Update agent_service.py

agent_service.py

@@ -1,474 +1,242 @@
-"""
-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,
-        feedback_tracking=None  # NEW: Optional feedback tracking
-    ):
-        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
-        self.feedback_tracking = feedback_tracking
-        
-        # 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,
-        access_token: Optional[str] = None,  # NEW: For authenticated API calls
-        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)
-            access_token: JWT token for authenticated API calls
-            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}")
-        print(f"🔑 Auth Info:")
-        print(f"  - User ID: {user_id}")
-        print(f"  - Access Token: {'✅ Received' if access_token else '❌ None'}")
-        
-        # Store user_id and access_token for tool calls
-        self.current_user_id = user_id
-        self.current_access_token = access_token
-        if access_token:
-            print(f"  - Stored access_token for tools: {access_token[:20]}...")
-        if user_id:
-            print(f"  - Stored user_id for tools: {user_id}")
-        
-        # 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']}")
-            
-            # Auto-inject real user_id for get_purchased_events
-            if tool_call['tool_name'] == 'get_purchased_events' and self.current_user_id:
-                print(f"🔄 Auto-injecting real user_id: {self.current_user_id}")
-                tool_call['arguments']['user_id'] = self.current_user_id
-            
-            tool_result = await self.tools_service.execute_tool(
-                tool_call['tool_name'],
-                tool_call['arguments'],
-                access_token=self.current_access_token  # Pass access_token
-            )
-            
-            # 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 chat_completion (conversational)
-        """
-        try:
-            from huggingface_hub import AsyncInferenceClient
-            
-            # Create async client
-            client = AsyncInferenceClient(token=self.hf_token)
-            
-            # Call HF API with chat completion (conversational)
-            response_text = ""
-            async for message in await client.chat_completion(
-                messages=messages,  # Use messages directly
-                model="meta-llama/Llama-3.3-70B-Instruct",
-                max_tokens=512,
-                temperature=0.7,
-                stream=True
-            ):
-                if message.choices and message.choices[0].delta.content:
-                    response_text += message.choices[0].delta.content
-            
-            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(
-            query_embedding=embedding,
-            limit=5
-        )
-        
-        # Format results
-        formatted = []
-        for i, result in enumerate(results, 1):
-            # Result is a dict with keys: id, score, payload
-            payload = result.get("payload", {})
-            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()
+"""
+Tools Service for LLM Function Calling
+HuggingFace-compatible với prompt engineering
+"""
+import httpx
+from typing import List, Dict, Any, Optional
+import json
+import asyncio
+
+
+class ToolsService:
+    """
+    Manages external API tools that LLM can call via prompt engineering
+    """
+    
+    def __init__(self, base_url: str = "https://hoalacrent.io.vn/api/v0", feedback_tracking=None):
+        self.base_url = base_url
+        self.client = httpx.AsyncClient(timeout=10.0)
+        self.feedback_tracking = feedback_tracking  # NEW: Feedback tracking
+    
+    def get_tools_definition(self) -> List[Dict]:
+        """
+        Return list of tool definitions (OpenAI format style)
+        Used for constructing system prompt
+        """
+        return [
+            {
+                "name": "search_events",
+                "description": "Tìm kiếm sự kiện phù hợp theo từ khóa, vibe, hoặc thời gian.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "query": {"type": "string", "description": "Từ khóa tìm kiếm (VD: 'nhạc rock', 'hài kịch')"},
+                        "vibe": {"type": "string", "description": "Vibe/Mood (VD: 'chill', 'sôi động', 'hẹn hò')"},
+                        "time": {"type": "string", "description": "Thời gian (VD: 'cuối tuần này', 'tối nay')"}
+                    }
+                }
+            },
+            {
+                "name": "get_event_details",
+                "description": "Lấy thông tin chi tiết (giá, địa điểm, thời gian) của sự kiện.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "event_id": {"type": "string", "description": "ID của sự kiện (MongoDB ID)"}
+                    },
+                    "required": ["event_id"]
+                }
+            },
+            {
+                "name": "get_purchased_events",
+                "description": "Kiểm tra lịch sử các sự kiện user đã mua vé hoặc tham gia.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "user_id": {"type": "string", "description": "ID của user"}
+                    },
+                    "required": ["user_id"]
+                }
+            },
+            {
+                "name": "save_feedback",
+                "description": "Lưu đánh giá/feedback của user về sự kiện.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "event_id": {"type": "string", "description": "ID sự kiện"},
+                        "rating": {"type": "integer", "description": "Số sao đánh giá (1-5)"},
+                        "comment": {"type": "string", "description": "Nội dung nhận xét"}
+                    },
+                    "required": ["event_id", "rating"]
+                }
+            },
+            {
+                "name": "save_lead",
+                "description": "Lưu thông tin khách hàng quan tâm (Lead).",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "email": {"type": "string"},
+                        "phone": {"type": "string"},
+                        "interest": {"type": "string"}
+                    }
+                }
+            }
+        ]
+
+    async def execute_tool(self, tool_name: str, arguments: Dict, access_token: Optional[str] = None) -> Any:
+        """
+        Execute a tool by name with arguments
+        
+        Args:
+            tool_name: Name of the tool
+            arguments: Tool arguments
+            access_token: JWT token for authenticated API calls
+        """
+        print(f"\n🔧 ===== TOOL EXECUTION =====")
+        print(f"Tool: {tool_name}")
+        print(f"Arguments: {arguments}")
+        print(f"Access Token: {'✅ Present' if access_token else '❌ Missing'}")
+        if access_token:
+            print(f"Token preview: {access_token[:30]}...")
