src/llm/adapters.py

"""LLM client adapters for different providers."""

from typing import Dict, Any, List, Optional, Union
from abc import ABC, abstractmethod
from dataclasses import dataclass

# LangChain imports
from langchain_mistralai.chat_models import ChatMistralAI
from langchain_openai.chat_models import ChatOpenAI
from langchain_ollama import ChatOllama

# Legacy client dependencies
from huggingface_hub import InferenceClient
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_community.llms import HuggingFaceEndpoint
from langchain_community.chat_models.huggingface import ChatHuggingFace

# Configuration loader
from ..config.loader import load_config

# Load configuration once at module level
_config = load_config()


# Legacy client factory functions (inlined from auditqa_old.reader)
def _create_inf_provider_client():
    """Create INF_PROVIDERS client."""
    reader_config = _config.get("reader", {})
    inf_config = reader_config.get("INF_PROVIDERS", {})
    
    api_key = inf_config.get("api_key")
    if not api_key:
        raise ValueError("INF_PROVIDERS api_key not found in configuration")
    
    provider = inf_config.get("provider")
    if not provider:
        raise ValueError("INF_PROVIDERS provider not found in configuration")

    return InferenceClient(
        provider=provider,
        api_key=api_key,
        bill_to="GIZ",
    )


def _create_nvidia_client():
    """Create NVIDIA client."""
    reader_config = _config.get("reader", {})
    nvidia_config = reader_config.get("NVIDIA", {})
    
    api_key = nvidia_config.get("api_key")
    if not api_key:
        raise ValueError("NVIDIA api_key not found in configuration")
    
    endpoint = nvidia_config.get("endpoint")
    if not endpoint:
        raise ValueError("NVIDIA endpoint not found in configuration")
    
    return InferenceClient(
        base_url=endpoint,
        api_key=api_key
    )


def _create_serverless_client():
    """Create serverless API client."""
    reader_config = _config.get("reader", {})
    serverless_config = reader_config.get("SERVERLESS", {})
    
    api_key = serverless_config.get("api_key")
    if not api_key:
        raise ValueError("SERVERLESS api_key not found in configuration")
    
    model_id = serverless_config.get("model", "meta-llama/Meta-Llama-3-8B-Instruct")
    
    return InferenceClient(
        model=model_id,
        api_key=api_key,
    )


def _create_dedicated_endpoint_client():
    """Create dedicated endpoint client."""
    reader_config = _config.get("reader", {})
    dedicated_config = reader_config.get("DEDICATED", {})
    
    api_key = dedicated_config.get("api_key")
    if not api_key:
        raise ValueError("DEDICATED api_key not found in configuration")
    
    endpoint = dedicated_config.get("endpoint")
    if not endpoint:
        raise ValueError("DEDICATED endpoint not found in configuration")
    
    max_tokens = dedicated_config.get("max_tokens", 768)
    
    # Set up the streaming callback handler
    callback = StreamingStdOutCallbackHandler()
    
    # Initialize the HuggingFaceEndpoint with streaming enabled
    llm_qa = HuggingFaceEndpoint(
        endpoint_url=endpoint,
        max_new_tokens=int(max_tokens),
        repetition_penalty=1.03,
        timeout=70,
        huggingfacehub_api_token=api_key,
        streaming=True,
        callbacks=[callback]
    )
    
    # Create a ChatHuggingFace instance with the streaming-enabled endpoint
    return ChatHuggingFace(llm=llm_qa)


@dataclass
class LLMResponse:
    """Standardized LLM response format."""
    content: str
    model: str
    provider: str
    metadata: Dict[str, Any] = None


class BaseLLMAdapter(ABC):
    """Base class for LLM adapters."""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
    
    @abstractmethod
    def generate(self, messages: List[Dict[str, str]], **kwargs) -> LLMResponse:
        """Generate response from messages."""
        pass
    
    @abstractmethod
    def stream_generate(self, messages: List[Dict[str, str]], **kwargs):
        """Generate streaming response from messages."""
        pass


class MistralAdapter(BaseLLMAdapter):
    """Adapter for Mistral AI models."""
    
