# Phase 1 Implementation Summary: Client Caching

## Changes Made

### 1. Created `langroid/language_models/client_cache.py`

A new module implementing singleton pattern for LLM clients with the following features:

- **Consistent with existing caching**: Uses SHA256 hashing for cache keys, matching the approach in `OpenAIGPT._cache_lookup`
- **Wrapper functions** for each client type:
  - `get_openai_client()` / `get_async_openai_client()`
  - `get_groq_client()` / `get_async_groq_client()`
  - `get_cerebras_client()` / `get_async_cerebras_client()`
- **Configuration-based caching**: Clients are cached based on their full configuration (API key, base URL, timeout, headers, etc.)
- **Lifecycle management**: Uses `atexit` hook for cleanup and weak references to track clients

### 2. Key Implementation Details

#### Cache Key Generation
```python
def _get_cache_key(client_type: str, **kwargs: Any) -> str:
    # Convert kwargs to sorted string representation
    sorted_kwargs_str = str(sorted(kwargs.items()))
    
    # Create raw key combining client type and sorted kwargs
    raw_key = f"{client_type}:{sorted_kwargs_str}"
    
    # Hash the key for consistent length and to handle complex objects
    hashed_key = hashlib.sha256(raw_key.encode()).hexdigest()
    
    return hashed_key
```

This approach:
- Ensures deterministic keys through sorting
- Handles complex objects via string representation
- Produces fixed-length keys (64 chars)
- Matches the existing Redis cache key generation pattern

### 3. Created Comprehensive Tests

`tests/main/test_client_cache.py` includes tests for:
- Singleton behavior (same config returns same client)
- Different configurations return different clients
- Different client types are cached separately
- Proper handling of timeout objects and headers
- Type differences are preserved (e.g., `30` vs `30.0` are different)

### 4. All Quality Checks Pass
- ✅ All 9 tests pass
- ✅ Type checking passes (mypy)
- ✅ Linting passes (ruff, black)

## Design Decisions

1. **Used SHA256 hashing instead of tuple keys**: More robust for complex objects and consistent with existing caching approach
2. **Type strictness**: `30` and `30.0` create different cache entries - better to be overly strict than risk bugs
3. **Weak references**: Allow garbage collection of unused clients while maintaining cleanup capability
4. **Simple atexit cleanup**: Accepted that async clients will be cleaned by OS on exit

## Next Steps (Phase 2)

Update `OpenAIGPT.__init__` to use these wrapper functions instead of directly creating clients:
```python
# Current
self.client = OpenAI(api_key=self.api_key, ...)

# New  
from langroid.language_models.client_cache import get_openai_client
self.client = get_openai_client(api_key=self.api_key, ...)
```

This will require careful updating of all client creation locations in `openai_gpt.py`.