Iyeoluwa Akinrinola
496 lines
17 KiB
Python
496 lines
17 KiB
Python
"""
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FastAPI application for the AI service.
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This service acts as a backend for OpenWebUI, providing OpenWebUI-compatible API endpoints.
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The service supports document-based question answering using OpenWebUI's knowledge database:
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- Set use_rag=True in API requests to enable Retrieval Augmented Generation
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- When enabled, the service will use OpenWebUI's knowledge database to find relevant information
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- Documents uploaded to OpenWebUI will be used to augment the model's responses
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"""
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from fastapi import FastAPI, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel, Field
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from typing import List, Optional
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import uuid
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import asyncio
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from datetime import datetime, timezone
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from ai_service.models.model_service import model_service
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from ai_service.models.chat_service import chat_service
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from ai_service.openwebui_api import router as openwebui_router
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from ai_service.config import config
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from ai_service import bot_manager
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# Create FastAPI app
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app = FastAPI(
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title="AI Service API",
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description="Backend API for OpenWebUI",
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version="1.0.0"
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)
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# Add CORS middleware
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"], # Allow all origins
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allow_credentials=True,
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allow_methods=["*"], # Allow all methods
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allow_headers=["*"], # Allow all headers
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)
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# Include OpenWebUI-compatible API routes
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app.include_router(openwebui_router, prefix="/api")
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# Include Ollama proxy routes
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app.include_router(openwebui_router, prefix="/ollama")
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# API startup event
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@app.on_event("startup")
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async def startup_event():
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"""
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Startup event for the API.
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"""
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print("=" * 50)
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print(f"Starting AI Service API on {config.API_HOST}:{config.API_PORT}")
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print(f"OpenWebUI URL: {config.OPENWEBUI_URL}")
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print(f"Default model: {config.DEFAULT_MODEL}")
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# Start the OpenWebUI bot if enabled
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if config.BOT_ENABLED:
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print("=" * 50)
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print("Starting OpenWebUI bot...")
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# Start the bot with configuration
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success = await bot_manager.start_bot(
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openwebui_url=config.OPENWEBUI_URL,
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api_key=config.OPENWEBUI_API_KEY,
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model_id=config.DEFAULT_MODEL,
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system_prompt=config.BOT_SYSTEM_PROMPT,
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temperature=config.BOT_TEMPERATURE,
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max_tokens=config.BOT_MAX_TOKENS,
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top_p=config.BOT_TOP_P,
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triggers=config.BOT_TRIGGERS,
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respond_to_all=config.BOT_RESPOND_TO_ALL
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)
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if success:
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print("Bot started successfully!")
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else:
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print("Failed to start bot. Check the logs for details.")
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print("=" * 50)
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else:
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print("OpenWebUI bot is disabled. Set BOT_ENABLED=true in .env to enable it.")
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print("=" * 50)
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# API shutdown event
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@app.on_event("shutdown")
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async def shutdown_event():
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"""
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Shutdown event for the API.
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"""
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print("=" * 50)
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print("Shutting down AI Service API...")
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# Stop the OpenWebUI bot if it's running
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if bot_manager.is_bot_running():
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print("Stopping OpenWebUI bot...")
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success = await bot_manager.stop_bot()
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if success:
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print("Bot stopped successfully!")
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else:
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print("Failed to stop bot. Check the logs for details.")
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print("=" * 50)
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# Define API models for health check
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class HealthResponse(BaseModel):
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"""Response model for health check."""
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status: str = Field(..., description="Health status")
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class ModelInfo(BaseModel):
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"""Model for model information."""
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id: str = Field(..., description="Model ID")
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name: str = Field(..., description="Model name")
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description: str = Field(..., description="Model description")
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provider: str = Field(..., description="Model provider")
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max_tokens: int = Field(..., description="Maximum tokens")
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is_default: bool = Field(..., description="Whether this is the default model")
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class ChatRequest(BaseModel):
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"""Request model for creating a chat."""
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user_id: str = Field(..., description="User ID")
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title: Optional[str] = Field(None, description="Chat title")
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model_id: Optional[str] = Field(None, description="Model ID")
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is_team_chat: bool = Field(False, description="Whether this is a team chat")
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class MessageRequest(BaseModel):
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"""Request model for sending a message."""
