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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
2026-03-04 00:11:41 +00:00
committed by GitHub
parent 5ba1c6f0cc
commit b5edb529b7
12 changed files with 120 additions and 105 deletions
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python/packages/azure-ai/agent_framework_azure_ai/_client.py
+20 -11
View File
@@ -37,12 +37,13 @@ from agent_framework.openai._responses_client import RawOpenAIResponsesClient
from azure.ai.projects.aio import AIProjectClient
from azure.ai.projects.models import (
ApproximateLocation,
CodeInterpreterContainerAuto,
CodeInterpreterTool,
CodeInterpreterToolAuto,
FoundryFeaturesOptInKeys,
ImageGenTool,
MCPTool,
PromptAgentDefinition,
PromptAgentDefinitionText,
PromptAgentDefinitionTextOptions,
RaiConfig,
Reasoning,
WebSearchPreviewTool,
@@ -78,6 +79,9 @@ class AzureAIProjectAgentOptions(OpenAIResponsesOptions, total=False):
reasoning: Reasoning # type: ignore[misc]
"""Configuration for enabling reasoning capabilities (requires azure.ai.projects.models.Reasoning)."""
foundry_features: FoundryFeaturesOptInKeys | str
"""Optional Foundry preview feature opt-in for agent version creation."""
AzureAIClientOptionsT = TypeVar(
"AzureAIClientOptionsT",
@@ -392,7 +396,7 @@ class RawAzureAIClient(RawOpenAIResponsesClient[AzureAIClientOptionsT], Generic[
# response_format is accessed from chat_options or additional_properties
# since the base class excludes it from run_options
if chat_options and (response_format := chat_options.get("response_format")):
args["text"] = PromptAgentDefinitionText(format=create_text_format_config(response_format))
args["text"] = PromptAgentDefinitionTextOptions(format=create_text_format_config(response_format))
# Combine instructions from messages and options
# instructions is accessed from chat_options since the base class excludes it from run_options
@@ -404,11 +408,15 @@ class RawAzureAIClient(RawOpenAIResponsesClient[AzureAIClientOptionsT], Generic[
if combined_instructions:
args["instructions"] = "".join(combined_instructions)
created_agent = await self.project_client.agents.create_version(
agent_name=self.agent_name,
definition=PromptAgentDefinition(**args),
description=self.agent_description,
)
create_version_kwargs: dict[str, Any] = {
"agent_name": self.agent_name,
"definition": PromptAgentDefinition(**args),
"description": self.agent_description,
}
if foundry_features := run_options.get("foundry_features"):
create_version_kwargs["foundry_features"] = foundry_features
created_agent = await self.project_client.agents.create_version(**create_version_kwargs)
self.agent_version = created_agent.version
self.warn_runtime_tools_and_structure_changed = True
@@ -500,6 +508,7 @@ class RawAzureAIClient(RawOpenAIResponsesClient[AzureAIClientOptionsT], Generic[
"temperature": ("temperature",),
"top_p": ("top_p",),
"reasoning": ("reasoning",),
"foundry_features": ("foundry_features",),
}
for run_keys in agent_level_option_to_run_keys.values():
@@ -526,9 +535,9 @@ class RawAzureAIClient(RawOpenAIResponsesClient[AzureAIClientOptionsT], Generic[
run_options["input"] = self._transform_input_for_azure_ai(cast(list[dict[str, Any]], run_options["input"]))
if not self._is_application_endpoint:
# Application-scoped response APIs do not support "agent" property.
# Application-scoped response APIs do not support "agent_reference" property.
agent_reference = await self._get_agent_reference_or_create(run_options, instructions, options)
