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Python: Azure AI client based on new azure-ai-projects package (#1910)
* Added changes (#1909)
* Python: [Feature Branch] Renamed Azure AI agent and small fixes (#1919)
* Renaming
* Small fixes
* Update python/packages/core/agent_framework/openai/_shared.py
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---------
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* Small fix
* Python: [Feature Branch] Added use_latest_version parameter to AzureAIClient (#1959)
* Added use_latest_version parameter to AzureAIClient
* Added unit tests
* Update python/samples/getting_started/agents/azure_ai/azure_ai_use_latest_version.py
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* Update python/packages/azure-ai/agent_framework_azure_ai/_client.py
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---------
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* Python: [Feature Branch] Structured Outputs and more examples for AzureAIClient (#1987)
* Small updates
* Added support for structured outputs
* Added code interpreter example
* More examples and fixes
* Added more examples and README
* Small fix
* Addressed PR feedback
* Removed optional ID from FunctionResultContent (#2011)
* Added hosted MCP support (#2018)
* Python: [Feature Branch] Fixed "store" parameter handling (#2069)
* Fixed store parameter handling
* Small fix
* Python: [Feature Branch] Added more examples and fixes for Azure AI agent (#2077)
* Updated azure-ai-projects package version
* Added an example of hosted MCP with approval required
* Updated code interpreter example
* Added file search example
* Update python/samples/getting_started/agents/azure_ai/azure_ai_with_file_search.py
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* Update python/samples/getting_started/agents/azure_ai/azure_ai_with_file_search.py
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* Small fix
---------
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* Added handling for conversation_id (#2098)
* Merge from main
* Revert "Merge from main"
This reverts commit b8206a85d7.
* Python: [Feature Branch] Merge from main to Azure AI branch (#2111)
* Do not build DevUI assets during .NET project build (#2010)
* .NET: Add unit tests for declarative executor SetMultipleVariables (#2016)
* Add unit tests for create conversation executor
* Update indentation and comment typo.
* Added unit tests for declarative executor SetMultipleVariablesExecutor
* Updated comments and syntactic sugar
* Python: DevUI: Use metadata.entity_id instead of model field (#1984)
* DevUI: Use metadata.entity_id for agent/workflow name instead of model field
* OpenAI Responses: add explicit request validation
* Review feedback
* .NET: DevUI - Do not automatically add/map OpenAI services/endpoints (#2014)
* Don't add OpenAIResponses as part of Dev UI
You should be able to add and remove Dev UI without impacting your other production endpoints.
* Remove `AddDevUI()` and do not map OpenAI endpoints from `MapDevUI()`
* Fix comment wording
* Revise documentation
---------
Co-authored-by: Daniel Roth <daroth@microsoft.com>
* Python: DevUI: Add OpenAI Responses API proxy support + HIL for Workflows (#1737)
* DevUI: Add OpenAI Responses API proxy support with enhanced UI features
This commit adds support for proxying requests to OpenAI's Responses API,
allowing DevUI to route conversations to OpenAI models when configured to enable testing.
Backend changes:
- Add OpenAI proxy executor with conversation routing logic
- Enhance event mapper to support OpenAI Responses API format
- Extend server endpoints to handle OpenAI proxy mode
- Update models with OpenAI-specific response types
- Remove emojis from logging and CLI output for cleaner text
Frontend changes:
- Add settings modal with OpenAI proxy configuration UI
- Enhance agent and workflow views with improved state management
- Add new UI components (separator, switch) for settings
- Update debug panel with better event filtering
- Improve message renderers for OpenAI content types
- Update types and API client for OpenAI integration
* update ui, settings modal and workflow input form, add register cleanup hooks.
* add workflow HIL support, user mode, other fixes
* feat(devui): add human-in-the-loop (HIL) support with dynamic response schemas
Implement HIL workflow support allowing workflows to pause for user input
with dynamically generated JSON schemas based on response handler type hints.
Key Features:
- Automatic response schema extraction from @response_handler decorators
- Dynamic form generation in UI based on Pydantic/dataclass response types
- Checkpoint-based conversation storage for HIL requests/responses
- Resume workflow execution after user provides HIL response
Backend Changes:
- Add extract_response_type_from_executor() to introspect response handlers
- Enrich RequestInfoEvent with response_schema via _enrich_request_info_event_with_response_schema()
- Map RequestInfoEvent to response.input.requested OpenAI event format
- Store HIL responses in conversation history and restore checkpoints
Frontend Changes:
- Add HILInputModal component with SchemaFormRenderer for dynamic forms
- Support Pydantic BaseModel and dataclass response types
- Render enum fields as dropdowns, strings as text/textarea, numbers, booleans, arrays, objects
- Display original request context alongside response form
Testing:
- Add tests for checkpoint storage (test_checkpoints.py)
- Add schema generation tests for all input types (test_schema_generation.py)
- Validate end-to-end HIL flow with spam workflow sample
This enables workflows to seamlessly pause execution and request structured user input
with type-safe, validated forms generated automatically from response type annotations.
* improve HIL support, improve workflow execution view
* ui updates
* ui updates
* improve HIL for workflows, add auth and view modes
* update workflow
* security improvements , ui fixes
* fix mypy error
* update loading spinner in ui
---------
Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com>
* .NET: Remove launchSettings.json from .gitignore in dotnet/samples (#2006)
* Remove launchSettings.json from .gitignore in dotnet/samples
* Update dotnet/samples/GettingStarted/DevUI/DevUI_Step01_BasicUsage/Properties/launchSettings.json
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* Update dotnet/samples/AGUIClientServer/AGUIServer/Properties/launchSettings.json
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---------
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* DevUI: Serialize workflow input as string to maintain conformance with OpenAI Responses format (#2021)
Co-authored-by: Victor Dibia <chuvidi2003@gmail.com>
* Add Microsoft Agent Framework logo to assets (#2007)
* Updated package versions (#2027)
* DevUI: Prevent line breaks within words in the agent view (#2024)
Co-authored-by: Victor Dibia <chuvidi2003@gmail.com>
* .NET [AG-UI]: Adds support for shared state. (#1996)
* Product changes
* Tests
* Dojo project
* Cleanups
* Python: Fix underlying tool choice bug and all for return to previous Handoff subagent (#2037)
* Fix tool_choice override bug and add enable_return_to_previous support
* Add unit test for handoff checkpointing
* Handle tools when we have them
* added missing chatAgent params (#2044)
* .NET: fix ChatCompletions Tools serialization (#2043)
* fix serialization in chat completions on tools
* nit
* .NET: assign AgentCard's URL to mapped-endpoint if not defined explicitly (#2047)
* fix serialization in chat completions on tools
* nit
* write e2e test for agent card resolve + adjust behavior
* nit
* Version 1.0.0-preview.251110.1 (#2048)
* .NET: Remove moved OpenAPI sample and point to SK one. (#1997)
* Remove moved OpenAPI sample and point to SK one.
* Update dotnet/samples/GettingStarted/Agents/README.md
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---------
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* Bump AWSSDK.Extensions.Bedrock.MEAI from 4.0.4.2 to 4.0.4.6 (#2031)
---
updated-dependencies:
- dependency-name: AWSSDK.Extensions.Bedrock.MEAI
dependency-version: 4.0.4.6
dependency-type: direct:production
update-type: version-update:semver-patch
...
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* .NET: Separate all memory and rag samples into their own folders (#2000)
* Separate all memory and rag samples into their own folders
* Fix broken link.
* Python: .Net: Dotnet devui compatibility fixes (#2026)
* DevUI: Add OpenAI Responses API proxy support with enhanced UI features
This commit adds support for proxying requests to OpenAI's Responses API,
allowing DevUI to route conversations to OpenAI models when configured to enable testing.
Backend changes:
- Add OpenAI proxy executor with conversation routing logic
- Enhance event mapper to support OpenAI Responses API format
- Extend server endpoints to handle OpenAI proxy mode
- Update models with OpenAI-specific response types
- Remove emojis from logging and CLI output for cleaner text
Frontend changes:
- Add settings modal with OpenAI proxy configuration UI
- Enhance agent and workflow views with improved state management
- Add new UI components (separator, switch) for settings
- Update debug panel with better event filtering
- Improve message renderers for OpenAI content types
- Update types and API client for OpenAI integration
* update ui, settings modal and workflow input form, add register cleanup hooks.
* add workflow HIL support, user mode, other fixes
* feat(devui): add human-in-the-loop (HIL) support with dynamic response schemas
Implement HIL workflow support allowing workflows to pause for user input
with dynamically generated JSON schemas based on response handler type hints.
Key Features:
- Automatic response schema extraction from @response_handler decorators
- Dynamic form generation in UI based on Pydantic/dataclass response types
- Checkpoint-based conversation storage for HIL requests/responses
- Resume workflow execution after user provides HIL response
Backend Changes:
- Add extract_response_type_from_executor() to introspect response handlers
- Enrich RequestInfoEvent with response_schema via _enrich_request_info_event_with_response_schema()
- Map RequestInfoEvent to response.input.requested OpenAI event format
- Store HIL responses in conversation history and restore checkpoints
Frontend Changes:
- Add HILInputModal component with SchemaFormRenderer for dynamic forms
- Support Pydantic BaseModel and dataclass response types
- Render enum fields as dropdowns, strings as text/textarea, numbers, booleans, arrays, objects
- Display original request context alongside response form
Testing:
- Add tests for checkpoint storage (test_checkpoints.py)
- Add schema generation tests for all input types (test_schema_generation.py)
- Validate end-to-end HIL flow with spam workflow sample
This enables workflows to seamlessly pause execution and request structured user input
with type-safe, validated forms generated automatically from response type annotations.
* improve HIL support, improve workflow execution view
* ui updates
* ui updates
* improve HIL for workflows, add auth and view modes
* update workflow
* security improvements , ui fixes
* fix mypy error
* update loading spinner in ui
* DevUI: Serialize workflow input as string to maintain conformance with OpenAI Responses format
* Phase 1: Add /meta endpoint and fix workflow event naming for .NET DevUI compatibility
* additional fixes for .NET DevUI workflow visualization item ID tracking
**Problem:**
.NET DevUI was generating different item IDs for ExecutorInvokedEvent and
ExecutorCompletedEvent, causing only the first executor to highlight in the
workflow graph. Long executor names and error messages also broke UI layout.
**Changes:**
- Add ExecutorActionItemResource to match Python DevUI implementation
- Track item IDs per executor using dictionary in AgentRunResponseUpdateExtensions
- Reuse same item ID across invoked/completed/failed events for proper pairing
- Add truncateText() utility to workflow-utils.ts
- Truncate executor names to 35 chars in execution timeline
- Truncate error messages to 150 chars in workflow graph nodes
** Details:**
- ExecutorActionItemResource registered with JSON source generation context
- Dictionary cleaned up after executor completion/failure to prevent memory leaks
- Frontend item tracking by unique item.id supports multiple executor runs
- All changes follow existing codebase patterns and conventions
Tested with review-workflow showing correct executor highlighting and state
transitions for sequential and concurrent executors.
