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참고/instructor-main/tests/llm/test_anthropic/conftest.py

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+# conftest.py
+import os
+import pytest
+import importlib.util
+
+
+if not os.getenv("ANTHROPIC_API_KEY"):
+    pytest.skip(
+        "ANTHROPIC_API_KEY environment variable not set",
+        allow_module_level=True,
+    )
+
+if (
+    importlib.util.find_spec("anthropic") is None
+):  # pragma: no cover - optional dependency
+    pytest.skip("anthropic package is not installed", allow_module_level=True)

참고/instructor-main/tests/llm/test_anthropic/test_multimodal.py

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+import pytest
+from instructor.processing.multimodal import Image, PDF, PDFWithCacheControl
+import instructor
+from pydantic import Field, BaseModel
+from itertools import product
+from .util import models, modes
+import os
+import base64
+
+# Models that support PDF input (Claude 3.5+ only)
+_PDF_CAPABLE = ["claude-3-5", "claude-3-7", "claude-haiku-4", "claude-sonnet-4"]
+pdf_supported = any(cap in m for cap in _PDF_CAPABLE for m in models)
+
+
+class ImageDescription(BaseModel):
+    objects: list[str] = Field(..., description="The objects in the image")
+    scene: str = Field(..., description="The scene of the image")
+    colors: list[str] = Field(..., description="The colors in the image")
+
+
+image_url = "https://raw.githubusercontent.com/instructor-ai/instructor/main/tests/assets/image.jpg"
+
+pdf_url = "https://raw.githubusercontent.com/instructor-ai/instructor/main/tests/assets/invoice.pdf"
+
+
+curr_file = os.path.dirname(__file__)
+pdf_path = os.path.join(curr_file, "../../assets/invoice.pdf")
+pdf_base64 = base64.b64encode(open(pdf_path, "rb").read()).decode("utf-8")
+pdf_base64_string = f"data:application/pdf;base64,{pdf_base64}"
+
+
+@pytest.mark.parametrize("model, mode", product(models, modes))
+def test_multimodal_image_description(model, mode):
+    client = instructor.from_provider(model, mode=mode)
+    response = client.chat.completions.create(
+        response_model=ImageDescription,
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a helpful assistant that can describe images",
+            },
+            {
+                "role": "user",
+                "content": [
+                    "What is this?",
+                    Image.from_url(image_url),
+                ],
+            },
+        ],
+        temperature=1,
+        max_tokens=1000,
+    )
+
+    # Assertions to validate the response
+    assert isinstance(response, ImageDescription)
+    assert len(response.objects) > 0
+    assert response.scene != ""
+    assert len(response.colors) > 0
+
+
+@pytest.mark.parametrize("model, mode", product(models, modes))
+def test_multimodal_image_description_autodetect(model, mode):
+    client = instructor.from_provider(model, mode=mode)
+    response = client.chat.completions.create(
+        response_model=ImageDescription,
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a helpful assistant that can describe images",
+            },
+            {
+                "role": "user",
+                "content": [
+                    "What is this?",
+                    image_url,
+                ],
+            },
+        ],
+        max_tokens=1000,
+        temperature=1,
+        autodetect_images=True,
+    )
+
+    # Assertions to validate the response
+    assert isinstance(response, ImageDescription)
+    assert len(response.objects) > 0
+    assert response.scene != ""
+    assert len(response.colors) > 0
+
+    # Additional assertions can be added based on expected content of the sample image
+
+
+@pytest.mark.parametrize("model, mode", product(models, modes))
+def test_multimodal_image_description_autodetect_image_params(model, mode):
+    client = instructor.from_provider(model, mode=mode)
+    response = client.chat.completions.create(
+        response_model=ImageDescription,
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a helpful assistant that can describe images",
+            },
+            {
+                "role": "user",
+                "content": [
+                    "What is this?",
+                    {
+                        "type": "image",
+                        "source": image_url,
+                    },
+                ],
+            },
+        ],
+        max_tokens=1000,
+        temperature=1,
+        autodetect_images=True,
+    )
+
+    # Assertions to validate the response
+    assert isinstance(response, ImageDescription)
+    assert len(response.objects) > 0