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message: str = Field(..., description="Message content")
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user_id: str = Field(..., description="User ID")
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use_rag: bool = Field(False, description="Whether to use RAG")
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# Model parameters
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temperature: Optional[float] = Field(None, description="Controls randomness: higher values mean more random completions")
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max_tokens: Optional[int] = Field(None, description="Maximum number of tokens to generate")
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top_p: Optional[float] = Field(None, description="Nucleus sampling parameter")
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frequency_penalty: Optional[float] = Field(None, description="Penalizes repeated tokens")
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presence_penalty: Optional[float] = Field(None, description="Penalizes repeated topics")
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stop_sequences: Optional[List[str]] = Field(None, description="Sequences where the API will stop generating")
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system_prompt: Optional[str] = Field(None, description="System prompt to guide the model's behavior")
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# Additional advanced parameters
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min_p: Optional[float] = Field(None, description="Minimum probability threshold for token selection")
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top_k: Optional[int] = Field(None, description="Only sample from the top k tokens")
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repeat_penalty: Optional[float] = Field(None, description="Penalty for repeating tokens")
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function_calling: Optional[bool] = Field(None, description="Whether to enable function calling")
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class Message(BaseModel):
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"""Model for a message."""
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id: str = Field(..., description="Message ID")
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content: str = Field(..., description="Message content")
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user_id: Optional[str] = Field(None, description="User ID")
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is_user_message: bool = Field(..., description="Whether this is a user message")
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timestamp: str = Field(..., description="Message timestamp")
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class Chat(BaseModel):
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"""Model for a chat."""
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id: str = Field(..., description="Chat ID")
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title: str = Field(..., description="Chat title")
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user_id: str = Field(..., description="User ID")
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model_id: str = Field(..., description="Model ID")
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is_team_chat: bool = Field(..., description="Whether this is a team chat")
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created_at: str = Field(..., description="Creation timestamp")
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updated_at: str = Field(..., description="Update timestamp")
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messages: List[Message] = Field(..., description="Chat messages")
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team_members: List[str] = Field(..., description="Team members")
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# Define API endpoints
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@app.get("/health", response_model=HealthResponse)
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async def health_check():
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"""
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Health check endpoint.
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Returns:
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Health status.
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"""
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return {"status": "healthy"}
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@app.get("/config")
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async def get_config():
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"""
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Get the current configuration.
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Returns:
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Current configuration settings.
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"""
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return {
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"api_host": config.API_HOST,
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"api_port": config.API_PORT,
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"openwebui_url": config.OPENWEBUI_URL,
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"ollama_api_url": config.OLLAMA_API_URL,
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"default_model": config.DEFAULT_MODEL,
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"api_timeout": config.API_TIMEOUT,
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"bot": {
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"enabled": config.BOT_ENABLED,
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"running": bot_manager.is_bot_running(),
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"model_id": config.DEFAULT_MODEL,
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"system_prompt": config.BOT_SYSTEM_PROMPT[:50] + "..." if len(config.BOT_SYSTEM_PROMPT) > 50 else config.BOT_SYSTEM_PROMPT,
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"temperature": config.BOT_TEMPERATURE,
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"max_tokens": config.BOT_MAX_TOKENS,
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"top_p": config.BOT_TOP_P,
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"triggers": config.BOT_TRIGGERS,
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"respond_to_all": config.BOT_RESPOND_TO_ALL
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},
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"available_models": list(model_service.AVAILABLE_MODELS.keys())
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}
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@app.get("/bot/status")
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async def get_bot_status():
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"""
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Get the status and configuration of the OpenWebUI bot.
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Returns:
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Bot status and configuration.
