test: Add readme tests.

Author: Raphael Mitsch

sieves/tests/docs/test_readme.py

@@ -0,0 +1,67 @@
+def test_simple_example() -> None:
+    import outlines
+    import transformers
+    from sieves import Pipeline, tasks, Doc
+
+    # Set up model.
+    model_name = "HuggingFaceTB/SmolLM2-135M-Instruct"
+    model = outlines.models.from_transformers(
+        transformers.AutoModelForCausalLM.from_pretrained(model_name),
+        transformers.AutoTokenizer.from_pretrained(model_name)
+    )
+
+    # Define task.
+    task = tasks.Classification(labels=["science", "politics"], model=model)
+
+    # Define pipeline with the classification task.
+    pipeline = Pipeline(task)
+
+    # Define documents to analyze.
+    doc = Doc(text="The new telescope captures images of distant galaxies.")
+
+    # Run pipeline and print results.
+    docs = list(pipeline([doc]))
+    # The `results` field contains the structured task output as a unified Pydantic model.
+    print(docs[0].results["Classification"])  # ResultMultiLabel(label_scores=[('science', 1.0), ('politics', 0.0)])
+    # The `meta` field contains more information helpful for observability and debugging, such as raw model output and token count information.
+    print(docs[0].meta)  # {'Classification': {
+    #    'raw': ['{ "science": 1.0, "politics": 0 }'],
+    #    'usage': {'input_tokens': 2, 'output_tokens': 2, 'chunks': [{'input_tokens': 2, 'output_tokens': 2}]}}, 'usage': {'input_tokens': 2, 'output_tokens': 2}
+    #  }
+
+def test_advanced_example() -> None:
+    import dspy
+    import os
+    import pydantic
+    import chonkie
+    import tokenizers
+    from sieves import tasks, Doc
+
+    # Define which schema of entity to extract.
+    class Equation(pydantic.BaseModel, frozen=True):
+        id: str = pydantic.Field(description="ID/index of equation in paper.")
+        equation: str = pydantic.Field(description="Equation as shown in paper.")
+
+    # Setup DSPy model.
+    model = dspy.LM(
+        "openrouter/google/gemini-3-flash-preview",
+        api_base="https://openrouter.ai/api/v1/",
+        api_key=os.environ["OPENROUTER_API_KEY"]
+    )
+
+    # Build pipeline: ingest -> chunk -> extract.
+    pipeline = (
+        tasks.Ingestion() +
+        tasks.Chunking(chonkie.TokenChunker(tokenizers.Tokenizer.from_pretrained("gpt2"))) +
+        tasks.InformationExtraction(entity_type=Equation, model=model)
+    )
+
+    # Define docs to analyze.
+    doc = Doc(uri="https://arxiv.org/pdf/1204.0162")
+
+    # Run pipeline.
+    results = list(pipeline([doc]))
+
+    # Print results.
+    for equation in results[0].results["InformationExtraction"].entities:
+        print(equation)