Update chat_endpoint.py

chat_endpoint.py

@@ -1,261 +1,282 @@
-"""
-Chat endpoint với Multi-turn Conversation + Function Calling
-"""
-from fastapi import HTTPException
-from datetime import datetime
-from huggingface_hub import InferenceClient
-from typing import Dict, List
-import json
-
-
-async def chat_endpoint(
-    request,  # ChatRequest
-    conversation_service,
-    tools_service,
-    advanced_rag,
-    embedding_service,
-    qdrant_service,
-    chat_history_collection,
-    hf_token
-):
-    """
-    Multi-turn conversational chatbot với RAG + Function Calling
-    
-    Flow:
-    1. Session management - create hoặc load existing session
-    2. RAG search - retrieve context nếu enabled
-    3. Build messages với conversation history +  tools prompt
-    4. LLM generation - có thể trigger tool calls
-    5. Execute tools nếu cần
-    6. Final LLM response với tool results
-    7. Save to conversation history
-    """
-    try:
-        # ===== 1. SESSION MANAGEMENT =====
-        session_id = request.session_id
-        if not session_id:
-            # Create new session (server-side)
-            session_id = conversation_service.create_session(
-                metadata={"user_agent": "api", "created_via": "chat_endpoint"}
-            )
-            print(f"Created new session: {session_id}")
-        else:
-            # Validate existing session
-            if not conversation_service.session_exists(session_id):
-                raise HTTPException(
-                    status_code=404, 
-                    detail=f"Session {session_id} not found. It may have expired."
-                )
-        
-        # Load conversation history
-        conversation_history = conversation_service.get_conversation_history(session_id)
-        
-        # ===== 2. RAG SEARCH =====
-        context_used = []
-        rag_stats = None
-        context_text = ""
-        
-        if request.use_rag:
-            if request.use_advanced_rag:
-                # Use Advanced RAG Pipeline
-                hf_client = None
-                if request.hf_token or hf_token:
-                    hf_client = InferenceClient(token=request.hf_token or hf_token)
-                
-                documents, stats = advanced_rag.hybrid_rag_pipeline(
-                    query=request.message,
-                    top_k=request.top_k,
-                    score_threshold=request.score_threshold,
-                    use_reranking=request.use_reranking,
-                    use_compression=request.use_compression,
-                    use_query_expansion=request.use_query_expansion,
-                    max_context_tokens=500,
-                    hf_client=hf_client
-                )
-                
-                # Convert to dict format
-                context_used = [
-                    {
-                        "id": doc.id,
-                        "confidence": doc.confidence,
-                        "metadata": doc.metadata
-                    }
-                    for doc in documents
-                ]
-                rag_stats = stats
-                
-                # Format context
-                context_text = advanced_rag.format_context_for_llm(documents)
-            else:
-                # Basic RAG
-                query_embedding = embedding_service.encode_text(request.message)
-                results = qdrant_service.search(
-                    query_embedding=query_embedding,
-                    limit=request.top_k,
-                    score_threshold=request.score_threshold
-                )
-                context_used = results
-                
-                context_text = "\n\nRelevant Context:\n"
-                for i, doc in enumerate(context_used, 1):
-                    doc_text = doc["metadata"].get("text", "")
-                    if not doc_text:
-                        doc_text = " ".join(doc["metadata"].get("texts", []))
-                    confidence = doc["confidence"]
-                    context_text += f"\n[{i}] (Confidence: {confidence:.2f})\n{doc_text}\n"
-        
-        # ===== 3. BUILD MESSAGES với TOOLS PROMPT =====
-        messages = []
-        
-        # System message với RAG context + Tools instruction
-        if request.use_rag and context_used:
-            if request.use_advanced_rag:
-                base_prompt = advanced_rag.build_rag_prompt(
-                    query="",  # Query sẽ đi trong user message
-                    context=context_text,
-                    system_message=request.system_message
-                )
-            else:
-                base_prompt = f"""{request.system_message}
-
-{context_text}
-
-HƯỚNG DẪN:
-- Sử dụng thông tin từ context trên để trả lời câu hỏi.
-- Trả lời tự nhiên, thân thiện, không copy nguyên văn.
-- Nếu tìm thấy sự kiện, hãy tóm tắt các thông tin quan trọng nhất.