* format fixes, remove cors tests
* remove unecessary attributes
---------
Co-authored-by: Mark Wallace <127216156+markwallace-microsoft@users.noreply.github.com>
Co-authored-by: Reuben Bond <reuben.bond@gmail.com>
* DevUI: support having both an agent and a workflow with the same id in discovery (#2023)
* Python: Fix Model ID attribute not showing up in `invoke_agent` span (#2061)
* Best effort to surface the model id to invoke agent span
* Fix tests
* Fix tests
* Version 1.0.0-preview.251107.2 (#2065)
* Version 1.0.0-preview.251110.2 (#2067)
* Update README.md to change Grafana links to Azure portal links for dashboard access (#1983)
* .NET - Enable build & test on branch `feature-foundry-agents` (#2068)
* Tests good, mkay
* Update .github/workflows/dotnet-build-and-test.yml
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* Enable feature build pipelines
---------
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* Python: Add concrete AGUIChatClient (#2072)
* Add concrete AGUIChatClient
* Update logging docstrings and conventions
* PR feedback
* Updates to support client-side tool calls
* .NET: Move catalog samples to the HostedAgents folder (#2090)
* move catalog samples to the HostedAgents folder
* move the catalog samples' projects to the HostedAgents folder
* Bump OpenTelemetry.Instrumentation.Runtime from 1.12.0 to 1.13.0 (#1856)
---
updated-dependencies:
- dependency-name: OpenTelemetry.Instrumentation.Runtime
dependency-version: 1.13.0
dependency-type: direct:production
update-type: version-update:semver-minor
...
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* .NET: Bump Microsoft.SemanticKernel.Agents.Abstractions from 1.66.0 to 1.67.0 (#1962)
* Bump Microsoft.SemanticKernel.Agents.Abstractions from 1.66.0 to 1.67.0
---
updated-dependencies:
- dependency-name: Microsoft.SemanticKernel.Agents.Abstractions
dependency-version: 1.67.0
dependency-type: direct:production
update-type: version-update:semver-minor
...
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* .NET: Bump all Microsoft.SemanticKernel packages from 1.66.* to 1.67.* (#1969)
* Initial plan
* Update all Microsoft.SemanticKernel packages to 1.67.*
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* Remove unrelated changes to package-lock.json and yarn.lock
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* .NET: fix: WorkflowAsAgent Sample (#1787)
* fix: WorkflowAsAgent Sample
* Also makes ChatForwardingExecutor public
* feat: Expand ChatForwardingExecutor handled types
Make ChatForwardingExecutor match the input types of ChatProtocolExecutor.
* fix: Update for the new AgentRunResponseUpdate merge logic
AIAgent always sends out List<ChatMessage> now.
* Updated (#2076)
* Bump vite in /python/samples/demos/chatkit-integration/frontend (#1918)
Bumps [vite](https://github.com/vitejs/vite/tree/HEAD/packages/vite) from 7.1.9 to 7.1.12.
- [Release notes](https://github.com/vitejs/vite/releases)
- [Changelog](https://github.com/vitejs/vite/blob/v7.1.12/packages/vite/CHANGELOG.md)
- [Commits](https://github.com/vitejs/vite/commits/v7.1.12/packages/vite)
---
updated-dependencies:
- dependency-name: vite
dependency-version: 7.1.12
dependency-type: direct:development
...
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* Bump Roslynator.Analyzers from 4.14.0 to 4.14.1 (#1857)
---
updated-dependencies:
- dependency-name: Roslynator.Analyzers
dependency-version: 4.14.1
dependency-type: direct:production
update-type: version-update:semver-patch
...
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* Bump MishaKav/pytest-coverage-comment from 1.1.57 to 1.1.59 (#2034)
Bumps [MishaKav/pytest-coverage-comment](https://github.com/mishakav/pytest-coverage-comment) from 1.1.57 to 1.1.59.
- [Release notes](https://github.com/mishakav/pytest-coverage-comment/releases)
- [Changelog](https://github.com/MishaKav/pytest-coverage-comment/blob/main/CHANGELOG.md)
- [Commits](https://github.com/mishakav/pytest-coverage-comment/compare/v1.1.57...v1.1.59)
---
updated-dependencies:
- dependency-name: MishaKav/pytest-coverage-comment
dependency-version: 1.1.59
dependency-type: direct:production
update-type: version-update:semver-patch
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* Python: Handle agent user input request in AgentExecutor (#2022)
* Handle agent user input request in AgentExecutor
* fix test
* Address comments
* Fix tests
* Fix tests
* Address comments
* Address comments
* Python: OpenAI Responses Image Generation Stream Support, Sample and Unit Tests (#1853)
* support for image gen streaming
* small fixes
* fixes
* added comment
* Python: Fix MCP Tool Parameter Descriptions Not Propagated to LLMs (#1978)
* mcp tool description fix
* small fix
* .NET: Allow extending agent run options via additional properties (#1872)
* Allow extending agent run options via additional properties
This mirrors the M.E.AI model in ChatOptions.AdditionalProperties which is very useful when building functionality pipelines.
Fixes https://github.com/microsoft/agent-framework/issues/1815
* Expand XML documentation
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* Add AdditionalProperties tests to AgentRunOptions
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* Python: Use the last entry in the task history to avoid empty responses (#2101)
* Use the last entry in the task history to avoid empty responses
* History only contains Messages
* Updated package versions (#2104)
---------
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* Updated azure-ai-projects package version and small fixes (#2139)
* Python: [Feature Branch] Resolve CI issues (#2143)
* Small documentation and code fixes
* Small fix in documentation
* Addressed PR feedback
* Added AI Search example
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This commit is contained in:
Dmytro Struk
committed by
GitHub
Unverified
GitHub
parent
4bffe1ebc8
commit
edb367a2b9
@@ -3,6 +3,7 @@
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import importlib.metadata
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from ._chat_client import AzureAIAgentClient
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from ._client import AzureAIClient
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from ._shared import AzureAISettings
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try:
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@@ -12,6 +13,7 @@ except importlib.metadata.PackageNotFoundError:
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__all__ = [
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"AzureAIAgentClient",
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"AzureAIClient",
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"AzureAISettings",
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"__version__",
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]
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@@ -0,0 +1,354 @@
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# Copyright (c) Microsoft. All rights reserved.
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import sys
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from collections.abc import MutableSequence
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from typing import Any, ClassVar, TypeVar
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from agent_framework import (
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AGENT_FRAMEWORK_USER_AGENT,
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ChatMessage,
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ChatOptions,
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HostedMCPTool,
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TextContent,
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get_logger,
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use_chat_middleware,
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use_function_invocation,
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)
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from agent_framework.exceptions import ServiceInitializationError
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from agent_framework.observability import use_observability
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from agent_framework.openai._responses_client import OpenAIBaseResponsesClient
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from azure.ai.projects.aio import AIProjectClient
|
||||
from azure.ai.projects.models import (
|
||||
MCPTool,
|
||||
PromptAgentDefinition,
|
||||
PromptAgentDefinitionText,
|
||||
ResponseTextFormatConfigurationJsonSchema,
|
||||
)
|
||||
from azure.core.credentials_async import AsyncTokenCredential
|
||||
from azure.core.exceptions import ResourceNotFoundError
|
||||
from openai.types.responses.parsed_response import (
|
||||
ParsedResponse,
|
||||
)
|
||||
from openai.types.responses.response import Response as OpenAIResponse
|
||||
from pydantic import BaseModel, ValidationError
|
||||
|
||||
from ._shared import AzureAISettings
|
||||
|
||||
if sys.version_info >= (3, 11):
|
||||
from typing import Self # pragma: no cover
|
||||
else:
|
||||
from typing_extensions import Self # pragma: no cover
|
||||
|
||||
|
||||
logger = get_logger("agent_framework.azure")
|
||||
|
||||
|
||||
TAzureAIClient = TypeVar("TAzureAIClient", bound="AzureAIClient")
|
||||
|
||||
|
||||
@use_function_invocation
|
||||
@use_observability
|
||||
@use_chat_middleware
|
||||
class AzureAIClient(OpenAIBaseResponsesClient):
|
||||
"""Azure AI Agent client."""
|
||||
|
||||
OTEL_PROVIDER_NAME: ClassVar[str] = "azure.ai" # type: ignore[reportIncompatibleVariableOverride, misc]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
project_client: AIProjectClient | None = None,
|
||||
agent_name: str | None = None,
|
||||
agent_version: str | None = None,
|
||||
conversation_id: str | None = None,
|
||||
project_endpoint: str | None = None,
|
||||
model_deployment_name: str | None = None,
|
||||
async_credential: AsyncTokenCredential | None = None,
|
||||
use_latest_version: bool | None = None,
|
||||
env_file_path: str | None = None,
|
||||
env_file_encoding: str | None = None,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
"""Initialize an Azure AI Agent client.
|
||||
|
||||
Keyword Args:
|
||||
project_client: An existing AIProjectClient to use. If not provided, one will be created.
|
||||
agent_name: The name to use when creating new agents.
|
||||
agent_version: The version of the agent to use.
|
||||
conversation_id: Default conversation ID to use for conversations. Can be overridden by
|
||||
conversation_id property when making a request.
|
||||
project_endpoint: The Azure AI Project endpoint URL.
|
||||
Can also be set via environment variable AZURE_AI_PROJECT_ENDPOINT.
|
||||
Ignored when a project_client is passed.
|
||||
model_deployment_name: The model deployment name to use for agent creation.
|
||||
Can also be set via environment variable AZURE_AI_MODEL_DEPLOYMENT_NAME.
|
||||
async_credential: Azure async credential to use for authentication.
|
||||
use_latest_version: Boolean flag that indicates whether to use latest agent version
|
||||
if it exists in the service.
|
||||
env_file_path: Path to environment file for loading settings.
|
||||
env_file_encoding: Encoding of the environment file.
|
||||
kwargs: Additional keyword arguments passed to the parent class.
|
||||
|
||||
Examples:
|
||||
.. code-block:: python
|
||||
|
||||
from agent_framework.azure import AzureAIClient
|
||||
from azure.identity.aio import DefaultAzureCredential
|
||||
|
||||
# Using environment variables
|
||||
# Set AZURE_AI_PROJECT_ENDPOINT=https://your-project.cognitiveservices.azure.com
|
||||
# Set AZURE_AI_MODEL_DEPLOYMENT_NAME=gpt-4
|
||||
credential = DefaultAzureCredential()
|
||||
client = AzureAIClient(async_credential=credential)
|
||||
|
||||
# Or passing parameters directly
|
||||
client = AzureAIClient(
|
||||
project_endpoint="https://your-project.cognitiveservices.azure.com",
|
||||
model_deployment_name="gpt-4",
|
||||
async_credential=credential,
|
||||
)
|
||||
|
||||
# Or loading from a .env file
|
||||
client = AzureAIClient(async_credential=credential, env_file_path="path/to/.env")
|
||||
"""
|
||||
try:
|
||||
azure_ai_settings = AzureAISettings(
|
||||
project_endpoint=project_endpoint,
|
||||
model_deployment_name=model_deployment_name,
|
||||
env_file_path=env_file_path,
|
||||
env_file_encoding=env_file_encoding,
|
||||
)
|
||||
except ValidationError as ex:
|
||||
raise ServiceInitializationError("Failed to create Azure AI settings.", ex) from ex
|
||||
|
||||
# If no project_client is provided, create one
|
||||
should_close_client = False
|
||||
if project_client is None:
|
||||
if not azure_ai_settings.project_endpoint:
|
||||
raise ServiceInitializationError(
|
||||
"Azure AI project endpoint is required. Set via 'project_endpoint' parameter "
|
||||
"or 'AZURE_AI_PROJECT_ENDPOINT' environment variable."
|
||||
)
|
||||
|
||||
# Use provided credential
|
||||
if not async_credential:
|
||||
raise ServiceInitializationError("Azure credential is required when project_client is not provided.")