+    assert response.scene != ""
+    assert len(response.colors) > 0
+
+    # Additional assertions can be added based on expected content of the sample image
+
+
+@pytest.mark.parametrize("model, mode", product(models, modes))
+def test_multimodal_image_description_autodetect_image_params_cache(model, mode):
+    client = instructor.from_provider(model, mode=mode)
+    messages = client.chat.completions.create(
+        response_model=None,
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a helpful assistant that can describe images and stuff",
+            },
+            {
+                "role": "user",
+                "content": [
+                    "Describe these images",
+                    # Large images to activate caching
+                    {
+                        "type": "image",
+                        "source": "https://assets.entrepreneur.com/content/3x2/2000/20200429211042-GettyImages-1164615296.jpeg",
+                        "cache_control": {"type": "ephemeral"},
+                    },
+                    {
+                        "type": "image",
+                        "source": "https://www.bigbear.com/imager/s3_us-west-1_amazonaws_com/big-bear/images/Scenic-Snow/89xVzXp1_00588cdef1e3d54756582b576359604b.jpeg",
+                        "cache_control": {"type": "ephemeral"},
+                    },
+                ],
+            },
+        ],
+        max_tokens=1000,
+        temperature=1,
+        autodetect_images=True,
+    )
+
+    # Cache tokens are non-deterministic (Anthropic may not always activate cache
+    # on first call or for small payloads). Just verify the fields are present.
+    assert hasattr(messages.usage, "cache_creation_input_tokens")
+    assert hasattr(messages.usage, "cache_read_input_tokens")
+
+
+class LineItem(BaseModel):
+    name: str
+    price: int
+    quantity: int
+
+
+class Receipt(BaseModel):
+    total: int
+    items: list[str]
+
+
+@pytest.mark.skipif(not pdf_supported, reason="PDF input requires Claude 3.5+ models")
+@pytest.mark.parametrize("pdf_source", [pdf_path, pdf_url, pdf_base64_string])
+@pytest.mark.parametrize("model, mode", product(models, modes))
+def test_multimodal_pdf_file(model, mode, pdf_source):
+    client = instructor.from_provider(model, mode=mode)
+
+    # Retry logic for flaky LLM responses
+    max_retries = 3
+    for attempt in range(max_retries):
+        response = client.chat.completions.create(
+            messages=[
+                {
+                    "role": "system",
+                    "content": "Extract the total and items from the invoice. Be precise and only extract the final total amount and list of item names. The total should be exactly 220.",
+                },
+                {
+                    "role": "user",
+                    "content": PDF.autodetect(pdf_source),
+                },
+            ],
+            max_tokens=1000,
+            temperature=0,  # Keep at 0 for consistent responses
+            autodetect_images=False,
+            response_model=Receipt,
+        )
+
+        if response.total == 220 and len(response.items) == 2:
+            break
+        elif attempt == max_retries - 1:
+            pytest.fail(
+                f"After {max_retries} attempts, got total={response.total}, items={response.items}, expected total=220, items=2"
+            )
+
+    assert response.total == 220
+    assert len(response.items) == 2
+
+
+@pytest.mark.skipif(not pdf_supported, reason="PDF input requires Claude 3.5+ models")
+@pytest.mark.parametrize("pdf_source", [pdf_path, pdf_url, pdf_base64_string])
+@pytest.mark.parametrize("model, mode", product(models, modes))
+def test_multimodal_pdf_file_with_cache_control(model, mode, pdf_source):
+    client = instructor.from_provider(model, mode=mode)
+
+    response, completion = client.chat.completions.create_with_completion(
+        messages=[
+            {
+                "role": "system",
+                "content": "Extract the total and items from the invoice",
+            },
+            {
+                "role": "user",
+                "content": PDFWithCacheControl.autodetect(pdf_source),
+            },
+        ],
+        max_tokens=1000,
+        autodetect_images=False,
+        response_model=Receipt,
+    )
+
+    assert response.total == 220
+    # Cache tokens are non-deterministic. Just verify the fields exist.
+    assert hasattr(completion.usage, "cache_creation_input_tokens")
+    assert hasattr(completion.usage, "cache_read_input_tokens")
+    assert len(response.items) == 2

