AI-VideoAssistant/engine/providers/llm/openai.py

"""LLM (Large Language Model) Service implementations.

Provides OpenAI-compatible LLM integration with streaming support
for real-time voice conversation.
"""

import os
import asyncio
import uuid
from typing import AsyncIterator, Optional, List, Dict, Any, Callable, Awaitable
from loguru import logger

from adapters.control_plane.backend import build_backend_adapter_from_settings
from providers.common.base import BaseLLMService, LLMMessage, LLMStreamEvent, ServiceState

# Try to import openai
try:
    from openai import AsyncOpenAI
    OPENAI_AVAILABLE = True
except ImportError:
    OPENAI_AVAILABLE = False
    logger.warning("openai package not available - LLM service will be disabled")


class OpenAILLMService(BaseLLMService):
    """
    OpenAI-compatible LLM service.
    
    Supports streaming responses for low-latency voice conversation.
    Works with OpenAI API, Azure OpenAI, and compatible APIs.
    """
    
    def __init__(
        self,
        model: str = "gpt-4o-mini",
        api_key: Optional[str] = None,
        base_url: Optional[str] = None,
        system_prompt: Optional[str] = None,
        knowledge_config: Optional[Dict[str, Any]] = None,
        knowledge_searcher: Optional[Callable[..., Awaitable[List[Dict[str, Any]]]]] = None,
    ):
        """
        Initialize OpenAI LLM service.
        
        Args:
            model: Model name (e.g., "gpt-4o-mini", "gpt-4o")
            api_key: Provider API key
            base_url: Custom API base URL (for Azure or compatible APIs)
            system_prompt: Default system prompt for conversations
        """
        super().__init__(model=model)
        
        self.api_key = api_key
        self.base_url = base_url or os.getenv("LLM_API_URL") or os.getenv("OPENAI_API_URL")
        self.system_prompt = system_prompt or (
            "You are a helpful, friendly voice assistant. "
            "Keep your responses concise and conversational. "
            "Respond naturally as if having a phone conversation."
        )
        
        self.client: Optional[AsyncOpenAI] = None
        self._cancel_event = asyncio.Event()
        self._knowledge_config: Dict[str, Any] = knowledge_config or {}
        if knowledge_searcher is None:
            adapter = build_backend_adapter_from_settings()
            self._knowledge_searcher = adapter.search_knowledge_context
        else:
            self._knowledge_searcher = knowledge_searcher
        self._tool_schemas: List[Dict[str, Any]] = []

    _RAG_DEFAULT_RESULTS = 5
    _RAG_MAX_RESULTS = 8
    _RAG_MAX_CONTEXT_CHARS = 4000
    
    async def connect(self) -> None:
        """Initialize OpenAI client."""
        if not OPENAI_AVAILABLE:
            raise RuntimeError("openai package not installed")
        
        if not self.api_key:
            raise ValueError("OpenAI API key not provided")
        
        self.client = AsyncOpenAI(
            api_key=self.api_key,
            base_url=self.base_url
        )
        self.state = ServiceState.CONNECTED
        logger.info(f"OpenAI LLM service connected: model={self.model}")
    
    async def disconnect(self) -> None:
        """Close OpenAI client."""
        if self.client:
            await self.client.close()
            self.client = None
        self.state = ServiceState.DISCONNECTED
        logger.info("OpenAI LLM service disconnected")
    
    def _prepare_messages(self, messages: List[LLMMessage]) -> List[Dict[str, Any]]:
        """Prepare messages list with system prompt."""
        result = []
        
        # Add system prompt if not already present
        has_system = any(m.role == "system" for m in messages)
        if not has_system and self.system_prompt:
            result.append({"role": "system", "content": self.system_prompt})
        
        # Add all messages
        for msg in messages:
            result.append(msg.to_dict())
        
        return result

    def set_knowledge_config(self, config: Optional[Dict[str, Any]]) -> None:
        """Update runtime knowledge retrieval config."""
        self._knowledge_config = config or {}

    def set_tool_schemas(self, schemas: Optional[List[Dict[str, Any]]]) -> None:
        """Update runtime tool schemas."""
        self._tool_schemas = []
        if not isinstance(schemas, list):
            return
        for item in schemas:
            if not isinstance(item, dict):
                continue
            fn = item.get("function")
            if isinstance(fn, dict) and fn.get("name"):
                self._tool_schemas.append(item)
            elif item.get("name"):
                self._tool_schemas.append(
                    {
                        "type": "function",
                        "function": {
                            "name": str(item.get("name")),
                            "description": str(item.get("description") or ""),
                            "parameters": item.get("parameters") or {"type": "object", "properties": {}},
                        },
                    }
                )