|
||||
project_client = AIProjectClient(
|
||||
endpoint=azure_ai_settings.project_endpoint,
|
||||
credential=async_credential,
|
||||
user_agent=AGENT_FRAMEWORK_USER_AGENT,
|
||||
)
|
||||
should_close_client = True
|
||||
|
||||
# Initialize parent
|
||||
super().__init__(
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
# Initialize instance variables
|
||||
self.agent_name = agent_name
|
||||
self.agent_version = agent_version
|
||||
self.use_latest_version = use_latest_version
|
||||
self.project_client = project_client
|
||||
self.credential = async_credential
|
||||
self.model_id = azure_ai_settings.model_deployment_name
|
||||
self.conversation_id = conversation_id
|
||||
self._should_close_client = should_close_client # Track whether we should close client connection
|
||||
|
||||
async def setup_azure_ai_observability(self, enable_sensitive_data: bool | None = None) -> None:
|
||||
"""Use this method to setup tracing in your Azure AI Project.
|
||||
|
||||
This will take the connection string from the project project_client.
|
||||
It will override any connection string that is set in the environment variables.
|
||||
It will disable any OTLP endpoint that might have been set.
|
||||
"""
|
||||
try:
|
||||
conn_string = await self.project_client.telemetry.get_application_insights_connection_string()
|
||||
except ResourceNotFoundError:
|
||||
logger.warning(
|
||||
"No Application Insights connection string found for the Azure AI Project, "
|
||||
"please call setup_observability() manually."
|
||||
)
|
||||
return
|
||||
from agent_framework.observability import setup_observability
|
||||
|
||||
setup_observability(
|
||||
applicationinsights_connection_string=conn_string, enable_sensitive_data=enable_sensitive_data
|
||||
)
|
||||
|
||||
async def __aenter__(self) -> "Self":
|
||||
"""Async context manager entry."""
|
||||
return self
|
||||
|
||||
async def __aexit__(self, exc_type: type[BaseException] | None, exc_val: BaseException | None, exc_tb: Any) -> None:
|
||||
"""Async context manager exit."""
|
||||
await self.close()
|
||||
|
||||
async def close(self) -> None:
|
||||
"""Close the project_client."""
|
||||
await self._close_client_if_needed()
|
||||
|
||||
async def _get_agent_reference_or_create(
|
||||
self, run_options: dict[str, Any], messages_instructions: str | None
|
||||
) -> dict[str, str]:
|
||||
"""Determine which agent to use and create if needed.
|
||||
|
||||
Returns:
|
||||
str: The agent_name to use
|
||||
"""
|
||||
agent_name = self.agent_name or "UnnamedAgent"
|
||||
|
||||
# If no agent_version is provided, either use latest version or create a new agent:
|
||||
if self.agent_version is None:
|
||||
# Try to use latest version if requested and agent exists
|
||||
if self.use_latest_version:
|
||||
try:
|
||||
existing_agent = await self.project_client.agents.get(agent_name)
|
||||
self.agent_name = existing_agent.name
|
||||
self.agent_version = existing_agent.versions.latest.version
|
||||
return {"name": self.agent_name, "version": self.agent_version, "type": "agent_reference"}
|
||||
except ResourceNotFoundError:
|
||||
# Agent doesn't exist, fall through to creation logic
|
||||
pass
|
||||
|
||||
if "model" not in run_options or not run_options["model"]:
|
||||
raise ServiceInitializationError(
|
||||
"Model deployment name is required for agent creation, "
|
||||
"can also be passed to the get_response methods."
|
||||
)
|
||||
|
||||
args: dict[str, Any] = {"model": run_options["model"]}
|
||||
|
||||
if "tools" in run_options:
|
||||
args["tools"] = run_options["tools"]
|
||||
|
||||
if "response_format" in run_options:
|
||||
response_format = run_options["response_format"]
|
||||
args["text"] = PromptAgentDefinitionText(
|
||||
format=ResponseTextFormatConfigurationJsonSchema(
|
||||
name=response_format.__name__,
|
||||
schema=response_format.model_json_schema(),
|
||||
)
|
||||
)
|
||||
|
||||
# Combine instructions from messages and options
|
||||
combined_instructions = [
|
||||
instructions
|
||||
for instructions in [messages_instructions, run_options.get("instructions")]
|
||||
if instructions
|
||||
]
|
||||
if combined_instructions:
|
||||
args["instructions"] = "".join(combined_instructions)
|
||||
|
||||
created_agent = await self.project_client.agents.create_version(
|
||||
agent_name=agent_name, definition=PromptAgentDefinition(**args)
|
||||
)
|
||||
|
||||
self.agent_name = created_agent.name
|
||||
self.agent_version = created_agent.version
|
||||
|
||||
return {"name": agent_name, "version": self.agent_version, "type": "agent_reference"}
|
||||
|
||||
async def _close_client_if_needed(self) -> None:
|
||||
"""Close project_client session if we created it."""
|
||||
if self._should_close_client:
|
||||
await self.project_client.close()
|
||||
|
||||
def _prepare_input(self, messages: MutableSequence[ChatMessage]) -> tuple[list[ChatMessage], str | None]:
|
||||
"""Prepare input from messages and convert system/developer messages to instructions."""
|
||||
result: list[ChatMessage] = []
|
||||
instructions_list: list[str] = []
|
||||
instructions: str | None = None
|
||||
|
||||
# System/developer messages are turned into instructions, since there is no such message roles in Azure AI.
|
||||
for message in messages:
|
||||
if message.role.value in ["system", "developer"]:
|
||||
for text_content in [content for content in message.contents if isinstance(content, TextContent)]:
|
||||
instructions_list.append(text_content.text)
|
||||
else:
|
||||
result.append(message)
|
||||
|
||||
if len(instructions_list) > 0:
|
||||
instructions = "".join(instructions_list)
|
||||
|
||||
return result, instructions
|
||||
|
||||
async def prepare_options(
|
||||
self, messages: MutableSequence[ChatMessage], chat_options: ChatOptions
|
||||
) -> dict[str, Any]:
|
||||
chat_options.store = bool(chat_options.store or chat_options.store is None)
|
||||
prepared_messages, instructions = self._prepare_input(messages)
|
||||
run_options = await super().prepare_options(prepared_messages, chat_options)
|
||||
agent_reference = await self._get_agent_reference_or_create(run_options, instructions)
|
||||
|
||||
run_options["extra_body"] = {"agent": agent_reference}
|
||||
|
||||
conversation_id = chat_options.conversation_id or self.conversation_id
|
||||
|
||||
# Handle different conversation ID formats
|
||||
if conversation_id:
|
||||
if conversation_id.startswith("resp_"):
|
||||
# For response IDs, set previous_response_id and remove conversation property
|
||||
run_options.pop("conversation", None)
|
||||
run_options["previous_response_id"] = conversation_id
|
||||
elif conversation_id.startswith("conv_"):
|
||||
# For conversation IDs, set conversation and remove previous_response_id property
|
||||
run_options.pop("previous_response_id", None)
|
||||
run_options["conversation"] = conversation_id
|
||||
|
||||
# Remove properties that are not supported on request level
|
||||
# but were configured on agent level
|
||||
exclude = ["model", "tools", "response_format"]
|
||||
|
||||
for property in exclude:
|
||||
run_options.pop(property, None)
|
||||
|
||||
return run_options
|
||||
|
||||
async def initialize_client(self) -> None:
|
||||
"""Initialize OpenAI client asynchronously."""
|
||||
self.client = await self.project_client.get_openai_client() # type: ignore
|
||||
|
||||
def _update_agent_name(self, agent_name: str | None) -> None:
|
||||
"""Update the agent name in the chat client.
|
||||
|
||||
Args:
|
||||
agent_name: The new name for the agent.
|
||||
"""
|
||||
# This is a no-op in the base class, but can be overridden by subclasses
|
||||
# to update the agent name in the client.
|
||||
if agent_name and not self.agent_name:
|
||||
self.agent_name = agent_name
|
||||
|
||||
def get_mcp_tool(self, tool: HostedMCPTool) -> Any:
|
||||
"""Get MCP tool from HostedMCPTool."""
|
||||
mcp = MCPTool(server_label=tool.name.replace(" ", "_"), server_url=str(tool.url))
|
||||
|
||||
if tool.allowed_tools:
|
||||
mcp["allowed_tools"] = list(tool.allowed_tools)
|
||||
|
||||
if tool.approval_mode:
|
||||
match tool.approval_mode:
|
||||
case str():
|
||||
mcp["require_approval"] = "always" if tool.approval_mode == "always_require" else "never"
|
||||
case _:
|
||||
if always_require_approvals := tool.approval_mode.get("always_require_approval"):
|
||||
mcp["require_approval"] = {"always": {"tool_names": list(always_require_approvals)}}
|
||||
if never_require_approvals := tool.approval_mode.get("never_require_approval"):
|
||||
mcp["require_approval"] = {"never": {"tool_names": list(never_require_approvals)}}
|
||||
|
||||
return mcp
|
||||
|
||||
def get_conversation_id(
|
||||
self, response: OpenAIResponse | ParsedResponse[BaseModel], store: bool | None
|
||||
) -> str | None:
|
||||
"""Get the conversation ID from the response if store is True."""
|
||||
if store:
|
||||
# If conversation ID exists, it means that we operate with conversation
|
||||
# so we use conversation ID as input and output.
|
||||
if response.conversation and response.conversation.id:
|
||||
return response.conversation.id
|
||||
# If conversation ID doesn't exist, we operate with responses
|
||||
# so we use response ID as input and output.
|
||||
return response.id
|
||||
return None
|
||||
@@ -24,7 +24,7 @@ classifiers = [
|
||||
]
|
||||
dependencies = [
|
||||
"agent-framework-core",
|
||||
"azure-ai-projects >= 1.0.0b11",
|
||||
"azure-ai-projects >= 2.0.0b1",
|
||||
"azure-ai-agents == 1.2.0b5",
|
||||
"aiohttp",
|
||||
]
|
||||
|
||||
@@ -0,0 +1,743 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
from unittest.mock import AsyncMock, MagicMock, patch
|
||||
|
||||
import pytest
|
||||
from agent_framework import (
|
||||
ChatClientProtocol,
|
||||
ChatMessage,
|
||||
ChatOptions,
|
||||
Role,
|
||||
TextContent,
|
||||
)
|
||||
from agent_framework.exceptions import ServiceInitializationError
|
||||
from azure.ai.projects.models import (
|
||||
ResponseTextFormatConfigurationJsonSchema,
|
||||
)
|
||||
from openai.types.responses.parsed_response import ParsedResponse
|
||||
from openai.types.responses.response import Response as OpenAIResponse
|
||||
from pydantic import BaseModel, ConfigDict, ValidationError
|
||||
|
||||
from agent_framework_azure_ai import AzureAIClient, AzureAISettings
|
||||
|
||||
|
||||
def create_test_azure_ai_client(
|
||||
mock_project_client: MagicMock,
|
||||
agent_name: str | None = None,
|
||||
agent_version: str | None = None,
|
||||
conversation_id: str | None = None,
|
||||
azure_ai_settings: AzureAISettings | None = None,
|
||||
should_close_client: bool = False,
|
||||
use_latest_version: bool | None = None,
|
||||
) -> AzureAIClient:
|
||||
"""Helper function to create AzureAIClient instances for testing, bypassing normal validation."""
|
||||
if azure_ai_settings is None:
|
||||
azure_ai_settings = AzureAISettings(env_file_path="test.env")
|
||||
|
||||
# Create client instance directly
|
||||
client = object.__new__(AzureAIClient)
|
||||
|
||||
# Set attributes directly
|
||||
client.project_client = mock_project_client
|
||||
client.credential = None
|
||||
client.agent_name = agent_name
|
||||
client.agent_version = agent_version
|
||||
client.use_latest_version = use_latest_version
|
||||
client.model_id = azure_ai_settings.model_deployment_name
|
||||
client.conversation_id = conversation_id
|
||||
client._should_close_client = should_close_client # type: ignore
|
||||
client.additional_properties = {}
|
||||
client.middleware = None
|
||||
|
||||
# Mock the OpenAI client attribute
|
||||
mock_openai_client = MagicMock()
|
||||
mock_openai_client.conversations = MagicMock()
|
||||
mock_openai_client.conversations.create = AsyncMock()
|
||||
client.client = mock_openai_client
|
||||
|
||||
return client
|
||||
|
||||
|
||||
def test_azure_ai_settings_init(azure_ai_unit_test_env: dict[str, str]) -> None:
|
||||
"""Test AzureAISettings initialization."""