참고/instructor-main/tests/llm/test_anthropic/test_reasoning.py

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+import pytest
+import instructor
+from pydantic import BaseModel
+
+
+class Answer(BaseModel):
+    answer: float
+
+
+def test_reasoning():
+    client = instructor.from_provider(
+        "anthropic/claude-sonnet-4-5-20250514",
+        mode=instructor.Mode.ANTHROPIC_REASONING_TOOLS,
+    )
+    try:
+        response = client.chat.completions.create(
+            response_model=Answer,
+            messages=[
+                {
+                    "role": "user",
+                    "content": "Which is larger, 9.11 or 9.8? Think carefully about decimal places.",
+                },
+            ],
+            temperature=1,  # Required when thinking is enabled
+            max_tokens=2000,
+            thinking={"type": "enabled", "budget_tokens": 1024},
+            max_retries=3,  # Retry if the model gets it wrong
+        )
+    except Exception as e:
+        if "404" in str(e) or "not_found_error" in str(e):
+            pytest.skip(
+                "Model claude-sonnet-4-5-20250514 not available with current API key"
+            )
+        raise
+
+    # Assertions to validate the response
+    assert isinstance(response, Answer)
+    assert response.answer == 9.8

참고/instructor-main/tests/llm/test_anthropic/test_system.py

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+import pytest
+import instructor
+from pydantic import BaseModel
+from itertools import product
+from .util import models, modes
+from anthropic.types.message import Message
+
+
+class User(BaseModel):
+    name: str
+    age: int
+
+
+@pytest.mark.parametrize("model, mode", product(models, modes))
+def test_creation(model, mode):
+    client = instructor.from_provider(model, mode=mode)
+    response = client.chat.completions.create(
+        response_model=User,
+        messages=[
+            {
+                "role": "system",
+                "content": [
+                    {"type": "text", "text": "<story>Mike is 37 years old</story>"}
+                ],
+            },
+            {
+                "role": "user",
+                "content": "Extract a user from the story.",
+            },
+        ],
+        temperature=1,
+        max_tokens=1000,
+    )
+
+    # Assertions to validate the response
+    assert isinstance(response, User)
+    assert response.name == "Mike"
+    assert response.age == 37
+
+
+@pytest.mark.parametrize("model, mode", product(models, modes))
+def test_creation_with_system_cache(model, mode):
+    client = instructor.from_provider(model, mode=mode)
+    response, message = client.chat.completions.create_with_completion(
+        response_model=User,
+        messages=[
+            {
+                "role": "system",
+                "content": [
+                    {
+                        "type": "text",
+                        "text": "<story>Mike is 37 years old " * 200 + "</story>",
+                        "cache_control": {"type": "ephemeral"},
+                    },
+                    {
+                        "type": "text",
+                        "text": "You are a helpful assistant who extracts users from stories.",
+                    },
+                ],
+            },
+            {
+                "role": "user",
+                "content": "Extract a user from the story.",
+            },
+        ],
+        temperature=1,
+        max_tokens=1000,
+    )
+
+    # Assertions to validate the response
+    assert isinstance(response, User)
+    assert response.name == "Mike"
+    assert response.age == 37
+
+    # Assert a cache write or cache hit
+    assert (
+        message.usage.cache_creation_input_tokens > 0
+        or message.usage.cache_read_input_tokens > 0
+    )
+
+
+@pytest.mark.parametrize("model, mode", product(models, modes))
+def test_creation_with_system_cache_anthropic_style(model, mode):
+    client = instructor.from_provider(model, mode=mode)
+    response, message = client.chat.completions.create_with_completion(
+        system=[
+            {
+                "type": "text",
+                "text": "<story>Mike is 37 years old " * 200 + "</story>",
+                "cache_control": {"type": "ephemeral"},
+            },
+            {
+                "type": "text",
+                "text": "You are a helpful assistant who extracts users from stories.",
+            },
+        ],
+        response_model=User,
+        messages=[
+            {
+                "role": "user",
+                "content": "Extract a user from the story.",
+            },
+        ],
+        temperature=1,
+        max_tokens=1000,
+    )
+
+    # Assertions to validate the response
+    assert isinstance(response, User)
+    assert response.name == "Mike"
+    assert response.age == 37
+
+    # Assert a cache write or cache hit
+    assert (
+        message.usage.cache_creation_input_tokens > 0
+        or message.usage.cache_read_input_tokens > 0
+    )
+
+
+@pytest.mark.parametrize("model, mode", product(models, modes))
+def test_creation_no_response_model(model, mode):
+    client = instructor.from_provider(model, mode=mode)
+    response = client.chat.completions.create(
+        response_model=None,
+        messages=[
+            {
+                "role": "system",
+                "content": [{"type": "text", "text": "Mike is 37 years old"}],
+            },
+            {
+                "role": "user",
+                "content": "Extract a user from the story.",
+            },
+        ],
+        temperature=1,
+        max_tokens=1000,
+    )
+
+    # Assertions to validate the response
+    assert isinstance(response, Message)

참고/instructor-main/tests/llm/test_anthropic/util.py

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+import instructor
+
+models = ["anthropic/claude-3-haiku-20240307"]
+modes = [
+    instructor.Mode.ANTHROPIC_TOOLS,
+]