"""Example demonstrating LLMClient usage.

This example shows how to use LLMClient for simple LLM calls
without the overhead of an Agent.
"""

import asyncio

from agentlite import LLMClient, llm_complete, llm_stream
from agentlite.config import AgentConfig, ProviderConfig, ModelConfig


async def main():
    """Run LLM client examples."""

    # Example 1: Using simple function interface
    print("=== Example 1: Simple Function ===")
    print("Using llm_complete() function:")

    # Note: This requires a valid API key
    # response = await llm_complete(
    #     user_prompt="What is Python?",
    #     api_key="your-api-key",
    #     model="gpt-4",
    # )
    # print(response)

    print("(Requires API key - uncomment to run)")

    # Example 2: Using configuration-based client
    print("\n=== Example 2: Configuration-Based Client ===")

    config = AgentConfig(
        name="simple_llm",
        system_prompt="You are a helpful coding assistant.",
        providers={
            "openai": ProviderConfig(
                type="openai",
                api_key="your-api-key",  # Replace with actual key
            )
        },
        models={
            "gpt4": ModelConfig(
                provider="openai",
                model="gpt-4",
                temperature=0.7,
            ),
            "gpt35": ModelConfig(
                provider="openai",
                model="gpt-3.5-turbo",
                temperature=0.5,
            ),
        },
        default_model="gpt4",
    )

    # Create client
    client = LLMClient(config)

    # Make a call
    # response = await client.complete(
    #     user_prompt="Explain async/await in Python",
    # )
    # print(f"Response: {response.content}")
    # print(f"Model: {response.model}")
    # if response.usage:
    #     print(f"Tokens: {response.usage.total}")

    print("(Requires API key - uncomment to run)")

    # Example 3: Streaming
    print("\n=== Example 3: Streaming ===")
    print("Using llm_stream() function:")

    # async for chunk in llm_stream(
    #     user_prompt="Write a haiku about programming",
    #     api_key="your-api-key",
    # ):
    #     print(chunk, end="")

    print("\n(Requires API key - uncomment to run)")

    # Example 4: Direct provider usage
    print("\n=== Example 4: Direct Provider ===")

    from agentlite import OpenAIProvider

    provider = OpenAIProvider(
        api_key="your-api-key",
        model="gpt-4",
        temperature=0.8,
    )

    client = LLMClient(provider=provider)

    # response = await client.complete(
    #     user_prompt="What are the benefits of type hints?",
    #     system_prompt="You are a Python expert.",
    # )
    # print(response.content)

    print("(Requires API key - uncomment to run)")

    # Example 5: Model switching
    print("\n=== Example 5: Model Switching ===")

    # Use default model (gpt4)
    # response1 = await client.complete(user_prompt="Hello!")

    # Switch to different model
    # client_gpt35 = LLMClient(config, model="gpt35")
    # response2 = await client_gpt35.complete(user_prompt="Hello!")

    print("(Requires API key - uncomment to run)")

    print("\n=== Examples Complete ===")
    print("To run these examples:")
    print("1. Set your OpenAI API key")
    print("2. Uncomment the example code")
    print("3. Run: python examples/llm_client_example.py")


if __name__ == "__main__":
    asyncio.run(main())