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Python: Upgraded azure-ai-projects to 2.0.0b4 (#4438)

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* Upgraded azure-ai-projects to 2.0.0b4

* Fixed tests
This commit is contained in:
Dmytro Struk
2026-03-03 16:11:41 -08:00
committed by GitHub
Unverified
parent 5ba1c6f0cc
commit b5edb529b7
12 changed files with 120 additions and 105 deletions
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python/samples/02-agents/providers/azure_ai/azure_ai_provider_methods.py
+4 -4
View File
@@ -8,7 +8,7 @@ from typing import Annotated
from agent_framework import tool
from agent_framework.azure import AzureAIProjectAgentProvider
from azure.ai.projects.aio import AIProjectClient
from azure.ai.projects.models import AgentReference, PromptAgentDefinition
from azure.ai.projects.models import PromptAgentDefinition
from azure.identity.aio import AzureCliCredential
from dotenv import load_dotenv
from pydantic import Field
@@ -116,7 +116,7 @@ async def get_agent_by_name_example() -> None:
async def get_agent_by_reference_example() -> None:
"""Example of using provider.get_agent(reference=...) to retrieve a specific agent version.
This method fetches a specific version of an agent using an AgentReference.
This method fetches a specific version of an agent using a reference mapping.
Use this when you need to use a particular version of an agent.
"""
print("=== provider.get_agent(reference=...) Example ===")
@@ -136,9 +136,9 @@ async def get_agent_by_reference_example() -> None:
)
try:
# Get the agent using an AgentReference with specific version
# Get the agent using a reference mapping with specific version
provider = AzureAIProjectAgentProvider(project_client=project_client)
reference = AgentReference(name=created_agent.name, version=created_agent.version)
reference = {"name": created_agent.name, "version": created_agent.version}
agent = await provider.get_agent(reference=reference)
print(f"Retrieved agent: {agent.name} (version via reference)")
python/samples/02-agents/providers/azure_ai/azure_ai_with_memory_search.py
+3 -3
View File
@@ -43,7 +43,7 @@ async def main() -> None:
options=MemoryStoreDefaultOptions(user_profile_enabled=True, chat_summary_enabled=True),
)
memory_store = await project_client.memory_stores.create(
memory_store = await project_client.beta.memory_stores.create(
name=memory_store_name,
description="Memory store for Agent Framework conversations",
definition=memory_store_definition,
@@ -57,7 +57,7 @@ async def main() -> None:
instructions="""You are a helpful assistant that remembers past conversations.
Use the memory search tool to recall relevant information from previous interactions.""",
tools={
"type": "memory_search",
"type": "memory_search_preview",
"memory_store_name": memory_store.name,
"scope": "user_123",
"update_delay": 1, # Wait 1 second before updating memories (use higher value in production)
@@ -84,7 +84,7 @@ async def main() -> None:
# Clean up - delete the memory store
async with AIProjectClient(endpoint=endpoint, credential=credential) as project_client:
await project_client.memory_stores.delete(memory_store_name)
await project_client.beta.memory_stores.delete(memory_store_name)
print("Memory store deleted")