# FastLLM Overview

FastLLM is a powerful Python library that enables efficient parallel processing of Large Language Model (LLM) API requests. It provides a robust solution for applications that need to make multiple LLM API calls simultaneously while managing resources effectively.

## Key Features

### 1. Parallel Request Processing
- Efficient handling of concurrent API requests
- Configurable concurrency limits
- Built-in request batching and chunking
- Progress tracking with detailed statistics

### 2. Intelligent Caching
- Multiple cache provider options (In-memory and Disk-based)
- Consistent request hashing for cache keys
- Configurable TTL for cached responses
- Automatic cache management

### 3. Multiple Provider Support
- Modular provider architecture
- Built-in OpenAI provider implementation
- Extensible base classes for adding new providers
- Provider-agnostic request/response models

### 4. Resource Management
- Automatic rate limiting
- Configurable timeout handling
- Retry mechanism with exponential backoff
- Connection pooling

### 5. Progress Monitoring
- Real-time progress tracking
- Token usage statistics
- Cache hit ratio monitoring
- Performance metrics (tokens/second)

### 6. Developer-Friendly Interface
- OpenAI-like API design
- Type hints throughout the codebase
- Comprehensive error handling
- Async/await support

## Use Cases

FastLLM is particularly useful for:

1. **Batch Processing**: Processing large numbers of LLM requests efficiently
   - Data analysis pipelines
   - Content generation at scale
   - Bulk text processing

2. **High-Performance Applications**: Applications requiring optimal LLM API usage
   - Real-time applications
   - Interactive systems
   - API proxies and middleware

3. **Resource-Conscious Systems**: Systems that need to optimize API usage
   - Cost optimization through caching
   - Rate limit management
   - Token usage optimization

4. **Development and Testing**: Tools for LLM application development
   - Rapid prototyping
   - Testing and benchmarking
   - Performance optimization

## Performance Considerations

FastLLM is designed with performance in mind:

- **Parallel Processing**: Efficiently processes multiple requests concurrently
- **Smart Batching**: Automatically chunks requests for optimal throughput
- **Cache Optimization**: Reduces API calls through intelligent caching
- **Resource Management**: Prevents overload through concurrency control
- **Memory Efficiency**: Manages memory usage through chunked processing