    def __init__(self, config: Dict[str, Any]):
        super().__init__(config)
        self.model = ChatMistralAI(
            model=config.get("model", "mistral-medium-latest")
        )
    
    def generate(self, messages: List[Dict[str, str]], **kwargs) -> LLMResponse:
        """Generate response using Mistral."""
        response = self.model.invoke(messages)
        
        return LLMResponse(
            content=response.content,
            model=self.config.get("model", "mistral-medium-latest"),
            provider="mistral",
            metadata={"usage": getattr(response, 'usage_metadata', {})}
        )
    
    def stream_generate(self, messages: List[Dict[str, str]], **kwargs):
        """Generate streaming response using Mistral."""
        for chunk in self.model.stream(messages):
            if chunk.content:
                yield chunk.content


class OpenAIAdapter(BaseLLMAdapter):
    """Adapter for OpenAI models."""
    
    def __init__(self, config: Dict[str, Any]):
        super().__init__(config)
        self.model = ChatOpenAI(
            model=config.get("model", "gpt-4o-mini")
        )
    
    def generate(self, messages: List[Dict[str, str]], **kwargs) -> LLMResponse:
        """Generate response using OpenAI."""
        response = self.model.invoke(messages)
        
        return LLMResponse(
            content=response.content,
            model=self.config.get("model", "gpt-4o-mini"),
            provider="openai",
            metadata={"usage": getattr(response, 'usage_metadata', {})}
        )
    
    def stream_generate(self, messages: List[Dict[str, str]], **kwargs):
        """Generate streaming response using OpenAI."""
        for chunk in self.model.stream(messages):
            if chunk.content:
                yield chunk.content


class OllamaAdapter(BaseLLMAdapter):
    """Adapter for Ollama models."""
    
    def __init__(self, config: Dict[str, Any]):
        super().__init__(config)
        self.model = ChatOllama(
            model=config.get("model", "mistral-small3.1:24b-instruct-2503-q8_0"),
            base_url=config.get("base_url", "http://localhost:11434/"),
            temperature=config.get("temperature", 0.8),
            num_predict=config.get("num_predict", 256)
        )
    
    def generate(self, messages: List[Dict[str, str]], **kwargs) -> LLMResponse:
        """Generate response using Ollama."""
        response = self.model.invoke(messages)
        
        return LLMResponse(
            content=response.content,
            model=self.config.get("model", "mistral-small3.1:24b-instruct-2503-q8_0"),
            provider="ollama",
            metadata={}
        )
    
    def stream_generate(self, messages: List[Dict[str, str]], **kwargs):
        """Generate streaming response using Ollama."""
        for chunk in self.model.stream(messages):
            if chunk.content:
                yield chunk.content


class OpenRouterAdapter(BaseLLMAdapter):
    """Adapter for OpenRouter models."""
    
    def __init__(self, config: Dict[str, Any]):
        super().__init__(config)

        # Prepare custom headers for OpenRouter (optional)
        headers = {}
        if config.get("site_url"):
            headers["HTTP-Referer"] = config["site_url"]
        if config.get("site_name"):
            headers["X-Title"] = config["site_name"]
        
        # Initialize ChatOpenAI with OpenRouter configuration
        self.model = ChatOpenAI(
            model=config.get("model", "openai/gpt-3.5-turbo"),
            api_key=config.get("api_key"),
            base_url=config.get("base_url", "https://openrouter.ai/api/v1"),
            default_headers= headers if headers else {},
            temperature=config.get("temperature", 0.7),
            max_tokens=config.get("max_tokens", 1000)
        )
    
    def generate(self, messages: List[Dict[str, str]], **kwargs) -> LLMResponse:
        """Generate response using OpenRouter."""
        response = self.model.invoke(messages)
        
        return LLMResponse(
            content=response.content,
            model=self.config.get("model", "openai/gpt-3.5-turbo"),
            provider="openrouter",
            metadata={"usage": getattr(response, 'usage_metadata', {})}
        )
    
    def stream_generate(self, messages: List[Dict[str, str]], **kwargs):
        """Generate streaming response using OpenRouter."""
        for chunk in self.model.stream(messages):
            if chunk.content:
                yield chunk.content


class LegacyAdapter(BaseLLMAdapter):
    """Adapter for legacy LLM clients (INF_PROVIDERS, NVIDIA, etc.)."""
    
