of this unit, your agent will seamlessly combine its existing tool capabilities with powerful document retrieval, enabling it to answer complex questions about your organization's documentation, policies, and technical specifications. This integration represents the foundation of modern Retrieval-Augmented Generation (RAG) systems, where AI reasoning meets curated knowledge repositories.

Knowledge Base integration revolutionizes agent capabilities by providing access to curated document collections that extend far beyond the training data of foundation models. When agents connect to knowledge bases, they gain the ability to retrieve relevant information from your organization's documentation, technical manuals, policy documents, and other structured knowledge sources in real time during conversations.

The retrieve tool operates through semantic similarity search, where your queries are converted into vector representations and matched against pre-indexed document embeddings stored in your knowledge base. This approach enables sophisticated information discovery that goes beyond simple keyword matching, finding conceptually relevant content even when exact terms don't appear in the source documents.

Note that we will be reusing the same Bedrock Knowledge Base we developed in the previous course.

Let's begin by configuring our agent with both familiar tools and the new knowledge base retrieval capability, building upon the foundational setup patterns you've mastered in previous lessons.

import os
from strands import Agent
from strands.models import BedrockModel
from strands_tools import calculator, retrieve

# Define the Bedrock model ID
model_id = "us.anthropic.claude-sonnet-5"

# Read guardrail info from environment variables
guardrail_id = os.getenv("GUARDRAIL_ID")
guardrail_version = os.getenv("GUARDRAIL_VERSION", "DRAFT")

# Read knowledge base and region from environment variables
KNOWLEDGE_BASE_ID = os.getenv("KNOWLEDGE_BASE_ID")
REGION = os.getenv("AWS_REGION")

As you recall from previous units, we're maintaining the same model configuration and guardrail setup patterns that ensure secure, compliant AI operations. The new additions here include the retrieve import from strands_tools and the environment variables for KNOWLEDGE_BASE_ID and REGION, which specify which knowledge base your agent should connect to and in which AWS region it's deployed.