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# pylint: disable=line-too-long,useless-suppression
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
DESCRIPTION:
This sample demonstrates how to perform CRUD operations on a memory store
using the synchronous AIProjectClient.
See also /samples/agents/tools/sample_agent_memory_search.py that shows
how to use the Memory Search Tool in a prompt agent.
USAGE:
python sample_memory_crud.py
Before running the sample:
pip install "azure-ai-projects>=2.0.0b1" azure-identity openai python-dotenv
Set these environment variables with your own values:
1) AZURE_AI_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
page of your Microsoft Foundry portal.
2) AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME - The deployment name of the chat model, as found under the "Name" column in
the "Models + endpoints" tab in your Microsoft Foundry project.
3) AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME - The deployment name of the embedding model, as found under the
"Name" column in the "Models + endpoints" tab in your Microsoft Foundry project.
"""
import os
from dotenv import load_dotenv
from azure.core.exceptions import ResourceNotFoundError
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import MemoryStoreDefaultDefinition
load_dotenv()
endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
with (
DefaultAzureCredential(exclude_interactive_browser_credential=False) as credential,
AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
):
# Delete memory store, if it already exists
memory_store_name = "my_memory_store"
try:
project_client.memory_stores.delete(memory_store_name)
print(f"Memory store `{memory_store_name}` deleted")
except ResourceNotFoundError:
pass
# Create Memory Store
definition = MemoryStoreDefaultDefinition(
chat_model=os.environ["AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME"],
embedding_model=os.environ["AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME"],
)
memory_store = project_client.memory_stores.create(
name=memory_store_name, description="Example memory store for conversations", definition=definition
)
print(f"Created memory store: {memory_store.name} ({memory_store.id}): {memory_store.description}")
# Get Memory Store
get_store = project_client.memory_stores.get(memory_store.name)
print(f"Retrieved: {get_store.name} ({get_store.id}): {get_store.description}")
# Update Memory Store
updated_store = project_client.memory_stores.update(name=memory_store.name, description="Updated description")
print(f"Updated: {updated_store.name} ({updated_store.id}): {updated_store.description}")
# List Memory Store
memory_stores = list(project_client.memory_stores.list(limit=10))
print(f"Found {len(memory_stores)} memory stores")
for store in memory_stores:
print(f" - {store.name} ({store.id}): {store.description}")
# Delete Memory Store
delete_response = project_client.memory_stores.delete(memory_store.name)
print(f"Deleted: {delete_response.deleted}")
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