run_options["extra_body"] = {"agent": agent_reference}
run_options["extra_body"] = {"agent_reference": agent_reference}
# Remove only keys that map to this client's declared options TypedDict.
self._remove_agent_level_run_options(run_options, options)
@@ -922,7 +931,7 @@ class RawAzureAIClient(RawOpenAIResponsesClient[AzureAIClientOptionsT], Generic[
if file_ids is None and isinstance(container, dict):
file_ids = container.get("file_ids")
resolved = resolve_file_ids(file_ids)
tool_container = CodeInterpreterToolAuto(file_ids=resolved)
tool_container = CodeInterpreterContainerAuto(file_ids=resolved)
return CodeInterpreterTool(container=tool_container, **kwargs)
@staticmethod
python/packages/azure-ai/agent_framework_azure_ai/_foundry_memory_provider.py
+10 -11
View File
@@ -18,7 +18,6 @@ from agent_framework._sessions import AgentSession, BaseContextProvider, Session
from agent_framework._settings import load_settings
from agent_framework.azure._entra_id_authentication import AzureCredentialTypes
from azure.ai.projects.aio import AIProjectClient
from azure.ai.projects.models import ItemParam, ResponsesAssistantMessageItemParam, ResponsesUserMessageItemParam
from ._shared import AzureAISettings
@@ -149,7 +148,7 @@ class FoundryMemoryProvider(BaseContextProvider):
# On first run, retrieve static memories (user profile memories)
if not state.get("initialized"):
try:
static_search_result = await self.project_client.memory_stores.search_memories(
static_search_result = await self.project_client.beta.memory_stores.search_memories(
name=self.memory_store_name,
scope=self.scope or context.session_id, # type: ignore[arg-type]
)
@@ -169,15 +168,15 @@ class FoundryMemoryProvider(BaseContextProvider):
if not has_input:
return
# Convert input messages to ItemParam format for search
# Convert input messages to memory search item format
items = [
ItemParam({"type": "text", "text": msg.text})
{"type": "text", "text": msg.text}
for msg in context.input_messages
if msg and msg.text and msg.text.strip()
]
try:
search_result = await self.project_client.memory_stores.search_memories(
search_result = await self.project_client.beta.memory_stores.search_memories(
name=self.memory_store_name,
scope=self.scope or context.session_id, # type: ignore[arg-type]
items=items,
@@ -224,24 +223,24 @@ class FoundryMemoryProvider(BaseContextProvider):
if context.response and context.response.messages:
messages_to_store.extend(context.response.messages)
# Filter and convert messages to ItemParam format
items: list[ResponsesUserMessageItemParam | ResponsesAssistantMessageItemParam] = []
# Filter and convert messages to memory update item format
items: list[dict[str, str]] = []
for message in messages_to_store:
if message.role in {"user", "assistant", "system"} and message.text and message.text.strip():
if message.role == "user":
items.append(ResponsesUserMessageItemParam(content=message.text))
items.append({"role": "user", "type": "message", "content": message.text})
elif message.role == "assistant":
items.append(ResponsesAssistantMessageItemParam(content=message.text))
items.append({"role": "assistant", "type": "message", "content": message.text})
if not items:
return
try:
# Fire and forget - don't wait for the update to complete
update_poller = await self.project_client.memory_stores.begin_update_memories(
update_poller = await self.project_client.beta.memory_stores.begin_update_memories(
name=self.memory_store_name,
scope=self.scope or context.session_id, # type: ignore[arg-type]
items=items, # type: ignore[arg-type]
items=items,
previous_update_id=state.get("previous_update_id"),
update_delay=self.update_delay,
)
python/packages/azure-ai/agent_framework_azure_ai/_project_provider.py
+21 -14
View File
@@ -4,7 +4,7 @@ from __future__ import annotations
import logging
import sys
from collections.abc import Callable, MutableMapping, Sequence
from collections.abc import Callable, Mapping, MutableMapping, Sequence
from typing import Any, Generic
from agent_framework import (
@@ -21,10 +21,9 @@ from agent_framework._tools import ToolTypes
from agent_framework.azure._entra_id_authentication import AzureCredentialTypes
from azure.ai.projects.aio import AIProjectClient
from azure.ai.projects.models import (
AgentReference,
AgentVersionDetails,
PromptAgentDefinition,
PromptAgentDefinitionText,
PromptAgentDefinitionTextOptions,
)
from azure.ai.projects.models import (
FunctionTool as AzureFunctionTool,
@@ -200,13 +199,14 @@ class AzureAIProjectAgentProvider(Generic[OptionsCoT]):
response_format = opts.get("response_format")
rai_config = opts.get("rai_config")
reasoning = opts.get("reasoning")
foundry_features = opts.get("foundry_features")
args: dict[str, Any] = {"model": resolved_model}
if instructions:
args["instructions"] = instructions
if response_format and isinstance(response_format, (type, dict)):
args["text"] = PromptAgentDefinitionText(
args["text"] = PromptAgentDefinitionTextOptions(
format=create_text_format_config(response_format) # type: ignore[arg-type]
)
if rai_config:
@@ -241,11 +241,15 @@ class AzureAIProjectAgentProvider(Generic[OptionsCoT]):
if all_tools_for_azure:
args["tools"] = to_azure_ai_tools(all_tools_for_azure)
created_agent = await self._project_client.agents.create_version(
agent_name=name,
definition=PromptAgentDefinition(**args),
description=description,
)
create_version_kwargs: dict[str, Any] = {
"agent_name": name,
"definition": PromptAgentDefinition(**args),
"description": description,
}
if foundry_features:
create_version_kwargs["foundry_features"] = foundry_features
created_agent = await self._project_client.agents.create_version(**create_version_kwargs)
return self._to_chat_agent_from_details(
created_agent,
@@ -259,7 +263,7 @@ class AzureAIProjectAgentProvider(Generic[OptionsCoT]):
self,
*,
name: str | None = None,
reference: AgentReference | None = None,
reference: Mapping[str, str | None] | None = None,
tools: ToolTypes | Callable[..., Any] | Sequence[ToolTypes | Callable[..., Any]] | None = None,
default_options: OptionsCoT | None = None,
middleware: Sequence[MiddlewareTypes] | None = None,
@@ -272,7 +276,7 @@ class AzureAIProjectAgentProvider(Generic[OptionsCoT]):
Args:
name: The name of the agent to retrieve (fetches latest version).
