From adb6dcd2af7d9910ec6a22f7967597bb5163eb1e Mon Sep 17 00:00:00 2001 From: Giles Odigwe <79032838+giles17@users.noreply.github.com> Date: Mon, 22 Sep 2025 15:38:44 -0700 Subject: [PATCH] Python: OpenAI Responses Image Generation Tool (#842) * Image generation + Added Samples * extended image gen tool for responses * uv fix * removed hosted image gen * copilot suggestions * Update python/packages/main/agent_framework/openai/_responses_client.py Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com> --------- Co-authored-by: Dmytro Struk <13853051+dmytrostruk@users.noreply.github.com> --- .../packages/main/agent_framework/_agents.py | 1 + .../packages/main/agent_framework/_types.py | 4 + .../openai/_responses_client.py | 73 ++- .../openai/test_openai_responses_client.py | 270 +++++++++++ python/pyproject.toml | 2 +- .../agents/openai_responses_client/README.md | 18 + .../openai_responses_client_image_analysis.py | 38 ++ ...penai_responses_client_image_generation.py | 72 +++ ...responses_client_with_structured_output.py | 44 ++ python/uv.lock | 421 ++++++++++-------- 10 files changed, 754 insertions(+), 189 deletions(-) create mode 100644 python/samples/getting_started/agents/openai_responses_client/openai_responses_client_image_analysis.py create mode 100644 python/samples/getting_started/agents/openai_responses_client/openai_responses_client_image_generation.py create mode 100644 python/samples/getting_started/agents/openai_responses_client/openai_responses_client_with_structured_output.py diff --git a/python/packages/main/agent_framework/_agents.py b/python/packages/main/agent_framework/_agents.py index 6977388588..8b66cb4b16 100644 --- a/python/packages/main/agent_framework/_agents.py +++ b/python/packages/main/agent_framework/_agents.py @@ -485,6 +485,7 @@ class ChatAgent(BaseAgent): response_id=response.response_id, created_at=response.created_at, usage_details=response.usage_details, + value=response.value, raw_representation=response, additional_properties=response.additional_properties, ) diff --git a/python/packages/main/agent_framework/_types.py b/python/packages/main/agent_framework/_types.py index cf6aa1964f..9f0a6d7080 100644 --- a/python/packages/main/agent_framework/_types.py +++ b/python/packages/main/agent_framework/_types.py @@ -2033,6 +2033,7 @@ class AgentRunResponse(AFBaseModel): response_id: str | None = None created_at: CreatedAtT | None = None # use a datetimeoffset type? usage_details: UsageDetails | None = None + value: Any | None = None raw_representation: Any | None = None additional_properties: dict[str, Any] | None = None @@ -2042,6 +2043,7 @@ class AgentRunResponse(AFBaseModel): response_id: str | None = None, created_at: CreatedAtT | None = None, usage_details: UsageDetails | None = None, + value: Any | None = None, raw_representation: Any | None = None, additional_properties: dict[str, Any] | None = None, **kwargs: Any, @@ -2053,6 +2055,7 @@ class AgentRunResponse(AFBaseModel): response_id: The ID of the chat response. created_at: A timestamp for the chat response. usage_details: The usage details for the chat response. + value: The structured output of the agent run response, if applicable. additional_properties: Any additional properties associated with the chat response. raw_representation: The raw representation of the chat response from an underlying implementation. **kwargs: Additional properties to set on the response. @@ -2069,6 +2072,7 @@ class AgentRunResponse(AFBaseModel): response_id=response_id, # type: ignore[reportCallIssue] created_at=created_at, # type: ignore[reportCallIssue] usage_details=usage_details, # type: ignore[reportCallIssue] + value=value, # type: ignore[reportCallIssue] additional_properties=additional_properties, # type: ignore[reportCallIssue] raw_representation=raw_representation, # type: ignore[reportCallIssue] **kwargs, diff --git a/python/packages/main/agent_framework/openai/_responses_client.py b/python/packages/main/agent_framework/openai/_responses_client.py index a205ff14c5..7f4e21d54e 100644 --- a/python/packages/main/agent_framework/openai/_responses_client.py +++ b/python/packages/main/agent_framework/openai/_responses_client.py @@ -272,7 +272,28 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient): case _: logger.debug("Unsupported tool passed (type: %s)", type(tool)) else: - response_tools.append(tool if isinstance(tool, dict) else dict(tool)) + # Handle raw dictionary tools + tool_dict = tool if isinstance(tool, dict) else dict(tool) + + # Special handling for image_generation tools + if tool_dict.get("type") == "image_generation": + # Create a copy to avoid modifying the original + mapped_tool = tool_dict.copy() + + # Map user-friendly parameter names to OpenAI API parameter names + parameter_mapping = { + "format": "output_format", + "compression": "output_compression", + } + + for user_param, api_param in parameter_mapping.items(): + if user_param in mapped_tool: + # Map the parameter name and remove the old one + mapped_tool[api_param] = mapped_tool.pop(user_param) + + response_tools.append(mapped_tool) + else: + response_tools.append(tool_dict) return response_tools def _prepare_options(self, messages: MutableSequence[ChatMessage], chat_options: ChatOptions) -> dict[str, Any]: @@ -304,13 +325,12 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient): options_dict["tools"] = self._tools_to_response_tools(chat_options.tools) # other settings - if chat_options.store: - options_dict["store"] = True - else: + if "store" not in options_dict: options_dict["store"] = False - if chat_options.conversation_id: - options_dict["previous_response_id"] = chat_options.conversation_id - if chat_options.ai_model_id is None: + if "conversation_id" in options_dict: + options_dict["previous_response_id"] = options_dict["conversation_id"] + options_dict.pop("conversation_id") + if "model" not in options_dict: options_dict["model"] = self.ai_model_id return options_dict @@ -634,9 +654,46 @@ class OpenAIBaseResponsesClient(OpenAIBase, BaseChatClient): ) case "image_generation_call": # ResponseOutputImageGenerationCall if item.result: + # Handle the result as either a proper data URI or raw base64 string + uri = item.result + media_type = None + if not uri.startswith("data:"): + # Raw base64 string - convert to proper data URI format + # Detect format from base64 data + import base64 + + try: + # Decode a small portion to detect format + decoded_data = base64.b64decode(uri[:100]) # First ~75 bytes should be enough + if decoded_data.startswith(b"\x89PNG"): + format_type = "png" + elif decoded_data.startswith(b"\xff\xd8\xff"): + format_type = "jpeg" + elif decoded_data.startswith(b"RIFF") and b"WEBP" in decoded_data[:12]: + format_type = "webp" + elif decoded_data.startswith(b"GIF87a") or decoded_data.startswith(b"GIF89a"): + format_type = "gif" + else: + # Default to png if format cannot be detected + format_type = "png" + except Exception: + # Fallback to png if decoding fails + format_type = "png" + uri = f"data:image/{format_type};base64,{uri}" + media_type = f"image/{format_type}" + else: + # Parse media type from existing data URI + try: + # Extract media type from data URI (e.g., "data:image/png;base64,...") + if ";" in uri and uri.startswith("data:"): + media_type = uri.split(";")[0].split(":", 1)[1] + except Exception: + # Fallback if parsing fails + media_type = "image" contents.append( DataContent( - uri=item.result, + uri=uri, + media_type=media_type, raw_representation=item, ) ) diff --git a/python/packages/main/tests/openai/test_openai_responses_client.py b/python/packages/main/tests/openai/test_openai_responses_client.py index 945aec9a39..5bd22ebec0 100644 --- a/python/packages/main/tests/openai/test_openai_responses_client.py +++ b/python/packages/main/tests/openai/test_openai_responses_client.py @@ -1,6 +1,7 @@ # Copyright (c) Microsoft. All rights reserved. import asyncio +import base64 import os from typing import Annotated from unittest.mock import MagicMock, patch @@ -23,6 +24,7 @@ from agent_framework import ( ChatMessage, ChatResponse, ChatResponseUpdate, + DataContent, FunctionApprovalRequestContent, FunctionApprovalResponseContent, FunctionCallContent, @@ -677,6 +679,85 @@ def test_create_response_content_with_mcp_approval_request() -> None: assert req.function_call.additional_properties["server_label"] == "My_MCP" +def