agent_service.py

"""
Agent Service - Central Brain for Sales & Feedback Agents
Manages LLM conversation loop with tool calling
"""
from typing import Dict, Any, List, Optional
import os
from tools_service import ToolsService


class AgentService:
    """
    Manages the conversation loop between User -> LLM -> Tools -> Response
    """
    
    def __init__(
        self,
        tools_service: ToolsService,
        embedding_service,
        qdrant_service,
        advanced_rag,
        hf_token: str
    ):
        self.tools_service = tools_service
        self.embedding_service = embedding_service
        self.qdrant_service = qdrant_service
        self.advanced_rag = advanced_rag
        self.hf_token = hf_token
        
        # Load system prompts
        self.prompts = self._load_prompts()
    
    def _load_prompts(self) -> Dict[str, str]:
        """Load system prompts from files"""
        prompts = {}
        prompts_dir = "prompts"
        
        for mode in ["sales_agent", "feedback_agent"]:
            filepath = os.path.join(prompts_dir, f"{mode}.txt")
            try:
                with open(filepath, 'r', encoding='utf-8') as f:
                    prompts[mode] = f.read()
                print(f"✓ Loaded prompt: {mode}")
            except Exception as e:
                print(f"⚠️ Error loading {mode} prompt: {e}")
                prompts[mode] = ""
        
        return prompts
    
    async def chat(
        self,
        user_message: str,
        conversation_history: List[Dict],
        mode: str = "sales",  # "sales" or "feedback"
        user_id: Optional[str] = None,
        max_iterations: int = 3
    ) -> Dict[str, Any]:
        """
        Main conversation loop
        
        Args:
            user_message: User's input
            conversation_history: Previous messages [{"role": "user", "content": ...}, ...]
            mode: "sales" or "feedback"
            user_id: User ID (for feedback mode to check purchase history)
            max_iterations: Maximum tool call iterations to prevent infinite loops
        
        Returns:
            {
                "message": "Bot response",
                "tool_calls": [...],  # List of tools called (for debugging)
                "mode": mode
            }
        """
        print(f"\n🤖 Agent Mode: {mode}")
        print(f"👤 User Message: {user_message}")
        
        # Select system prompt
        system_prompt = self._get_system_prompt(mode)
        
        # Build conversation context
        messages = self._build_messages(system_prompt, conversation_history, user_message)
        
        # Agentic loop: LLM may call tools multiple times
        tool_calls_made = []
        current_response = None
        
        for iteration in range(max_iterations):
            print(f"\n🔄 Iteration {iteration + 1}")
            
            # Call LLM
            llm_response = await self._call_llm(messages)
            print(f"🧠 LLM Response: {llm_response[:200]}...")
            
            # Check if LLM wants to call a tool
            tool_call = self._parse_tool_call(llm_response)
            
            if not tool_call:
                # No tool call -> This is the final response
                current_response = llm_response
                break
            
            # Execute tool
            print(f"🔧 Tool Called: {tool_call['tool_name']}")
            tool_result = await self.tools_service.execute_tool(
                tool_call['tool_name'],
                tool_call['arguments']
            )
            
            # Record tool call
            tool_calls_made.append({
                "function": tool_call['tool_name'],
                "arguments": tool_call['arguments'],
                "result": tool_result
            })
            
            # Add tool result to conversation
            messages.append({
                "role": "assistant",
                "content": llm_response
            })
            messages.append({
                "role": "system",
                "content": f"Tool Result:\n{self._format_tool_result({'result': tool_result})}"
            })
            
            # If tool returns "run_rag_search", handle it specially
            if isinstance(tool_result, dict) and tool_result.get("action") == "run_rag_search":
                rag_results = await self._execute_rag_search(tool_result["query"])
                messages[-1]["content"] = f"RAG Search Results:\n{rag_results}"
        
        # Clean up response
        final_response = current_response or llm_response
        final_response = self._clean_response(final_response)
        
        return {
            "message": final_response,
            "tool_calls": tool_calls_made,
            "mode": mode
        }
    
    def _get_system_prompt(self, mode: str) -> str:
        """Get system prompt for selected mode with tools definition"""
        prompt_key = f"{mode}_agent" if mode in ["sales", "feedback"] else "sales_agent"
        base_prompt = self.prompts.get(prompt_key, "")
        
