1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
|
# pylint: disable=line-too-long,useless-suppression
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
DESCRIPTION:
This sample demonstrates how to interact with the memory store to add and retrieve memory.
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_basic.py
Before running the sample:
pip install "azure-ai-projects>=2.0.0b1" azure-identity openai python-dotenv
Deploy a chat model (e.g. gpt-4.1) and an embedding model (e.g. text-embedding-3-small).
Once you have deployed models, set the deployment name in the variables below.
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,
MemoryStoreDefaultOptions,
MemorySearchOptions,
ResponsesUserMessageItemParam,
)
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 a memory store
definition = MemoryStoreDefaultDefinition(
chat_model=os.environ["AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME"],
embedding_model=os.environ["AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME"],
options=MemoryStoreDefaultOptions(
user_profile_enabled=True, chat_summary_enabled=True
), # Note: This line will not be needed once the service is fixed to use correct defaults
)
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}")
if isinstance(memory_store.definition, MemoryStoreDefaultDefinition):
print(f" - Chat model: {memory_store.definition.chat_model}")
print(f" - Embedding model: {memory_store.definition.embedding_model}")
# Set scope to associate the memories with
# You can also use "{{$userId}}" to take the oid of the request authentication header
scope = "user_123"
# Add memories to the memory store
user_message = ResponsesUserMessageItemParam(
content="I prefer dark roast coffee and usually drink it in the morning"
)
update_poller = project_client.memory_stores.begin_update_memories(
name=memory_store.name,
scope=scope,
items=[user_message], # Pass conversation items that you want to add to memory
update_delay=0, # Trigger update immediately without waiting for inactivity
)
# Wait for the update operation to complete, but can also fire and forget
update_result = update_poller.result()
print(f"Updated with {len(update_result.memory_operations)} memory operations")
for operation in update_result.memory_operations:
print(
f" - Operation: {operation.kind}, Memory ID: {operation.memory_item.memory_id}, Content: {operation.memory_item.content}"
)
# Retrieve memories from the memory store
query_message = ResponsesUserMessageItemParam(content="What are my coffee preferences?")
search_response = project_client.memory_stores.search_memories(
name=memory_store.name, scope=scope, items=[query_message], options=MemorySearchOptions(max_memories=5)
)
print(f"Found {len(search_response.memories)} memories")
for memory in search_response.memories:
print(f" - Memory ID: {memory.memory_item.memory_id}, Content: {memory.memory_item.content}")
# Delete memories for a specific scope
project_client.memory_stores.delete_scope(name=memory_store.name, scope=scope)
print(f"Deleted memories for scope '{scope}'")
# Delete memory store
project_client.memory_stores.delete(memory_store.name)
print(f"Deleted memory store `{memory_store.name}`")
|