    @staticmethod
    def _coerce_int(value: Any, default: int) -> int:
        try:
            return int(value)
        except (TypeError, ValueError):
            return default

    def _resolve_kb_id(self) -> Optional[str]:
        cfg = self._knowledge_config if isinstance(self._knowledge_config, dict) else {}
        kb_id = str(
            cfg.get("kbId")
            or cfg.get("knowledgeBaseId")
            or cfg.get("knowledge_base_id")
            or ""
        ).strip()
        return kb_id or None

    def _build_knowledge_prompt(self, results: List[Dict[str, Any]]) -> Optional[str]:
        if not results:
            return None

        lines = [
            "You have retrieved the following knowledge base snippets.",
            "Use them only when relevant to the latest user request.",
            "If snippets are insufficient, say you are not sure instead of guessing.",
            "",
        ]

        used_chars = 0
        used_count = 0
        for item in results:
            content = str(item.get("content") or "").strip()
            if not content:
                continue
            if used_chars >= self._RAG_MAX_CONTEXT_CHARS:
                break

            metadata = item.get("metadata") if isinstance(item.get("metadata"), dict) else {}
            doc_id = metadata.get("document_id")
            chunk_index = metadata.get("chunk_index")
            distance = item.get("distance")

            source_parts = []
            if doc_id:
                source_parts.append(f"doc={doc_id}")
            if chunk_index is not None:
                source_parts.append(f"chunk={chunk_index}")
            source = f" ({', '.join(source_parts)})" if source_parts else ""

            distance_text = ""
            try:
                if distance is not None:
                    distance_text = f", distance={float(distance):.4f}"
            except (TypeError, ValueError):
                distance_text = ""

            remaining = self._RAG_MAX_CONTEXT_CHARS - used_chars
            snippet = content[:remaining].strip()
            if not snippet:
                continue

            used_count += 1
            lines.append(f"[{used_count}{source}{distance_text}] {snippet}")
            used_chars += len(snippet)

        if used_count == 0:
            return None

        return "\n".join(lines)

    async def _with_knowledge_context(self, messages: List[LLMMessage]) -> List[LLMMessage]:
        cfg = self._knowledge_config if isinstance(self._knowledge_config, dict) else {}
        enabled = cfg.get("enabled", True)
        if isinstance(enabled, str):
            enabled = enabled.strip().lower() not in {"false", "0", "off", "no"}
        if not enabled:
            return messages

        kb_id = self._resolve_kb_id()
        if not kb_id:
            return messages

        latest_user = ""
        for msg in reversed(messages):
            if msg.role == "user":
                latest_user = (msg.content or "").strip()
                break
        if not latest_user:
            return messages

        n_results = self._coerce_int(cfg.get("nResults"), self._RAG_DEFAULT_RESULTS)
        n_results = max(1, min(n_results, self._RAG_MAX_RESULTS))

        results = await self._knowledge_searcher(
            kb_id=kb_id,
            query=latest_user,
            n_results=n_results,
        )
        prompt = self._build_knowledge_prompt(results)
        if not prompt:
            return messages

        logger.debug(f"RAG context injected (kb_id={kb_id}, chunks={len(results)})")
        rag_system = LLMMessage(role="system", content=prompt)
        if messages and messages[0].role == "system":
            return [messages[0], rag_system, *messages[1:]]
        return [rag_system, *messages]
    
    async def generate(
        self,
        messages: List[LLMMessage],
        temperature: float = 0.7,
        max_tokens: Optional[int] = None
    ) -> str:
        """
        Generate a complete response.
        
        Args:
            messages: Conversation history
            temperature: Sampling temperature
            max_tokens: Maximum tokens to generate
            
        Returns:
            Complete response text
        """
        if not self.client:
            raise RuntimeError("LLM service not connected")
        
        rag_messages = await self._with_knowledge_context(messages)
        prepared = self._prepare_messages(rag_messages)
        
        try:
            response = await self.client.chat.completions.create(
                model=self.model,
                messages=prepared,
                temperature=temperature,
                max_tokens=max_tokens
            )
            
            content = response.choices[0].message.content or ""
            logger.debug(f"LLM response: {content[:100]}...")
            return content
            
        except Exception as e:
            logger.error(f"LLM generation error: {e}")
            raise
    
    async def generate_stream(
        self,
        messages: List[LLMMessage],
        temperature: float = 0.7,
        max_tokens: Optional[int] = None
    ) -> AsyncIterator[LLMStreamEvent]:
        """
        Generate response in streaming mode.
        
