chore(azure) add built-in tools tests for azure provider Responses API (#9431)

## Background

This pull request doesn't update any features to @ai-sdk/azure.
However, it adds tests that will help ensure the soundness of future
updates.
I would like to add tests for when doStream and doGenerate are processed
in the Responses API.
In the tests for the Responses API in @ai-sdk/openai, it is possible to
obtain idempotent results for doGenerate and doStream by passing the
result data chunks to the function in advance.
I would like to add similar tests for the Responses API in
@ai-sdk/azure.


## Summary

This pull request doesn't update any features to @ai-sdk/azure.
I created the Azure test code by referring to the OpenAI test code. I
copied some parts.

- add `createModel` function for idempotent results for doGenerate and
doStream.
- add tool using test
- add code interpreter test
- add file search test
- add image generation test ( doGenerate only because not supported
stream on azure platform )

## Manual Verification

Since there were no functional changes, I confirmed that there was no
change in the test results.
- ai-core
src/generate-text/azure-responses-code-interpreter.ts
src/stream-text/azure-responses-code-interpreter.ts


---------

Co-authored-by: tsuzaki430 <[email protected]>
Co-authored-by: Gregor Martynus <[email protected]>

Author: tsuzaki430

.changeset/orange-peaches-buy.md

@@ -0,0 +1,5 @@
+---
+'@ai-sdk/azure': patch
+---
+
+Support idempotent doGenerate/doStream with pre-supplied result chunks in Responses API tests.

packages/azure/src/__fixtures__/azure-code-interpreter-tool.1.chunks.txt

@@ -0,0 +1,90 @@
+{
+  "id": "resp_02ffbc38ae6fca1a0068ec87605cfc819793efdf27e5496675",
+  "object": "response",
+  "created_at": 1760331616,
+  "status": "completed",
+  "background": false,
+  "content_filters": null,
+  "error": null,
+  "incomplete_details": null,
+  "instructions": null,
+  "max_output_tokens": null,
+  "max_tool_calls": null,
+  "model": "gpt-5-mini",
+  "output": [
+    {
+      "id": "rs_02ffbc38ae6fca1a0068ec8762f61881979bf5191564930c23",
+      "type": "reasoning",
+      "summary": []
+    },
+    {
+      "id": "ci_02ffbc38ae6fca1a0068ec876654a08197af4d648fe8bba210",
+      "type": "code_interpreter_call",
+      "status": "completed",
+      "code": "import random\n\n# Generate five random numbers between 1 and 100 with two decimal places\nnumbers = [round(random.uniform(1, 100), 2) for _ in range(5)]\n\n# Print the program and its execution results\nprint(\"Program: Generate five random numbers between 1 and 100 with two decimal places\")\nprint(\"Generated numbers:\")\nfor i, num in enumerate(numbers, start=1):\n    print(f\"{i}: {num:.2f}\")",
+      "container_id": "cntr_68ec87613e58819097ec47888dc686d8088a1d37157e7565",
+      "outputs": [
+        {
+          "type": "logs",
+          "logs": "Program: Generate five random numbers between 1 and 100 with two decimal places\nGenerated numbers:\n1: 74.07\n2: 94.14\n3: 53.74\n4: 77.29\n5: 87.68\n"
+        }
+      ]
+    },
+    {
+      "id": "msg_02ffbc38ae6fca1a0068ec8768f35c81979428e19fa11468f1",
+      "type": "message",
+      "status": "completed",
+      "content": [
+        {
+          "type": "output_text",
+          "annotations": [],
+          "logprobs": [],
+          "text": "I ran the program and produced five random numbers between 1 and 100 with two decimal places. Results shown above:\n\n1: 74.07\n2: 94.14\n3: 53.74\n4: 77.29\n5: 87.68\n\nIf you want the code to be saved to a file, run multiple sets, or use a fixed seed for reproducible results, tell me and I can adjust."
+        }
+      ],
+      "role": "assistant"
+    }
+  ],
+  "parallel_tool_calls": true,
+  "previous_response_id": null,
+  "prompt_cache_key": null,
+  "reasoning": {
+    "effort": "medium",
+    "summary": null
+  },
+  "safety_identifier": null,
+  "service_tier": "default",
+  "store": true,
+  "temperature": 1,
+  "text": {
+    "format": {
+      "type": "text"
+    },
+    "verbosity": "medium"
+  },
+  "tool_choice": "auto",
+  "tools": [
+    {
+      "type": "code_interpreter",
+      "container": {
+        "type": "auto"
+      }
+    }
+  ],
+  "top_logprobs": 0,
+  "top_p": 1,
+  "truncation": "disabled",
+  "usage": {
+    "input_tokens": 1750,
+    "input_tokens_details": {
+      "cached_tokens": 0
+    },
+    "output_tokens": 400,
+    "output_tokens_details": {
+      "reasoning_tokens": 192
+    },
+    "total_tokens": 2150
+  },
+  "user": null,
+  "metadata": {}
+}

