# Advanced Features

LLMPromptKit provides several advanced features for sophisticated prompt engineering.

## Advanced Templating

LLMPromptKit's templating system goes beyond simple variable substitution, offering conditionals and loops.

### Basic Variable Substitution

```python
from llmpromptkit import PromptTemplate

# Simple variable substitution
template = PromptTemplate("Hello, {name}!")
rendered = template.render(name="John")
# Result: "Hello, John!"
```

### Conditional Logic

```python
# Conditionals
template = PromptTemplate("""
{if is_formal}
Dear {name},

I hope this message finds you well.
{else}
Hey {name}!
{endif}

{message}
""")

formal = template.render(is_formal=True, name="Dr. Smith", message="Please review the attached document.")
casual = template.render(is_formal=False, name="Bob", message="Want to grab lunch?")
```

### Loops

```python
# Loops
template = PromptTemplate("""
Here are your tasks:

{for task in tasks}
- {task.priority}: {task.description}
{endfor}
""")

rendered = template.render(tasks=[
    {"priority": "High", "description": "Complete the report"},
    {"priority": "Medium", "description": "Schedule meeting"},
    {"priority": "Low", "description": "Organize files"}
])
```

### Nested Structures

```python
# Combining loops and conditionals
template = PromptTemplate("""
{system_message}

{for example in examples}
User: {example.input}
{if example.has_reasoning}
Reasoning: {example.reasoning}
{endif}
Assistant: {example.output}
{endfor}

User: {query}
Assistant:
""")

rendered = template.render(
    system_message="You are a helpful assistant.",
    examples=[
        {
            "input": "What's 2+2?",
            "has_reasoning": True,
            "reasoning": "Adding 2 and 2 gives 4",
            "output": "4"
        },
        {
            "input": "Hello",
            "has_reasoning": False,
            "output": "Hi there! How can I help you today?"
        }
    ],
    query="What's the capital of France?"
)
```

## Custom Evaluation Metrics

You can create custom metrics to evaluate prompt outputs based on your specific requirements.

### Creating a Custom Metric

```python
from llmpromptkit import EvaluationMetric

class RelevanceMetric(EvaluationMetric):
    """Evaluates relevance of output to a given topic."""
    
    def __init__(self, topics):
        super().__init__("relevance", "Evaluates relevance to specified topics")
        self.topics = topics
    
    def compute(self, generated_output, expected_output=None, **kwargs):
        """
        Compute relevance score based on topic presence.
        Returns a float between 0 and 1.
        """
        score = 0
        output_lower = generated_output.lower()
        
        for topic in self.topics:
            if topic.lower() in output_lower:
                score += 1
                
        # Normalize to 0-1 range
        return min(1.0, score / len(self.topics)) if self.topics else 0.0
```

### Using Custom Metrics

```python
from llmpromptkit import Evaluator, PromptManager

# Initialize components
prompt_manager = PromptManager()
evaluator = Evaluator(prompt_manager)

# Register custom metric
climate_relevance = RelevanceMetric(["climate", "temperature", "warming", "environment"])
evaluator.register_metric(climate_relevance)

# Use in evaluation
async def my_llm(prompt, vars):
    # Call your LLM API here
    return "Climate change is causing global temperature increases..."

results = await evaluator.evaluate_prompt(
    prompt_id="abc123",
    inputs=[{"topic": "climate change"}],
    llm_callback=my_llm,
    metric_names=["relevance"]  # Use our custom metric
)

print(f"Relevance score: {results['aggregated_metrics']['relevance']}")
```

## Customizing Storage

LLMPromptKit allows you to customize where and how prompts and related data are stored.

### Custom Storage Locations

```python
# Specify a custom storage location
prompt_manager = PromptManager("/path/to/my/prompts")

# Export/import prompts
import json

# Export a prompt to a file
prompt = prompt_manager.get("abc123")
with open("exported_prompt.json", "w") as f:
    json.dump(prompt.to_dict(), f, indent=2)

# Import a prompt from a file
with open("exported_prompt.json", "r") as f:
    data = json.load(f)
    imported_prompt = prompt_manager.import_prompt(data)
```

## LLM Integration

LLMPromptKit is designed to work with any LLM through callback functions. Here are examples of integrating with popular LLM APIs.

### OpenAI Integration

```python
import openai
from llmpromptkit import PromptManager, PromptTesting

prompt_manager = PromptManager()
testing = PromptTesting(prompt_manager)

# Configure OpenAI
openai.api_key = "your-api-key"

# OpenAI callback function
async def openai_callback(prompt, vars):
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.7,
        max_tokens=150
    )
    return response.choices[0].message.content

# Run tests with OpenAI
test_results = await testing.run_all_tests("abc123", openai_callback)
```

### Anthropic Integration

```python
import anthropic
from llmpromptkit import PromptManager, Evaluator

prompt_manager = PromptManager()
evaluator = Evaluator(prompt_manager)

# Configure Anthropic
client = anthropic.Anthropic(api_key="your-api-key")

# Anthropic callback function
async def anthropic_callback(prompt, vars):
    response = client.messages.create(
        model="claude-2",
        messages=[{"role": "user", "content": prompt}],
        max_tokens=150
    )
    return response.content[0].text

# Evaluate with Anthropic
eval_results = await evaluator.evaluate_prompt(
    prompt_id="abc123",
    inputs=[{"query": "What is machine learning?"}],
    llm_callback=anthropic_callback
)
```

### Hugging Face Integration

```python
from transformers import pipeline
import asyncio
from llmpromptkit import PromptManager, VersionControl

prompt_manager = PromptManager()
version_control = VersionControl(prompt_manager)

# Set up Hugging Face pipeline
generator = pipeline('text-generation', model='gpt2')

# Hugging Face callback function
async def hf_callback(prompt, vars):
    # Run synchronously but in a way that doesn't block the asyncio event loop
    loop = asyncio.get_event_loop()
    result = await loop.run_in_executor(None, lambda: generator(prompt, max_length=100)[0]['generated_text'])
    return result

# Use with version control
prompt = prompt_manager.create(
    content="Complete this: {text}",
    name="Text Completion"
)
version_control.commit(prompt.id, "Initial version")

# Test with different models by swapping the callback
```