"""
LLM service client (vLLM/OpenAI-compatible).
"""
import logging
from typing import Optional, AsyncIterator

import httpx

from handler_base.config import LLMSettings
from handler_base.telemetry import create_span

logger = logging.getLogger(__name__)


class LLMClient:
    """
    Client for the LLM service (vLLM with OpenAI-compatible API).
    
    Usage:
        client = LLMClient()
        response = await client.generate("Hello, how are you?")
        
        # With context for RAG
        response = await client.generate(
            "What is the capital?",
            context="France is a country in Europe..."
        )
        
        # Streaming
        async for chunk in client.stream("Tell me a story"):
            print(chunk, end="")
    """
    
    def __init__(self, settings: Optional[LLMSettings] = None):
        self.settings = settings or LLMSettings()
        self._client = httpx.AsyncClient(
            base_url=self.settings.llm_url,
            timeout=self.settings.http_timeout,
        )
    
    async def close(self) -> None:
        """Close the HTTP client."""
        await self._client.aclose()
    
    async def generate(
        self,
        prompt: str,
        context: Optional[str] = None,
        system_prompt: Optional[str] = None,
        max_tokens: Optional[int] = None,
        temperature: Optional[float] = None,
        top_p: Optional[float] = None,
        stop: Optional[list[str]] = None,
    ) -> str:
        """
        Generate a response from the LLM.
        
        Args:
            prompt: User prompt/query
            context: Optional context for RAG
            system_prompt: Optional system prompt
            max_tokens: Maximum tokens to generate
            temperature: Sampling temperature
            top_p: Top-p sampling
            stop: Stop sequences
            
        Returns:
            Generated text response
        """
        with create_span("llm.generate") as span:
            messages = self._build_messages(prompt, context, system_prompt)
            
            if span:
                span.set_attribute("llm.model", self.settings.llm_model)
                span.set_attribute("llm.prompt_length", len(prompt))
                if context:
                    span.set_attribute("llm.context_length", len(context))
            
            payload = {
                "model": self.settings.llm_model,
                "messages": messages,
                "max_tokens": max_tokens or self.settings.llm_max_tokens,
                "temperature": temperature or self.settings.llm_temperature,
                "top_p": top_p or self.settings.llm_top_p,
            }
            if stop:
                payload["stop"] = stop
            
            response = await self._client.post("/v1/chat/completions", json=payload)
            response.raise_for_status()
            
            result = response.json()
            content = result["choices"][0]["message"]["content"]
            
            if span:
                span.set_attribute("llm.response_length", len(content))
                usage = result.get("usage", {})
                span.set_attribute("llm.prompt_tokens", usage.get("prompt_tokens", 0))
                span.set_attribute("llm.completion_tokens", usage.get("completion_tokens", 0))
            
            return content
    
    async def stream(
        self,
        prompt: str,
        context: Optional[str] = None,
        system_prompt: Optional[str] = None,
        max_tokens: Optional[int] = None,
        temperature: Optional[float] = None,
    ) -> AsyncIterator[str]:
        """
        Stream a response from the LLM.
        
        Args:
            prompt: User prompt/query
            context: Optional context for RAG
            system_prompt: Optional system prompt
            max_tokens: Maximum tokens to generate
            temperature: Sampling temperature
            
        Yields:
            Text chunks as they're generated
        """
        messages = self._build_messages(prompt, context, system_prompt)
        
        payload = {
            "model": self.settings.llm_model,
            "messages": messages,
            "max_tokens": max_tokens or self.settings.llm_max_tokens,
            "temperature": temperature or self.settings.llm_temperature,
            "stream": True,
        }
        
        async with self._client.stream(
            "POST", "/v1/chat/completions", json=payload
        ) as response:
            response.raise_for_status()
            
            async for line in response.aiter_lines():
                if line.startswith("data: "):
                    data = line[6:]
                    if data == "[DONE]":
                        break
                    
                    import json
                    chunk = json.loads(data)
                    delta = chunk["choices"][0].get("delta", {})
                    content = delta.get("content", "")
                    if content:
                        yield content
    
    def _build_messages(
        self,
        prompt: str,
        context: Optional[str] = None,
        system_prompt: Optional[str] = None,
    ) -> list[dict]:
        """Build the messages list for the API call."""
        messages = []
        
        # System prompt
        if system_prompt:
            messages.append({"role": "system", "content": system_prompt})
        elif context:
            # Default RAG system prompt
            messages.append({
                "role": "system",
                "content": (
                    "You are a helpful assistant. Use the provided context to answer "
                    "the user's question. If the context doesn't contain relevant "
                    "information, say so."
                ),
            })
        
        # Add context as a separate message if provided
        if context:
            messages.append({
                "role": "user",
                "content": f"Context:\n{context}\n\nQuestion: {prompt}",
            })
        else:
            messages.append({"role": "user", "content": prompt})
        
        return messages
    
    async def health(self) -> bool:
        """Check if the LLM service is healthy."""
        try:
            response = await self._client.get("/health")
            return response.status_code == 200
        except Exception:
            return False