Summary

This PR refactors the ChatBot class to use our modular LLMFactory architecture, eliminating 400+ lines of hardcoded provider logic while maintaining full backward compatibility. The change reduces code complexity, standardizes provider management, and improves maintainability across all LLM integrations.

Key Changes

• Reduced ChatBot complexity: From 436+ lines to ~170 lines (60% reduction)
• Unified provider management: All 4 providers (OpenAI, Anthropic, DeepSeek, Gemini) now use consistent factory pattern
• Standardized configuration: All providers use config.json and official API endpoints
• Enhanced testing: Added comprehensive test suites with 100% pass rate
• Preserved compatibility: All existing ChatBot methods work unchanged

Architecture Improvements

Before: Hardcoded Provider Logic

# 400+ lines of if/elif provider switching
if self.api_logic == "openai":
    response = await self.llm.chat.completions.create(...)
elif self.api_logic == "anthropic":
    response = await self.llm.messages.create(...)
# ... hundreds more lines of duplicated logic

After: Clean Factory Pattern

# Simple delegation to factory-created provider
self.llm = LLMFactory.create(self.llm_provider)
response = await self.llm.chat(messages=formatted_messages, system_msgs=system_msgs)

Technical Details

Provider Consolidation

• Moved providers from scattered locations to unified spoon_ai/llm/providers/ directory
• Standardized config loading using config.json via ConfigManager
• Fixed API endpoints to use official provider URLs (no more custom endpoints)
• Unified error handling across all providers

Backward Compatibility

• ✅ All existing ChatBot constructor parameters preserved
• ✅ ask() and ask_tool() methods work unchanged
• ✅ Memory system continues tracking conversation history
• ✅ Cache metrics accessible via same API
• ✅ Configuration fallback logic maintained

Provider-Specific Fixes

• Gemini: Fixed missing output_queue attribute causing crashes
• Anthropic: Added empty message validation to prevent API errors
• OpenAI: Enhanced connection error handling
• DeepSeek: Standardized to use official API endpoint

Test Results

Integration Tests (7/7 Passing)

✅ ChatBot-Factory Integration
✅ Multi-Provider Support  
✅ Memory System Functionality
✅ Cache Metrics Integration
✅ Backward Compatibility
✅ Error Handling
✅ Configuration Loading

Provider Tests (7/7 Passing)

✅ Provider Registration (4/4 providers)
✅ Provider Instantiation
✅ Configuration Loading (env vars + config.json)
✅ Basic Chat Functionality
✅ Tool-based Chat Functionality
✅ Error Handling & Edge Cases
✅ API Endpoint Validation

Dependencies

• Added: pytest>=8.4.1 for improved test infrastructure
• Added: toml>=0.10.2 for configuration parsing

Breaking Changes

None. This is a purely internal refactoring that maintains the exact same public API.

Migration Notes

No action required for existing code. All ChatBot usage patterns continue to work:

# All these patterns still work unchanged
chatbot = ChatBot()  # Auto-detects provider
chatbot = ChatBot(llm_provider="anthropic")  # Explicit provider
response = await chatbot.ask(messages, system_msg="...")  # Basic chat
response = await chatbot.ask_tool(messages, tools=tools)  # Tool chat
metrics = chatbot.get_cache_metrics()  # Cache metrics

Files Changed

Core Refactoring

• spoon_ai/chat.py: Complete ChatBot refactor using LLMFactory
• spoon_ai/llm/factory.py: Enhanced with provider registration
• spoon_ai/llm/providers/: New unified provider directory

Provider Standardization

• spoon_ai/llm/providers/anthropic.py: Official API endpoint, cache metrics
• spoon_ai/llm/providers/openai.py: Config.json integration
• spoon_ai/llm/providers/deepseek.py: Official API endpoint
• spoon_ai/llm/providers/gemini.py: Fixed streaming issues

Testing Infrastructure

• tests/test_chatbot_integration.py: Comprehensive integration tests
• tests/test_providers.py: Provider functionality tests
• pyproject.toml: Added testing dependencies

Validation

• ✅ All existing functionality preserved
• ✅ Memory system working correctly
• ✅ Cache metrics properly delegated
• ✅ Provider auto-detection functional
• ✅ Configuration loading from both config.json and env vars
• ✅ Error handling robust across all providers
• ✅ Tool functionality working with Anthropic and OpenAI

Future Benefits

This refactoring enables:

• Easier provider additions: New LLM providers can be added with minimal code
• Consistent behavior: All providers follow the same patterns
• Better testing: Isolated provider testing and mocking
• Reduced maintenance: Single source of truth for provider logic
• Improved reliability: Standardized error handling and configuration

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