|
||||
settings = AzureAISettings()
|
||||
|
||||
assert settings.project_endpoint == azure_ai_unit_test_env["AZURE_AI_PROJECT_ENDPOINT"]
|
||||
assert settings.model_deployment_name == azure_ai_unit_test_env["AZURE_AI_MODEL_DEPLOYMENT_NAME"]
|
||||
|
||||
|
||||
def test_azure_ai_settings_init_with_explicit_values() -> None:
|
||||
"""Test AzureAISettings initialization with explicit values."""
|
||||
settings = AzureAISettings(
|
||||
project_endpoint="https://custom-endpoint.com/",
|
||||
model_deployment_name="custom-model",
|
||||
)
|
||||
|
||||
assert settings.project_endpoint == "https://custom-endpoint.com/"
|
||||
assert settings.model_deployment_name == "custom-model"
|
||||
|
||||
|
||||
def test_azure_ai_client_init_with_project_client(mock_project_client: MagicMock) -> None:
|
||||
"""Test AzureAIClient initialization with existing project_client."""
|
||||
with patch("agent_framework_azure_ai._client.AzureAISettings") as mock_settings:
|
||||
mock_settings.return_value.project_endpoint = None
|
||||
mock_settings.return_value.model_deployment_name = "test-model"
|
||||
|
||||
client = AzureAIClient(
|
||||
project_client=mock_project_client,
|
||||
agent_name="test-agent",
|
||||
agent_version="1.0",
|
||||
)
|
||||
|
||||
assert client.project_client is mock_project_client
|
||||
assert client.agent_name == "test-agent"
|
||||
assert client.agent_version == "1.0"
|
||||
assert not client._should_close_client # type: ignore
|
||||
assert isinstance(client, ChatClientProtocol)
|
||||
|
||||
|
||||
def test_azure_ai_client_init_auto_create_client(
|
||||
azure_ai_unit_test_env: dict[str, str],
|
||||
mock_azure_credential: MagicMock,
|
||||
) -> None:
|
||||
"""Test AzureAIClient initialization with auto-created project_client."""
|
||||
with patch("agent_framework_azure_ai._client.AIProjectClient") as mock_ai_project_client:
|
||||
mock_project_client = MagicMock()
|
||||
mock_ai_project_client.return_value = mock_project_client
|
||||
|
||||
client = AzureAIClient(
|
||||
project_endpoint=azure_ai_unit_test_env["AZURE_AI_PROJECT_ENDPOINT"],
|
||||
model_deployment_name=azure_ai_unit_test_env["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
async_credential=mock_azure_credential,
|
||||
agent_name="test-agent",
|
||||
)
|
||||
|
||||
assert client.project_client is mock_project_client
|
||||
assert client.agent_name == "test-agent"
|
||||
assert client._should_close_client # type: ignore
|
||||
|
||||
# Verify AIProjectClient was called with correct parameters
|
||||
mock_ai_project_client.assert_called_once()
|
||||
|
||||
|
||||
def test_azure_ai_client_init_missing_project_endpoint() -> None:
|
||||
"""Test AzureAIClient initialization when project_endpoint is missing and no project_client provided."""
|
||||
with patch("agent_framework_azure_ai._client.AzureAISettings") as mock_settings:
|
||||
mock_settings.return_value.project_endpoint = None
|
||||
mock_settings.return_value.model_deployment_name = "test-model"
|
||||
|
||||
with pytest.raises(ServiceInitializationError, match="Azure AI project endpoint is required"):
|
||||
AzureAIClient(async_credential=MagicMock())
|
||||
|
||||
|
||||
def test_azure_ai_client_init_missing_credential(azure_ai_unit_test_env: dict[str, str]) -> None:
|
||||
"""Test AzureAIClient.__init__ when async_credential is missing and no project_client provided."""
|
||||
with pytest.raises(
|
||||
ServiceInitializationError, match="Azure credential is required when project_client is not provided"
|
||||
):
|
||||
AzureAIClient(
|
||||
project_endpoint=azure_ai_unit_test_env["AZURE_AI_PROJECT_ENDPOINT"],
|
||||
model_deployment_name=azure_ai_unit_test_env["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
|
||||
)
|
||||
|
||||
|
||||
def test_azure_ai_client_init_validation_error(mock_azure_credential: MagicMock) -> None:
|
||||
"""Test that ValidationError in AzureAISettings is properly handled."""
|
||||
with patch("agent_framework_azure_ai._client.AzureAISettings") as mock_settings:
|
||||
mock_settings.side_effect = ValidationError.from_exception_data("test", [])
|
||||
|
||||
with pytest.raises(ServiceInitializationError, match="Failed to create Azure AI settings"):
|
||||
AzureAIClient(async_credential=mock_azure_credential)
|
||||
|
||||
|
||||
async def test_azure_ai_client_get_agent_reference_or_create_existing_version(
|
||||
mock_project_client: MagicMock,
|
||||
) -> None:
|
||||
"""Test _get_agent_reference_or_create when agent_version is already provided."""
|
||||
client = create_test_azure_ai_client(mock_project_client, agent_name="existing-agent", agent_version="1.0")
|
||||
|
||||
agent_ref = await client._get_agent_reference_or_create({}, None) # type: ignore
|
||||
|
||||
assert agent_ref == {"name": "existing-agent", "version": "1.0", "type": "agent_reference"}
|
||||
|
||||
|
||||
async def test_azure_ai_client_get_agent_reference_or_create_new_agent(
|
||||
mock_project_client: MagicMock,
|
||||
azure_ai_unit_test_env: dict[str, str],
|
||||
) -> None:
|
||||
"""Test _get_agent_reference_or_create when creating a new agent."""
|
||||
azure_ai_settings = AzureAISettings(model_deployment_name=azure_ai_unit_test_env["AZURE_AI_MODEL_DEPLOYMENT_NAME"])
|
||||
client = create_test_azure_ai_client(
|
||||
mock_project_client, agent_name="new-agent", azure_ai_settings=azure_ai_settings
|
||||
)
|
||||
|
||||
# Mock agent creation response
|
||||
mock_agent = MagicMock()
|
||||
mock_agent.name = "new-agent"
|
||||
mock_agent.version = "1.0"
|
||||
mock_project_client.agents.create_version = AsyncMock(return_value=mock_agent)
|
||||
|
||||
run_options = {"model": azure_ai_settings.model_deployment_name}
|
||||
agent_ref = await client._get_agent_reference_or_create(run_options, None) # type: ignore
|
||||
|
||||
assert agent_ref == {"name": "new-agent", "version": "1.0", "type": "agent_reference"}
|
||||
assert client.agent_name == "new-agent"
|
||||
assert client.agent_version == "1.0"
|
||||
|
||||
|
||||
async def test_azure_ai_client_get_agent_reference_missing_model(
|
||||
mock_project_client: MagicMock,
|
||||
) -> None:
|
||||
"""Test _get_agent_reference_or_create when model is missing for agent creation."""
|
||||
client = create_test_azure_ai_client(mock_project_client, agent_name="test-agent")
|
||||
|
||||
with pytest.raises(ServiceInitializationError, match="Model deployment name is required for agent creation"):
|
||||
await client._get_agent_reference_or_create({}, None) # type: ignore
|
||||
|
||||
|
||||
async def test_azure_ai_client_prepare_input_with_system_messages(
|
||||
mock_project_client: MagicMock,
|
||||
) -> None:
|
||||
"""Test _prepare_input converts system/developer messages to instructions."""
|
||||
client = create_test_azure_ai_client(mock_project_client)
|
||||
|
||||
messages = [
|
||||
ChatMessage(role=Role.SYSTEM, contents=[TextContent(text="You are a helpful assistant.")]),
|
||||
ChatMessage(role=Role.USER, contents=[TextContent(text="Hello")]),
|
||||
ChatMessage(role=Role.ASSISTANT, contents=[TextContent(text="System response")]),
|
||||
]
|
||||
|
||||
result_messages, instructions = client._prepare_input(messages) # type: ignore
|
||||
|
||||
assert len(result_messages) == 2
|
||||
assert result_messages[0].role == Role.USER
|
||||
assert result_messages[1].role == Role.ASSISTANT
|
||||
assert instructions == "You are a helpful assistant."
|
||||
|
||||
|
||||
async def test_azure_ai_client_prepare_input_no_system_messages(
|
||||
mock_project_client: MagicMock,
|
||||
) -> None:
|
||||
"""Test _prepare_input with no system/developer messages."""
|
||||
client = create_test_azure_ai_client(mock_project_client)
|
||||
|
||||
messages = [
|
||||
ChatMessage(role=Role.USER, contents=[TextContent(text="Hello")]),
|
||||
ChatMessage(role=Role.ASSISTANT, contents=[TextContent(text="Hi there!")]),
|
||||
]
|
||||
|
||||
result_messages, instructions = client._prepare_input(messages) # type: ignore
|
||||
|
||||
assert len(result_messages) == 2
|
||||
assert instructions is None
|
||||
|
||||
|
||||
async def test_azure_ai_client_prepare_options_basic(mock_project_client: MagicMock) -> None:
|
||||
"""Test prepare_options basic functionality."""
|
||||
client = create_test_azure_ai_client(mock_project_client, agent_name="test-agent", agent_version="1.0")
|
||||
|
||||
messages = [ChatMessage(role=Role.USER, contents=[TextContent(text="Hello")])]
|
||||
chat_options = ChatOptions()
|
||||
|
||||
with (
|
||||
patch.object(client.__class__.__bases__[0], "prepare_options", return_value={"model": "test-model"}),
|
||||
patch.object(
|
||||
client,
|
||||
"_get_agent_reference_or_create",
|
||||
return_value={"name": "test-agent", "version": "1.0", "type": "agent_reference"},
|
||||
),
|
||||
):
|
||||
run_options = await client.prepare_options(messages, chat_options)
|
||||
|
||||
assert "extra_body" in run_options
|
||||
assert run_options["extra_body"]["agent"]["name"] == "test-agent"
|
||||
|
||||
|
||||
async def test_azure_ai_client_initialize_client(mock_project_client: MagicMock) -> None:
|
||||
"""Test initialize_client method."""
|
||||
client = create_test_azure_ai_client(mock_project_client)
|
||||
|
||||
mock_openai_client = MagicMock()
|
||||
mock_project_client.get_openai_client = AsyncMock(return_value=mock_openai_client)
|
||||
|
||||
await client.initialize_client()
|
||||
|
||||
assert client.client is mock_openai_client
|
||||
mock_project_client.get_openai_client.assert_called_once()
|
||||
|
||||
|
||||
def test_azure_ai_client_update_agent_name(mock_project_client: MagicMock) -> None:
|
||||
"""Test _update_agent_name method."""
|
||||
client = create_test_azure_ai_client(mock_project_client)
|
||||
|
||||
# Test updating agent name when current is None
|
||||
with patch.object(client, "_update_agent_name") as mock_update:
|
||||
mock_update.return_value = None
|
||||
client._update_agent_name("new-agent") # type: ignore
|
||||
mock_update.assert_called_once_with("new-agent")
|
||||
|
||||
# Test behavior when agent name is updated
|
||||
assert client.agent_name is None # Should remain None since we didn't actually update
|
||||
client.agent_name = "test-agent" # Manually set for the test
|
||||
|
||||
# Test with None input
|
||||
with patch.object(client, "_update_agent_name") as mock_update:
|
||||
mock_update.return_value = None
|
||||
client._update_agent_name(None) # type: ignore
|
||||
mock_update.assert_called_once_with(None)
|
||||
|
||||
|
||||
async def test_azure_ai_client_async_context_manager(mock_project_client: MagicMock) -> None:
|
||||
"""Test async context manager functionality."""