test_tools_to_response_tools_with_raw_image_generation() -> None: + """Test that raw image_generation tool dict is handled correctly with parameter mapping.""" + client = OpenAIResponsesClient(ai_model_id="test-model", api_key="test-key") + + # Test with raw tool dict using user-friendly parameter names + tool = { + "type": "image_generation", + "size": "1536x1024", + "quality": "high", + "format": "webp", # Will be mapped to output_format + "compression": 75, # Will be mapped to output_compression + "background": "transparent", + } + + resp_tools = client._tools_to_response_tools([tool]) + assert isinstance(resp_tools, list) + assert len(resp_tools) == 1 + + image_tool = resp_tools[0] + assert isinstance(image_tool, dict) + assert image_tool["type"] == "image_generation" + assert image_tool["size"] == "1536x1024" + assert image_tool["quality"] == "high" + assert image_tool["background"] == "transparent" + # Check parameter name mapping + assert image_tool["output_format"] == "webp" + assert image_tool["output_compression"] == 75 + + +def test_tools_to_response_tools_with_raw_image_generation_openai_responses_params() -> None: + """Test raw image_generation tool with OpenAI-specific parameters.""" + client = OpenAIResponsesClient(ai_model_id="test-model", api_key="test-key") + + # Test with OpenAI-specific parameters + tool = { + "type": "image_generation", + "size": "1024x1024", + "model": "gpt-image-1", + "input_fidelity": "high", + "moderation": "strict", + "partial_images": 2, # Should be integer 0-3 + } + + resp_tools = client._tools_to_response_tools([tool]) + assert isinstance(resp_tools, list) + assert len(resp_tools) == 1 + + image_tool = resp_tools[0] + assert isinstance(image_tool, dict) + assert image_tool["type"] == "image_generation" + + # Cast to dict for easier access to ImageGeneration-specific fields + tool_dict = dict(image_tool) + assert tool_dict["size"] == "1024x1024" + # Check OpenAI-specific parameters are included + assert tool_dict["model"] == "gpt-image-1" + assert tool_dict["input_fidelity"] == "high" + assert tool_dict["moderation"] == "strict" + assert tool_dict["partial_images"] == 2 + + +def test_tools_to_response_tools_with_raw_image_generation_minimal() -> None: + """Test raw image_generation tool with minimal configuration.""" + client = OpenAIResponsesClient(ai_model_id="test-model", api_key="test-key") + + # Test with minimal parameters (just type) + tool = {"type": "image_generation"} + + resp_tools = client._tools_to_response_tools([tool]) + assert isinstance(resp_tools, list) + assert len(resp_tools) == 1 + + image_tool = resp_tools[0] + assert isinstance(image_tool, dict) + assert image_tool["type"] == "image_generation" + # Should only have the type parameter when created with minimal config + assert len(image_tool) == 1 + + def test_create_streaming_response_content_with_mcp_approval_request() -> None: """Test that a streaming mcp_approval_request event is parsed into FunctionApprovalRequestContent.""" client = OpenAIResponsesClient(ai_model_id="test-model", api_key="test-key") @@ -1329,6 +1410,42 @@ async def test_openai_responses_client_agent_hosted_code_interpreter_tool(): assert contains_relevant_content or len(response.text.strip()) > 10 +@skip_if_openai_integration_tests_disabled +async def test_openai_responses_client_agent_raw_image_generation_tool(): + """Test OpenAI Responses Client agent with raw image_generation tool through OpenAIResponsesClient.""" + async with ChatAgent( + chat_client=OpenAIResponsesClient(), + instructions="You are a helpful assistant that can generate images.", + tools=[{"type": "image_generation", "size": "1024x1024", "quality": "low", "format": "png"}], + ) as agent: + # Test image generation functionality + response = await agent.run("Generate an image of a cute red panda sitting on a tree branch in a forest.") + + assert isinstance(response, AgentRunResponse) + + # For image generation, we expect to get some response content + # This could be DataContent with image data, UriContent + assert response.messages is not None and len(response.messages) > 0 + + # Check that we have some kind of content in the response + total_contents = sum(len(message.contents) for message in response.messages) + assert total_contents > 0, f"Expected some content in response messages, got {total_contents} contents" + + # Verify we got image content - look for DataContent with URI starting with "data:image" + image_content_found = False + for message in response.messages: + for content in message.contents: + uri = getattr(content, "uri", None) + if uri and uri.startswith("data:image"): + image_content_found = True + break + if image_content_found: + break + + # The test passes if we got image content (which we did based on the visible base64 output) + assert image_content_found, "Expected to find image content in response" + + @skip_if_openai_integration_tests_disabled async def test_openai_responses_client_agent_level_tool_persistence(): """Test that agent-level tools persist across multiple runs with OpenAI Responses Client.""" @@ -1774,3 +1891,156 @@ def test_streaming_reasoning_events_preserve_metadata() -> None: # Content types should be different assert isinstance(text_response.contents[0], TextContent) assert isinstance(reasoning_response.contents[0], TextReasoningContent) + + +def test_create_response_content_image_generation_raw_base64(): + """Test image generation response parsing with raw base64 string.""" + client = OpenAIResponsesClient(ai_model_id="test-model", api_key="test-key") + + # Create a mock response with raw base64 image data (PNG signature) + mock_response = MagicMock() + mock_response.output_parsed = None + mock_response.metadata = {} + mock_response.usage = None + mock_response.id = "test-response-id" + mock_response.model = "test-model" + mock_response.created_at = 1234567890 + + # Mock image generation output item with raw base64 (PNG format) + png_signature = b"\x89PNG\r\n\x1a\n" + mock_base64 = base64.b64encode(png_signature + b"fake_png_data_here").decode() + + mock_item = MagicMock() + mock_item.type = "image_generation_call" + mock_item.result = mock_base64 + + mock_response.output = [mock_item] + + with patch.object(client, "_get_metadata_from_response", return_value={}): + response = client._create_response_content(mock_response, chat_options=ChatOptions()) # type: ignore + + # Verify the response contains DataContent with proper URI and media_type + assert len(response.messages[0].contents) == 1 + content = response.messages[0].contents[0] + assert isinstance(content, DataContent) + assert content.uri.startswith("data:image/png;base64,") + assert content.media_type == "image/png" + + +def test_create_response_content_image_generation_existing_data_uri(): + """Test image generation response parsing with existing data URI.""" + client = OpenAIResponsesClient(ai_model_id="test-model", api_key="test-key") + + # Create a mock response with existing data URI + mock_response = MagicMock() + mock_response.output_parsed = None + mock_response.metadata = {} + mock_response.usage = None + mock_response.id = "test-response-id" + mock_response.model = "test-model" + mock_response.created_at = 1234567890 + + # Mock image generation output item with existing data URI (valid WEBP header) + webp_signature = b"RIFF" + b"\x12\x00\x00\x00" + b"WEBP" + valid_webp_base64 = base64.b64encode(webp_signature + b"VP8 fake_data").decode() + mock_item = MagicMock() + mock_item.type = "image_generation_call" + mock_item.result = f"data:image/webp;base64,{valid_webp_base64}" + + mock_response.output = [mock_item] + + with patch.object(client, "_get_metadata_from_response", return_value={}): + response = client._create_response_content(mock_response, chat_options=ChatOptions()) # type: ignore + + # Verify the response contains DataContent with proper media_type parsed from URI + assert len(response.messages[0].contents) == 1 + content = response.messages[0].contents[0] + assert isinstance(content, DataContent) + assert content.uri == f"data:image/webp;base64,{valid_webp_base64}" + assert content.media_type == "image/webp" + + +def test_create_response_content_image_generation_format_detection(): + """Test different image format detection from base64 data.""" + client = OpenAIResponsesClient(ai_model_id="test-model", api_key="test-key") + + # Test JPEG