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change to use Pypi
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‎src/backend/01_create_agents.py‎

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import asyncio
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import json
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import logging
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import os
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import struct
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from datetime import date, datetime
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from decimal import Decimal
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from xmlrpc import client
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from agent_framework import ChatAgent, HostedFileSearchTool
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from agent_framework.azure import AzureAIAgentClient
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from azure.ai.projects.aio import AIProjectClient
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from azure.identity.aio import AzureCliCredential
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# import pyodbc
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from dotenv import load_dotenv
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from pydantic import BaseModel, ConfigDict
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# Load environment variables from .env file
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load_dotenv()
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# import argparse
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# p = argparse.ArgumentParser()
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# p.add_argument("--ai_project_endpoint", required=True)
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# p.add_argument("--solution_name", required=True)
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# p.add_argument("--gpt_model_name", required=True)
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# args = p.parse_args()
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ai_project_endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
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#solutionName = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
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gptModelName = os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"]
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# # fetch all required env variables
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# ai_project_endpoint = os.getenv("AZURE_AI_AGENT_ENDPOINT")
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# solution_name = os.getenv("SOLUTION_NAME")
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# gpt_model_name = os.getenv("AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME")
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# app_env = os.getenv("APP_ENV", "prod").lower()
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# # ai_project_endpoint = 'https://aisa-ccblgmiensv4lga.services.ai.azure.com/api/projects/aifp-ccblgmiensv4lga'
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# ai_project_endpoint = 'https://testmodle.services.ai.azure.com/api/projects/testModle-project'
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# gpt_model_name = 'gpt-4o-mini'
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async def create_agents():
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"""Create and return orchestrator, SQL, and chart agent IDs."""
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async with (
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AzureCliCredential() as credential,
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AIProjectClient(
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endpoint=ai_project_endpoint,
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credential=credential,
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) as project_client,
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):
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# Create agents
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agents_client = project_client.agents
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# print("Creating agents...")
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# Create the client and manually create an agent with Azure AI Search tool
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from azure.ai.projects.models import ConnectionType
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ai_search_conn_id = ""
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async for connection in project_client.connections.list():
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if connection.type == ConnectionType.AZURE_AI_SEARCH:
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ai_search_conn_id = connection.id
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break
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# 1. Create Azure AI agent with the search tool
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product_agent_instructions = '''You are a helpful agent that searches product information using Azure AI Search.
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Always use the search tool and index to find product data and provide accurate information.
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If you can not find the answer in the search tool, respond that you can't answer the question.
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Do not add any other information from your general knowledge.'''
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product_agent = await agents_client.create_agent(
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model=gptModelName,
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name="product_agent",
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instructions=product_agent_instructions,
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tools=[{"type": "azure_ai_search"}],
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tool_resources={
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"azure_ai_search": {
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"indexes": [
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{
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"index_connection_id": ai_search_conn_id,
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"index_name": "products_index",
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"query_type": "vector_simple_hybrid", # Use vector hybrid search
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}
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]
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}
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},
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)
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# 1. Create Azure AI agent with the search tool
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policy_agent_instructions = '''You are a helpful agent that searches policy information using Azure AI Search.
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Always use the search tool and index to find policy data and provide accurate information.
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If you can not find the answer in the search tool, respond that you can't answer the question.
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Do not add any other information from your general knowledge.'''
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policy_agent = await agents_client.create_agent(
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model=gptModelName,
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name="policy_agent",
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instructions=policy_agent_instructions,
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tools=[{"type": "azure_ai_search"}],
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tool_resources={
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"azure_ai_search": {
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"indexes": [
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{
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"index_connection_id": ai_search_conn_id,
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"index_name": "policies_index",
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"query_type": "vector_simple_hybrid", # Use vector hybrid search
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}
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]
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}
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},
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)
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chat_agent_instructions = '''You are a helpful assistant that can use the product agent and policy agent to answer user questions.
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If you don't find any information in the knowledge source, please say no data found.'''
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chat_agent = await agents_client.create_agent(
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model=gptModelName,
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name=f"chat_agent",
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instructions=chat_agent_instructions
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)
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# Return agent IDs
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return product_agent.id, policy_agent.id, chat_agent.id
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product_agent_id, policy_agent_id, chat_agent_id = asyncio.run(create_agents())
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print(f"chatAgentId={chat_agent_id}")
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print(f"productAgentId={product_agent_id}")
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print(f"policyAgentId={policy_agent_id}")
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# import json
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# from azure.ai.projects import AIProjectClient
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# import sys
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# import os
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# import argparse
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# sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
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# from azure_credential_utils import get_azure_credential
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# from azure.ai.projects.models import ConnectionType
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# p = argparse.ArgumentParser()
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# p.add_argument("--ai_project_endpoint", required=True)
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# p.add_argument("--solution_name", required=True)
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# p.add_argument("--gpt_model_name", required=True)
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# args = p.parse_args()
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# ai_project_endpoint = args.ai_project_endpoint
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# solutionName = args.solution_name
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# gptModelName = args.gpt_model_name
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# project_client = AIProjectClient(
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# endpoint= ai_project_endpoint,
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# credential=get_azure_credential(),
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# )
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# with project_client:
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# # Create agents
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# agents_client = project_client.agents
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# print("Creating agents...")
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# # Create the client and manually create an agent with Azure AI Search tool
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# ai_search_conn_id = ""
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# for connection in project_client.connections.list():
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# if connection.type == ConnectionType.AZURE_AI_SEARCH:
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# ai_search_conn_id = connection.id
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# break
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# # 1. Create Azure AI agent with the search tool
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# product_agent_instructions = '''You are a helpful agent that searches product information using Azure AI Search.
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# Always use the search tool and index to find product data and provide accurate information.
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# If you can not find the answer in the search tool, respond that you can't answer the question.
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# Do not add any other information from your general knowledge.'''
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# product_agent = agents_client.create_agent(
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# model=gptModelName,
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# name="product_agent",
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# instructions=product_agent_instructions,
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# tools=[{"type": "azure_ai_search"}],
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# tool_resources={
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# "azure_ai_search": {
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# "indexes": [
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# {
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# "index_connection_id": ai_search_conn_id,
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# "index_name": "products_index",
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# "query_type": "vector_simple_hybrid", # Use vector hybrid search
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# }
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# ]
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# }
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# },
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# )
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# # 1. Create Azure AI agent with the search tool
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# policy_agent_instructions = '''You are a helpful agent that searches policy information using Azure AI Search.
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# Always use the search tool and index to find policy data and provide accurate information.
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# If you can not find the answer in the search tool, respond that you can't answer the question.
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# Do not add any other information from your general knowledge.'''
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# policy_agent = agents_client.create_agent(
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# model=gptModelName,
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# name="policy_agent",
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# instructions=policy_agent_instructions,
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# tools=[{"type": "azure_ai_search"}],
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# tool_resources={
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# "azure_ai_search": {
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# "indexes": [
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# {
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# "index_connection_id": ai_search_conn_id,
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# "index_name": "policies_index",
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# "query_type": "vector_simple_hybrid", # Use vector hybrid search
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# }
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# ]
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# }
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# },
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# )
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# chat_agent_instructions = '''You are a helpful assistant that can use the product agent and policy agent to answer user questions.
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# If you don't find any information in the knowledge source, please say no data found.'''
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# chat_agent = agents_client.create_agent(
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# model=gptModelName,
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# name=f"chat_agent",
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# instructions=chat_agent_instructions
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# )
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# print(f"chatAgentId={chat_agent.id}")
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# print(f"productAgentId={product_agent.id}")
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# print(f"policyAgentId={policy_agent.id}")
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# # agents_client = project_client.agents
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# # print("Creating agents...")
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# # product_agent_instructions = "You are a helpful assistant that uses knowledge sources to help find products. If you don't find any products in the knowledge source, please say no data found."
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# # product_agent = agents_client.create_agent(
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# # model=gptModelName,
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# # name=f"product_agent",
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# # instructions=product_agent_instructions
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# # )
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# # print(f"Created Product Agent with ID: {product_agent.id}")
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# # policy_agent_instructions = "You are a helpful assistant that searches policies to answer user questions.If you don't find any information in the knowledge source, please say no data found"
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# # policy_agent = agents_client.create_agent(
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# # model=gptModelName,
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# # name=f"policy_agent",
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# # instructions=policy_agent_instructions
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# # )
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# # print(f"Created Policy Agent with ID: {policy_agent.id}")
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# # chat_agent_instructions = "You are a helpful assistant that can use the product agent and policy agent to answer user questions. If you don't find any information in the knowledge source, please say no data found"
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# # chat_agent = agents_client.create_agent(
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# # model=gptModelName,
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# # name=f"chat_agent",
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# # instructions=chat_agent_instructions
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# # )
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# # print(f"chatAgentId={chat_agent.id}")
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# # print(f"productAgentId={product_agent.id}")
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# # print(f"policyAgentId={policy_agent.id}")

