langextract https://github.com/google/langextract ollama example

ollama_example.py

# /// script
# dependencies = [
#   "langextract"
# ]
# ///


import langextract as lx
import textwrap

# 1. Define the prompt and extraction rules
prompt = textwrap.dedent("""\
    Extract characters, emotions, and relationships in order of appearance.
    Use exact text for extractions. Do not paraphrase or overlap entities.
    Provide meaningful attributes for each entity to add context.""")

# 2. Provide a high-quality example to guide the model
examples = [
    lx.data.ExampleData(
        text="ROMEO. But soft! What light through yonder window breaks? It is the east, and Juliet is the sun.",
        extractions=[
            lx.data.Extraction(
                extraction_class="character",
                extraction_text="ROMEO",
                attributes={"emotional_state": "wonder"}
            ),
            lx.data.Extraction(
                extraction_class="emotion",
                extraction_text="But soft!",
                attributes={"feeling": "gentle awe"}
            ),
            lx.data.Extraction(
                extraction_class="relationship",
                extraction_text="Juliet is the sun",
                attributes={"type": "metaphor"}
            ),
        ]
    )
]


# The input text to be processed
input_text = "Lady Juliet gazed longingly at the stars, her heart aching for Romeo"

# Run the extraction
result = lx.extract(
    text_or_documents=input_text,
    prompt_description=prompt,
    examples=examples,
    language_model_type=lx.inference.OllamaLanguageModel,
    language_model_params={"model": "gemma3:latest"},
    model_url="http://localhost:11434"
)

# Save the results to a JSONL file
lx.io.save_annotated_documents([result], output_name="extraction_results.jsonl")

# Generate the visualization from the file
html_content = lx.visualize("test_output/extraction_results.jsonl")
with open("test_output/visualization.html", "w") as f:
    f.write(html_content)