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34e27fb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 | from fastapi import APIRouter, HTTPException, status, Depends
from sqlmodel import Session
from typing import Optional
from uuid import UUID
from pydantic import BaseModel
import json
import logging
from ..models.user import User
from ..models.conversation import Conversation
from ..models.message import Message
from ..database import get_session_dep
from ..utils.deps import get_current_user
from ..services.conversation_service import ConversationService
from ..agent_config import todo_agent_config
from ..mcp_server import get_mcp_tools_for_gemin_api
from ..mcp_tools.task_tools import (
execute_add_task,
execute_list_tasks,
execute_complete_task,
execute_delete_task,
execute_update_task,
execute_create_project,
execute_list_projects,
execute_get_calendar,
AddTaskParams,
ListTasksParams,
CompleteTaskParams,
DeleteTaskParams,
UpdateTaskParams,
CreateProjectParams,
ListProjectsParams,
GetCalendarParams
)
router = APIRouter(prefix="/api/{user_id}/chat", tags=["chat"])
logger = logging.getLogger(__name__)
class ChatRequest(BaseModel):
conversation_id: Optional[int] = None
message: str
class ChatResponse(BaseModel):
conversation_id: int
response: str
tool_calls: list = []
@router.post("/", response_model=ChatResponse)
def chat(
user_id: UUID,
chat_request: ChatRequest,
current_user: User = Depends(get_current_user),
session: Session = Depends(get_session_dep)
):
"""
Handle chat requests from users using AI assistant with tool calling.
"""
logger.info(f"Chat endpoint called with user_id: {user_id}, current_user.id: {current_user.id}")
# Verify that the user_id in the URL matches the authenticated user
if current_user.id != user_id:
logger.warning(f"User ID mismatch: path user_id={user_id}, auth user_id={current_user.id}")
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail="Access denied"
)
# Get or create conversation
conversation_id = chat_request.conversation_id
if conversation_id is None:
conversation = Conversation(user_id=user_id)
session.add(conversation)
session.commit()
session.refresh(conversation)
conversation_id = conversation.id
else:
conversation = session.get(Conversation, conversation_id)
if not conversation or conversation.user_id != user_id:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail="Conversation not found"
)
# Store user message
user_message = Message(
conversation_id=conversation_id,
user_id=user_id,
role="user",
content=chat_request.message
)
session.add(user_message)
session.commit()
# Get conversation history (last 10 messages for context)
conversation_history = ConversationService.get_messages(
conversation_id=conversation_id,
user_id=user_id,
db_session=session,
limit=10
)
history_for_agent = []
for msg in conversation_history:
history_for_agent.append({
"role": msg.role,
"content": msg.content
})
agent_config = todo_agent_config
tools = get_mcp_tools_for_gemin_api()
messages = [
{"role": "system", "content": agent_config["instructions"]},
*history_for_agent,
{"role": "user", "content": chat_request.message}
]
try:
# Call the AI agent with tools
response = agent_config["client"].chat.completions.create(
model=agent_config["model"],
messages=messages,
tools=tools,
tool_choice="auto"
)
response_message = response.choices[0].message
tool_calls = response_message.tool_calls
# If there are tool calls, execute them
if tool_calls:
# Add assistant's tool call message to history
messages.append(response_message)
for tool_call in tool_calls:
function_name = tool_call.function.name
function_args = json.loads(tool_call.function.arguments)
# Force the user_id to be the current user's ID for security
function_args["user_id"] = str(user_id)
logger.info(f"Executing tool: {function_name} with args: {function_args}")
result = None
try:
if function_name == "add_task":
result = execute_add_task(AddTaskParams(**function_args))
elif function_name == "list_tasks":
result = execute_list_tasks(ListTasksParams(**function_args))
elif function_name == "complete_task":
result = execute_complete_task(CompleteTaskParams(**function_args))
elif function_name == "delete_task":
result = execute_delete_task(DeleteTaskParams(**function_args))
elif function_name == "update_task":
result = execute_update_task(UpdateTaskParams(**function_args))
elif function_name == "create_project":
result = execute_create_project(CreateProjectParams(**function_args))
elif function_name == "list_projects":
result = execute_list_projects(ListProjectsParams(**function_args))
elif function_name == "get_calendar":
result = execute_get_calendar(GetCalendarParams(**function_args))
tool_result_content = json.dumps(result.dict() if result else {"error": "Unknown tool"})
except Exception as e:
logger.error(f"Error executing tool {function_name}: {str(e)}")
tool_result_content = json.dumps({"error": str(e)})
messages.append({
"tool_call_id": tool_call.id,
"role": "tool",
"name": function_name,
"content": tool_result_content,
})
# Get final response from AI after tool results
second_response = agent_config["client"].chat.completions.create(
model=agent_config["model"],
messages=messages,
)
ai_response = second_response.choices[0].message.content
else:
ai_response = response_message.content
except Exception as e:
logger.error(f"Error in AI processing: {str(e)}")
ai_response = f"I encountered an error processing your request. Please try again later. (Error: {str(e)})"
# Store assistant response
assistant_message = Message(
conversation_id=conversation_id,
user_id=user_id,
role="assistant",
content=ai_response
)
session.add(assistant_message)
session.commit()
return ChatResponse(
conversation_id=conversation_id,
response=ai_response,
tool_calls=[] # We already handled them
) |