from pydantic_ai import Agent
from pydantic_ai.models import Model
from dcc_backend_common.config.app_config import LlmConfig
from dcc_backend_common.llm_agent import BaseAgent

class MyAgent(BaseAgent[None, str]):
    def create_agent(self, model: Model) -> Agent[None, str]:
        return Agent(
            model=model,
            system_prompt="You are a helpful assistant.",
        )

config = LlmConfig(
    llm_model="gemma-3-27b-it",
    llm_url="https://your-vllm-endpoint/v1",
    llm_api_key="your-key",
)

agent = MyAgent(config)
result = await agent.run("What is the capital of Switzerland?")

agent_with_thinking = MyAgent(config, enable_thinking=True)
result = await agent_with_thinking.run("Solve this step by step: ...")

agent_no_thinking = MyAgent(config, enable_thinking=False)  # default

# Stream text deltas
async for chunk in agent.run_stream_text("Tell me a story"):
    print(chunk, end="", flush=True)

# Stream full accumulated text (delta=False)
async for text in agent.run_stream_text("Tell me a story", delta=False):
    print(text)

# Stream structured output chunks
async for chunk in agent.run_stream_output("List three cities"):
    print(chunk)

# Stream raw pydantic-ai events
async for event in agent.run_stream_events("Hello"):
    print(event)

from pydantic import BaseModel

class CityList(BaseModel):
    cities: list[str]
    country: str

class CityAgent(BaseAgent[None, CityList]):
    def create_agent(self, model: Model) -> Agent[None, CityList]:
        return Agent(model=model, output_type=CityList)

agent = CityAgent(config, output_type=CityList)
result = await agent.run("List three Swiss cities")
# result.cities == ["Zurich", "Basel", "Bern"]

class ItemAgent(BaseAgent[None, str]):
    def create_agent(self, model: Model) -> Agent[None, str]:
        return Agent(model=model)

agent = ItemAgent(config)
async for item in agent.stream_list("Name five fruits"):
    print(item)  # prints each fruit as soon as it is ready

class MyAgent(BaseAgent[None, str]):
    def _get_postprocessors(self):
        return super()._get_postprocessors() + [my_custom_processor]

class MyAgent(BaseAgent[None, str]):
    def process_prompt(self, prompt, deps):
        return f"[context] {prompt}"

from dcc_backend_common.llm_agent.debugging import withDebbugger

class MyAgent(BaseAgent[None, str]):
    @withDebbugger
    async def run(self, *args, **kwargs):
        return await super().run(*args, **kwargs)

from dcc_backend_common.llm_agent.debugging import create_event_debugger

async for event in agent.run_stream_events(
    "Hello",
    event_stream_handler=create_event_debugger("my-agent"),
):
    ...

from fastapi import FastAPI
from dcc_backend_common.fastapi_health_probes import health_probe_router

app = FastAPI()

service_dependencies = [
    {
        "name": "database",
        "health_check_url": "http://postgres:5432/health",
        "api_key": None,
    },
    {
        "name": "external-api",
        "health_check_url": "https://api.example.com/health",
        "api_key": "your-api-key-here",
    },
]

app.include_router(health_probe_router(service_dependencies))

from dcc_backend_common.logger import init_logger, get_logger

init_logger()  # JSON in production (IS_PROD=true), colored console otherwise
logger = get_logger(__name__)

logger.info("request_received", extra={"user_id": 42})

from dcc_backend_common.config.app_config import AppConfig, LlmConfig

config = AppConfig.from_env()
print(config)  # secrets are redacted in __str__

llm = LlmConfig(
    llm_model="gemma-3-27b-it",
    llm_url="http://vllm:8000/v1",
    llm_api_key="your-key",
)