reference: Reference containing the agent's name and optionally a specific version.
reference: Mapping containing the agent's ``name`` and optionally a specific ``version``.
tools: Tools to make available to the agent. Required if the agent has function tools.
default_options: A TypedDict containing default chat options for the agent.
These options are applied to every run unless overridden.
@@ -287,12 +291,15 @@ class AzureAIProjectAgentProvider(Generic[OptionsCoT]):
"""
existing_agent: AgentVersionDetails
if reference and reference.version:
reference_name = str(reference.get("name")) if reference and reference.get("name") else None
reference_version = str(reference.get("version")) if reference and reference.get("version") else None
if reference_name and reference_version:
# Fetch specific version
existing_agent = await self._project_client.agents.get_version(
agent_name=reference.name, agent_version=reference.version
agent_name=reference_name, agent_version=reference_version
)
elif agent_name := (reference.name if reference else name):
elif agent_name := (reference_name if reference_name else name):
# Fetch latest version
details = await self._project_client.agents.get(agent_name=agent_name)
existing_agent = details.versions.latest
python/packages/azure-ai/agent_framework_azure_ai/_shared.py
+10 -10
View File
@@ -19,9 +19,9 @@ from azure.ai.agents.models import (
from azure.ai.projects.models import (
CodeInterpreterTool,
MCPTool,
ResponseTextFormatConfigurationJsonObject,
ResponseTextFormatConfigurationJsonSchema,
ResponseTextFormatConfigurationText,
TextResponseFormatConfigurationResponseFormatJsonObject,
TextResponseFormatConfigurationResponseFormatText,
TextResponseFormatJsonSchema,
Tool,
WebSearchPreviewTool,
)
@@ -463,9 +463,9 @@ def _prepare_mcp_tool_dict_for_azure_ai(tool_dict: dict[str, Any]) -> MCPTool:
def create_text_format_config(
response_format: type[BaseModel] | Mapping[str, Any],
) -> (
ResponseTextFormatConfigurationJsonSchema
| ResponseTextFormatConfigurationJsonObject
| ResponseTextFormatConfigurationText
TextResponseFormatJsonSchema
| TextResponseFormatConfigurationResponseFormatJsonObject
| TextResponseFormatConfigurationResponseFormatText
):
"""Convert response_format into Azure text format configuration."""
if isinstance(response_format, type) and issubclass(response_format, BaseModel):
@@ -473,7 +473,7 @@ def create_text_format_config(
# Ensure additionalProperties is explicitly false to satisfy Azure validation
if isinstance(schema, dict):
schema.setdefault("additionalProperties", False)
return ResponseTextFormatConfigurationJsonSchema(
return TextResponseFormatJsonSchema(
name=response_format.__name__,
schema=schema,
strict=True,
@@ -494,11 +494,11 @@ def create_text_format_config(
config_kwargs["strict"] = format_config["strict"]
if "description" in format_config:
config_kwargs["description"] = format_config["description"]
return ResponseTextFormatConfigurationJsonSchema(**config_kwargs)
return TextResponseFormatJsonSchema(**config_kwargs)
if format_type == "json_object":
return ResponseTextFormatConfigurationJsonObject()
return TextResponseFormatConfigurationResponseFormatJsonObject()
if format_type == "text":
return ResponseTextFormatConfigurationText()
return TextResponseFormatConfigurationResponseFormatText()
raise IntegrationInvalidRequestException("response_format must be a Pydantic model or mapping.")