        Args:
            messages: Conversation history
            temperature: Sampling temperature
            max_tokens: Maximum tokens to generate
            
        Yields:
            Structured stream events
        """
        if not self.client:
            raise RuntimeError("LLM service not connected")
        
        rag_messages = await self._with_knowledge_context(messages)
        prepared = self._prepare_messages(rag_messages)
        self._cancel_event.clear()
        tool_accumulator: Dict[int, Dict[str, str]] = {}
        openai_tools = self._tool_schemas or None
        
        try:
            create_args: Dict[str, Any] = dict(
                model=self.model,
                messages=prepared,
                temperature=temperature,
                max_tokens=max_tokens,
                stream=True,
            )
            if openai_tools:
                create_args["tools"] = openai_tools
                create_args["tool_choice"] = "auto"
            stream = await self.client.chat.completions.create(**create_args)
            
            async for chunk in stream:
                # Check for cancellation
                if self._cancel_event.is_set():
                    logger.info("LLM stream cancelled")
                    break

                if not chunk.choices:
                    continue

                choice = chunk.choices[0]
                delta = getattr(choice, "delta", None)
                if delta and getattr(delta, "content", None):
                    content = delta.content
                    yield LLMStreamEvent(type="text_delta", text=content)

                # OpenAI streams function calls via incremental tool_calls deltas.
                tool_calls = getattr(delta, "tool_calls", None) if delta else None
                if tool_calls:
                    for tc in tool_calls:
                        index = getattr(tc, "index", 0) or 0
                        item = tool_accumulator.setdefault(
                            int(index),
                            {"id": "", "name": "", "arguments": ""},
                        )
                        tc_id = getattr(tc, "id", None)
                        if tc_id:
                            item["id"] = str(tc_id)
                        fn = getattr(tc, "function", None)
                        if fn:
                            fn_name = getattr(fn, "name", None)
                            if fn_name:
                                item["name"] = str(fn_name)
                            fn_args = getattr(fn, "arguments", None)
                            if fn_args:
                                item["arguments"] += str(fn_args)

                finish_reason = getattr(choice, "finish_reason", None)
                if finish_reason == "tool_calls" and tool_accumulator:
                    for _, payload in sorted(tool_accumulator.items(), key=lambda row: row[0]):
                        call_name = payload.get("name", "").strip()
                        if not call_name:
                            continue
                        call_id = payload.get("id", "").strip() or f"call_{uuid.uuid4().hex[:10]}"
                        yield LLMStreamEvent(
                            type="tool_call",
                            tool_call={
                                "id": call_id,
                                "type": "function",
                                "function": {
                                    "name": call_name,
                                    "arguments": payload.get("arguments", "") or "{}",
                                },
                            },
                        )
                    yield LLMStreamEvent(type="done")
                    return

                if finish_reason in {"stop", "length", "content_filter"}:
                    yield LLMStreamEvent(type="done")
                    return
            
        except asyncio.CancelledError:
            logger.info("LLM stream cancelled via asyncio")
            raise
        except Exception as e:
            logger.error(f"LLM streaming error: {e}")
            raise
    
    def cancel(self) -> None:
        """Cancel ongoing generation."""
        self._cancel_event.set()


class MockLLMService(BaseLLMService):
    """
    Mock LLM service for testing without API calls.
    """
    
    def __init__(self, response_delay: float = 0.5):
        super().__init__(model="mock")
        self.response_delay = response_delay
        self.responses = [
            "Hello! How can I help you today?",
            "That's an interesting question. Let me think about it.",
            "I understand. Is there anything else you'd like to know?",
            "Great! I'm here if you need anything else.",
        ]
        self._response_index = 0
    
    async def connect(self) -> None:
        self.state = ServiceState.CONNECTED
        logger.info("Mock LLM service connected")
    
    async def disconnect(self) -> None:
        self.state = ServiceState.DISCONNECTED
        logger.info("Mock LLM service disconnected")
    
    async def generate(
        self,
        messages: List[LLMMessage],
        temperature: float = 0.7,
        max_tokens: Optional[int] = None
    ) -> str:
        await asyncio.sleep(self.response_delay)
        response = self.responses[self._response_index % len(self.responses)]
        self._response_index += 1
        return response
    
    async def generate_stream(
        self,
        messages: List[LLMMessage],
        temperature: float = 0.7,
        max_tokens: Optional[int] = None
    ) -> AsyncIterator[LLMStreamEvent]:
        response = await self.generate(messages, temperature, max_tokens)
        
        # Stream word by word
        words = response.split()
        for i, word in enumerate(words):
            if i > 0:
                yield LLMStreamEvent(type="text_delta", text=" ")
            yield LLMStreamEvent(type="text_delta", text=word)
            await asyncio.sleep(0.05)  # Simulate streaming delay
        yield LLMStreamEvent(type="done")