        # Add tools definition
        tools_definition = self._get_tools_definition()
        
        return f"{base_prompt}\n\n{tools_definition}"
    
    def _get_tools_definition(self) -> str:
        """Get tools definition in text format for prompt"""
        return """
# AVAILABLE TOOLS

You can call the following tools when needed. To call a tool, output a JSON block like this:

```json
{
  "tool_call": "tool_name",
  "arguments": {
    "arg1": "value1",
    "arg2": "value2"
  }
}
```

## Tools List:

### 1. search_events
Search for events matching user criteria.
Arguments:
- query (string): Search keywords
- vibe (string, optional): Mood/vibe (e.g., "chill", "sôi động")
- time (string, optional): Time period (e.g., "cuối tuần này")

Example:
```json
{"tool_call": "search_events", "arguments": {"query": "nhạc rock", "vibe": "sôi động"}}
```

### 2. get_event_details
Get detailed information about a specific event.
Arguments:
- event_id (string): Event ID from search results

Example:
```json
{"tool_call": "get_event_details", "arguments": {"event_id": "6900ae38eb03f29702c7fd1d"}}
```

### 3. get_purchased_events (Feedback mode only)
Check which events the user has attended.
Arguments:
- user_id (string): User ID

Example:
```json
{"tool_call": "get_purchased_events", "arguments": {"user_id": "user_123"}}
```

### 4. save_feedback
Save user's feedback/review for an event.
Arguments:
- event_id (string): Event ID
- rating (integer): 1-5 stars
- comment (string, optional): User's comment

Example:
```json
{"tool_call": "save_feedback", "arguments": {"event_id": "abc123", "rating": 5, "comment": "Tuyệt vời!"}}
```

### 5. save_lead
Save customer contact information.
Arguments:
- email (string, optional): Email address
- phone (string, optional): Phone number
- interest (string, optional): What they're interested in

Example:
```json
{"tool_call": "save_lead", "arguments": {"email": "user@example.com", "interest": "Rock show"}}
```

**IMPORTANT:**
- Call tools ONLY when you need real-time data
- After receiving tool results, respond naturally to the user
- Don't expose raw JSON to users - always format nicely
"""
    
    def _build_messages(
        self,
        system_prompt: str,
        history: List[Dict],
        user_message: str
    ) -> List[Dict]:
        """Build messages array for LLM"""
        messages = [{"role": "system", "content": system_prompt}]
        
        # Add conversation history
        messages.extend(history)
        
        # Add current user message
        messages.append({"role": "user", "content": user_message})
        
        return messages
    
    async def _call_llm(self, messages: List[Dict]) -> str:
        """
        Call HuggingFace LLM directly using InferenceClient
        """
        try:
            from huggingface_hub import AsyncInferenceClient
            
            # Build prompt from messages
            prompt = self._messages_to_prompt(messages)
            
            # Create async client
            client = AsyncInferenceClient(token=self.hf_token)
            
            # Call HF API
            response_text = ""
            async for token in await client.text_generation(
                prompt=prompt,
                model="openai/gpt-oss-20b",
                max_new_tokens=512,
                temperature=0.7,
                stream=True
            ):
                response_text += token
            
            return response_text
        except Exception as e:
            print(f"⚠️ LLM Call Error: {e}")
            return "Xin lỗi, tôi đang gặp chút vấn đề kỹ thuật. Bạn thử lại sau nhé!"
    
    def _messages_to_prompt(self, messages: List[Dict]) -> str:
        """Convert messages array to single prompt string"""
        prompt_parts = []
        
        for msg in messages:
            role = msg["role"]
            content = msg["content"]
            
            if role == "system":
                prompt_parts.append(f"[SYSTEM]\n{content}\n")
            elif role == "user":
                prompt_parts.append(f"[USER]\n{content}\n")
            elif role == "assistant":
                prompt_parts.append(f"[ASSISTANT]\n{content}\n")
        
        return "\n".join(prompt_parts)
    
    def _format_tool_result(self, tool_result: Dict) -> str:
        """Format tool result for feeding back to LLM"""
        result = tool_result.get("result", {})
        
        if isinstance(result, dict):
            # Pretty print key info
            formatted = []
            for key, value in result.items():
                if key not in ["success", "error"]:
                    formatted.append(f"{key}: {value}")
            return "\n".join(formatted)
        
        return str(result)
    
    async def _execute_rag_search(self, query_params: Dict) -> str:
        """
        Execute RAG search for event discovery
        Called when LLM wants to search_events
        """
        query = query_params.get("query", "")
        vibe = query_params.get("vibe", "")
        
