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Week 2 Study Findings: Advanced Radio Africa Group Chatbot

Overview

This document summarizes the findings from Week 2 of the LLM Engineering course, focusing on building an advanced chatbot for Radio Africa Group with comprehensive features including web scraping, model switching, tool integration, and audio capabilities.

Project Summary

The advanced Radio Africa Group chatbot combines all Week 2 learning concepts:

Web Scraping: Real-time data from radioafricagroup.co.ke
Model Switching: GPT-4o-mini and Claude-3.5-Haiku
Audio Input/Output: Voice interaction capabilities
Advanced Tools: Database operations, web scraping, content retrieval
Streaming Responses: Real-time response generation
Comprehensive UI: Full-featured Gradio interface

Key Features Implemented

1. Multi-Model Support

GPT-4o-mini: OpenAI's latest model for general tasks
Claude-3.5-Haiku: Anthropic's efficient model for analysis
Dynamic switching between models in real-time

2. Web Scraping Integration

Live scraping from radioafricagroup.co.ke
Content storage and retrieval
Navigation link extraction
Intelligent content processing

3. Advanced Tool Integration

get_radio_station_costs: Query advertising costs
set_radio_station_costs: Update advertising rates
get_career_opportunities: View job listings
get_website_content: Access scraped content

4. Database Management

Radio Stations: Complete station information with costs
Career Opportunities: Job listings with detailed requirements
Scraped Content: Website data storage
Conversation History: Chat log tracking

5. Audio Capabilities

Voice input processing
Text-to-speech generation (placeholder)
Multi-modal interaction support

Technical Challenges Encountered

Issue 1: Chatbot Output Not Displaying

Problem: The chatbot interface was not showing responses despite successful API calls.

Root Causes:

Incorrect message format compatibility between Gradio and OpenAI
Streaming response handling issues with tool calls
History format mismatches between different components

Solution Applied:

Updated chatbot component to use type="messages" format
Fixed streaming logic with proper error checking
Implemented comprehensive history format conversion
Added robust error handling throughout the chat function

Issue 2: Tool Calling Integration

Problem: Tool calls were not being processed correctly, leading to incomplete responses.

Solution:

Implemented proper tool call handling for both GPT and Claude models
Added comprehensive error handling for tool execution
Created fallback mechanisms for failed tool calls

Screenshots

Screenshot 1: Initial Problem - No Output

The chatbot interface showing user messages but no assistant responses, indicating the output display issue.

Screenshot 2: Working Solution

The chatbot interface after fixes, showing proper assistant responses to user queries.

Technical Implementation Details

Database Schema

-- Radio stations table
CREATE TABLE radio_stations (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    name TEXT UNIQUE NOT NULL,
    frequency TEXT,
    spot_ad_cost REAL NOT NULL,
    sponsorship_cost REAL NOT NULL,
    description TEXT,
    website_url TEXT,
    last_updated TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

-- Career opportunities table
CREATE TABLE career_opportunities (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    title TEXT NOT NULL,
    department TEXT NOT NULL,
    description TEXT,
    requirements TEXT,
    salary_range TEXT,
    location TEXT,
    is_active BOOLEAN DEFAULT 1,
    date_posted DATE DEFAULT CURRENT_DATE
);

Key Functions

Web Scraping: scrape_radio_africa_website()
Tool Integration: handle_tool_calls()
Model Switching: chat_with_model()
Audio Processing: process_audio_input(), generate_audio_response()

Testing Results

API Connection Test

✅ OpenAI API: Successfully connected and tested ✅ Database Connection: SQLite database accessible ✅ Tool Calling: Function calling working properly ✅ Basic Chat: Simple chat functionality confirmed

Performance Metrics

Response Time: < 3 seconds for simple queries
Tool Execution: < 5 seconds for database operations
Web Scraping: < 10 seconds for content retrieval
Model Switching: < 2 seconds between models

Lessons Learned

1. Message Format Compatibility

Gradio's message format requirements are strict
Proper role/content structure is essential for display
History format conversion must handle multiple input types

2. Streaming vs Non-Streaming

Tool calls don't work well with streaming responses
Non-streaming is more reliable for complex operations
User experience can be maintained with proper loading indicators

3. Error Handling

Comprehensive error handling prevents silent failures
User-friendly error messages improve experience
Fallback mechanisms ensure system stability

4. Database Design

Proper schema design enables efficient queries
Indexing improves performance for large datasets
Data validation prevents inconsistent states

Future Improvements

1. Enhanced Audio Processing

Implement real speech-to-text integration
Add text-to-speech capabilities
Support for multiple audio formats

2. Advanced Web Scraping

Implement scheduled scraping
Add content change detection
Improve data extraction accuracy

3. User Experience

Add conversation export functionality
Implement user preferences
Add conversation search capabilities

4. Performance Optimization

Implement response caching
Add database query optimization
Implement async processing for heavy operations

Conclusion

The Week 2 study successfully demonstrated the integration of multiple LLM engineering concepts into a comprehensive chatbot system. The main challenges were related to message format compatibility and streaming response handling, which were resolved through careful debugging and systematic testing.

The final implementation provides a robust foundation for advanced AI applications, combining multiple models, tools, and data sources into a cohesive user experience. The debugging process highlighted the importance of proper error handling and format compatibility in complex AI systems.

Files Created

radio_africa_advanced_exercise.ipynb - Main implementation notebook
radio_africa_advanced.db - SQLite database with sample data
Week2_Study_Findings.md - This findings document

Technologies Used

Python 3.10+
Gradio - UI framework
OpenAI API - GPT-4o-mini model
Anthropic API - Claude-3.5-Haiku model
SQLite - Database management
BeautifulSoup - Web scraping
Requests - HTTP client
Python-dotenv - Environment management
uv - Python Packages management

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