Health Resource Assistant
Health Resource Assistant: Building an AI Agent for Community Health Information
Access to accurate health information is essential for communities, particularly for underserved populations who may not know where to turn for services like free flu shots, Medicaid providers, mental health resources, or emergency services. The Health Resource Assistant project demonstrates how AI agents can provide reliable, accessible health information through natural language interaction with a curated knowledge base.
The notebook for this project can be found at Health Resource Assistant on GitHub
The Challenge: Accessible Health Information Retrieval
Community health resources are often scattered across multiple sources, making it difficult for individuals to find the specific information they need. The Health Resource Assistant addresses several key challenges:
- Providing quick access to categorized health resource information
- Enabling natural language queries instead of rigid database searches
- Maintaining accurate, up-to-date health information through a structured knowledge base
- Ensuring responses are relevant to the specific health questions asked
- Delivering consistent information quality across different query types
Technical Architecture: How Health Resource Assistant Works
The Health Resource Assistant is built on Google's Agent Development Kit (ADK) combined with the Gemini API, creating an agent that can search and retrieve information from a structured FAQ knowledge base.
- Structured FAQ Knowledge Base
- Categorization across different health domains (general health, mental health, emergency services)
- Systematic organization of frequently asked questions and answers
- Easy updates and additions to the knowledge base without code changes
- Structured data that the AI agent can efficiently search and retrieve
- MCP (Model Context Protocol) Integration
- Search across FAQ files using semantic understanding of queries
- Retrieve relevant information based on question context
- Access structured health information efficiently
- Provide accurate responses grounded in the knowledge base
- Google Agent Development Kit (ADK)
- Processing natural language health queries
- Determining which tools to invoke based on the question
- Coordinating file searches across the FAQ knowledge base
- Generating responses based on retrieved information
- Interactive Testing and Evaluation
- Interactive interface for real-time question testing with conversation history
- Automated evaluation system with scoring metrics to assess response quality
- Performance visualization and analysis capabilities
- Systematic testing across different health information categories
The system organizes health information into categorized FAQ files stored in a designated directory structure. This approach enables:
The agent uses MCP toolset integration to implement file search capabilities. This enables the agent to:
The ADK framework provides the agent architecture that coordinates between the Gemini language model and the MCP file search tools. This architecture handles:
The implementation includes both an interactive testing interface and an automated evaluation framework:
Implementation Benefits
This architecture delivers several key advantages for health information access:
- Natural Language Queries
- Categorized Information Structure
- Accurate Information Retrieval
- Measurable Performance
- Maintainable Architecture
Users can ask questions in natural language like "Where can I get a free flu shot?" rather than navigating complex healthcare databases or websites.
Health information is organized into logical categories (general health, mental health, emergency services), making it easier to maintain and update specific domains of health information.
By grounding responses in a curated FAQ knowledge base, the agent provides reliable information rather than generating potentially inaccurate health advice.
The automated evaluation framework enables systematic assessment of response quality, ensuring the agent maintains accuracy across different types of health queries.
The separation of knowledge base content from agent logic allows health information to be updated independently of the core system, making it practical for real-world deployment.
Potential Applications and Future Enhancements
While the Health Resource Assistant focuses on community health FAQs, this architecture could be extended to:
- Healthcare provider directories with specialty and insurance filtering
- Medication information systems with interaction checking
- Health insurance navigation assistance
- Public health information dissemination during emergencies
Future enhancements might include:
- Multi-language support for diverse communities
- Integration with real-time service availability data
- Personalization based on user location and insurance status
- Analytics for identifying gaps in available health resources
Conclusion
The Health Resource Assistant demonstrates how combining Google's Agent Development Kit, MCP toolset integration, and structured knowledge bases can create AI agents that provide reliable access to community health information. By addressing the challenges of information accessibility, accuracy, and natural language interaction, systems like this can help bridge gaps in healthcare access for underserved communities.
The implementation showcases how AI agents can move beyond generic chatbots to become specialized information retrieval systems that solve specific community needs while maintaining accuracy through structured knowledge bases.
Project Context and Attribution
This AI Agents Capstone Project demonstrates the application of Google's Agent Development Kit for building specialized information retrieval agents.
Development Environment
- Python
- Google Agent Development Kit (ADK)
- Google Gemini API
- MCP (Model Context Protocol)