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"""
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return {
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"enabled": config.BOT_ENABLED,
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"running": bot_manager.is_bot_running(),
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"config": {
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"model_id": config.DEFAULT_MODEL,
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"system_prompt": config.BOT_SYSTEM_PROMPT[:50] + "..." if len(config.BOT_SYSTEM_PROMPT) > 50 else config.BOT_SYSTEM_PROMPT,
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"temperature": config.BOT_TEMPERATURE,
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"max_tokens": config.BOT_MAX_TOKENS,
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"top_p": config.BOT_TOP_P,
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"triggers": config.BOT_TRIGGERS,
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"respond_to_all": config.BOT_RESPOND_TO_ALL
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}
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}
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@app.post("/bot/start")
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async def start_bot(
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model_id: str = None,
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system_prompt: str = None,
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temperature: float = None,
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max_tokens: int = None,
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top_p: float = None,
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respond_to_all: bool = None
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):
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"""
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Start the OpenWebUI bot with optional configuration.
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Args:
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model_id: ID of the model to use (default: config.DEFAULT_MODEL)
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system_prompt: System prompt for the bot (default: config.BOT_SYSTEM_PROMPT)
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temperature: Temperature for response generation (default: config.BOT_TEMPERATURE)
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max_tokens: Maximum number of tokens to generate (default: config.BOT_MAX_TOKENS)
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top_p: Top-p sampling parameter (default: config.BOT_TOP_P)
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respond_to_all: Whether to respond to all messages (default: config.BOT_RESPOND_TO_ALL)
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Returns:
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Start status.
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"""
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if bot_manager.is_bot_running():
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return {"status": "already_running", "message": "Bot is already running"}
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# Use provided values or defaults from config
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success = await bot_manager.start_bot(
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openwebui_url=config.OPENWEBUI_URL,
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api_key=config.OPENWEBUI_API_KEY,
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model_id=model_id or config.DEFAULT_MODEL,
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system_prompt=system_prompt or config.BOT_SYSTEM_PROMPT,
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temperature=temperature if temperature is not None else config.BOT_TEMPERATURE,
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max_tokens=max_tokens if max_tokens is not None else config.BOT_MAX_TOKENS,
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top_p=top_p if top_p is not None else config.BOT_TOP_P,
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triggers=config.BOT_TRIGGERS,
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respond_to_all=respond_to_all if respond_to_all is not None else config.BOT_RESPOND_TO_ALL
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)
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if success:
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return {
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"status": "success",
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"message": "Bot started successfully",
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"config": {
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"model_id": model_id or config.DEFAULT_MODEL,
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"system_prompt": (system_prompt or config.BOT_SYSTEM_PROMPT)[:50] + "..." if len(system_prompt or config.BOT_SYSTEM_PROMPT) > 50 else (system_prompt or config.BOT_SYSTEM_PROMPT),
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"temperature": temperature if temperature is not None else config.BOT_TEMPERATURE,
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"max_tokens": max_tokens if max_tokens is not None else config.BOT_MAX_TOKENS,
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"top_p": top_p if top_p is not None else config.BOT_TOP_P,
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"respond_to_all": respond_to_all if respond_to_all is not None else config.BOT_RESPOND_TO_ALL
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}
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}
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else:
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return {"status": "error", "message": "Failed to start bot"}
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@app.post("/bot/stop")
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async def stop_bot():
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"""
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Stop the OpenWebUI bot.
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Returns:
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Stop status.
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"""
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if not bot_manager.is_bot_running():
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return {"status": "not_running", "message": "Bot is not running"}
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success = await bot_manager.stop_bot()
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if success:
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return {"status": "success", "message": "Bot stopped successfully"}
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else:
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return {"status": "error", "message": "Failed to stop bot"}
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# Model endpoints
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@app.get("/models", response_model=List[ModelInfo])
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async def get_available_models():
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"""
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Get available models.
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Returns:
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List of model information.
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"""
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models = model_service.get_available_models()
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# Debug log
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print(f"API models: {models}")
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return models
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@app.get("/models/{model_id}", response_model=ModelInfo)
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async def get_model_info(model_id: str):
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"""
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Get information about a model.
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Args:
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model_id: Model ID.
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Returns:
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Model information.
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"""
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model_info = model_service.get_model_info(model_id)
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if not model_info:
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raise HTTPException(status_code=404, detail="Model not found")
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return model_info
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# Chat endpoints
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@app.post("/chats", response_model=Chat)
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async def create_chat(request: ChatRequest):
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"""
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Create a new chat.
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Args:
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request: Chat creation request.
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Returns:
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Created chat.
