Revolutionize Your Applications with Amazon Bedrock Agents and Web Search APIs
Amazon Bedrock Agents offer developers the ability to build and configure autonomous agents in their applications. These agents help users complete actions based on organizational data and user input, orchestrating interactions between foundation models (FMs), data sources, software applications, and user conversations.
By leveraging the power of large language models (LLMs), Amazon Bedrock agents can perform complex reasoning and action generation inspired by the ReAct paradigm. This approach combines reasoning traces and task-specific actions in an interleaved manner.
Amazon Bedrock agents use LLMs to break down tasks, interact dynamically with users, run actions through API calls, and augment knowledge using Amazon Bedrock Knowledge Bases. The ReAct approach enables agents to generate reasoning traces and actions while seamlessly integrating with company systems through action groups. By offering accelerated development, simplified infrastructure, enhanced capabilities through chain-of-thought (CoT) prompting, and improved accuracy, Amazon Bedrock Agents allow developers to rapidly build sophisticated AI solutions that combine the power of LLMs with custom actions and knowledge bases, all without managing underlying complexity.
Benefits of integrating a web search API with Amazon Bedrock Agents
Let’s explore how this integration can revolutionize your chatbot experience:
- Seamless in-chat web search – By incorporating web search APIs into your Amazon Bedrock agents, you can empower your chatbot to perform real-time web searches without forcing users to leave the chat interface. This keeps users engaged within your application, improving overall user experience and retention.
- Dynamic information retrieval – Amazon Bedrock agents can use web search APIs to fetch up-to-date information on a wide range of topics. This ensures that your chatbot provides the most current and relevant responses, enhancing its utility and user trust.
- Contextual responses – Amazon Bedrock agents use CoT prompting, enabling FMs to plan and run actions dynamically. Through this approach, agents can analyze user queries and determine when a web search is necessary or gather more information from the user to complete the task. This allows your chatbot to blend information from APIs, knowledge bases, and up-to-date web-sourced content, creating a more natural and informative conversation flow. With these capabilities, agents can provide responses that are better tailored to the user’s needs and the current context of the interaction.
- Enhanced problem solving – By integrating web search APIs, your Amazon Bedrock agent can tackle a broader range of user inquiries. Whether it’s troubleshooting a technical issue or providing industry insights, your chatbot becomes a more versatile and valuable resource for users.
- Minimal setup, maximum impact – Amazon Bedrock agents simplify the process of adding web search functionality to your chatbot. With just a few configuration steps, you can dramatically expand your chatbot’s knowledge base and capabilities, all while maintaining a streamlined UI.
- Infrastructure as code – You can use AWS CloudFormation or the AWS Cloud Development Kit (AWS CDK) to deploy and manage Amazon Bedrock agents.
By addressing the customer challenge of expanding chatbot functionality without complicating the user experience, the combination of web search APIs and Amazon Bedrock agents offers a compelling solution. This integration allows businesses to create more capable, informative, and user-friendly chatbots that keep users engaged and satisfied within a single interface.
Solution Overview
This solution uses Amazon Bedrock Agents with a web search capability that integrates external search APIs (SerpAPI and Tavily AI) with the agent. The architecture consists of the following key components:
- An Amazon Bedrock agent orchestrates the interaction between the user and search APIs, handling the chat sessions and optionally long-term memory
- An AWS Lambda function implements the logic for calling external search APIs and processing results
- External search APIs (SerpAPI and Tavily AI) provide web search capabilities
- Amazon Bedrock FMs generate natural language responses based on search results
- AWS Secrets Manager securely stores API keys for external services
Prerequisites
Make sure you have the following prerequisites:
Configure the Web Search APIs
Both SERPER (SerpAPI) and Tavily AI provide web search APIs that can be integrated with Amazon Bedrock agents by calling their REST-based API endpoints from a Lambda function. However, they have some key differences that can influence when you would use each one:
- SerpAPI provides access to multiple search engines, including Google, Bing, Yahoo, and others. It offers granular control over search parameters and result types.
- Tavily AI is specifically designed for AI agents and LLMs, focusing on delivering relevant and factual results with customization options.
Ultimately, the choice between SerpAPI and Tavily AI depends on your specific research requirements, the level of control you need over search parameters, and whether you prioritize general search engine capabilities or AI-optimized results.
For both APIs, API keys are required and are available from Serper and Tavily.
Now that the APIs are configured, you can start building the web search Amazon Bedrock agent.