|
||||
client = create_test_azure_ai_client(mock_project_client, should_close_client=True)
|
||||
|
||||
mock_project_client.close = AsyncMock()
|
||||
|
||||
async with client as ctx_client:
|
||||
assert ctx_client is client
|
||||
|
||||
# Should call close after exiting context
|
||||
mock_project_client.close.assert_called_once()
|
||||
|
||||
|
||||
async def test_azure_ai_client_close_method(mock_project_client: MagicMock) -> None:
|
||||
"""Test close method."""
|
||||
client = create_test_azure_ai_client(mock_project_client, should_close_client=True)
|
||||
|
||||
mock_project_client.close = AsyncMock()
|
||||
|
||||
await client.close()
|
||||
|
||||
mock_project_client.close.assert_called_once()
|
||||
|
||||
|
||||
async def test_azure_ai_client_close_client_when_should_close_false(mock_project_client: MagicMock) -> None:
|
||||
"""Test _close_client_if_needed when should_close_client is False."""
|
||||
client = create_test_azure_ai_client(mock_project_client, should_close_client=False)
|
||||
|
||||
mock_project_client.close = AsyncMock()
|
||||
|
||||
await client._close_client_if_needed() # type: ignore
|
||||
|
||||
# Should not call close when should_close_client is False
|
||||
mock_project_client.close.assert_not_called()
|
||||
|
||||
|
||||
async def test_azure_ai_client_agent_creation_with_instructions(
|
||||
mock_project_client: MagicMock,
|
||||
) -> None:
|
||||
"""Test agent creation with combined instructions."""
|
||||
client = create_test_azure_ai_client(mock_project_client, agent_name="test-agent")
|
||||
|
||||
# Mock agent creation response
|
||||
mock_agent = MagicMock()
|
||||
mock_agent.name = "test-agent"
|
||||
mock_agent.version = "1.0"
|
||||
mock_project_client.agents.create_version = AsyncMock(return_value=mock_agent)
|
||||
|
||||
run_options = {"model": "test-model", "instructions": "Option instructions. "}
|
||||
messages_instructions = "Message instructions. "
|
||||
|
||||
await client._get_agent_reference_or_create(run_options, messages_instructions) # type: ignore
|
||||
|
||||
# Verify agent was created with combined instructions
|
||||
call_args = mock_project_client.agents.create_version.call_args
|
||||
assert call_args[1]["definition"].instructions == "Message instructions. Option instructions. "
|
||||
|
||||
|
||||
async def test_azure_ai_client_agent_creation_with_tools(
|
||||
mock_project_client: MagicMock,
|
||||
) -> None:
|
||||
"""Test agent creation with tools."""
|
||||
client = create_test_azure_ai_client(mock_project_client, agent_name="test-agent")
|
||||
|
||||
# Mock agent creation response
|
||||
mock_agent = MagicMock()
|
||||
mock_agent.name = "test-agent"
|
||||
mock_agent.version = "1.0"
|
||||
mock_project_client.agents.create_version = AsyncMock(return_value=mock_agent)
|
||||
|
||||
test_tools = [{"type": "function", "function": {"name": "test_tool"}}]
|
||||
run_options = {"model": "test-model", "tools": test_tools}
|
||||
|
||||
await client._get_agent_reference_or_create(run_options, None) # type: ignore
|
||||
|
||||
# Verify agent was created with tools
|
||||
call_args = mock_project_client.agents.create_version.call_args
|
||||
assert call_args[1]["definition"].tools == test_tools
|
||||
|
||||
|
||||
async def test_azure_ai_client_use_latest_version_existing_agent(
|
||||
mock_project_client: MagicMock,
|
||||
) -> None:
|
||||
"""Test _get_agent_reference_or_create when use_latest_version=True and agent exists."""
|
||||
client = create_test_azure_ai_client(mock_project_client, agent_name="existing-agent", use_latest_version=True)
|
||||
|
||||
# Mock existing agent response
|
||||
mock_existing_agent = MagicMock()
|
||||
mock_existing_agent.name = "existing-agent"
|
||||
mock_existing_agent.versions.latest.version = "2.5"
|
||||
mock_project_client.agents.get = AsyncMock(return_value=mock_existing_agent)
|
||||
|
||||
run_options = {"model": "test-model"}
|
||||
agent_ref = await client._get_agent_reference_or_create(run_options, None) # type: ignore
|
||||
|
||||
# Verify existing agent was retrieved and used
|
||||
mock_project_client.agents.get.assert_called_once_with("existing-agent")
|
||||
mock_project_client.agents.create_version.assert_not_called()
|
||||
|
||||
assert agent_ref == {"name": "existing-agent", "version": "2.5", "type": "agent_reference"}
|
||||
assert client.agent_name == "existing-agent"
|
||||
assert client.agent_version == "2.5"
|
||||
|
||||
|
||||
async def test_azure_ai_client_use_latest_version_agent_not_found(
|
||||
mock_project_client: MagicMock,
|
||||
) -> None:
|
||||
"""Test _get_agent_reference_or_create when use_latest_version=True but agent doesn't exist."""
|
||||
from azure.core.exceptions import ResourceNotFoundError
|
||||
|
||||
client = create_test_azure_ai_client(mock_project_client, agent_name="non-existing-agent", use_latest_version=True)
|
||||
|
||||
# Mock ResourceNotFoundError when trying to retrieve agent
|
||||
mock_project_client.agents.get = AsyncMock(side_effect=ResourceNotFoundError("Agent not found"))
|
||||
|
||||
# Mock agent creation response for fallback
|
||||
mock_created_agent = MagicMock()
|
||||
mock_created_agent.name = "non-existing-agent"
|
||||
mock_created_agent.version = "1.0"
|
||||
mock_project_client.agents.create_version = AsyncMock(return_value=mock_created_agent)
|
||||
|
||||
run_options = {"model": "test-model"}
|
||||
agent_ref = await client._get_agent_reference_or_create(run_options, None) # type: ignore
|
||||
|
||||
# Verify retrieval was attempted and creation was used as fallback
|
||||
mock_project_client.agents.get.assert_called_once_with("non-existing-agent")
|
||||
mock_project_client.agents.create_version.assert_called_once()
|
||||
|
||||
assert agent_ref == {"name": "non-existing-agent", "version": "1.0", "type": "agent_reference"}
|
||||
assert client.agent_name == "non-existing-agent"
|
||||
assert client.agent_version == "1.0"
|
||||
|
||||
|
||||
async def test_azure_ai_client_use_latest_version_false(
|
||||
mock_project_client: MagicMock,
|
||||
) -> None:
|
||||
"""Test _get_agent_reference_or_create when use_latest_version=False (default behavior)."""
|
||||
client = create_test_azure_ai_client(mock_project_client, agent_name="test-agent", use_latest_version=False)
|
||||
|
||||
# Mock agent creation response
|
||||
mock_created_agent = MagicMock()
|
||||
mock_created_agent.name = "test-agent"
|
||||
mock_created_agent.version = "1.0"
|
||||
mock_project_client.agents.create_version = AsyncMock(return_value=mock_created_agent)
|
||||
|
||||
run_options = {"model": "test-model"}
|
||||
agent_ref = await client._get_agent_reference_or_create(run_options, None) # type: ignore
|
||||
|
||||
# Verify retrieval was not attempted and creation was used directly
|
||||
mock_project_client.agents.get.assert_not_called()
|
||||
mock_project_client.agents.create_version.assert_called_once()
|
||||
|
||||
assert agent_ref == {"name": "test-agent", "version": "1.0", "type": "agent_reference"}
|
||||
|
||||
|
||||
async def test_azure_ai_client_use_latest_version_with_existing_agent_version(
|
||||
mock_project_client: MagicMock,
|
||||
) -> None:
|
||||
"""Test that use_latest_version is ignored when agent_version is already provided."""
|
||||
client = create_test_azure_ai_client(
|
||||
mock_project_client, agent_name="test-agent", agent_version="3.0", use_latest_version=True
|
||||
)
|
||||
|
||||
agent_ref = await client._get_agent_reference_or_create({}, None) # type: ignore
|
||||
|
||||
# Verify neither retrieval nor creation was attempted since version is already set
|
||||
mock_project_client.agents.get.assert_not_called()
|
||||
mock_project_client.agents.create_version.assert_not_called()
|
||||
|
||||
assert agent_ref == {"name": "test-agent", "version": "3.0", "type": "agent_reference"}
|
||||
|
||||
|
||||
class ResponseFormatModel(BaseModel):
|
||||
"""Test Pydantic model for response format testing."""
|
||||
|
||||
name: str
|
||||
value: int
|
||||
description: str
|
||||
model_config = ConfigDict(extra="forbid")
|
||||
|
||||
|
||||
async def test_azure_ai_client_agent_creation_with_response_format(
|
||||
mock_project_client: MagicMock,
|
||||
) -> None:
|
||||
"""Test agent creation with response_format configuration."""
|
||||
client = create_test_azure_ai_client(mock_project_client, agent_name="test-agent")
|
||||
|
||||
# Mock agent creation response
|
||||
mock_agent = MagicMock()
|
||||
mock_agent.name = "test-agent"
|
||||
mock_agent.version = "1.0"
|
||||
mock_project_client.agents.create_version = AsyncMock(return_value=mock_agent)
|
||||
|
||||
run_options = {"model": "test-model", "response_format": ResponseFormatModel}
|
||||
|
||||
await client._get_agent_reference_or_create(run_options, None) # type: ignore
|
||||
|
||||
# Verify agent was created with response format configuration
|
||||
call_args = mock_project_client.agents.create_version.call_args
|
||||
created_definition = call_args[1]["definition"]
|
||||
|
||||
# Check that text format configuration was set
|
||||
assert hasattr(created_definition, "text")
|
||||
assert created_definition.text is not None
|
||||
|
||||
# Check that the format is a ResponseTextFormatConfigurationJsonSchema
|
||||
assert hasattr(created_definition.text, "format")
|
||||
format_config = created_definition.text.format
|
||||
assert isinstance(format_config, ResponseTextFormatConfigurationJsonSchema)
|
||||
|
||||
# Check the schema name matches the model class name
|
||||
assert format_config.name == "ResponseFormatModel"
|
||||
|
||||
# Check that schema was generated correctly
|
||||
assert format_config.schema is not None
|
||||
schema = format_config.schema
|
||||
assert "properties" in schema
|
||||
assert "name" in schema["properties"]
|
||||
assert "value" in schema["properties"]
|
||||
assert "description" in schema["properties"]
|
||||
|
||||
|
||||
async def test_azure_ai_client_prepare_options_excludes_response_format(
|
||||
mock_project_client: MagicMock,
|
||||
) -> None:
|
||||
"""Test that prepare_options excludes response_format from final run options."""
