fix(api): add error logging for agent card generation and request processing

src/api/agent_routes.py

@@ -206,6 +206,7 @@ async def get_agent_json(
         }
         return AGENT_CARD
     except Exception as e:
+        logger.error(f"Error generating agent card: {str(e)}")
         raise HTTPException(
             status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
             detail="Error generating agent card",
@@ -240,6 +241,7 @@ async def handle_task(
         try:
             task_request = await request.json()
         except Exception as e:
+            logger.error(f"Error processing request: {str(e)}")
             raise HTTPException(
                 status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid request format"
             )

src/config/settings.py

@@ -32,11 +32,6 @@ class Settings(BaseSettings):
     LOG_LEVEL: str = os.getenv("LOG_LEVEL", "INFO")
     LOG_DIR: str = "logs"
 
-    # Knowledge API settings
-    KNOWLEDGE_API_URL: str = os.getenv("KNOWLEDGE_API_URL", "http://localhost:5540")
-    KNOWLEDGE_API_KEY: str = os.getenv("KNOWLEDGE_API_KEY", "")
-    TENANT_ID: str = os.getenv("TENANT_ID", "")
-
     # Redis settings
     REDIS_HOST: str = os.getenv("REDIS_HOST", "localhost")
     REDIS_PORT: int = int(os.getenv("REDIS_PORT", 6379))

src/schemas/schemas.py

@@ -1,4 +1,4 @@
-from pydantic import BaseModel, Field, validator, EmailStr
+from pydantic import BaseModel, Field, validator
 from typing import Optional, Dict, Any, Union, List
 from datetime import datetime
 from uuid import UUID

src/services/agent_builder.py

@@ -9,157 +9,13 @@ from src.services.custom_tools import CustomToolBuilder
 from src.services.mcp_service import MCPService
 from sqlalchemy.orm import Session
 from contextlib import AsyncExitStack
-from google.adk.agents.callback_context import CallbackContext
-from google.adk.models import LlmResponse, LlmRequest
 from google.adk.tools import load_memory
 
-import requests
-import os
 from datetime import datetime
 
 logger = setup_logger(__name__)
 