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"""
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chat_id = chat_service.create_chat(
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user_id=request.user_id,
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title=request.title,
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model_id=request.model_id,
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is_team_chat=request.is_team_chat
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)
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return chat_service.get_chat(chat_id)
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@app.get("/chats/user/{user_id}", response_model=List[Chat])
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async def get_user_chats(user_id: str):
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"""
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Get all chats for a user.
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Args:
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user_id: User ID.
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Returns:
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List of chats.
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"""
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return chat_service.get_user_chats(user_id)
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@app.get("/chats/{chat_id}", response_model=Chat)
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async def get_chat(chat_id: str):
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"""
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Get a chat by ID.
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Args:
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chat_id: Chat ID.
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Returns:
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Chat information.
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"""
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chat = chat_service.get_chat(chat_id)
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if not chat:
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raise HTTPException(status_code=404, detail="Chat not found")
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return chat
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@app.post("/chats/{chat_id}/messages", response_model=Message)
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async def send_message(chat_id: str, request: MessageRequest):
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"""
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Send a message to a chat.
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Args:
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chat_id: Chat ID.
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request: Message request with optional model parameters.
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Returns:
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Bot response message.
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"""
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try:
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print(f"Processing message for chat {chat_id} from user {request.user_id}")
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print(f"Message: {request.message[:50]}...") # Print first 50 chars of message
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print(f"Using RAG: {request.use_rag}")
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print(f"Model parameters: temperature={request.temperature}, max_tokens={request.max_tokens}")
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# Extract model parameters from the request
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response = chat_service.get_chat_response(
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chat_id=chat_id,
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message=request.message,
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user_id=request.user_id,
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use_rag=request.use_rag,
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temperature=request.temperature,
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max_tokens=request.max_tokens,
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top_p=request.top_p,
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frequency_penalty=request.frequency_penalty,
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presence_penalty=request.presence_penalty,
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stop_sequences=request.stop_sequences,
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system_prompt=request.system_prompt,
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min_p=request.min_p,
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top_k=request.top_k,
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repeat_penalty=request.repeat_penalty,
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function_calling=request.function_calling
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)
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print(f"Response received. Length: {len(response.get('content', ''))}")
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return response
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except ValueError as e:
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error_msg = f"Chat not found: {str(e)}"
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print(f"ERROR: {error_msg}")
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raise HTTPException(status_code=404, detail=error_msg)
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except Exception as e:
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error_msg = f"Error processing message: {str(e)}"
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print(f"ERROR: {error_msg}")
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# Return an error message instead of raising an exception
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# This ensures the client gets a proper response
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return {
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"id": str(uuid.uuid4()),
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"content": f"Error processing message: {str(e)}",
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"user_id": None,
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"is_user_message": False,
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"timestamp": datetime.now(timezone.utc).isoformat()
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}
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@app.post("/chats/{chat_id}/members/{user_id}")
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async def add_team_member(chat_id: str, user_id: str):
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"""
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Add a user to a team chat.
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Args:
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chat_id: Chat ID.
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user_id: User ID.
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Returns:
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Addition status.
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"""
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success = chat_service.add_team_member(chat_id, user_id)
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if not success:
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raise HTTPException(status_code=400, detail="Failed to add team member")
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return {"status": "success", "message": "Team member added"}
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@app.delete("/chats/{chat_id}/members/{user_id}")
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async def remove_team_member(chat_id: str, user_id: str):
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"""
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Remove a user from a team chat.
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Args:
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chat_id: Chat ID.
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user_id: User ID.
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Returns:
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Removal status.
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"""
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success = chat_service.remove_team_member(chat_id, user_id)
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if not success:
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raise HTTPException(status_code=400, detail="Failed to remove team member")
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return {"status": "success", "message": "Team member removed"}
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@app.delete("/chats/{chat_id}")
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async def delete_chat(chat_id: str):
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"""
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Delete a chat.
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Args:
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chat_id: Chat ID.
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Returns:
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Deletion status.
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"""
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success = chat_service.delete_chat(chat_id)
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if not success:
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raise HTTPException(status_code=404, detail="Chat not found")
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return {"status": "success", "message": "Chat deleted"}
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