detection + jpeg_signature = b"\xff\xd8\xff" + mock_base64_jpeg = base64.b64encode(jpeg_signature + b"fake_jpeg_data").decode() + + mock_response_jpeg = MagicMock() + mock_response_jpeg.output_parsed = None + mock_response_jpeg.metadata = {} + mock_response_jpeg.usage = None + mock_response_jpeg.id = "test-id" + mock_response_jpeg.model = "test-model" + mock_response_jpeg.created_at = 1234567890 + + mock_item_jpeg = MagicMock() + mock_item_jpeg.type = "image_generation_call" + mock_item_jpeg.result = mock_base64_jpeg + mock_response_jpeg.output = [mock_item_jpeg] + + with patch.object(client, "_get_metadata_from_response", return_value={}): + response_jpeg = client._create_response_content(mock_response_jpeg, chat_options=ChatOptions()) # type: ignore + content_jpeg = response_jpeg.messages[0].contents[0] + assert isinstance(content_jpeg, DataContent) + assert content_jpeg.media_type == "image/jpeg" + assert "data:image/jpeg;base64," in content_jpeg.uri + + # Test WEBP detection + webp_signature = b"RIFF" + b"\x00\x00\x00\x00" + b"WEBP" + mock_base64_webp = base64.b64encode(webp_signature + b"fake_webp_data").decode() + + mock_response_webp = MagicMock() + mock_response_webp.output_parsed = None + mock_response_webp.metadata = {} + mock_response_webp.usage = None + mock_response_webp.id = "test-id" + mock_response_webp.model = "test-model" + mock_response_webp.created_at = 1234567890 + + mock_item_webp = MagicMock() + mock_item_webp.type = "image_generation_call" + mock_item_webp.result = mock_base64_webp + mock_response_webp.output = [mock_item_webp] + + with patch.object(client, "_get_metadata_from_response", return_value={}): + response_webp = client._create_response_content(mock_response_webp, chat_options=ChatOptions()) # type: ignore + content_webp = response_webp.messages[0].contents[0] + assert isinstance(content_webp, DataContent) + assert content_webp.media_type == "image/webp" + assert "data:image/webp;base64," in content_webp.uri + + +def test_create_response_content_image_generation_fallback(): + """Test image generation with invalid base64 falls back to PNG.""" + client = OpenAIResponsesClient(ai_model_id="test-model", api_key="test-key") + + # Create a mock response with invalid base64 + mock_response = MagicMock() + mock_response.output_parsed = None + mock_response.metadata = {} + mock_response.usage = None + mock_response.id = "test-response-id" + mock_response.model = "test-model" + mock_response.created_at = 1234567890 + + # Mock image generation output item with unrecognized format (should fall back to PNG) + unrecognized_data = b"UNKNOWN_FORMAT" + b"some_binary_data" + unrecognized_base64 = base64.b64encode(unrecognized_data).decode() + mock_item = MagicMock() + mock_item.type = "image_generation_call" + mock_item.result = unrecognized_base64 + + mock_response.output = [mock_item] + + with patch.object(client, "_get_metadata_from_response", return_value={}): + response = client._create_response_content(mock_response, chat_options=ChatOptions()) # type: ignore + + # Verify it falls back to PNG format for unrecognized binary data + assert len(response.messages[0].contents) == 1 + content = response.messages[0].contents[0] + assert isinstance(content, DataContent) + assert content.media_type == "image/png" + assert f"data:image/png;base64,{unrecognized_base64}" == content.uri diff --git a/python/pyproject.toml b/python/pyproject.toml index cdb8bde07b..34e5ad4b3c 100644 --- a/python/pyproject.toml +++ b/python/pyproject.toml @@ -222,4 +222,4 @@ sequence = [ [tool.setuptools.packages.find] where = ["packages"] include = ["agent_framework**"] -namespaces = true +namespaces = true \ No newline at end of file diff --git a/python/samples/getting_started/agents/openai_responses_client/README.md b/python/samples/getting_started/agents/openai_responses_client/README.md index 59aef7f39e..fe588e0a62 100644 --- a/python/samples/getting_started/agents/openai_responses_client/README.md +++ b/python/samples/getting_started/agents/openai_responses_client/README.md @@ -12,7 +12,11 @@ This folder contains examples demonstrating different ways to create and use age | [`openai_responses_client_with_function_tools.py`](openai_responses_client_with_function_tools.py) | Demonstrates how to use function tools with agents. Shows both agent-level tools (defined when creating the agent) and query-level tools (provided with specific queries). | | [`openai_responses_client_with_code_interpreter.py`](openai_responses_client_with_code_interpreter.py) | Shows how to use the HostedCodeInterpreterTool with OpenAI agents to write and execute Python code. Includes helper methods for accessing code interpreter data from response chunks. | | [`openai_responses_client_with_file_search.py`](openai_responses_client_with_file_search.py) | Demonstrates how to use file search capabilities with OpenAI agents, allowing the agent to search through uploaded files to answer questions. | +| [`openai_responses_client_with_hosted_mcp.py`](openai_responses_client_with_hosted_mcp.py) | Shows how to integrate OpenAI agents with hosted Model Context Protocol (MCP) servers, including approval workflows and tool management for remote MCP services. | +| [`openai_responses_client_image_analysis.py`](openai_responses_client_image_analysis.py) | Demonstrates how to use vision capabilities with agents to analyze images. | +| [`openai_responses_client_image_generation.py`](openai_responses_client_image_generation.py) | Shows how to use image generation capabilities with agents to create images from text descriptions. Requires PIL (Pillow) for image display. | | [`openai_responses_client_with_local_mcp.py`](openai_responses_client_with_local_mcp.py) | Shows how to integrate OpenAI agents with local Model Context Protocol (MCP) servers for enhanced functionality and tool integration. | +| [`openai_responses_client_with_structured_output.py`](openai_responses_client_with_structured_output.py) | Demonstrates how to use structured outputs with OpenAI agents to get structured data responses in predefined formats. | | [`openai_responses_client_with_thread.py`](openai_responses_client_with_thread.py) | Demonstrates thread management with OpenAI agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. | | [`openai_responses_client_with_web_search.py`](openai_responses_client_with_web_search.py) | Shows how to use web search capabilities with OpenAI agents to retrieve and use information from the internet in responses. | @@ -22,3 +26,17 @@ Make sure to set the following environment variables before running the examples - `OPENAI_API_KEY`: Your OpenAI API key - `OPENAI_RESPONSES_MODEL_ID`: The OpenAI model to use (e.g., `gpt-4o`, `gpt-4o-mini`, `gpt-3.5-turbo`) +- For image processing examples, use a vision-capable model like `gpt-4o` or `gpt-4o-mini` + +## Optional Dependencies + +Some examples require additional dependencies: + +- **Image Generation Example**: The `openai_responses_client_image_generation.py` example requires PIL (Pillow) for image display. Install with: + ```bash + # Using uv + uv add pillow + + # Or using pip + pip install pillow + ``` diff --git a/python/samples/getting_started/agents/openai_responses_client/openai_responses_client_image_analysis.py b/python/samples/getting_started/agents/openai_responses_client/openai_responses_client_image_analysis.py new file mode 100644 index 0000000000..7464308cb6 --- /dev/null +++ b/python/samples/getting_started/agents/openai_responses_client/openai_responses_client_image_analysis.py @@ -0,0 +1,38 @@ +# Copyright (c) Microsoft. All rights reserved. + +import asyncio + +from agent_framework import ChatMessage, TextContent, UriContent +from agent_framework.openai import OpenAIResponsesClient + + +async def main(): + print("=== OpenAI Responses Agent with Image Analysis ===") + + # 1. Create an OpenAI Responses agent with vision capabilities + agent = OpenAIResponsesClient().create_agent( + name="VisionAgent", + instructions="You are a helpful agent that can analyze images.", + ) + + # 2. Create a simple message with both text and image content + user_message = ChatMessage( + role="user", + contents=[ + TextContent(text="What do you see in this image?"), + UriContent( + uri="https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg", + media_type="image/jpeg", + ), + ], + ) + + # 3. Get the agent's response + print("User: What do you see in this image? [Image provided]") + result = await agent.run(user_message) + print(f"Agent: {result.text}") + print() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/python/samples/getting_started/agents/openai_responses_client/openai_responses_client_image_generation.py b/python/samples/getting_started/agents/openai_responses_client/openai_responses_client_image_generation.py new file mode 100644 index 0000000000..711ec454d1 --- /dev/null +++ b/python/samples/getting_started/agents/openai_responses_client/openai_responses_client_image_generation.py @@ -0,0 +1,72 @@ +# Copyright (c) Microsoft. All rights reserved. + +import asyncio +import base64 + +from agent_framework import DataContent, UriContent +from agent_framework.openai import OpenAIResponsesClient + + +def show_image_info(data_uri: str) -> None: + """Display information about the generated image.""" + try: + # Extract format and size info from data URI + if data_uri.startswith("data:image/"): + format_info = data_uri.split(";")[0].split("/")[1] + base64_data = data_uri.split(",", 1)[1] + image_bytes = base64.b64decode(base64_data) + size_kb = len(image_bytes) / 1024 + + print(" Image successfully generated!") + print(f" Format: {format_info.upper()}") + print(f" Size: {size_kb:.1f} KB") + print(f" Data URI length: {len(data_uri)} characters") + print("") + print(" To save and view the image:") + print(' 1. Install Pillow: "pip install pillow" or "uv add pillow"') + print(" 2. Use the data URI in your code to save/display the image") + print(" 3. Or copy the base64 data to an online base64 image decoder") + else: + print(f" Image URL generated: {data_uri}") + print(" You can open this URL in a browser to view the image") + + except Exception as e: + print(f" Error processing image data: {e}") + print(" Image generated but couldn't parse details") + + +async def main() -> None: + print("=== OpenAI Responses Image Generation Agent Example ===") + + # Create an agent with customized image generation options + agent = OpenAIResponsesClient().create_agent( + instructions="You are a helpful AI that can generate images.", + tools=[ + { + "type": "image_generation", + # Core parameters + "size": "1024x1024", + "background": "transparent", + "quality": "low", + "format": "webp", + } + ], + ) + + query = "Generate a nice beach scenery with blue skies in summer time." + print(f"User: {query}") + print("Generating image with parameters: 1024x1024 size, transparent background, low quality, WebP format...") + + result = await agent.run(query) + print(f"Agent: {result.text}") + + # Show information about the generated image + for message in result.messages: + for content in message.contents: + if isinstance(content, (DataContent, UriContent)) and content.uri: + show_image_info(content.uri) + break + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/python/samples/getting_started/agents/openai_responses_client/openai_responses_client_with_structured_output.py b/python/samples/getting_started/agents/openai_responses_client/openai_responses_client_with_structured_output.py new file mode 100644 index 0000000000..59c298c5da --- /dev/null +++ b/python/samples/getting_started/agents/openai_responses_client/openai_responses_client_with_structured_output.py @@ -0,0 +1,44 @@ +# Copyright (c) Microsoft. All rights reserved. + +import asyncio + +from agent_framework.openai import OpenAIResponsesClient +from pydantic import BaseModel + + +class OutputStruct(BaseModel): + """A structured output for testing purposes.""" + + city: str + description: str + + +async def main(): + print("=== OpenAI Responses Agent with Structured Output ===") + + # 1. Create an OpenAI Responses agent + agent = OpenAIResponsesClient().create_agent( + name="CityAgent", + instructions="You are a helpful agent that describes cities in a structured format.", + ) + + # 2. Ask the agent about a city + query = "Tell me about Paris, France" + + print(f"User: {query}") + + # 3. Get structured response from the agent using response_format parameter + result = await agent.run(query, response_format=OutputStruct) + + # 4. 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