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Implementation Details

Architecture

The Congress.gov Chatbot is built using a clean architecture approach with the following components:

  1. API Layer - Handles communication with the Congress.gov API
  2. LLM Integration - Uses LangChainGo to integrate with the GenAI LLM service
  3. Service Layer - Orchestrates the interaction between the user, LLM, and Congress.gov API
  4. Handler Layer - Manages HTTP requests/responses using Fiber
  5. Web Interface - A simple HTML/CSS/JS frontend for the chatbot

Components

Congress.gov API Client (api/congress_client.go)

The API client provides a clean interface to the Congress.gov API endpoints:

  • Search Bills
  • Get Bill Details
  • Get Bill Summaries
  • Search Members
  • Get Member Details
  • Search Amendments

LLM Client (pkg/llm/llm.go)

The LLM client wraps LangChainGo to provide a simplified interface for interacting with the GenAI LLM service:

  • Managing conversation history
  • Generating responses from the LLM
  • Adding system, user, and assistant messages

Chatbot Service (internal/service/chatbot.go)

The service layer implements the core logic of the chatbot:

  1. User sends a query
  2. LLM analyzes the query to determine which Congress.gov API to call
  3. API call is made to fetch relevant information
  4. LLM interprets the API response to generate a helpful answer
  5. Response is returned to the user

HTTP Handler (internal/handler/handler.go)

The handler layer manages HTTP requests and responses:

  • /api/chat - Process user messages
  • /api/history - Get conversation history
  • /api/clear - Clear conversation history
  • /api/health - Health check endpoint

Web Interface

A simple single-page application using HTML, CSS, and JavaScript provides the user interface for the chatbot.

Deployment

Cloud Foundry Configuration

The application is configured to be deployed to Tanzu Platform for Cloud Foundry:

  • manifest.yml defines the application configuration
  • The application can be pushed using cf push
  • It can be bound to a GenAI LLM service instance

Service Binding

The application demonstrates two approaches to service binding:

  1. Automatic Service Binding:

    • The application is bound to a service instance using cf bind-service
    • Service credentials are automatically injected via VCAP_SERVICES

  2. Manual Configuration:

    • Service keys are created using cf create-service-key
    • The application is configured with environment variables

Design Decisions

Why Go + Fiber + LangChainGo?

  • Go: Lightweight, efficient, and perfect for cloud-native applications
  • Fiber: Fast, Express-like web framework with good middleware support
  • LangChainGo: Provides a clean interface to various LLM providers

Conversation Management

The application maintains conversation history to provide context to the LLM, enabling more coherent multi-turn conversations.

Error Handling

Comprehensive error handling ensures that the application can recover from failures and provide helpful error messages to the user.

Security

API keys and service credentials are securely handled through environment variables and service bindings.

Future Improvements

  • Add authentication for the API endpoints
  • Implement streaming responses for a more interactive experience
  • Add more advanced conversation capabilities
  • Expand the range of Congress.gov API endpoints used
  • Add better error handling and retry logic
  • Implement unit and integration tests