mirror of
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Evan Mattson
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* Python: Support GPT-5 verbosity option and restore Foundry agent_reference Adds verbosity as a typed Literal["low","medium","high"] field on OpenAIChatOptions (Responses API) and OpenAIChatCompletionOptions (Chat Completions API), set in the same way as the existing reasoning options. For the Responses API, top-level verbosity is translated to the nested text.verbosity shape the OpenAI service expects. The same field flows through to FoundryChatClient via the existing FoundryChatOptions alias. Also fixes #5582: PR #5447 removed the agent_reference injection from RawFoundryAgentChatClient._prepare_options, so first-turn calls against a Foundry Prompt Agent went out without model and without agent_reference and were rejected by the Responses API with "Missing required parameter: 'model'". Restores the injection on the non-preview path (allow_preview=False) and adds a guard test that asserts the preview path does not inject agent_reference, since the preview SDK injects it via project_client.get_openai_client(agent_name=...). Closes #5516 Closes #5582 * Python: Address Copilot review on PR #5619 - Foundry verbosity sample docstring: replace the misleading "set deployment name on model=" instruction with the actual env-var pattern the sample relies on (FOUNDRY_PROJECT_ENDPOINT and FOUNDRY_MODEL). - _build_agent_reference docstring: clarify the helper is used for both Prompt Agents and HostedAgents on the non-preview path. - Add a Responses API test that locks in the documented precedence rule: when both top-level verbosity and text["verbosity"] are supplied, the top-level value wins. * Python: Drop redundant Foundry verbosity sample and list OpenAI sample in README - Remove samples/02-agents/providers/foundry/foundry_chat_client_verbosity.py per review feedback. The verbosity functionality is identical across the OpenAI and Foundry clients (FoundryChatOptions is an alias of OpenAIChatOptions), so a single sample on the OpenAI side is sufficient. - Add the new client_verbosity.py entry to the OpenAI samples README.
65 lines
4.3 KiB
Markdown
65 lines
4.3 KiB
Markdown
# OpenAI Provider Samples
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This folder contains OpenAI provider samples for the generic clients in
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`agent_framework.openai`.
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## Chat Completions API samples (`OpenAIChatCompletionClient`)
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| File | Description |
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|------|-------------|
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| [`chat_completion_client_basic.py`](chat_completion_client_basic.py) | Basic non-streaming and streaming chat completion sample with an explicit `gpt-5.4-nano` model and API key. |
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| [`chat_completion_client_with_explicit_settings.py`](chat_completion_client_with_explicit_settings.py) | Chat completion sample with explicit model and API key settings. |
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| [`chat_completion_client_with_function_tools.py`](chat_completion_client_with_function_tools.py) | Function tools with agent-level and run-level patterns. |
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| [`chat_completion_client_with_local_mcp.py`](chat_completion_client_with_local_mcp.py) | Local MCP integration with the chat completions client. |
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| [`chat_completion_client_with_runtime_json_schema.py`](chat_completion_client_with_runtime_json_schema.py) | Runtime JSON schema output with the chat completions client. |
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| [`chat_completion_client_with_session.py`](chat_completion_client_with_session.py) | Session management with the chat completions client. |
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| [`chat_completion_client_with_web_search.py`](chat_completion_client_with_web_search.py) | Web search with the chat completions client. |
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## Responses API samples (`OpenAIChatClient`)
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| File | Description |
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| [`client_basic.py`](client_basic.py) | Basic non-streaming and streaming responses sample with an explicit `gpt-5.4-nano` model and API key. |
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| [`client_image_analysis.py`](client_image_analysis.py) | Analyze images with the responses client. |
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| [`client_image_generation.py`](client_image_generation.py) | Generate images from text prompts. |
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| [`client_reasoning.py`](client_reasoning.py) | Reasoning-focused sample for models such as `gpt-5`. |
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| [`client_streaming_image_generation.py`](client_streaming_image_generation.py) | Streaming image generation sample. |
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| [`client_verbosity.py`](client_verbosity.py) | GPT-5 `verbosity` option (`low`/`medium`/`high`) with default and per-call overrides. |
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| [`client_with_agent_as_tool.py`](client_with_agent_as_tool.py) | Agent-as-tool orchestration pattern. |
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| [`client_with_code_interpreter.py`](client_with_code_interpreter.py) | Code interpreter sample. |
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| [`client_with_code_interpreter_files.py`](client_with_code_interpreter_files.py) | Code interpreter sample with uploaded files. |
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| [`client_with_explicit_settings.py`](client_with_explicit_settings.py) | Responses client with explicit model and API key settings. |
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| [`client_with_file_search.py`](client_with_file_search.py) | Hosted file search sample. |
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| [`client_with_function_tools.py`](client_with_function_tools.py) | Function tools with agent-level and run-level patterns. |
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| [`client_with_hosted_mcp.py`](client_with_hosted_mcp.py) | Hosted MCP tools and approval workflows. |
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| [`client_with_local_mcp.py`](client_with_local_mcp.py) | Local MCP integration with the responses client. |
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| [`client_with_local_shell.py`](client_with_local_shell.py) | Local shell tool sample. |
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| [`client_with_runtime_json_schema.py`](client_with_runtime_json_schema.py) | Runtime JSON schema output with the responses client. |
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| [`client_with_session.py`](client_with_session.py) | Session management with the responses client. |
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| [`client_with_shell.py`](client_with_shell.py) | Hosted shell tool sample. |
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| [`client_with_structured_output.py`](client_with_structured_output.py) | Structured output with Pydantic models. |
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| [`client_with_web_search.py`](client_with_web_search.py) | Web search with the responses client. |
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## Environment Variables
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Set these before running the OpenAI provider samples:
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- `OPENAI_API_KEY`
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- `OPENAI_MODEL`
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Optionally, you can also set:
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- `OPENAI_ORG_ID`
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- `OPENAI_BASE_URL`
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If your shell also contains `AZURE_OPENAI_*` variables, these samples still stay on OpenAI as long as
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`OPENAI_API_KEY` is present. To force Azure routing with the generic clients, pass an explicit Azure
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input such as `credential`, `azure_endpoint`, or `api_version`, or use the Azure provider samples.
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## Optional Dependencies
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Some samples need extra packages:
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- `client_image_generation.py` and `client_streaming_image_generation.py` use Pillow for image display.
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- MCP samples require the relevant MCP server/tooling you configure locally.
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