|
||||
client = create_test_azure_ai_client(mock_project_client, agent_name="test-agent", agent_version="1.0")
|
||||
|
||||
messages = [ChatMessage(role=Role.USER, contents=[TextContent(text="Hello")])]
|
||||
chat_options = ChatOptions()
|
||||
|
||||
with (
|
||||
patch.object(
|
||||
client.__class__.__bases__[0],
|
||||
"prepare_options",
|
||||
return_value={"model": "test-model", "response_format": ResponseFormatModel},
|
||||
),
|
||||
patch.object(
|
||||
client,
|
||||
"_get_agent_reference_or_create",
|
||||
return_value={"name": "test-agent", "version": "1.0", "type": "agent_reference"},
|
||||
),
|
||||
):
|
||||
run_options = await client.prepare_options(messages, chat_options)
|
||||
|
||||
# response_format should be excluded from final run options
|
||||
assert "response_format" not in run_options
|
||||
# But extra_body should contain agent reference
|
||||
assert "extra_body" in run_options
|
||||
assert run_options["extra_body"]["agent"]["name"] == "test-agent"
|
||||
|
||||
|
||||
async def test_azure_ai_client_prepare_options_with_resp_conversation_id(
|
||||
mock_project_client: MagicMock,
|
||||
) -> None:
|
||||
"""Test prepare_options with conversation ID starting with 'resp_'."""
|
||||
client = create_test_azure_ai_client(mock_project_client, agent_name="test-agent", agent_version="1.0")
|
||||
|
||||
messages = [ChatMessage(role=Role.USER, contents=[TextContent(text="Hello")])]
|
||||
chat_options = ChatOptions(conversation_id="resp_12345")
|
||||
|
||||
with (
|
||||
patch.object(
|
||||
client.__class__.__bases__[0],
|
||||
"prepare_options",
|
||||
return_value={"model": "test-model", "previous_response_id": "old_value", "conversation": "old_conv"},
|
||||
),
|
||||
patch.object(
|
||||
client,
|
||||
"_get_agent_reference_or_create",
|
||||
return_value={"name": "test-agent", "version": "1.0", "type": "agent_reference"},
|
||||
),
|
||||
):
|
||||
run_options = await client.prepare_options(messages, chat_options)
|
||||
|
||||
# Should set previous_response_id and remove conversation property
|
||||
assert run_options["previous_response_id"] == "resp_12345"
|
||||
assert "conversation" not in run_options
|
||||
|
||||
|
||||
async def test_azure_ai_client_prepare_options_with_conv_conversation_id(
|
||||
mock_project_client: MagicMock,
|
||||
) -> None:
|
||||
"""Test prepare_options with conversation ID starting with 'conv_'."""
|
||||
client = create_test_azure_ai_client(mock_project_client, agent_name="test-agent", agent_version="1.0")
|
||||
|
||||
messages = [ChatMessage(role=Role.USER, contents=[TextContent(text="Hello")])]
|
||||
chat_options = ChatOptions(conversation_id="conv_67890")
|
||||
|
||||
with (
|
||||
patch.object(
|
||||
client.__class__.__bases__[0],
|
||||
"prepare_options",
|
||||
return_value={"model": "test-model", "previous_response_id": "old_value", "conversation": "old_conv"},
|
||||
),
|
||||
patch.object(
|
||||
client,
|
||||
"_get_agent_reference_or_create",
|
||||
return_value={"name": "test-agent", "version": "1.0", "type": "agent_reference"},
|
||||
),
|
||||
):
|
||||
run_options = await client.prepare_options(messages, chat_options)
|
||||
|
||||
# Should set conversation and remove previous_response_id property
|
||||
assert run_options["conversation"] == "conv_67890"
|
||||
assert "previous_response_id" not in run_options
|
||||
|
||||
|
||||
async def test_azure_ai_client_prepare_options_with_client_conversation_id(
|
||||
mock_project_client: MagicMock,
|
||||
) -> None:
|
||||
"""Test prepare_options using client's default conversation ID when chat options don't have one."""
|
||||
client = create_test_azure_ai_client(
|
||||
mock_project_client, agent_name="test-agent", agent_version="1.0", conversation_id="resp_client_default"
|
||||
)
|
||||
|
||||
messages = [ChatMessage(role=Role.USER, contents=[TextContent(text="Hello")])]
|
||||
chat_options = ChatOptions() # No conversation_id specified
|
||||
|
||||
with (
|
||||
patch.object(
|
||||
client.__class__.__bases__[0],
|
||||
"prepare_options",
|
||||
return_value={"model": "test-model", "previous_response_id": "old_value", "conversation": "old_conv"},
|
||||
),
|
||||
patch.object(
|
||||
client,
|
||||
"_get_agent_reference_or_create",
|
||||
return_value={"name": "test-agent", "version": "1.0", "type": "agent_reference"},
|
||||
),
|
||||
):
|
||||
run_options = await client.prepare_options(messages, chat_options)
|
||||
|
||||
# Should use client's default conversation_id and set previous_response_id
|
||||
assert run_options["previous_response_id"] == "resp_client_default"
|
||||
assert "conversation" not in run_options
|
||||
|
||||
|
||||
def test_get_conversation_id_with_store_true_and_conversation_id() -> None:
|
||||
"""Test get_conversation_id returns conversation ID when store is True and conversation exists."""
|
||||
client = create_test_azure_ai_client(MagicMock())
|
||||
|
||||
# Mock OpenAI response with conversation
|
||||
mock_response = MagicMock(spec=OpenAIResponse)
|
||||
mock_response.id = "resp_12345"
|
||||
mock_conversation = MagicMock()
|
||||
mock_conversation.id = "conv_67890"
|
||||
mock_response.conversation = mock_conversation
|
||||
|
||||
result = client.get_conversation_id(mock_response, store=True)
|
||||
|
||||
assert result == "conv_67890"
|
||||
|
||||
|
||||
def test_get_conversation_id_with_store_true_and_no_conversation() -> None:
|
||||
"""Test get_conversation_id returns response ID when store is True and no conversation exists."""
|
||||
client = create_test_azure_ai_client(MagicMock())
|
||||
|
||||
# Mock OpenAI response without conversation
|
||||
mock_response = MagicMock(spec=OpenAIResponse)
|
||||
mock_response.id = "resp_12345"
|
||||
mock_response.conversation = None
|
||||
|
||||
result = client.get_conversation_id(mock_response, store=True)
|
||||
|
||||
assert result == "resp_12345"
|
||||
|
||||
|
||||
def test_get_conversation_id_with_store_true_and_empty_conversation_id() -> None:
|
||||
"""Test get_conversation_id returns response ID when store is True and conversation ID is empty."""
|
||||
client = create_test_azure_ai_client(MagicMock())
|
||||
|
||||
# Mock OpenAI response with conversation but empty ID
|
||||
mock_response = MagicMock(spec=OpenAIResponse)
|
||||
mock_response.id = "resp_12345"
|
||||
mock_conversation = MagicMock()
|
||||
mock_conversation.id = ""
|
||||
mock_response.conversation = mock_conversation
|
||||
|
||||
result = client.get_conversation_id(mock_response, store=True)
|
||||
|
||||
assert result == "resp_12345"
|
||||
|
||||
|
||||
def test_get_conversation_id_with_store_false() -> None:
|
||||
"""Test get_conversation_id returns None when store is False."""
|
||||
client = create_test_azure_ai_client(MagicMock())
|
||||
|
||||
# Mock OpenAI response with conversation
|
||||
mock_response = MagicMock(spec=OpenAIResponse)
|
||||
mock_response.id = "resp_12345"
|
||||
mock_conversation = MagicMock()
|
||||
mock_conversation.id = "conv_67890"
|
||||
mock_response.conversation = mock_conversation
|
||||
|
||||
result = client.get_conversation_id(mock_response, store=False)
|
||||
|
||||
assert result is None
|
||||
|
||||
|
||||
def test_get_conversation_id_with_parsed_response_and_store_true() -> None:
|
||||
"""Test get_conversation_id works with ParsedResponse when store is True."""
|
||||
client = create_test_azure_ai_client(MagicMock())
|
||||
|
||||
# Mock ParsedResponse with conversation
|
||||
mock_response = MagicMock(spec=ParsedResponse[BaseModel])
|
||||
mock_response.id = "resp_parsed_12345"
|
||||
mock_conversation = MagicMock()
|
||||
mock_conversation.id = "conv_parsed_67890"
|
||||
mock_response.conversation = mock_conversation
|
||||
|
||||
result = client.get_conversation_id(mock_response, store=True)
|
||||
|
||||
assert result == "conv_parsed_67890"
|
||||
|
||||
|
||||
def test_get_conversation_id_with_parsed_response_no_conversation() -> None:
|
||||
"""Test get_conversation_id returns response ID with ParsedResponse when no conversation exists."""
|
||||
client = create_test_azure_ai_client(MagicMock())
|
||||
|
||||
# Mock ParsedResponse without conversation
|
||||
mock_response = MagicMock(spec=ParsedResponse[BaseModel])
|
||||
mock_response.id = "resp_parsed_12345"
|
||||
mock_response.conversation = None
|
||||
|
||||
result = client.get_conversation_id(mock_response, store=True)
|
||||
|
||||
assert result == "resp_parsed_12345"
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_project_client() -> MagicMock:
|
||||
"""Fixture that provides a mock AIProjectClient."""
|
||||
mock_client = MagicMock()
|
||||
|
||||
# Mock agents property
|
||||
mock_client.agents = MagicMock()
|
||||
mock_client.agents.create_version = AsyncMock()
|
||||
|
||||
# Mock conversations property
|
||||
mock_client.conversations = MagicMock()
|
||||
mock_client.conversations.create = AsyncMock()
|
||||
|
||||
# Mock telemetry property
|
||||
mock_client.telemetry = MagicMock()
|
||||
mock_client.telemetry.get_application_insights_connection_string = AsyncMock()
|
||||
|
||||
# Mock get_openai_client method
|
||||
mock_client.get_openai_client = AsyncMock()
|
||||
|
||||
# Mock close method
|
||||
mock_client.close = AsyncMock()
|
||||
|
||||
return mock_client
|
||||
@@ -564,10 +564,6 @@ class BaseChatClient(SerializationMixin, ABC):
|
||||
|
||||
# Validate that store is True when conversation_id is set
|
||||
if chat_options.conversation_id is not None and chat_options.store is not True:
|
||||
logger.warning(
|
||||
"When conversation_id is set, store must be True for service-managed threads. "
|
||||
"Automatically setting store=True."
|
||||
)
|
||||
chat_options.store = True
|
||||
|
||||
if chat_options.instructions:
|
||||
@@ -663,10 +659,6 @@ class BaseChatClient(SerializationMixin, ABC):
|
||||
|
||||
# Validate that store is True when conversation_id is set
|
||||
if chat_options.conversation_id is not None and chat_options.store is not True:
|
||||
logger.warning(
|
||||
"When conversation_id is set, store must be True for service-managed threads. "
|
||||
"Automatically setting store=True."