        # Build search query
        search_text = f"{query} {vibe}".strip()
        
        print(f"🔍 RAG Search: {search_text}")
        
        # Use embedding + qdrant
        embedding = self.embedding_service.encode_text(search_text)
        results = self.qdrant_service.search(
            collection_name="events",
            query_vector=embedding,
            limit=5
        )
        
        # Format results
        formatted = []
        for i, result in enumerate(results, 1):
            payload = result.payload or {}
            texts = payload.get("texts", [])
            text = texts[0] if texts else ""
            event_id = payload.get("id_use", "")
            
            formatted.append(f"{i}. {text[:100]}... (ID: {event_id})")
        
        return "\n".join(formatted) if formatted else "Không tìm thấy sự kiện phù hợp."
    
    def _parse_tool_call(self, llm_response: str) -> Optional[Dict]:
        """
        Parse LLM response to detect tool calls using structured JSON
        
        Returns:
            {"tool_name": "...", "arguments": {...}} or None
        """
        import json
        import re
        
        # Method 1: Look for JSON code block
        json_match = re.search(r'```json\s*(\{.*?\})\s*```', llm_response, re.DOTALL)
        if json_match:
            try:
                data = json.loads(json_match.group(1))
                return self._extract_tool_from_json(data)
            except json.JSONDecodeError:
                pass
        
        # Method 2: Look for inline JSON object
        # Find all potential JSON objects
        json_objects = re.findall(r'\{[^{}]*(?:\{[^{}]*\}[^{}]*)*\}', llm_response)
        for json_str in json_objects:
            try:
                data = json.loads(json_str)
                tool_call = self._extract_tool_from_json(data)
                if tool_call:
                    return tool_call
            except json.JSONDecodeError:
                continue
        
        # Method 3: Nested JSON (for complex structures)
        try:
            # Find outermost curly braces
            if '{' in llm_response and '}' in llm_response:
                start = llm_response.find('{')
                # Find matching closing brace
                count = 0
                for i, char in enumerate(llm_response[start:], start):
                    if char == '{':
                        count += 1
                    elif char == '}':
                        count -= 1
                        if count == 0:
                            json_str = llm_response[start:i+1]
                            data = json.loads(json_str)
                            return self._extract_tool_from_json(data)
        except (json.JSONDecodeError, ValueError):
            pass
        
        return None
    
    def _extract_tool_from_json(self, data: dict) -> Optional[Dict]:
        """
        Extract tool call information from parsed JSON
        
        Supports multiple formats:
        - {"tool_call": "search_events", "arguments": {...}}
        - {"function": "search_events", "parameters": {...}}
        - {"name": "search_events", "args": {...}}
        """
        # Format 1: tool_call + arguments
        if "tool_call" in data and isinstance(data["tool_call"], str):
            return {
                "tool_name": data["tool_call"],
                "arguments": data.get("arguments", {})
            }
        
        # Format 2: function + parameters
        if "function" in data:
            return {
                "tool_name": data["function"],
                "arguments": data.get("parameters", data.get("arguments", {}))
            }
        
        # Format 3: name + args
        if "name" in data:
            return {
                "tool_name": data["name"],
                "arguments": data.get("args", data.get("arguments", {}))
            }
        
        # Format 4: Direct tool name as key
        valid_tools = ["search_events", "get_event_details", "get_purchased_events", "save_feedback", "save_lead"]
        for tool in valid_tools:
            if tool in data:
                return {
                    "tool_name": tool,
                    "arguments": data[tool] if isinstance(data[tool], dict) else {}
                }
        
        return None
    
    def _clean_response(self, response: str) -> str:
        """Remove JSON artifacts from final response"""
        # Remove JSON blocks
        if "```json" in response:
            response = response.split("```json")[0]
        if "```" in response:
            response = response.split("```")[0]
        
        # Remove tool call markers
        if "{" in response and "tool_call" in response:
            # Find the last natural sentence before JSON
            lines = response.split("\n")
            cleaned = []
            for line in lines:
                if "{" in line and "tool_call" in line:
                    break
                cleaned.append(line)
            response = "\n".join(cleaned)
        
        return response.strip()