 
-def before_model_callback(
-    callback_context: CallbackContext, llm_request: LlmRequest
-) -> Optional[LlmResponse]:
-    """
-    Callback executed before the model generates a response.
-    Always executes a search in the knowledge base before proceeding.
-    """
-    try:
-        agent_name = callback_context.agent_name
-        logger.debug(f"🔄 Before model call for agent: {agent_name}")
-
-        # Extract the last user message
-        last_user_message = ""
-        if llm_request.contents and llm_request.contents[-1].role == "user":
-            if llm_request.contents[-1].parts:
-                last_user_message = llm_request.contents[-1].parts[0].text
-                logger.debug(f"📝 Última mensagem do usuário: {last_user_message}")
-
-        # Extract and format the history of messages
-        history = []
-        for content in llm_request.contents:
-            if content.parts and content.parts[0].text:
-                # Replace 'model' with 'assistant' in the role
-                role = "assistant" if content.role == "model" else content.role
-                history.append(
-                    {
-                        "role": role,
-                        "content": {
-                            "type": "text",
-                            "text": content.parts[0].text,
-                        },
-                    }
-                )
-
-        # log the history of messages
-        logger.debug(f"📝 History of messages: {history}")
-
-        if last_user_message:
-            logger.info("🔍 Executing knowledge base search")
-            # Execute the knowledge base search synchronously
-            search_results = search_knowledge_base_function_sync(
-                last_user_message, history
-            )
-
-            if search_results:
-                logger.info("✅ Resultados encontrados, adicionando ao contexto")
-
-                # Get the original system instruction
-                original_instruction = llm_request.config.system_instruction or ""
-
-                # Add the search results and history to the system context
-                modified_text = (
-                    original_instruction
-                    + "\n\n<knowledge_context>\n"
-                    + str(search_results)
-                    + "\n</knowledge_context>\n\n<history>\n"
-                    + str(history)
-                    + "\n</history>"
-                )
-                llm_request.config.system_instruction = modified_text
-
-                logger.debug(
-                    "📝 System instruction updated with search results and history"
-                )
-            else:
-                logger.warning("⚠️ No results found in the search")
-        else:
-            logger.warning("⚠️ No user message found")
-
-        logger.info("✅ before_model_callback finished")
-        return None
-    except Exception as e:
-        logger.error(f"❌ Error in before_model_callback: {str(e)}", exc_info=True)
-        return None
-
-
-def search_knowledge_base_function_sync(query: str, history=[]):
-    """
-    Search knowledge base synchronously.
-
-    Args:
-        query (str): The search query, with user message and history messages, all in one string
-
-    Returns:
-        dict: The search results
-    """
-    try:
-        logger.info("🔍 Starting knowledge base search")
-        logger.debug(f"Received query: {query}")
-
-        # url = os.getenv("KNOWLEDGE_API_URL") + "/api/v1/search"
-        url = os.getenv("KNOWLEDGE_API_URL") + "/api/v1/knowledge"
-        tenant_id = os.getenv("TENANT_ID")
-        url = url + "?tenant_id=" + tenant_id
-        logger.debug(f"API URL: {url}")
-        logger.debug(f"Tenant ID: {tenant_id}")
-
-        headers = {
-            "x-api-key": f"{os.getenv('KNOWLEDGE_API_KEY')}",
-            "Content-Type": "application/json",
-        }
-        logger.debug(f"Headers configured: {headers}")
-
-        payload = {
-            "gemini_api_key": os.getenv("GOOGLE_API_KEY"),
-            "gemini_model": "gemini-2.0-flash-lite-001",
-            "gemini_temperature": 0.7,
-            "query": query,
-            "tenant_id": tenant_id,
-            "history": history,
-        }
-
-        logger.debug(f"Request payload: {payload}")
-
-        # Using requests to make a synchronous request with timeout
-        logger.info("🔄 Making synchronous request to the knowledge API")
-        # response = requests.post(url, headers=headers, json=payload)
-        response = requests.get(url, headers=headers, timeout=10)
-
-        if response.status_code == 200:
-            logger.info("✅ Search executed successfully")
-            result = response.json()
-            logger.debug(f"Search result: {result}")
-            return result
-        else:
-            logger.error(
-                f"❌ Error performing search. Status code: {response.status_code}"
-            )
-            return None
-    except requests.exceptions.Timeout:
-        logger.error("❌ Timeout performing search")
-        return None
-    except requests.exceptions.RequestException as e:
-        logger.error(f"❌ Error in request: {str(e)}", exc_info=True)
-        return None
-    except Exception as e:
-        logger.error(f"❌ Error performing search: {str(e)}", exc_info=True)
-        return None
-
-
 class AgentBuilder:
     def __init__(self, db: Session):
         self.db = db
@@ -204,7 +60,6 @@ class AgentBuilder:
         # before_model_callback_func = None
         if agent.config.get("load_memory"):
             all_tools.append(load_memory)
-            # before_model_callback_func = before_model_callback
             formatted_prompt = (
                 formatted_prompt
                 + "\n\n<memory_instructions>ALWAYS use the load_memory tool to retrieve knowledge for your context</memory_instructions>\n\n"
@@ -217,7 +72,6 @@ class AgentBuilder:
                 instruction=formatted_prompt,
                 description=agent.description,
                 tools=all_tools,
-                # before_model_callback=before_model_callback_func,
             ),
             mcp_exit_stack,
         )

src/services/agent_service.py

@@ -1,4 +1,3 @@
-import os
 from sqlalchemy.orm import Session
 from sqlalchemy.exc import SQLAlchemyError
 from fastapi import HTTPException, status
@@ -51,8 +50,6 @@ def get_agents_by_client(
 
         agents = query.offset(skip).limit(limit).all()
 
-        # A propriedade virtual agent_card_url será automaticamente incluída
-        # quando os agentes forem serializados para JSON
         return agents
     except SQLAlchemyError as e:
         logger.error(f"Error searching for client agents {client_id}: {str(e)}")
@@ -146,8 +143,6 @@ def create_agent(db: Session, agent: AgentCreate) -> Agent:
         db.refresh(db_agent)
         logger.info(f"Agent created successfully: {db_agent.id}")
 
-        # A propriedade virtual agent_card_url será automaticamente incluída
-        # quando o agente for serializado para JSON
         return db_agent
     except SQLAlchemyError as e:
         db.rollback()

src/services/mcp_server_service.py

@@ -2,7 +2,7 @@ from sqlalchemy.orm import Session
 from sqlalchemy.exc import SQLAlchemyError
 from fastapi import HTTPException, status
 from src.models.models import MCPServer
-from src.schemas.schemas import MCPServerCreate, ToolConfig
+from src.schemas.schemas import MCPServerCreate
 from typing import List, Optional
 import uuid
 import logging