|
||||
)
|
||||
chat_options.store = True
|
||||
|
||||
if chat_options.instructions:
|
||||
|
||||
@@ -1636,7 +1636,7 @@ def _handle_function_calls_response(
|
||||
# this runs in every but the first run
|
||||
# we need to keep track of all function call messages
|
||||
fcc_messages.extend(response.messages)
|
||||
if getattr(kwargs.get("chat_options"), "store", False):
|
||||
if response.conversation_id is not None:
|
||||
prepped_messages.clear()
|
||||
prepped_messages.append(result_message)
|
||||
else:
|
||||
@@ -1839,7 +1839,7 @@ def _handle_function_calls_streaming_response(
|
||||
# this runs in every but the first run
|
||||
# we need to keep track of all function call messages
|
||||
fcc_messages.extend(response.messages)
|
||||
if getattr(kwargs.get("chat_options"), "store", False):
|
||||
if response.conversation_id is not None:
|
||||
prepped_messages.clear()
|
||||
prepped_messages.append(result_message)
|
||||
else:
|
||||
|
||||
@@ -6,6 +6,7 @@ from typing import Any
|
||||
|
||||
_IMPORTS: dict[str, tuple[str, str]] = {
|
||||
"AzureAIAgentClient": ("agent_framework_azure_ai", "azure-ai"),
|
||||
"AzureAIClient": ("agent_framework_azure_ai", "azure-ai"),
|
||||
"AzureOpenAIAssistantsClient": ("agent_framework.azure._assistants_client", "core"),
|
||||
"AzureOpenAIChatClient": ("agent_framework.azure._chat_client", "core"),
|
||||
"AzureAISettings": ("agent_framework_azure_ai", "azure-ai"),
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
|
||||
from agent_framework_azure_ai import AzureAIAgentClient, AzureAISettings
|
||||
from agent_framework_azure_ai import AzureAIAgentClient, AzureAIClient, AzureAISettings
|
||||
|
||||
from agent_framework.azure._assistants_client import AzureOpenAIAssistantsClient
|
||||
from agent_framework.azure._chat_client import AzureOpenAIChatClient
|
||||
@@ -10,6 +10,7 @@ from agent_framework.azure._shared import AzureOpenAISettings
|
||||
|
||||
__all__ = [
|
||||
"AzureAIAgentClient",
|
||||
"AzureAIClient",
|
||||
"AzureAISettings",
|
||||
"AzureOpenAIAssistantsClient",
|
||||
"AzureOpenAIChatClient",
|
||||
|
||||
@@ -161,7 +161,8 @@ class OpenAIAssistantsClient(OpenAIConfigMixin, BaseChatClient):
|
||||
async def close(self) -> None:
|
||||
"""Clean up any assistants we created."""
|
||||
if self._should_delete_assistant and self.assistant_id is not None:
|
||||
await self.client.beta.assistants.delete(self.assistant_id)
|
||||
client = await self.ensure_client()
|
||||
await client.beta.assistants.delete(self.assistant_id)
|
||||
object.__setattr__(self, "assistant_id", None)
|
||||
object.__setattr__(self, "_should_delete_assistant", False)
|
||||
|
||||
@@ -215,7 +216,11 @@ class OpenAIAssistantsClient(OpenAIConfigMixin, BaseChatClient):
|
||||
"""
|
||||
# If no assistant is provided, create a temporary assistant
|
||||
if self.assistant_id is None:
|
||||
created_assistant = await self.client.beta.assistants.create(name=self.assistant_name, model=self.model_id)
|
||||
if not self.model_id:
|
||||
raise ServiceInitializationError("Parameter 'model_id' is required for assistant creation.")
|
||||
|
||||
client = await self.ensure_client()
|
||||
created_assistant = await client.beta.assistants.create(name=self.assistant_name, model=self.model_id)
|
||||
self.assistant_id = created_assistant.id
|
||||
self._should_delete_assistant = True
|
||||
|
||||
@@ -233,6 +238,7 @@ class OpenAIAssistantsClient(OpenAIConfigMixin, BaseChatClient):
|
||||
Returns:
|
||||
tuple: (stream, final_thread_id)
|
||||
"""
|
||||
client = await self.ensure_client()
|
||||
# Get any active run for this thread
|
||||
thread_run = await self._get_active_thread_run(thread_id)
|
||||
|
||||
@@ -240,7 +246,7 @@ class OpenAIAssistantsClient(OpenAIConfigMixin, BaseChatClient):
|
||||
|
||||
if thread_run is not None and tool_run_id is not None and tool_run_id == thread_run.id and tool_outputs:
|
||||
# There's an active run and we have tool results to submit, so submit the results.
|
||||
stream = self.client.beta.threads.runs.submit_tool_outputs_stream( # type: ignore[reportDeprecated]
|
||||
stream = client.beta.threads.runs.submit_tool_outputs_stream( # type: ignore[reportDeprecated]
|
||||
run_id=tool_run_id, thread_id=thread_run.thread_id, tool_outputs=tool_outputs
|
||||
)
|
||||
final_thread_id = thread_run.thread_id
|
||||
@@ -249,7 +255,7 @@ class OpenAIAssistantsClient(OpenAIConfigMixin, BaseChatClient):
|
||||
final_thread_id = await self._prepare_thread(thread_id, thread_run, run_options)
|
||||
|
||||
# Now create a new run and stream the results.
|
||||
stream = self.client.beta.threads.runs.stream( # type: ignore[reportDeprecated]
|
||||
stream = client.beta.threads.runs.stream( # type: ignore[reportDeprecated]
|
||||
assistant_id=assistant_id, thread_id=final_thread_id, **run_options
|
||||
)
|
||||
|
||||
@@ -257,19 +263,21 @@ class OpenAIAssistantsClient(OpenAIConfigMixin, BaseChatClient):
|
||||
|
||||
async def _get_active_thread_run(self, thread_id: str | None) -> Run | None:
|
||||
"""Get any active run for the given thread."""
|
||||
client = await self.ensure_client()
|
||||
if thread_id is None:
|
||||
return None
|
||||
|
||||
async for run in self.client.beta.threads.runs.list(thread_id=thread_id, limit=1, order="desc"): # type: ignore[reportDeprecated]
|
||||
async for run in client.beta.threads.runs.list(thread_id=thread_id, limit=1, order="desc"): # type: ignore[reportDeprecated]
|
||||
if run.status not in ["completed", "cancelled", "failed", "expired"]:
|
||||
return run
|
||||
return None
|
||||
|
||||
async def _prepare_thread(self, thread_id: str | None, thread_run: Run | None, run_options: dict[str, Any]) -> str:
|
||||
"""Prepare the thread for a new run, creating or cleaning up as needed."""
|
||||
client = await self.ensure_client()
|
||||
if thread_id is None:
|
||||
# No thread ID was provided, so create a new thread.
|
||||
thread = await self.client.beta.threads.create( # type: ignore[reportDeprecated]
|
||||
thread = await client.beta.threads.create( # type: ignore[reportDeprecated]
|
||||
messages=run_options["additional_messages"],
|
||||
tool_resources=run_options.get("tool_resources"),
|
||||
metadata=run_options.get("metadata"),
|
||||
@@ -280,7 +288,7 @@ class OpenAIAssistantsClient(OpenAIConfigMixin, BaseChatClient):
|
||||
|
||||
if thread_run is not None:
|
||||
# There was an active run; we need to cancel it before starting a new run.
|
||||
await self.client.beta.threads.runs.cancel(run_id=thread_run.id, thread_id=thread_id) # type: ignore[reportDeprecated]
|
||||
await client.beta.threads.runs.cancel(run_id=thread_run.id, thread_id=thread_id) # type: ignore[reportDeprecated]
|
||||
|
||||
return thread_id
|
||||
|
||||
|
||||
@@ -69,10 +69,11 @@ class OpenAIBaseChatClient(OpenAIBase, BaseChatClient):
|
||||
chat_options: ChatOptions,
|
||||
**kwargs: Any,
|
||||
) -> ChatResponse:
|
||||
client = await self.ensure_client()
|
||||
options_dict = self._prepare_options(messages, chat_options)
|
||||
try:
|
||||
return self._create_chat_response(
|
||||
await self.client.chat.completions.create(stream=False, **options_dict), chat_options
|
||||
await client.chat.completions.create(stream=False, **options_dict), chat_options
|
||||
)
|
||||
except BadRequestError as ex:
|
||||
if ex.code == "content_filter":
|
||||
@@ -97,10 +98,11 @@ class OpenAIBaseChatClient(OpenAIBase, BaseChatClient):
|
||||
chat_options: ChatOptions,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterable[ChatResponseUpdate]:
|
||||
client = await self.ensure_client()
|
||||
options_dict = self._prepare_options(messages, chat_options)
|
||||
options_dict["stream_options"] = {"include_usage": True}
|
||||
try:
|
||||
async for chunk in await self.client.chat.completions.create(stream=True, **options_dict):
|
||||
async for chunk in await client.chat.completions.create(stream=True, **options_dict):
|
||||
if len(chunk.choices) == 0 and chunk.usage is None:
|
||||
continue
|
||||
yield self._create_chat_response_update(chunk)
|
||||
|
||||
@@ -89,23 +89,24 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient):
|
||||
chat_options: ChatOptions,
|
||||
**kwargs: Any,
|
||||
) -> ChatResponse:
|
||||
options_dict = self._prepare_options(messages, chat_options)
|
||||
client = await self.ensure_client()
|
||||
run_options = await self.prepare_options(messages, chat_options)
|
||||
try:
|
||||
if not chat_options.response_format:
|
||||
response = await self.client.responses.create(
|
||||
response_format = run_options.pop("response_format", None)
|
||||
if not response_format:
|
||||
response = await client.responses.create(
|
||||
stream=False,
|
||||
**options_dict,
|
||||
**run_options,
|
||||
)
|
||||
chat_options.conversation_id = response.id if chat_options.store is True else None
|
||||
chat_options.conversation_id = self.get_conversation_id(response, chat_options.store)
|
||||
return self._create_response_content(response, chat_options=chat_options)
|
||||
# create call does not support response_format, so we need to handle it via parse call
|
||||
resp_format = chat_options.response_format
|
||||
parsed_response: ParsedResponse[BaseModel] = await self.client.responses.parse(
|
||||
text_format=resp_format,
|
||||
parsed_response: ParsedResponse[BaseModel] = await client.responses.parse(
|
||||
text_format=response_format,
|
||||
stream=False,
|
||||
**options_dict,
|
||||
**run_options,
|
||||
)
|
||||
chat_options.conversation_id = parsed_response.id if chat_options.store is True else None
|
||||
chat_options.conversation_id = self.get_conversation_id(parsed_response, chat_options.store)
|
||||
return self._create_response_content(parsed_response, chat_options=chat_options)
|
||||
except BadRequestError as ex:
|
||||
if ex.code == "content_filter":
|
||||
@@ -130,13 +131,15 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient):
|
||||
chat_options: ChatOptions,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterable[ChatResponseUpdate]:
|
||||
options_dict = self._prepare_options(messages, chat_options)
|
||||
client = await self.ensure_client()
|
||||
run_options = await self.prepare_options(messages, chat_options)
|
||||
function_call_ids: dict[int, tuple[str, str]] = {} # output_index: (call_id, name)
|
||||
try:
|
||||
if not chat_options.response_format:
|
||||
response = await self.client.responses.create(
|
||||
response_format = run_options.pop("response_format", None)
|
||||
if not response_format:
|
||||
response = await client.responses.create(
|
||||
stream=True,
|
||||
**options_dict,
|
||||
**run_options,
|
||||
)
|
||||
async for chunk in response:
|
||||
update = self._create_streaming_response_content(
|
||||
@@ -145,9 +148,9 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient):
|
||||
yield update
|
||||
return
|
||||
# create call does not support response_format, so we need to handle it via stream call
|
||||
async with self.client.responses.stream(
|
||||
text_format=chat_options.response_format,
|
||||
**options_dict,
|
||||
async with client.responses.stream(
|
||||
text_format=response_format,
|
||||
**run_options,
|
||||
) as response:
|
||||
async for chunk in response:
|
||||
update = self._create_streaming_response_content(
|
||||
@@ -170,6 +173,12 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient):
|
||||
inner_exception=ex,
|
||||
) from ex
|
||||
|
||||
def get_conversation_id(
|
||||
self, response: OpenAIResponse | ParsedResponse[BaseModel], store: bool | None
|
||||
) -> str | None:
|
||||
"""Get the conversation ID from the response if store is True."""