-"""
-        else:
-            base_prompt = request.system_message
-        
-        # Add tools instruction nếu enabled
-        if request.enable_tools:
-            tools_prompt = tools_service.get_tools_prompt()
-            system_message_with_tools = f"{base_prompt}\n\n{tools_prompt}"
-        else:
-            system_message_with_tools = base_prompt
-        
-        # Bắt đầu messages với system
-        messages.append({"role": "system", "content": system_message_with_tools})
-        
-        # Add conversation history (past turns)
-        messages.extend(conversation_history)
-        
-        # Add current user message
-        messages.append({"role": "user", "content": request.message})
-        
-        # ===== 4. LLM GENERATION =====
-        token = request.hf_token or hf_token
-        tool_calls_made = []
-        
-        if not token:
-            response = f"""[LLM Response Placeholder]
-
-Context retrieved: {len(context_used)} documents
-User question: {request.message}
-Session: {session_id}
-
-To enable actual LLM generation:
-1. Set HUGGINGFACE_TOKEN environment variable, OR
-2. Pass hf_token in request body
-"""
-        else:
-            try:
-                client = InferenceClient(
-                    token=token,
-                    model="openai/gpt-oss-20b"  # Hoặc model khác
-                )
-                
-                # First LLM call
-                first_response = ""
-                for msg in client.chat_completion(
-                    messages,
-                    max_tokens=request.max_tokens,
-                    stream=True,
-                    temperature=request.temperature,
-                    top_p=request.top_p,
-                ):
-                    choices = msg.choices
-                    if len(choices) and choices[0].delta.content:
-                        first_response += choices[0].delta.content
-                
-                # ===== 5. PARSE & EXECUTE TOOLS =====
-                if request.enable_tools:
-                    tool_result = await tools_service.parse_and_execute(first_response)
-                    
-                    if tool_result:
-                        # Tool was called!
-                        tool_calls_made.append(tool_result)
-                        
-                        # Add tool result to messages
-                        messages.append({"role": "assistant", "content": first_response})
-                        messages.append({
-                            "role": "user", 
-                            "content": f"TOOL RESULT:\n{json.dumps(tool_result['result'], ensure_ascii=False, indent=2)}\n\nHãy dùng thông tin này để trả lời câu hỏi của user."
-                        })
-                        
-                        # Second LLM call với tool results
-                        final_response = ""
-                        for msg in client.chat_completion(
-                            messages,
-                            max_tokens=request.max_tokens,
-                            stream=True,
-                            temperature=request.temperature,
-                            top_p=request.top_p,
-                        ):
-                            choices = msg.choices
-                            if len(choices) and choices[0].delta.content:
-                                final_response += choices[0].delta.content
-                        
-                        response = final_response
-                    else:
-                        # No tool call, use first response
-                        response = first_response
-                else:
-                    response = first_response
-                
-            except Exception as e:
-                response = f"Error generating response with LLM: {str(e)}\n\nContext was retrieved successfully, but LLM generation failed."
-        
-        # ===== 6. SAVE TO CONVERSATION HISTORY =====
-        conversation_service.add_message(
-            session_id, 
-            "user", 
-            request.message
-        )
-        conversation_service.add_message(
-            session_id, 
-            "assistant", 
-            response,
-            metadata={
-                "rag_stats": rag_stats,
-                "tool_calls": tool_calls_made,
-                "context_count": len(context_used)
-            }
-        )
-        
-        # Also save to legacy chat_history collection
-        chat_data = {
-            "session_id": session_id,
-            "user_message": request.message,
-            "assistant_response": response,
-            "context_used": context_used,
-            "tool_calls": tool_calls_made,
-            "timestamp": datetime.utcnow()
-        }
-        chat_history_collection.insert_one(chat_data)
-        
-        # ===== 7. RETURN RESPONSE =====
-        return {
-            "response": response,
-            "context_used": context_used,
-            "timestamp": datetime.utcnow().isoformat(),
-            "rag_stats": rag_stats,
-            "session_id": session_id,
-            "tool_calls": tool_calls_made if tool_calls_made else None
-        }
-        
-    except HTTPException:
-        raise
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error: {str(e)}")
+"""
+Chat endpoint với Multi-turn Conversation + Function Calling
+"""
+from fastapi import HTTPException
+from datetime import datetime
+from huggingface_hub import InferenceClient
+from typing import Dict, List
+import json
+
+
+async def chat_endpoint(
+    request,  # ChatRequest
+    conversation_service,
+    tools_service,
+    advanced_rag,
+    embedding_service,
+    qdrant_service,
+    chat_history_collection,
+    hf_token
+):
+    """
+    Multi-turn conversational chatbot với RAG + Function Calling
+    
+    Flow:
+    1. Session management - create hoặc load existing session
+    2. RAG search - retrieve context nếu enabled
+    3. Build messages với conversation history +  tools prompt
+    4. LLM generation - có thể trigger tool calls
+    5. Execute tools nếu cần
+    6. Final LLM response với tool results
+    7. Save to conversation history
+    """
+    try:
+        # ===== 1. SESSION MANAGEMENT =====
+        session_id = request.session_id
+        if not session_id:
+            # Create new session (server-side)
+            session_id = conversation_service.create_session(
+                metadata={"user_agent": "api", "created_via": "chat_endpoint"}
+            )
+            print(f"Created new session: {session_id}")
+        else:
+            # Validate existing session
+            if not conversation_service.session_exists(session_id):
+                raise HTTPException(
+                    status_code=404, 
+                    detail=f"Session {session_id} not found. It may have expired."