    def __init__(self, config: Dict[str, Any], client_type: str):
        super().__init__(config)
        self.client_type = client_type
        self.client = self._create_client()
    
    def _create_client(self):
        """Create legacy client based on type."""
        if self.client_type == "INF_PROVIDERS":
            return _create_inf_provider_client()
        elif self.client_type == "NVIDIA":
            return _create_nvidia_client()
        elif self.client_type == "DEDICATED":
            return _create_dedicated_endpoint_client()
        else:  # SERVERLESS
            return _create_serverless_client()
    
    def generate(self, messages: List[Dict[str, str]], **kwargs) -> LLMResponse:
        """Generate response using legacy client."""
        max_tokens = kwargs.get('max_tokens', self.config.get('max_tokens', 768))
        
        if self.client_type == "INF_PROVIDERS":
            response = self.client.chat.completions.create(
                model=self.config.get("model"),
                messages=messages,
                max_tokens=max_tokens
            )
            content = response.choices[0].message.content
            
        elif self.client_type == "NVIDIA":
            response = self.client.chat_completion(
                model=self.config.get("model"),
                messages=messages,
                max_tokens=max_tokens
            )
            content = response.choices[0].message.content
            
        else:  # DEDICATED or SERVERLESS
            response = self.client.chat_completion(
                messages=messages,
                max_tokens=max_tokens
            )
            content = response.choices[0].message.content
        
        return LLMResponse(
            content=content,
            model=self.config.get("model", "unknown"),
            provider=self.client_type.lower(),
            metadata={}
        )
    
    def stream_generate(self, messages: List[Dict[str, str]], **kwargs):
        """Generate streaming response using legacy client."""
        # Legacy clients may not support streaming in the same way
        # This is a simplified implementation
        response = self.generate(messages, **kwargs)
        words = response.content.split()
        for word in words:
            yield word + " "


class LLMRegistry:
    """Registry for managing different LLM adapters."""
    
    def __init__(self):
        self.adapters = {}
        self.adapter_configs = {}
    
    def register_adapter(self, name: str, adapter_class: type, config: Dict[str, Any]):
        """Register an LLM adapter (lazy instantiation)."""
        self.adapter_configs[name] = (adapter_class, config)
    
    def get_adapter(self, name: str) -> BaseLLMAdapter:
        """Get an LLM adapter by name (lazy instantiation)."""
        if name not in self.adapter_configs:
            raise ValueError(f"Unknown LLM adapter: {name}")
        
        # Lazy instantiation - only create when needed
        if name not in self.adapters:
            adapter_class, config = self.adapter_configs[name]
            self.adapters[name] = adapter_class(config)
        
        return self.adapters[name]
    
    def list_adapters(self) -> List[str]:
        """List available adapter names."""
        return list(self.adapter_configs.keys())


def create_llm_registry(config: Dict[str, Any]) -> LLMRegistry:
    """
    Create and populate LLM registry from configuration.
    
    Args:
        config: Configuration dictionary
        
    Returns:
        Populated LLMRegistry
    """
    registry = LLMRegistry()
    reader_config = config.get("reader", {})
    
    # Register simple adapters
    if "MISTRAL" in reader_config:
        registry.register_adapter("mistral", MistralAdapter, reader_config["MISTRAL"])
    
    if "OPENAI" in reader_config:
        registry.register_adapter("openai", OpenAIAdapter, reader_config["OPENAI"])
    
    if "OLLAMA" in reader_config:
        registry.register_adapter("ollama", OllamaAdapter, reader_config["OLLAMA"])
    
    if "OPENROUTER" in reader_config:
        registry.register_adapter("openrouter", OpenRouterAdapter, reader_config["OPENROUTER"])
    
    # Register legacy adapters
    # legacy_types = ["INF_PROVIDERS", "NVIDIA", "DEDICATED"]
    legacy_types = ["INF_PROVIDERS"]
    for legacy_type in legacy_types:
        if legacy_type in reader_config:
            registry.register_adapter(
                legacy_type.lower(),
                lambda cfg, lt=legacy_type: LegacyAdapter(cfg, lt),
                reader_config[legacy_type]
            )
    
    return registry


def get_llm_client(provider: str, config: Dict[str, Any]) -> BaseLLMAdapter:
    """
    Get LLM client for specified provider.
    
    Args:
        provider: Provider name (mistral, openai, ollama, etc.)
        config: Configuration dictionary
        
    Returns:
        LLM adapter instance
    """
    registry = create_llm_registry(config)
    return registry.get_adapter(provider)