app/MultiAgent.py

from agents import  Agent, ModelSettings, Runner, RunConfig,OpenAIResponsesModel ,AsyncOpenAI,function_tool,OpenAIChatCompletionsModel
from pydantic import BaseModel
from datetime import datetime, date
from dotenv import load_dotenv
load_dotenv(dotenv_path="./.env.local")
import asyncio
from typing import List
from .VectorDBManagers import VectorDBManager
from chatkit.agents import AgentContext
import os 
import nest_asyncio
nest_asyncio.apply()
from .function_tool import suggestion_ragtool
kimi_model = OpenAIResponsesModel(
    model="moonshotai/kimi-k2-instruct-0905",  # Valid Groq model
    openai_client=AsyncOpenAI(
        base_url="https://api.groq.com/openai/v1",
        api_key=os.getenv("GROQ_API_KEY"),
    )
)
google_model = OpenAIChatCompletionsModel(
    model="google/gemini-2.5-flash",  # Google Gemini via OpenRouter
    openai_client=AsyncOpenAI(
         base_url="https://openrouter.ai/api/v1",
        api_key=os.getenv("OPENROUTER_API_KEY"),
    )
)
deepseek_model = OpenAIChatCompletionsModel(
    model="deepseek/deepseek-chat",  # DeepSeek via OpenRouter
    openai_client=AsyncOpenAI(
        base_url="https://openrouter.ai/api/v1",
        api_key=os.getenv("OPENROUTER_API_KEY"),
    )
)

sumary_model = OpenAIResponsesModel(
    model="meta-llama/llama-4-scout-17b-16e-instruct",  # Valid Groq model
    openai_client=AsyncOpenAI(
        base_url="https://api.groq.com/openai/v1",
        api_key=os.getenv("GROQ_API_KEY")
    )
)
def build_sugguestion_information_agent()-> Agent[AgentContext]:
    

    current_time = datetime.now()
    current_date = date.today()
    current_day = datetime.today().strftime("%A")
    information_agent = Agent[AgentContext](
        name="company_suggestion_information",
        instructions=(
    "You are an information agent and customer service representative for the company. "
    "Your goal is to provide clear, concise answers using ONLY the suggestion_ragtool tool. "
    "For greeting do not call tool reply by your self add company name "
    "Always speak as the company using 'we'. Do NOT guess or assume. "
    "If information is not found, reply politely: "
    "phraphse according to your intellgence 'No information is available regarding to this . You may book an appointment or speak to our sales agent for more details.' praphrase it "
    
    "Make only ONE suggestion_ragtool query that fully represents the user’s request. "
    "All company-related answers must come strictly from the suggestion_ragtool tool. "
    "Do not create or assume any details. Always use the correct company name. "
    "The suggestion_ragtool result is your official answer. "
    
    "Respond in under not more than 80 words, in a friendly customer-service tone. "
    "Never leave incomplete replies and never ignore earlier conversation context. "
    
    "As a customer support agent, always answer using official company information found through the suggestion_ragtool tool. for partcular question liek greeting and user info if you have so do not use tool reply by self "
    f"Current system time : {current_time}, date: {current_date}, day: {current_day}. "
),
        model=deepseek_model,
        tools=[
            suggestion_ragtool
        ],
        model_settings=ModelSettings(
            temperature=1,
            top_p=1,
            max_tokens=2048,
        ),
        )
    return information_agent

def build_summarizer_agent() -> Agent[AgentContext]:
    """
    Creates a summarizer agent that condenses chat history
    into a short, factual summary for context preservation.
    """
    summarizer_agent = Agent[AgentContext](
        name="Summarizer Agent",
        instructions="""
        You are a summarization assistant.
        Your job is to take several user and assistant messages and produce a concise,
        factual summary that captures key intents, facts, and outcomes.

        Guidelines:
        - Keep the summary under 80 words.
        - Focus on what the user is asking for and the assistant's key responses.
        - Do NOT add new information.
        - Preserve important context like customer concerns, preferences, or goals.
        - Write in plain English.
        """,
        model=sumary_model,  # or use default_model if configured in your environment
        model_settings=ModelSettings(
            temperature=0.3,
            top_p=0.9,
            max_tokens=300,
        ),
    )

    return summarizer_agent
def build_kimi_information_agent()-> Agent[AgentContext]:
    

    current_time = datetime.now()
    current_date = date.today()
    current_day = datetime.today().strftime("%A")
    information_agent = Agent[AgentContext](
        name="company_suggestion_information",
        instructions=(
    "You are an information agent and customer service representative for the company. "
    "Your goal is to provide clear, concise answers using ONLY the suggestion_ragtool tool. "
    "For greeting do not call tool reply by your self add company name "
    "Always speak as the company using 'we'. Do NOT guess or assume. "
    "If information is not found, reply politely: "
    "phraphse according to your intellgence 'No information is available regarding to this . You may book an appointment or speak to our sales agent for more details.' praphrase it "
    
    "Make only ONE suggestion_ragtool query that fully represents the user’s request. "
    "All company-related answers must come strictly from the suggestion_ragtool tool. "
    "Do not create or assume any details. Always use the correct company name. "
    "The suggestion_ragtool result is your official answer. "
    
    "Respond in under not more than 80 words, in a friendly customer-service tone. "
    "Never leave incomplete replies and never ignore earlier conversation context. "
    
    "As a customer support agent, always answer using official company information found through the suggestion_ragtool tool. for partcular question liek greeting and user info if you have so do not use tool reply by self "
    f"Current system time : {current_time}, date: {current_date}, day: {current_day}. "
),
        model=kimi_model,
        tools=[
            suggestion_ragtool
        ],
        model_settings=ModelSettings(
            temperature=1,
            top_p=1,
            max_tokens=2048,
        ),
        )
    return information_agent


def build_google_information_agent()-> Agent[AgentContext]:
    

    current_time = datetime.now()
    current_date = date.today()
    current_day = datetime.today().strftime("%A")
    information_agent = Agent[AgentContext](
        name="company_suggestion_information",
        instructions=(
    "You are an information agent and customer service representative for the company. "
    "Your goal is to provide clear, concise answers using ONLY the suggestion_ragtool tool. "
    "For greeting do not call tool reply by your self add company name "
    "Always speak as the company using 'we'. Do NOT guess or assume. "
    "If information is not found, reply politely: "
    "phraphse according to your intellgence 'No information is available regarding to this . You may book an appointment or speak to our sales agent for more details.' praphrase it "
    
    "Make only ONE suggestion_ragtool query that fully represents the user’s request. "
    "All company-related answers must come strictly from the suggestion_ragtool tool. "
    "Do not create or assume any details. Always use the correct company name. "
    "The suggestion_ragtool result is your official answer. "
    
    "Respond in under not more than 80 words, in a friendly customer-service tone. "
    "Never leave incomplete replies and never ignore earlier conversation context. "
    
    "As a customer support agent, always answer using official company information found through the suggestion_ragtool tool. for partcular question liek greeting and user info if you have so do not use tool reply by self "
    f"Current system time : {current_time}, date: {current_date}, day: {current_day}. "
),
        model=google_model,
        tools=[
            suggestion_ragtool
        ],
        model_settings=ModelSettings(
            temperature=1,
            top_p=1,
            max_tokens=2048,
        ),
        )
    return information_agent