+                )
+        
+        # Load conversation history
+        conversation_history = conversation_service.get_conversation_history(session_id)
+        
+        # ===== 2. RAG SEARCH =====
+        context_used = []
+        rag_stats = None
+        context_text = ""
+        
+        if request.use_rag:
+            if request.use_advanced_rag:
+                # Use Advanced RAG Pipeline
+                hf_client = None
+                if request.hf_token or hf_token:
+                    hf_client = InferenceClient(token=request.hf_token or hf_token)
+                
+                documents, stats = advanced_rag.hybrid_rag_pipeline(
+                    query=request.message,
+                    top_k=request.top_k,
+                    score_threshold=request.score_threshold,
+                    use_reranking=request.use_reranking,
+                    use_compression=request.use_compression,
+                    use_query_expansion=request.use_query_expansion,
+                    max_context_tokens=500,
+                    hf_client=hf_client
+                )
+                
+                # Convert to dict format
+                context_used = [
+                    {
+                        "id": doc.id,
+                        "confidence": doc.confidence,
+                        "metadata": doc.metadata
+                    }
+                    for doc in documents
+                ]
+                rag_stats = stats
+                
+                # Format context
+                context_text = advanced_rag.format_context_for_llm(documents)
+            else:
+                # Basic RAG
+                query_embedding = embedding_service.encode_text(request.message)
+                results = qdrant_service.search(
+                    query_embedding=query_embedding,
+                    limit=request.top_k,
+                    score_threshold=request.score_threshold
+                )
+                context_used = results
+                
+                context_text = "\n\nRelevant Context:\n"
+                for i, doc in enumerate(context_used, 1):
+                    doc_text = doc["metadata"].get("text", "")
+                    if not doc_text:
+                        doc_text = " ".join(doc["metadata"].get("texts", []))
+                    confidence = doc["confidence"]
+                    context_text += f"\n[{i}] (Confidence: {confidence:.2f})\n{doc_text}\n"
+        
+        # ===== 3. BUILD MESSAGES với TOOLS PROMPT =====
+        messages = []
+        
+        # System message với RAG context + Tools instruction
+        if request.use_rag and context_used:
+            if request.use_advanced_rag:
+                base_prompt = advanced_rag.build_rag_prompt(
+                    query="",  # Query sẽ đi trong user message
+                    context=context_text,
+                    system_message=request.system_message
+                )
+            else:
+                base_prompt = f"""{request.system_message}
+
+{context_text}
+
+HƯỚNG DẪN:
+- Sử dụng thông tin từ context trên để trả lời câu hỏi.
+- Trả lời tự nhiên, thân thiện, không copy nguyên văn.
+- Nếu tìm thấy sự kiện, hãy tóm tắt các thông tin quan trọng nhất.
