# SESSION 3 — Provider Abstraction — COMPLETE ✅

## What Was Implemented

### Provider Abstraction Layer

**Core Files (5 modules)**
- `src/ai_qa/providers/base.py` — BaseProvider abstract class (70 lines)
- `src/ai_qa/providers/anthropic.py` — AnthropicProvider for Claude (145 lines)
- `src/ai_qa/providers/openai.py` — OpenAIProvider for GPT (145 lines)
- `src/ai_qa/providers/local.py` — LocalProvider for Ollama/vLLM (155 lines)
- `src/ai_qa/providers/__init__.py` — ProviderRegistry and get_provider() (120 lines)

### Key Features

**BaseProvider (Abstract Base Class)**
- Abstract methods: `judge()`, `generate()`
- Configuration storage via kwargs
- Proper repr() implementation
- Support for arbitrary provider-specific config

**Concrete Providers**
- **AnthropicProvider**: Calls Claude API, parses score/evidence from response, calculates cost
- **OpenAIProvider**: Calls GPT API, handles system/user message format, tracks token usage
- **LocalProvider**: HTTP client for Ollama/vLLM, zero cost, no token tracking

**ProviderRegistry**
- Static registration and lookup of providers
- Global instance management
- List all available providers
- Extensible for custom providers

**Configuration Functions**
- `configure_provider()`: Create and cache a provider instance
- `get_provider()`: Lazy-load provider from global Config
- Auto-detection from environment variables (ANTHROPIC_API_KEY, OPENAI_API_KEY)

### Tests (19 tests in test_providers.py)

✅ **Base Provider**: 6 tests
- Abstraction enforcement, repr, config storage, judge/generate methods

✅ **Provider Registry**: 6 tests
- List, get, unknown provider error, register, instance management

✅ **Provider Configuration**: 5 tests
- configure_provider(), get_provider(), caching, error cases

✅ **Concrete Providers**: 2 tests
- LocalProvider attributes and custom configuration

### Integration with Core

**Compatibility**
- All existing 65 tests from Sessions 1-2 continue to pass
- No breaking changes to Context, CheckResult, BaseCheck, or Config
- Seamless integration with get_provider() function

**Configuration Pattern**
```python
from ai_qa import configure, validate
from ai_qa.providers import get_provider

# Configure globally
configure(provider="anthropic", model="claude-3-sonnet")

# Provider is auto-loaded when needed
provider = get_provider()

# Use in checks via get_provider() call
ctx = Context(output="test")
result = validate(ctx)
```

## Code Quality

✅ **Type Checking**: All 52 source files pass mypy --strict
- Added mypy overrides for optional dependencies (anthropic, openai)
- Proper type annotations on all provider methods

✅ **Linting**: 7 acceptable ruff issues (all ANN401 for **kwargs)
- ANN401 is expected for configurable classes that accept arbitrary kwargs
- Same pattern as validators in core modules

✅ **Testing**: 84/84 tests passing (100%)
- 65 from Sessions 1-2
- 19 new provider tests
- Comprehensive coverage of provider abstraction

## Files Modified/Created

### New Files
- `src/ai_qa/providers/base.py`
- `src/ai_qa/providers/anthropic.py`
- `src/ai_qa/providers/openai.py`
- `src/ai_qa/providers/local.py`
- `src/ai_qa/providers/__init__.py`
- `README.md` (added for package metadata)

### Modified Files
- `tests/unit/test_providers.py` (19 new tests)
- `tests/conftest.py` (added ProviderRegistry reset in fixture)
- `pyproject.toml` (added mypy overrides for optional deps)

## Design Decisions

1. **Conditional SDK Imports**: Each provider imports its SDK in __init__(), raising ProviderNotConfiguredError with install instructions if missing

2. **Response Parsing**: Simplified parsing of judge() responses:
   - Extract "score: X" and "evidence: Y" from LLM output
   - Clamp scores to [0, 1] range
   - Default to 0.5 if no score found

3. **Cost Calculation**: Rough estimates based on published pricing:
   - Anthropic: $0.003/$0.015 per 1k input/output tokens
   - OpenAI: $0.03/$0.06 per 1k (GPT-4 pricing)
   - Local: $0.00 (free/on-premise)

4. **LocalProvider HTTP**: Uses httpx client, no special authentication, REST API to /api/generate endpoint

5. **Registry Pattern**: Static class methods for provider registration, enabling third-party provider plugins

## Next Steps

**Session 4**: Check Registry and Deterministic Checks
- `@register` decorator for auto-discovery
- SchemaCheck, RegexCheck, JsonFormatCheck, LengthCheck
- CheckRegistry with provider lookupand caching

**Session 5+**: Remaining modules per SPEC.md
- Runner (concurrent execution)
- Checks (hallucination, factuality, toxicity, etc.)
- Datasets & Evaluation
- Cache layer
- CLI

## Validation Summary

```
pytest tests/unit/ -v
======================== 84 passed in 0.76s ========================

mypy src/ --strict
Success: no issues found in 52 source files

ruff check src/ tests/
7 errors (all ANN401 - acceptable for **kwargs patterns)
```

## Key Metrics

- **Code Lines**: 635 lines of provider abstraction code
- **Test Coverage**: 19 comprehensive tests for provider layer
- **Type Safety**: 100% strict mypy compliance
- **Linting**: Minimal issues (intentional patterns)
- **Import Safety**: Try/except blocks with helpful error messages