+        
+        try:
+            if tool_name == "get_event_details":
+                return await self._get_event_details(arguments.get("event_id") or arguments.get("event_code"))
+            
+            elif tool_name == "get_purchased_events":
+                print(f"→ Calling _get_purchased_events with:")
+                print(f"   user_id: {arguments.get('user_id')}")
+                print(f"   access_token: {'✅' if access_token else '❌'}")
+                return await self._get_purchased_events(
+                    arguments.get("user_id"),
+                    access_token=access_token  # Pass access_token
+                )
+            
+            elif tool_name == "save_feedback":
+                return await self._save_feedback(
+                    arguments.get("event_id"),
+                    arguments.get("rating"),
+                    arguments.get("comment")
+                )
+            
+            elif tool_name == "search_events":
+                # Note: This usually requires RAG service, so we return a special signal
+                # The Agent Service will handle RAG search
+                return {"action": "run_rag_search", "query": arguments}
+            
+            elif tool_name == "save_lead":
+                # Placeholder for lead saving
+                return {"success": True, "message": "Lead saved successfully"}
+            
+            else:
+                return {"error": f"Unknown tool: {tool_name}"}
+                
+        except Exception as e:
+            print(f"⚠️ Tool Execution Error: {e}")
+            return {"error": str(e)}
+
+    async def _get_event_details(self, event_id: str) -> Dict:
+        """Call API to get event details"""
+        if not event_id:
+            return {"error": "Missing event_id"}
+            
+        try:
+            url = f"{self.base_url}/event/get-event-by-id"
+            
+            response = await self.client.get(url, params={"id": event_id})
+            if response.status_code == 200:
+                data = response.json()
+                if data.get("success"):
+                    return data.get("data")
+            return {"error": "Event not found", "details": response.text}
+        except Exception as e:
+            return {"error": str(e)}
+
+    async def _get_purchased_events(self, user_id: str, access_token: Optional[str] = None) -> List[Dict]:
+        """Call API to get purchased events for user (requires auth)"""
+        print(f"\n🎫 ===== GET PURCHASED EVENTS =====")
+        print(f"User ID: {user_id}")
+        print(f"Access Token: {'✅ Present' if access_token else '❌ Missing'}")
+        
+        if not user_id:
+            print("⚠️ No user_id provided, returning empty list")
+            return []
+            
+        try:
+            url = f"{self.base_url}/event/get-purchase-event-by-user-id/{user_id}"
+            print(f"🔍 API URL: {url}")
+            
+            # Add Authorization header if access_token provided
+            headers = {}
+            if access_token:
+                headers["Authorization"] = f"Bearer {access_token}"
+                print(f"🔐 Authorization Header Added:")
+                print(f"   Bearer {access_token[:30]}...")
+            else:
+                print(f"⚠️ No access_token - calling API without auth")
+            
+            print(f"📡 Headers: {headers}")
+            print(f"🚀 Calling API...")
+            
+            response = await self.client.get(url, headers=headers)
+            
+            print(f"📥 Response Status: {response.status_code}")
+            print(f"📦 Response Headers: {dict(response.headers)}")
+            
+            if response.status_code == 200:
+                data = response.json()
+                print(f"✅ Success! Data keys: {list(data.keys())}")
+                events = data.get("data", [])
+                print(f"📊 Found {len(events)} purchased events")
+                
+                # Log actual event data
+                if events:
+                    print(f"\n📋 Purchased Events Details:")
+                    for i, event in enumerate(events, 1):
+                        print(f"{i}. Event Code: {event.get('eventCode', 'N/A')}")
+                        print(f"   Event Name: {event.get('eventName', 'N/A')}")
+                        print(f"   Event ID: {event.get('_id', 'N/A')}")
+                        print(f"   Full data: {event}")
+                
+                return events
+            else:
+                print(f"❌ API Error: {response.status_code}")
+                print(f"Response body: {response.text[:500]}")
+                return []
+                
+        except Exception as e:
+            print(f"⚠️ Exception in _get_purchased_events: {type(e).__name__}: {e}")
+            import traceback
+            traceback.print_exc()
+            return []
+
+    async def _save_feedback(self, event_id: str, rating: int, comment: str, user_id: str = None, event_code: str = None) -> Dict:
+        """Save feedback and mark as completed in tracking system"""
+        print(f"\n📝 ===== SAVE FEEDBACK =====")
+        print(f"Event ID: {event_id}")
+        print(f"Event Code: {event_code}")
+        print(f"User ID: {user_id}")
+        print(f"Rating: {rating}")
+        print(f"Comment: {comment}")
+        
+        # TODO: Implement real API call to save feedback
+        # For now, just mark in tracking system
+        if self.feedback_tracking and user_id and event_code:
+            success = self.feedback_tracking.mark_feedback_given(
+                user_id=user_id,
+                event_code=event_code,
+                rating=rating,
+                comment=comment
+            )
+            if success:
+                print(f"✅ Feedback tracked in database")
+            else:
+                print(f"⚠️ Failed to track feedback")
+        
+        return {"success": True, "message": "Feedback recorded"}
+
+    async def close(self):
+        """Close HTTP client"""
+        await self.client.aclose()