+"""
+        else:
+            base_prompt = request.system_message
+        
+        # Add tools instruction nếu enabled
+        if request.enable_tools:
+            tools_prompt = tools_service.get_tools_prompt()
+            system_message_with_tools = f"{base_prompt}\n\n{tools_prompt}"
+        else:
+            system_message_with_tools = base_prompt
+        
+        # Bắt đầu messages với system
+        messages.append({"role": "system", "content": system_message_with_tools})
+        
+        # Add conversation history (past turns)
+        messages.extend(conversation_history)
+        
+        # Add current user message
+        messages.append({"role": "user", "content": request.message})
+        
+        # ===== 4. LLM GENERATION =====
+        token = request.hf_token or hf_token
+        tool_calls_made = []
+        
+        if not token:
+            response = f"""[LLM Response Placeholder]
+
+Context retrieved: {len(context_used)} documents
+User question: {request.message}
+Session: {session_id}
+
+To enable actual LLM generation:
+1. Set HUGGINGFACE_TOKEN environment variable, OR
+2. Pass hf_token in request body
+"""
+        else:
+            try:
+                client = InferenceClient(
+                    token=token,
+                    model="openai/gpt-oss-20b"  # Hoặc model khác
+                )
+                
+                # First LLM call
+                first_response = ""
+                try:
+                    for msg in client.chat_completion(
+                        messages,
+                        max_tokens=request.max_tokens,
+                        stream=True,
+                        temperature=request.temperature,
+                        top_p=request.top_p,
+                    ):
+                        choices = msg.choices
+                        if len(choices) and choices[0].delta.content:
+                            first_response += choices[0].delta.content
+                except Exception as e:
+                    # HF API throws error when LLM returns JSON (tool call)
+                    # Extract the "failed_generation" from error
+                    error_str = str(e)
+                    if "tool_use_failed" in error_str and "failed_generation" in error_str:
+                        # Parse error dict to get the actual JSON response
+                        import ast
+                        try:
+                            error_dict = ast.literal_eval(error_str)
+                            first_response = error_dict.get("failed_generation", "")
+                        except:
+                            # Fallback: extract JSON from string
+                            import re
+                            match = re.search(r"'failed_generation': '({.*?})'", error_str)
+                            if match:
+                                first_response = match.group(1)
+                            else:
+                                raise e
+                    else:
+                        raise e
+                
+                # ===== 5. PARSE & EXECUTE TOOLS =====
+                if request.enable_tools:
+                    tool_result = await tools_service.parse_and_execute(first_response)
+                    
+                    if tool_result:
+                        # Tool was called!
+                        tool_calls_made.append(tool_result)
+                        
+                        # Add tool result to messages
+                        messages.append({"role": "assistant", "content": first_response})
+                        messages.append({
+                            "role": "user", 
+                            "content": f"TOOL RESULT:\n{json.dumps(tool_result['result'], ensure_ascii=False, indent=2)}\n\nHãy dùng thông tin này để trả lời câu hỏi của user."
+                        })
+                        
+                        # Second LLM call với tool results
+                        final_response = ""
+                        for msg in client.chat_completion(
+                            messages,
+                            max_tokens=request.max_tokens,
+                            stream=True,
+                            temperature=request.temperature,
+                            top_p=request.top_p,
+                        ):
+                            choices = msg.choices
+                            if len(choices) and choices[0].delta.content:
+                                final_response += choices[0].delta.content
+                        
+                        response = final_response
+                    else:
+                        # No tool call, use first response
+                        response = first_response
+                else:
+                    response = first_response
+                
+            except Exception as e:
+                response = f"Error generating response with LLM: {str(e)}\n\nContext was retrieved successfully, but LLM generation failed."
+        
+        # ===== 6. SAVE TO CONVERSATION HISTORY =====
+        conversation_service.add_message(
+            session_id, 
+            "user", 
+            request.message
+        )
+        conversation_service.add_message(
+            session_id, 
+            "assistant", 
+            response,
+            metadata={
+                "rag_stats": rag_stats,
+                "tool_calls": tool_calls_made,
+                "context_count": len(context_used)
+            }
+        )
+        
+        # Also save to legacy chat_history collection
+        chat_data = {
+            "session_id": session_id,
+            "user_message": request.message,
+            "assistant_response": response,
+            "context_used": context_used,
+            "tool_calls": tool_calls_made,
+            "timestamp": datetime.utcnow()
+        }
+        chat_history_collection.insert_one(chat_data)
+        
+        # ===== 7. RETURN RESPONSE =====
+        return {
+            "response": response,
+            "context_used": context_used,
+            "timestamp": datetime.utcnow().isoformat(),
+            "rag_stats": rag_stats,
+            "session_id": session_id,
+            "tool_calls": tool_calls_made if tool_calls_made else None
+        }
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error: {str(e)}")