‎src/backend/pyproject.toml‎

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@@ -5,16 +5,10 @@ description = "Add your description here"
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readme = "README.md"
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requires-python = ">=3.11"
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dependencies = [
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"agent-framework-core @ git+https://github.com/microsoft/agent-framework.git@main#subdirectory=python/packages/core",
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"agent-framework-azure-ai @ git+https://github.com/microsoft/agent-framework.git@main#subdirectory=python/packages/azure-ai",
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"agent-framework-copilotstudio @ git+https://github.com/microsoft/agent-framework.git@main#subdirectory=python/packages/copilotstudio",
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"agent-framework-devui @ git+https://github.com/microsoft/agent-framework.git@main#subdirectory=python/packages/devui",
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"agent-framework-mem0 @ git+https://github.com/microsoft/agent-framework.git@main#subdirectory=python/packages/mem0",
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"agent-framework-redis @ git+https://github.com/microsoft/agent-framework.git@main#subdirectory=python/packages/redis",
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"azure-ai-evaluation==1.11.0",
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"azure-ai-inference==1.0.0b9",
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"azure-ai-projects==1.0.0",
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"azure-ai-agents==1.2.0b5",
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"azure-ai-agents==1.2.0b5",
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"azure-cosmos==4.9.0",
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"azure-identity==1.24.0",
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"azure-monitor-events-extension==0.1.0",
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"uvicorn==0.35.0",
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"pylint-pydantic==0.3.5",
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"pexpect==4.9.0",
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"mcp==1.13.1"
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"mcp==1.13.1",
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"agent-framework>=1.0.0b251105",
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]

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