packages/azure/src/__fixtures__/azure-code-interpreter-tool.1.json

@@ -0,0 +1,89 @@
+{
+  "id": "resp_0a098396a8feca410068caae39e7648196b346e99fa8ec494c",
+  "object": "response",
+  "created_at": 1758113338,
+  "status": "completed",
+  "background": false,
+  "error": null,
+  "incomplete_details": null,
+  "instructions": null,
+  "max_output_tokens": null,
+  "max_tool_calls": null,
+  "model": "gpt-5-mini-2025-08-07",
+  "output": [
+    {
+      "id": "rs_0a098396a8feca410068caae3b47208196957fe59419daad70",
+      "type": "reasoning",
+      "summary": []
+    },
+    {
+      "id": "fs_0a098396a8feca410068caae3cab5c8196a54fd00498464e62",
+      "type": "file_search_call",
+      "status": "completed",
+      "queries": [
+        "What is an embedding model according to this document?",
+        "What is an embedding model?",
+        "definition of embedding model in the document",
+        "embedding model description"
+      ],
+      "results": null
+    },
+    {
+      "id": "rs_0a098396a8feca410068caae3e21a081968e7ac588401c4a6a",
+      "type": "reasoning",
+      "summary": []
+    },
+    {
+      "id": "msg_0a098396a8feca410068caae457c508196b2fcd079d1d3ec74",
+      "type": "message",
+      "status": "completed",
+      "content": [
+        {
+          "type": "output_text",
+          "annotations": [
+            {
+              "type": "file_citation",
+              "file_id": "file-Ebzhf8H4DPGPr9pUhr7n7v",
+              "filename": "ai.pdf",
+              "index": 438
+            }
+          ],
+          "logprobs": [],
+          "text": "According to the document, an embedding model is used to convert complex data (like words or images) into a dense vector (a list of numbers) representation called an embedding, which captures semantic and syntactic relationships. Unlike generative models, embedding models do not generate new text or data; instead, they provide these vector representations to be used as input for other models or other natural language processing tasks ."
+        }
+      ],
+      "role": "assistant"
+    }
+  ],
+  "parallel_tool_calls": true,
+  "previous_response_id": null,
+  "prompt_cache_key": null,
+  "reasoning": { "effort": "medium", "summary": null },
+  "safety_identifier": null,
+  "service_tier": "default",
+  "store": true,
+  "temperature": 1,
+  "text": { "format": { "type": "text" }, "verbosity": "medium" },
+  "tool_choice": "auto",
+  "tools": [
+    {
+      "type": "file_search",
+      "filters": null,
+      "max_num_results": 20,
+      "ranking_options": { "ranker": "auto", "score_threshold": 0 },
+      "vector_store_ids": ["vs_68caad8bd5d88191ab766cf043d89a18"]
+    }
+  ],
+  "top_logprobs": 0,
+  "top_p": 1,
+  "truncation": "disabled",
+  "usage": {
+    "input_tokens": 3700,
+    "input_tokens_details": { "cached_tokens": 2560 },
+    "output_tokens": 741,
+    "output_tokens_details": { "reasoning_tokens": 640 },
+    "total_tokens": 4441
+  },
+  "user": null,
+  "metadata": {}
+}