|
||||
return response.id if store else None
|
||||
|
||||
# region Prep methods
|
||||
|
||||
def _tools_to_response_tools(
|
||||
@@ -180,31 +189,7 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient):
|
||||
if isinstance(tool, ToolProtocol):
|
||||
match tool:
|
||||
case HostedMCPTool():
|
||||
mcp: Mcp = {
|
||||
"type": "mcp",
|
||||
"server_label": tool.name.replace(" ", "_"),
|
||||
"server_url": str(tool.url),
|
||||
"server_description": tool.description,
|
||||
"headers": tool.headers,
|
||||
}
|
||||
if tool.allowed_tools:
|
||||
mcp["allowed_tools"] = list(tool.allowed_tools)
|
||||
if tool.approval_mode:
|
||||
match tool.approval_mode:
|
||||
case str():
|
||||
mcp["require_approval"] = (
|
||||
"always" if tool.approval_mode == "always_require" else "never"
|
||||
)
|
||||
case _:
|
||||
if always_require_approvals := tool.approval_mode.get("always_require_approval"):
|
||||
mcp["require_approval"] = {
|
||||
"always": {"tool_names": list(always_require_approvals)}
|
||||
}
|
||||
if never_require_approvals := tool.approval_mode.get("never_require_approval"):
|
||||
mcp["require_approval"] = {
|
||||
"never": {"tool_names": list(never_require_approvals)}
|
||||
}
|
||||
response_tools.append(mcp)
|
||||
response_tools.append(self.get_mcp_tool(tool))
|
||||
case HostedCodeInterpreterTool():
|
||||
tool_args: CodeInterpreterContainerCodeInterpreterToolAuto = {"type": "auto"}
|
||||
if tool.inputs:
|
||||
@@ -306,12 +291,36 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient):
|
||||
response_tools.append(tool_dict)
|
||||
return response_tools
|
||||
|
||||
def _prepare_options(self, messages: MutableSequence[ChatMessage], chat_options: ChatOptions) -> dict[str, Any]:
|
||||
def get_mcp_tool(self, tool: HostedMCPTool) -> Any:
|
||||
"""Get MCP tool from HostedMCPTool."""
|
||||
mcp: Mcp = {
|
||||
"type": "mcp",
|
||||
"server_label": tool.name.replace(" ", "_"),
|
||||
"server_url": str(tool.url),
|
||||
"server_description": tool.description,
|
||||
"headers": tool.headers,
|
||||
}
|
||||
if tool.allowed_tools:
|
||||
mcp["allowed_tools"] = list(tool.allowed_tools)
|
||||
if tool.approval_mode:
|
||||
match tool.approval_mode:
|
||||
case str():
|
||||
mcp["require_approval"] = "always" if tool.approval_mode == "always_require" else "never"
|
||||
case _:
|
||||
if always_require_approvals := tool.approval_mode.get("always_require_approval"):
|
||||
mcp["require_approval"] = {"always": {"tool_names": list(always_require_approvals)}}
|
||||
if never_require_approvals := tool.approval_mode.get("never_require_approval"):
|
||||
mcp["require_approval"] = {"never": {"tool_names": list(never_require_approvals)}}
|
||||
|
||||
return mcp
|
||||
|
||||
async def prepare_options(
|
||||
self, messages: MutableSequence[ChatMessage], chat_options: ChatOptions
|
||||
) -> dict[str, Any]:
|
||||
"""Take ChatOptions and create the specific options for Responses API."""
|
||||
options_dict: dict[str, Any] = chat_options.to_dict(
|
||||
run_options: dict[str, Any] = chat_options.to_dict(
|
||||
exclude={
|
||||
"type",
|
||||
"response_format", # handled in inner get methods
|
||||
"presence_penalty", # not supported
|
||||
"frequency_penalty", # not supported
|
||||
"logit_bias", # not supported
|
||||
@@ -320,6 +329,10 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient):
|
||||
"instructions", # already added as system message
|
||||
}
|
||||
)
|
||||
|
||||
if chat_options.response_format:
|
||||
run_options["response_format"] = chat_options.response_format
|
||||
|
||||
translations = {
|
||||
"model_id": "model",
|
||||
"allow_multiple_tool_calls": "parallel_tool_calls",
|
||||
@@ -327,35 +340,37 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient):
|
||||
"max_tokens": "max_output_tokens",
|
||||
}
|
||||
for old_key, new_key in translations.items():
|
||||
if old_key in options_dict and old_key != new_key:
|
||||
options_dict[new_key] = options_dict.pop(old_key)
|
||||
if old_key in run_options and old_key != new_key:
|
||||
run_options[new_key] = run_options.pop(old_key)
|
||||
|
||||
# tools
|
||||
if chat_options.tools is None:
|
||||
options_dict.pop("parallel_tool_calls", None)
|
||||
run_options.pop("parallel_tool_calls", None)
|
||||
else:
|
||||
options_dict["tools"] = self._tools_to_response_tools(chat_options.tools)
|
||||
run_options["tools"] = self._tools_to_response_tools(chat_options.tools)
|
||||
|
||||
# model id
|
||||
if not options_dict.get("model"):
|
||||
options_dict["model"] = self.model_id
|
||||
if not run_options.get("model"):
|
||||
if not self.model_id:
|
||||
raise ValueError("model_id must be a non-empty string")
|
||||
run_options["model"] = self.model_id
|
||||
|
||||
# messages
|
||||
request_input = self._prepare_chat_messages_for_request(messages)
|
||||
if not request_input:
|
||||
raise ServiceInvalidRequestError("Messages are required for chat completions")
|
||||
options_dict["input"] = request_input
|
||||
run_options["input"] = request_input
|
||||
|
||||
# additional provider specific settings
|
||||
if additional_properties := options_dict.pop("additional_properties", None):
|
||||
if additional_properties := run_options.pop("additional_properties", None):
|
||||
for key, value in additional_properties.items():
|
||||
if value is not None:
|
||||
options_dict[key] = value
|
||||
if "store" not in options_dict:
|
||||
options_dict["store"] = False
|
||||
if (tool_choice := options_dict.get("tool_choice")) and len(tool_choice.keys()) == 1:
|
||||
options_dict["tool_choice"] = tool_choice["mode"]
|
||||
return options_dict
|
||||
run_options[key] = value
|
||||
if "store" not in run_options:
|
||||
run_options["store"] = False
|
||||
if (tool_choice := run_options.get("tool_choice")) and len(tool_choice.keys()) == 1:
|
||||
run_options["tool_choice"] = tool_choice["mode"]
|
||||
return run_options
|
||||
|
||||
def _prepare_chat_messages_for_request(self, chat_messages: Sequence[ChatMessage]) -> list[dict[str, Any]]:
|
||||
"""Prepare the chat messages for a request.
|
||||
@@ -504,7 +519,6 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient):
|
||||
# call_id for the result needs to be the same as the call_id for the function call
|
||||
args: dict[str, Any] = {
|
||||
"call_id": content.call_id,
|
||||
"id": call_id_to_id.get(content.call_id),
|
||||
"type": "function_call_output",
|
||||
}
|
||||
if content.result:
|
||||
@@ -734,7 +748,7 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient):
|
||||
"raw_representation": response,
|
||||
}
|
||||
if chat_options.store:
|
||||
args["conversation_id"] = response.id
|
||||
args["conversation_id"] = self.get_conversation_id(response, chat_options.store)
|
||||
if response.usage and (usage_details := self._usage_details_from_openai(response.usage)):
|
||||
args["usage_details"] = usage_details
|
||||
if structured_response:
|
||||
@@ -834,7 +848,7 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient):
|
||||
contents.append(TextReasoningContent(text=event.text, raw_representation=event))
|
||||
metadata.update(self._get_metadata_from_response(event))
|
||||
case "response.completed":
|
||||
conversation_id = event.response.id if chat_options.store is True else None
|
||||
conversation_id = self.get_conversation_id(event.response, chat_options.store)
|
||||
model = event.response.model
|
||||
if event.response.usage:
|
||||
usage = self._usage_details_from_openai(event.response.usage)
|
||||
|
||||
@@ -127,18 +127,18 @@ class OpenAIBase(SerializationMixin):
|
||||
|
||||
INJECTABLE: ClassVar[set[str]] = {"client"}
|
||||
|
||||
def __init__(self, *, client: AsyncOpenAI, model_id: str, **kwargs: Any) -> None:
|
||||
def __init__(self, *, model_id: str | None = None, client: AsyncOpenAI | None = None, **kwargs: Any) -> None:
|
||||
"""Initialize OpenAIBase.
|
||||
|
||||
Keyword Args:
|
||||
client: The AsyncOpenAI client instance.
|
||||
model_id: The AI model ID to use (non-empty, whitespace stripped).
|
||||
model_id: The AI model ID to use.
|
||||
**kwargs: Additional keyword arguments.
|
||||
"""
|
||||
if not model_id or not model_id.strip():
|
||||
raise ValueError("model_id must be a non-empty string")
|
||||
self.client = client
|
||||
self.model_id = model_id.strip()
|
||||
self.model_id = None
|
||||
if model_id:
|
||||
self.model_id = model_id.strip()
|
||||
|
||||
# Call super().__init__() to continue MRO chain (e.g., BaseChatClient)
|
||||
# Extract known kwargs that belong to other base classes
|
||||
@@ -162,6 +162,21 @@ class OpenAIBase(SerializationMixin):
|
||||
for key, value in kwargs.items():
|
||||
setattr(self, key, value)
|
||||
|
||||
async def initialize_client(self) -> None:
|
||||
"""Initialize OpenAI client asynchronously.
|
||||
|
||||
Override in subclasses to initialize the OpenAI client asynchronously.
|
||||
"""
|
||||
pass
|
||||
|
||||
async def ensure_client(self) -> AsyncOpenAI:
|
||||
"""Ensure OpenAI client is initialized."""
|
||||
await self.initialize_client()
|
||||
if self.client is None:
|
||||
raise ServiceInitializationError("OpenAI client is not initialized")
|
||||
|
||||
return self.client
|
||||
|
||||
def _get_api_key(
|
||||
self, api_key: str | SecretStr | Callable[[], str | Awaitable[str]] | None
|
||||
) -> str | Callable[[], str | Awaitable[str]] | None:
|
||||
|
||||
@@ -1407,27 +1407,27 @@ def test_create_response_content_image_generation_fallback():
|
||||
assert f"data:image/png;base64,{unrecognized_base64}" == content.uri
|
||||
|
||||
|
||||
def test_prepare_options_store_parameter_handling() -> None:
|
||||
async def test_prepare_options_store_parameter_handling() -> None:
|
||||
client = OpenAIResponsesClient(model_id="test-model", api_key="test-key")
|
||||
messages = [ChatMessage(role="user", text="Test message")]
|
||||
|
||||
test_conversation_id = "test-conversation-123"
|
||||
chat_options = ChatOptions(store=True, conversation_id=test_conversation_id)
|
||||
options = client._prepare_options(messages, chat_options) # type: ignore
|
||||
options = await client.prepare_options(messages, chat_options)
|
||||
assert options["store"] is True
|
||||
assert options["previous_response_id"] == test_conversation_id
|
||||
|
||||
chat_options = ChatOptions(store=False, conversation_id="")
|
||||
options = client._prepare_options(messages, chat_options) # type: ignore
|
||||
options = await client.prepare_options(messages, chat_options)
|
||||
assert options["store"] is False
|
||||
|
||||
chat_options = ChatOptions(store=None, conversation_id=None)
|
||||
options = client._prepare_options(messages, chat_options) # type: ignore
|
||||
options = await client.prepare_options(messages, chat_options)
|
||||
assert options["store"] is False
|
||||
assert "previous_response_id" not in options
|
||||
|
||||
chat_options = ChatOptions()
|
||||
options = client._prepare_options(messages, chat_options) # type: ignore
|
||||
options = await client.prepare_options(messages, chat_options)
|
||||
assert options["store"] is False
|
||||
assert "previous_response_id" not in options
|
||||
|
||||
|
||||
Reference in New Issue
Block a user